This document summarizes a computational fluid dynamics (CFD) project analyzing heat transfer in a hot water tank. It describes the geometry, meshing, material properties, boundary conditions, and solution methodology for 4 cases (A, B, C, D) with different tank dimensions. The key results are the outlet water temperatures and coefficients of performance for each case. Removing buoyancy effects in Task 3 showed lower outlet temperatures compared to the previous cases.
Techniques of heat transfer enhancement and their application chapter 6ssusercf6d0e
This document discusses different types of externally finned tubes used in heat exchangers. It describes plain plate fins attached to round tubes, as well as enhanced fin geometries like wavy and interrupted fins. The effects of fin spacing and geometry on heat transfer and friction are examined. Reynolds number is defined based on both tube diameter and hydraulic diameter, and it is noted that tube diameter may be a better characteristic length for finned tubes. Friction factor definitions are also discussed.
Conjugate Heat Transfer Analysis in a Cryogenic Microchannel Heat ExchangerIRJET Journal
This document presents a numerical analysis of conjugate heat transfer in a cryogenic microchannel heat exchanger. A counterflow rectangular microchannel printed circuit heat exchanger model is designed and simulated. Performance is investigated numerically with helium at cryogenic temperatures for Reynolds numbers less than 100 and varying material thermal conductivity ratios. Results show axial conduction affects performance at low Reynolds numbers. Effectiveness decreases with increasing axial conduction and increases with Reynolds number. Nusselt number and heat flux along the channels are also determined.
JIMEC Thermal modeling and Simulation of HX Conference PaperAhmed Sohail Izhar
1) The document describes a thermal modeling and simulation of an industrial shell and tube heat exchanger used to cool raw natural gas.
2) A thermal model was developed using the effectiveness-NTU method to determine the required heat transfer area and estimate the tube-side and shell-side heat transfer coefficients.
3) The results of the model showed that a heat transfer area of about 1132 m2 is required to provide a thermal duty of 1.4 MW, with tube-side and shell-side heat transfer coefficients of 950 W/m2K and 495 W/m2K respectively.
This document describes a CFD analysis of fluid flow through tube banks in heat recovery steam generators (HRSGs). The authors developed a new procedure to define porous medium parameters like loss coefficients starting from 3D simulations of flow through tube banks. Both finned and bare tube banks were considered. The analysis was performed using the commercial CFD code Fluent to simulate flow through a single tube row and investigate the effects of Reynolds number, inlet yaw angle, and inlet pitch angle on pressure drop and outlet flow angles. Results were compared to experimental data for a real fired HRSG to validate the proposed porous media modeling approach.
The document presents a computational tool and methodology for simulating compact tube bundle heat exchangers used in recuperated gas turbine engines. Computational fluid dynamics analyses were performed on an oval tube bundle heat exchanger to derive resistance tensors for modeling the heat exchanger as a porous medium. The porous media model was validated against experimental hot gas channel and isothermal flow test data, showing acceptable agreement between calculated and measured velocity profiles and less than 10% deviation in pressure drop. While cold side heat transfer correlations predicted results reasonably well, hot gas calculations overpredicted heat transfer likely due to assumptions about flow regime.
This document describes using Excel to optimize the thermal design of systems. It provides an example of optimizing the insulation thickness and pipe diameter of an insulated pipe to minimize costs. Excel is able to calculate the total cost as a function of diameter and use its Solver tool to iteratively find the optimal diameter that minimizes cost. This provides an effective way for students to learn thermal design optimization compared to analytical optimization, as it can handle multiple design factors and parameters. The document then presents another example of using this approach to optimize the diameters and insulation thicknesses of two sections of an air conditioning duct to minimize owning costs.
This document summarizes a paper that analyzes heat storage using a thermocline tank for concentrated solar power plants. It presents:
- A simulation model of the charging and discharging processes in a thermocline tank that uses molten salt and quartzite rock as storage media.
- Validation of the model by comparing its results to experimental data and other models, showing good agreement.
- Application of the model to design an alternative storage system for the AndaSol I solar plant using a single thermocline tank instead of two separate tanks.
- Analysis of how the tank height-to-diameter ratio affects energy storage efficiency.
- Comparison of three thermal fluids to determine the best
This document discusses heat exchanger network capital cost targets, specifically heat exchange area targets. It provides context on how the minimum number of units in a heat exchanger network can be estimated from graph theory. It then discusses how the total heat exchange area target of a network can be estimated based on the streams' enthalpy intervals, duties, and heat transfer coefficients using an equation provided. The document uses an example process to demonstrate calculating the area target. The target area is then compared to the actual area of the designed network for that example, showing it was 13% above the target.
Techniques of heat transfer enhancement and their application chapter 6ssusercf6d0e
This document discusses different types of externally finned tubes used in heat exchangers. It describes plain plate fins attached to round tubes, as well as enhanced fin geometries like wavy and interrupted fins. The effects of fin spacing and geometry on heat transfer and friction are examined. Reynolds number is defined based on both tube diameter and hydraulic diameter, and it is noted that tube diameter may be a better characteristic length for finned tubes. Friction factor definitions are also discussed.
Conjugate Heat Transfer Analysis in a Cryogenic Microchannel Heat ExchangerIRJET Journal
This document presents a numerical analysis of conjugate heat transfer in a cryogenic microchannel heat exchanger. A counterflow rectangular microchannel printed circuit heat exchanger model is designed and simulated. Performance is investigated numerically with helium at cryogenic temperatures for Reynolds numbers less than 100 and varying material thermal conductivity ratios. Results show axial conduction affects performance at low Reynolds numbers. Effectiveness decreases with increasing axial conduction and increases with Reynolds number. Nusselt number and heat flux along the channels are also determined.
JIMEC Thermal modeling and Simulation of HX Conference PaperAhmed Sohail Izhar
1) The document describes a thermal modeling and simulation of an industrial shell and tube heat exchanger used to cool raw natural gas.
2) A thermal model was developed using the effectiveness-NTU method to determine the required heat transfer area and estimate the tube-side and shell-side heat transfer coefficients.
3) The results of the model showed that a heat transfer area of about 1132 m2 is required to provide a thermal duty of 1.4 MW, with tube-side and shell-side heat transfer coefficients of 950 W/m2K and 495 W/m2K respectively.
This document describes a CFD analysis of fluid flow through tube banks in heat recovery steam generators (HRSGs). The authors developed a new procedure to define porous medium parameters like loss coefficients starting from 3D simulations of flow through tube banks. Both finned and bare tube banks were considered. The analysis was performed using the commercial CFD code Fluent to simulate flow through a single tube row and investigate the effects of Reynolds number, inlet yaw angle, and inlet pitch angle on pressure drop and outlet flow angles. Results were compared to experimental data for a real fired HRSG to validate the proposed porous media modeling approach.
The document presents a computational tool and methodology for simulating compact tube bundle heat exchangers used in recuperated gas turbine engines. Computational fluid dynamics analyses were performed on an oval tube bundle heat exchanger to derive resistance tensors for modeling the heat exchanger as a porous medium. The porous media model was validated against experimental hot gas channel and isothermal flow test data, showing acceptable agreement between calculated and measured velocity profiles and less than 10% deviation in pressure drop. While cold side heat transfer correlations predicted results reasonably well, hot gas calculations overpredicted heat transfer likely due to assumptions about flow regime.
This document describes using Excel to optimize the thermal design of systems. It provides an example of optimizing the insulation thickness and pipe diameter of an insulated pipe to minimize costs. Excel is able to calculate the total cost as a function of diameter and use its Solver tool to iteratively find the optimal diameter that minimizes cost. This provides an effective way for students to learn thermal design optimization compared to analytical optimization, as it can handle multiple design factors and parameters. The document then presents another example of using this approach to optimize the diameters and insulation thicknesses of two sections of an air conditioning duct to minimize owning costs.
This document summarizes a paper that analyzes heat storage using a thermocline tank for concentrated solar power plants. It presents:
- A simulation model of the charging and discharging processes in a thermocline tank that uses molten salt and quartzite rock as storage media.
- Validation of the model by comparing its results to experimental data and other models, showing good agreement.
- Application of the model to design an alternative storage system for the AndaSol I solar plant using a single thermocline tank instead of two separate tanks.
- Analysis of how the tank height-to-diameter ratio affects energy storage efficiency.
- Comparison of three thermal fluids to determine the best
This document discusses heat exchanger network capital cost targets, specifically heat exchange area targets. It provides context on how the minimum number of units in a heat exchanger network can be estimated from graph theory. It then discusses how the total heat exchange area target of a network can be estimated based on the streams' enthalpy intervals, duties, and heat transfer coefficients using an equation provided. The document uses an example process to demonstrate calculating the area target. The target area is then compared to the actual area of the designed network for that example, showing it was 13% above the target.
Analytical Solution of Compartment Based Double Pipe Heat Exchanger using Di...IRJET Journal
This document discusses analytical solutions for a compartment-based double pipe heat exchanger model using the differential transform method. The model considers transformer oil as the hot fluid and water as the coolant fluid flowing in parallel through two compartments of the heat exchanger. Analytical expressions for the temperature profiles of the hot and cold fluids over time are derived. The solutions are shown to converge with increasing terms in the series solutions.
Optimization of Air Preheater for compactness of shell by evaluating performa...Nemish Kanwar
Designing of an Air Preheater with increased performance from an existing design through alteration in baffle placement. Analysis of 4 Baffle designs for segmented Baffle case was done using Ansys Fluent. The net heat recovery rate was computed by subtracting pump work from heat recovered. Based on the result, Air Preheater design was recommended.
Distillation Blending and Cutpoint Temperature Optimization in Scheduling Ope...Brenno Menezes
In oil refinery manufacturing, final products such as fuels, lubricants and petrochemicals are produced from crude-oil in process units considering their operations in coordination with tanks, pipelines, blenders, etc. In this process, the full range of hydrocarbon components (crude-oil) is transformed (separated, reacted, blended) into smaller boiling-point temperature ranges resulting in intermediate and final products, in which planning, scheduling and real-time optimization using distillation curves of the streams can be used to effectively model the unit-operations and predict yields and properties of their outlet streams.1 The hydrocarbon streams’ characterization or assays of both the crude-oil and its derivatives are decomposed, partitioned or characterized into several temperature cuts based on what are known as True Boiling Point (TBP) temperature distribution or distillation curves.2,3 These are one-dimensional representations of how quantity (yields) and quality (properties) data of hydrocarbon streams are distributed or profiled over its TBP temperatures where each cut is also referred to as a component, pseudocomponent or hypothetical in process simulation and optimization technology.4
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery, we proposed a new technique to optimize the blending of several streams’ distillation curves with also shifting or adjusting cutpoint temperatures of distilled streams, i.e, their initial boiling point (IBP) and final boiling point (FBP), in order to manipulate their TBP curves in either off-line or on-line environment. By shifting or adjusting the front-end and back-end of the TBP curve for one or more distillate blending streams, it allows for improved control and optimization of the final product demand quantity and quality, affording better maneuvering closer and around downstream bottlenecks such as tight property specifications and volatile demand flow and timing constrictions. This shifting or adjusting of the TBP curve’s IBP and FBP (front- and back-end respectively) ultimately requires that the unit-operation has sufficient handles or controls to allow this type of cutpoint variation where the solution from this higher-level optimization would provide set points or targets to a lower-level advanced process control systems, which are now commonplace in oil refineries.
By optimizing both the recipes of the blended material and its blending component distillation curves, very significant benefits can be achieved especially given the global push towards ultralow sulfur fuels (ULSF) due to the increase in natural gas plays reducing the demand for other oil distillates. One example is provided to highlight and demonstrate the technique.
Numerical Investigation of Mixed Convective Flow inside a Straight Pipe and B...iosrjce
The present study deals with a numerical investigation of steady laminar and turbulent mixed
convection heat transfer in a horizontal pipe and bend pipe using air as the working fluid.The thermal boundary
condition chosen is that of uniform temperature at the outer wall. Computations were performed to investigate
the effect of inlet Rayleigh number and Reynolds number in the velocity and temperature profile at inside of the
pipe. The secondary flow is more intense in the upper part of the cross-section. It increases throughout the
cross-section until its intensity reaches a maximum, and then it becomes weak at far downstream. For the
horizontal pipe the value of the L/D ratio becomes more than 10 the secondary flow effects are neutralized and
the velocity profile almost become constant throughout.
This document presents a mathematical model for determining effectiveness-NTU (ε-NTU) relations for cross-flow heat exchangers with complex flow arrangements. The model discretizes the heat exchanger into small control volumes called "elements" and applies the governing equations to each element. Each element is treated as a simple single-pass cross-flow heat exchanger. An iterative algorithm is used to compute the temperatures at the inlet and outlet of each element. This allows the model to simulate different flow arrangements by using equations valid for a simple mixed/unmixed cross-flow arrangement within each element. The model provides accurate ε-NTU curves for complex geometries not described by existing algebraic relations.
This document summarizes how modern computer dynamic analysis and detailed evaluations can yield significant savings in both weight and cost of flare and relief systems compared to traditional steady state calculation methods. It provides examples showing that dynamic simulation can predict substantially lower relief loads for vessels under fire or distillation column upsets. It also illustrates how dynamic flow analysis of flare header designs allows additional relief sources to be accommodated without exceeding pressure limits, avoiding the need for larger and more expensive systems.
This document reports on analyses and experimentation conducted on a Quadrafire Classic Bay 1200 pellet stove to increase its efficiency and reduce emissions. The researchers have developed a theoretical model that characterizes the stove's heating output as a continuous function of distance along the heat exchanger, accounting for both convection and radiation heat transfer. Their experiments involved upgrading instrumentation to directly measure airflow rates and temperatures, validating assumptions in the previous model. Key findings include radiation significantly influencing heat output and an air-rich firepot reaction that reduces combustion efficiency. The updated model and experiments provide a foundation for redesigning the stove.
This report reconciles the numerical results of both forms of the Colebrook-White Friction Factor equations. It should
be noted that although the Colebrook equation represents the data to within +/- 10 percent, the equations are not
reconciled to within the 5 decimal place. This reconciliation is of great importance to the researches of explicit-forms
of the Colebrook equation as it now can be compared to
either form-1 (Used in the generation of the Moody Diagram) or form-2, preferred by some investigators. The author prefers form-1 as it is the most compact-form
This document summarizes the design of urban hydraulic structures in Davis, CA, including a drainage channel, gutters, storm sewers, a culvert, and detention pond. The structures are designed to handle peak runoff from a 10-year storm. Runoff from a playing field and school yard enters the drainage channel, which flows through a culvert and into a detention pond located 4,000 feet away. Six methods were used to estimate peak runoff: Velocity Method, Wong's Formula, Kerby's Formula, FAA Formula, Morgali and Linsley's Formula, and Chen and Yen's Formula. Wong's Formula produced a peak runoff closest to the average when calculating individually for each watershed
Using Lattice Boltzmann Method to Investigate the Effects of Porous Media on ...A Behzadmehr
1) The document describes a numerical study using the lattice Boltzmann method to investigate heat transfer from a solid block inside a porous media-filled channel.
2) The effects of porosity and thermal conductivity ratio on fluid flow patterns and temperature fields were examined.
3) Higher porosity and lower thermal conductivity ratio resulted in lower fluid temperatures, as increased porosity reduces the effective thermal conductivity and thus heat transfer between the fluid and solid block.
Mech 0036 exam 12 13 with answers (revision)Mostafa Tamish
This document appears to be a past exam paper and marking scheme for a Thermal Power Plant and Heat Transfer course. It contains 6 questions testing various concepts related to thermodynamics, heat transfer, steam power plants, gas turbines, and refrigeration. For each question, it provides the question prompt, relevant figures or tables, and a detailed multi-part solution and marking scheme. The questions cover topics such as spark ignition engines, Rankine cycles, heat exchangers, radiation heat transfer, and gas turbine cycles.
The document discusses the construction of attainable regions (AR) to analyze chemical reaction systems and optimize reactor network design. The AR represents the collection of all possible outputs for all reactor configurations, even those not yet imagined. AR theory can be used to compare reactor performance, establish performance targets, interpret and optimize reactor networks. Examples demonstrate how to systematically construct the AR for a reaction system using different reactor types (CSTR, PFR) and represent it graphically to identify the optimal reactor configuration for a given reaction.
This document provides an overview of methods for calculating gas properties including:
1. Empirical correlations for calculating z-factors such as Hall-Yarborough and Dranchuk-Abu-Kassem.
2. Calculation of gas compressibility, gas formation volume factor, and gas expansion factor using real gas equations of state.
3. Empirical correlations for calculating gas viscosity including Carr-Kobayashi-Burrows and Lee-Gonzalez-Eakin.
This document discusses interparticle forces and phase diagrams. It covers hydrogen bonding, phase transitions like melting and boiling points, heat involved in phase changes of water, phase diagrams showing pressure and temperature relationships, ideal gas properties on 3D graphs, and two-component phase diagrams showing temperature and composition relationships. Gibbs' phase rule is also explained for two-component systems in determining the degrees of freedom based on the number of phases present.
A strategy for an efficient simulation of countcorrent flows in the iron blas...Josué Medeiros
This document summarizes a strategy for efficiently simulating countercurrent gas and solids flows in an iron blast furnace. Key aspects of the strategy include:
1) Modeling the gas flow using an anisotropic Ergun equation that accounts for layered porous media and can be solved using a computationally efficient algorithm.
2) Modeling the slow descending solids flow using an irrotational flow assumption and conservation of mass.
3) Modeling heat transfer between the gas and solids using energy balance equations that account for convection and heat exchange, with appropriate enthalpy-temperature relationships.
4) Accounting for the stagnant central "deadman" zone and high-flow "race
- The document summarizes computational fluid dynamics (CFD) simulations performed using the Data-Parallel Line Relaxation (DPLR) method to model various geometries tested in arc-jet experiments, including a 20-degree half wedge, flat-faced puck, spherical calorimeter, and nose of the space shuttle.
- The results of the CFD simulations will be compared to actual test and flight data to validate the models and improve accuracy for future simulations.
- Key geometries modeled include a 20-degree half wedge, flat-faced puck with corner radii of 0.25 and 0.0625 inches, and spherical calorimeter. Freestream conditions and surface properties were defined based on
The document summarizes the VISHNU hybrid model for describing the viscous QCD matter created in relativistic heavy ion collisions. VISHNU couples viscous hydrodynamics to model the quark-gluon plasma expansion with a hadron cascade model to describe the late hadronic stage. The model is used to extract the shear viscosity to entropy density ratio of the QGP (η/s)QGP by fitting elliptic flow data from RHIC and LHC. Analyses find 1 < 4π(η/s)QGP < 2.5 at RHIC and a slightly higher value at LHC, although the temperature dependence cannot be uniquely constrained. VISHNU provides an excellent description of spectra and differential flow
Analysis of Coiled-Tube Heat Exchangers to Improve Heat Transfer Rate With Sp...IJMER
Steady heat transfer enhancement has been studied in helically coiled-tube heat exchangers. The outer side of the wall of the heat exchanger contains a helical corrugation which makes a helical rib on the inner side of the tube wall to induce additional swirling motion of fluid particles. Numerical calculations have been carried out to examine different geometrical parameters and the impact of flow and thermal boundary conditions for the heat transfer rate in laminar and transitional flow regimes. Calculated results have been compared to existing empirical formula and experimental tests to investigate the validity of the numerical results in case of common helical tube heat exchanger and additionally results of the numerical computation of corrugated straight tubes for laminar and transition flow have been validated with experimental tests available in the literature. Comparison of the flow and temperature fields in case of common helical tube and the coil with spirally corrugated wall configuration are discussed. Heat exchanger coils with helically corrugated wall configuration show 80–100% increase for the inner side heat transfer rate due to the additionally developed swirling motion while the relative pressure drop is 10–600% larger compared to the common helically coiled heat exchangers. New empirical Co-relation has been proposed for the fully developed inner side heat transfer prediction in case of helically corrugated wall configuration.
1. Placing a full glass bottle of water in the freezer would cause it to break because water expands as it freezes and the sealed bottle provides no room for expansion.
2. The phase diagram shows that at higher altitudes, the boiling point of water decreases and the melting point increases due to lower atmospheric pressure. This could require longer cooking times in mountains.
3. If two cylinders made of materials A and B conduct heat at the same rate when subjected to the same temperature difference, and the diameter of A is twice the diameter of B, then the thermal conductivity of A is one fourth that of B.
This document summarizes the development of a high-power lithium target for accelerator-based boron neutron capture therapy (BNCT). Key points:
- A water-cooled conical target is being developed to accept a 50 kW proton beam and produce neutrons via the 7Li(p,n)7Be reaction for BNCT applications.
- Computational fluid dynamics modeling was used to design the target with 20 helical water channels to keep the lithium surface below 150°C with a water flow of 80 kg/min.
- An initial prototype target was fabricated and underwent preliminary hydraulic testing matching CFD predictions. Further electron beam thermal testing is planned at Sandia National Laboratories to validate the
This document provides the solutions to homework problems assigned in a thermodynamics course. It summarizes the key steps and conclusions for 6 problems involving concepts like approximations for enthalpy of compressed water, properties of water at different temperatures and pressures, heat transfer for a refrigerant, the Rankine cycle diagram, and properties of propane using different equations of state. The last problem calculates about 700 kJ/kg of work done by steam expanding adiabatically between two states.
Design Calculations for Solar Water Heating Systemsangeetkhule
Chapter 1 City of Residence
Chapter 2 Estimation of Available Solar Resources
Chapter 3 Site Survey
Chapter 4 Load Estimation
Chapter 5 Estimation of Required Absorber Area
Chapter 6 Market Survey & Estimation of No. of Tubes for ETC
Chapter 7 Economical Analysis & Estimation of Payback Period
Chapter 8 Conclusion
Analytical Solution of Compartment Based Double Pipe Heat Exchanger using Di...IRJET Journal
This document discusses analytical solutions for a compartment-based double pipe heat exchanger model using the differential transform method. The model considers transformer oil as the hot fluid and water as the coolant fluid flowing in parallel through two compartments of the heat exchanger. Analytical expressions for the temperature profiles of the hot and cold fluids over time are derived. The solutions are shown to converge with increasing terms in the series solutions.
Optimization of Air Preheater for compactness of shell by evaluating performa...Nemish Kanwar
Designing of an Air Preheater with increased performance from an existing design through alteration in baffle placement. Analysis of 4 Baffle designs for segmented Baffle case was done using Ansys Fluent. The net heat recovery rate was computed by subtracting pump work from heat recovered. Based on the result, Air Preheater design was recommended.
Distillation Blending and Cutpoint Temperature Optimization in Scheduling Ope...Brenno Menezes
In oil refinery manufacturing, final products such as fuels, lubricants and petrochemicals are produced from crude-oil in process units considering their operations in coordination with tanks, pipelines, blenders, etc. In this process, the full range of hydrocarbon components (crude-oil) is transformed (separated, reacted, blended) into smaller boiling-point temperature ranges resulting in intermediate and final products, in which planning, scheduling and real-time optimization using distillation curves of the streams can be used to effectively model the unit-operations and predict yields and properties of their outlet streams.1 The hydrocarbon streams’ characterization or assays of both the crude-oil and its derivatives are decomposed, partitioned or characterized into several temperature cuts based on what are known as True Boiling Point (TBP) temperature distribution or distillation curves.2,3 These are one-dimensional representations of how quantity (yields) and quality (properties) data of hydrocarbon streams are distributed or profiled over its TBP temperatures where each cut is also referred to as a component, pseudocomponent or hypothetical in process simulation and optimization technology.4
To improve efficiency, effectiveness and economy of mixing/blending, reacting/converting and separating/fractionating inside the oil-refinery, we proposed a new technique to optimize the blending of several streams’ distillation curves with also shifting or adjusting cutpoint temperatures of distilled streams, i.e, their initial boiling point (IBP) and final boiling point (FBP), in order to manipulate their TBP curves in either off-line or on-line environment. By shifting or adjusting the front-end and back-end of the TBP curve for one or more distillate blending streams, it allows for improved control and optimization of the final product demand quantity and quality, affording better maneuvering closer and around downstream bottlenecks such as tight property specifications and volatile demand flow and timing constrictions. This shifting or adjusting of the TBP curve’s IBP and FBP (front- and back-end respectively) ultimately requires that the unit-operation has sufficient handles or controls to allow this type of cutpoint variation where the solution from this higher-level optimization would provide set points or targets to a lower-level advanced process control systems, which are now commonplace in oil refineries.
By optimizing both the recipes of the blended material and its blending component distillation curves, very significant benefits can be achieved especially given the global push towards ultralow sulfur fuels (ULSF) due to the increase in natural gas plays reducing the demand for other oil distillates. One example is provided to highlight and demonstrate the technique.
Numerical Investigation of Mixed Convective Flow inside a Straight Pipe and B...iosrjce
The present study deals with a numerical investigation of steady laminar and turbulent mixed
convection heat transfer in a horizontal pipe and bend pipe using air as the working fluid.The thermal boundary
condition chosen is that of uniform temperature at the outer wall. Computations were performed to investigate
the effect of inlet Rayleigh number and Reynolds number in the velocity and temperature profile at inside of the
pipe. The secondary flow is more intense in the upper part of the cross-section. It increases throughout the
cross-section until its intensity reaches a maximum, and then it becomes weak at far downstream. For the
horizontal pipe the value of the L/D ratio becomes more than 10 the secondary flow effects are neutralized and
the velocity profile almost become constant throughout.
This document presents a mathematical model for determining effectiveness-NTU (ε-NTU) relations for cross-flow heat exchangers with complex flow arrangements. The model discretizes the heat exchanger into small control volumes called "elements" and applies the governing equations to each element. Each element is treated as a simple single-pass cross-flow heat exchanger. An iterative algorithm is used to compute the temperatures at the inlet and outlet of each element. This allows the model to simulate different flow arrangements by using equations valid for a simple mixed/unmixed cross-flow arrangement within each element. The model provides accurate ε-NTU curves for complex geometries not described by existing algebraic relations.
This document summarizes how modern computer dynamic analysis and detailed evaluations can yield significant savings in both weight and cost of flare and relief systems compared to traditional steady state calculation methods. It provides examples showing that dynamic simulation can predict substantially lower relief loads for vessels under fire or distillation column upsets. It also illustrates how dynamic flow analysis of flare header designs allows additional relief sources to be accommodated without exceeding pressure limits, avoiding the need for larger and more expensive systems.
This document reports on analyses and experimentation conducted on a Quadrafire Classic Bay 1200 pellet stove to increase its efficiency and reduce emissions. The researchers have developed a theoretical model that characterizes the stove's heating output as a continuous function of distance along the heat exchanger, accounting for both convection and radiation heat transfer. Their experiments involved upgrading instrumentation to directly measure airflow rates and temperatures, validating assumptions in the previous model. Key findings include radiation significantly influencing heat output and an air-rich firepot reaction that reduces combustion efficiency. The updated model and experiments provide a foundation for redesigning the stove.
This report reconciles the numerical results of both forms of the Colebrook-White Friction Factor equations. It should
be noted that although the Colebrook equation represents the data to within +/- 10 percent, the equations are not
reconciled to within the 5 decimal place. This reconciliation is of great importance to the researches of explicit-forms
of the Colebrook equation as it now can be compared to
either form-1 (Used in the generation of the Moody Diagram) or form-2, preferred by some investigators. The author prefers form-1 as it is the most compact-form
This document summarizes the design of urban hydraulic structures in Davis, CA, including a drainage channel, gutters, storm sewers, a culvert, and detention pond. The structures are designed to handle peak runoff from a 10-year storm. Runoff from a playing field and school yard enters the drainage channel, which flows through a culvert and into a detention pond located 4,000 feet away. Six methods were used to estimate peak runoff: Velocity Method, Wong's Formula, Kerby's Formula, FAA Formula, Morgali and Linsley's Formula, and Chen and Yen's Formula. Wong's Formula produced a peak runoff closest to the average when calculating individually for each watershed
Using Lattice Boltzmann Method to Investigate the Effects of Porous Media on ...A Behzadmehr
1) The document describes a numerical study using the lattice Boltzmann method to investigate heat transfer from a solid block inside a porous media-filled channel.
2) The effects of porosity and thermal conductivity ratio on fluid flow patterns and temperature fields were examined.
3) Higher porosity and lower thermal conductivity ratio resulted in lower fluid temperatures, as increased porosity reduces the effective thermal conductivity and thus heat transfer between the fluid and solid block.
Mech 0036 exam 12 13 with answers (revision)Mostafa Tamish
This document appears to be a past exam paper and marking scheme for a Thermal Power Plant and Heat Transfer course. It contains 6 questions testing various concepts related to thermodynamics, heat transfer, steam power plants, gas turbines, and refrigeration. For each question, it provides the question prompt, relevant figures or tables, and a detailed multi-part solution and marking scheme. The questions cover topics such as spark ignition engines, Rankine cycles, heat exchangers, radiation heat transfer, and gas turbine cycles.
The document discusses the construction of attainable regions (AR) to analyze chemical reaction systems and optimize reactor network design. The AR represents the collection of all possible outputs for all reactor configurations, even those not yet imagined. AR theory can be used to compare reactor performance, establish performance targets, interpret and optimize reactor networks. Examples demonstrate how to systematically construct the AR for a reaction system using different reactor types (CSTR, PFR) and represent it graphically to identify the optimal reactor configuration for a given reaction.
This document provides an overview of methods for calculating gas properties including:
1. Empirical correlations for calculating z-factors such as Hall-Yarborough and Dranchuk-Abu-Kassem.
2. Calculation of gas compressibility, gas formation volume factor, and gas expansion factor using real gas equations of state.
3. Empirical correlations for calculating gas viscosity including Carr-Kobayashi-Burrows and Lee-Gonzalez-Eakin.
This document discusses interparticle forces and phase diagrams. It covers hydrogen bonding, phase transitions like melting and boiling points, heat involved in phase changes of water, phase diagrams showing pressure and temperature relationships, ideal gas properties on 3D graphs, and two-component phase diagrams showing temperature and composition relationships. Gibbs' phase rule is also explained for two-component systems in determining the degrees of freedom based on the number of phases present.
A strategy for an efficient simulation of countcorrent flows in the iron blas...Josué Medeiros
This document summarizes a strategy for efficiently simulating countercurrent gas and solids flows in an iron blast furnace. Key aspects of the strategy include:
1) Modeling the gas flow using an anisotropic Ergun equation that accounts for layered porous media and can be solved using a computationally efficient algorithm.
2) Modeling the slow descending solids flow using an irrotational flow assumption and conservation of mass.
3) Modeling heat transfer between the gas and solids using energy balance equations that account for convection and heat exchange, with appropriate enthalpy-temperature relationships.
4) Accounting for the stagnant central "deadman" zone and high-flow "race
- The document summarizes computational fluid dynamics (CFD) simulations performed using the Data-Parallel Line Relaxation (DPLR) method to model various geometries tested in arc-jet experiments, including a 20-degree half wedge, flat-faced puck, spherical calorimeter, and nose of the space shuttle.
- The results of the CFD simulations will be compared to actual test and flight data to validate the models and improve accuracy for future simulations.
- Key geometries modeled include a 20-degree half wedge, flat-faced puck with corner radii of 0.25 and 0.0625 inches, and spherical calorimeter. Freestream conditions and surface properties were defined based on
The document summarizes the VISHNU hybrid model for describing the viscous QCD matter created in relativistic heavy ion collisions. VISHNU couples viscous hydrodynamics to model the quark-gluon plasma expansion with a hadron cascade model to describe the late hadronic stage. The model is used to extract the shear viscosity to entropy density ratio of the QGP (η/s)QGP by fitting elliptic flow data from RHIC and LHC. Analyses find 1 < 4π(η/s)QGP < 2.5 at RHIC and a slightly higher value at LHC, although the temperature dependence cannot be uniquely constrained. VISHNU provides an excellent description of spectra and differential flow
Analysis of Coiled-Tube Heat Exchangers to Improve Heat Transfer Rate With Sp...IJMER
Steady heat transfer enhancement has been studied in helically coiled-tube heat exchangers. The outer side of the wall of the heat exchanger contains a helical corrugation which makes a helical rib on the inner side of the tube wall to induce additional swirling motion of fluid particles. Numerical calculations have been carried out to examine different geometrical parameters and the impact of flow and thermal boundary conditions for the heat transfer rate in laminar and transitional flow regimes. Calculated results have been compared to existing empirical formula and experimental tests to investigate the validity of the numerical results in case of common helical tube heat exchanger and additionally results of the numerical computation of corrugated straight tubes for laminar and transition flow have been validated with experimental tests available in the literature. Comparison of the flow and temperature fields in case of common helical tube and the coil with spirally corrugated wall configuration are discussed. Heat exchanger coils with helically corrugated wall configuration show 80–100% increase for the inner side heat transfer rate due to the additionally developed swirling motion while the relative pressure drop is 10–600% larger compared to the common helically coiled heat exchangers. New empirical Co-relation has been proposed for the fully developed inner side heat transfer prediction in case of helically corrugated wall configuration.
1. Placing a full glass bottle of water in the freezer would cause it to break because water expands as it freezes and the sealed bottle provides no room for expansion.
2. The phase diagram shows that at higher altitudes, the boiling point of water decreases and the melting point increases due to lower atmospheric pressure. This could require longer cooking times in mountains.
3. If two cylinders made of materials A and B conduct heat at the same rate when subjected to the same temperature difference, and the diameter of A is twice the diameter of B, then the thermal conductivity of A is one fourth that of B.
This document summarizes the development of a high-power lithium target for accelerator-based boron neutron capture therapy (BNCT). Key points:
- A water-cooled conical target is being developed to accept a 50 kW proton beam and produce neutrons via the 7Li(p,n)7Be reaction for BNCT applications.
- Computational fluid dynamics modeling was used to design the target with 20 helical water channels to keep the lithium surface below 150°C with a water flow of 80 kg/min.
- An initial prototype target was fabricated and underwent preliminary hydraulic testing matching CFD predictions. Further electron beam thermal testing is planned at Sandia National Laboratories to validate the
This document provides the solutions to homework problems assigned in a thermodynamics course. It summarizes the key steps and conclusions for 6 problems involving concepts like approximations for enthalpy of compressed water, properties of water at different temperatures and pressures, heat transfer for a refrigerant, the Rankine cycle diagram, and properties of propane using different equations of state. The last problem calculates about 700 kJ/kg of work done by steam expanding adiabatically between two states.
Design Calculations for Solar Water Heating Systemsangeetkhule
Chapter 1 City of Residence
Chapter 2 Estimation of Available Solar Resources
Chapter 3 Site Survey
Chapter 4 Load Estimation
Chapter 5 Estimation of Required Absorber Area
Chapter 6 Market Survey & Estimation of No. of Tubes for ETC
Chapter 7 Economical Analysis & Estimation of Payback Period
Chapter 8 Conclusion
Article in Hydrocarbon Engineering September 2019 about K°BOND diffusion bonded heat exchanger, also known as Printed Circuit Heat Exchanger (PCHE).
PCHE is used as recuperator in supercritical CO2 (sCO2) Power Cycles. The Allam Fetvedt cycle is a sCO2 cycle with oxifuel combustion. After the CO2 stream has been used in the power cycle it can be stored underground in depletee oil fields as CCS.
The document outlines a 14-step process for manually designing a shell and tube heat exchanger using the Kern method. Key steps include: 1) obtaining thermo-physical properties of fluids, 2) performing an energy balance to determine heat duty, 3) assuming an overall heat transfer coefficient, 4) deciding tube passes and calculating the log mean temperature difference, 5) calculating required heat transfer area, 6) selecting tube materials and dimensions, 7) deciding exchanger type and tube pitch, 8) assigning fluids and selecting baffles, 9) calculating heat transfer coefficients, 10) checking the calculated overall heat transfer coefficient, 11) recalculating as needed, 12) calculating overdesign, 13) calculating pressure drops, and 14)
This document discusses the thermal and hydraulic design of brazed aluminum plate-fin heat exchangers. It covers topics such as single and multiple banking configurations, multi-stream designs, thermal design procedures and relationships, hydraulic design considerations, and the selection of fin geometry. Key points include defining the components of heat transfer surface area and pressure drop, methods for calculating overall heat transfer coefficients and temperature differences, and considerations for single-phase versus two-phase stream calculations.
The document describes a numerical simulation of the transient thermal behavior of a flat plate solar collector. The simulation applies finite differences to a two-dimensional grid representing the absorber plate and calculates temperatures and heat transfer. It examines the effects of irradiance, mass flow rate, and other parameters on temperatures, heat loss coefficient, and collector efficiency over time. Results are compared to previous studies and conclusions discuss future research opportunities.
CENE 599 Sp16 Lecture 19 1
CENE 599 Sp16 Lecture 19 2
Aerated Pond Example Problem
Use the data below to design a partial mix aerated pond with three cells of equal volume.
Parameter Value
Design flow rate = 3,850 m3/day
Influent BOD5 = 310 mg/L
Effluent BOD5 = 30 mg/L
Reaction rate at 20 oC = 0.204 day-1
Influent temperature oC = 17 oC
Summer air temp. oC = Ta = 31 oC
Winter air temp. oC = Ta = 11 oC
Temperature correction coefficient = 1.03
a. Design for winter conditions.
b. Estimate the reaction rate k using Equation 3-5 and a temperature higher
than the winter air temperature, but lower than the influent water
temperature.
CENE 599 Sp16 Lecture 19 3
b. Estimate the reaction rate k using Equation 3-5 and a temperature higher
than the winter air temperature, but lower than the influent water
temperature.
Aerated Pond Design Example
Use T = 14oC as a first trial value.
CENE 599 Sp16 Lecture 19 4
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 5
c. Calculate the total detention time using Equation 3-7.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 6
d. Calculate the volume of each reactor using the flow rate, number of cells,
and detention time.
Aerated Pond Design Example
e. Calculate the surface area of each cell using L:W=3, and a depth of 4.0 m.
For this problem, assume the ponds have vertical walls.
CENE 599 Sp16 Lecture 19 7
f. Check the pond temperature using the temperatures provided for this
problem, the surface area you just calculated, and Equation 3-6. If this
temperature is more than 10% different than your assumed temperature,
then use the calculated temperature from Equation 3-6 and start over
from Step b above.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 8
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 9
g. Calculate the effluent from Cell 1 using the equation we derived on
Lecture 18 Page 2.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 10
h. For the second cell, calculate the water temperature (Tw) and rate
constant k at that temperature, then calculate the BOD. The volume,
surface area, and HRT stay the same for all cells.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 11
i. For the third cell, make the same calculations using the temperature of
the influent water from Cell 2.
Aerated Pond Design Example
j. The concentration is slightly higher than 30 mg/L because of the
decreasing k values as temperature decreased in the cells.
CENE 599 Sp16 Lecture 19 12
k. Prepare a summary table showing the volume, length, width, and depth
of your ponds and provide a sketch.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 13
If you set this up as a spreadsheet it is easy to alter the HRT for the cells and
adjust the outflow concentration of the last cell to <= 30 mg/L.
Aerated Pond Design Example
CENE 599 Sp16 Lecture 19 14
If you set this up as a spreadsheet it is easy to alter the HRT for the cells and
adju ...
This document discusses the advantages of considering compact heat exchangers like plate-and-frame exchangers early in the process design stage. Plate-and-frame exchangers can be significantly smaller than traditional shell-and-tube exchangers while meeting the same heat transfer needs. Specifying design requirements without considering the characteristics of different exchanger types can lead to oversized and more expensive designs. Charts are provided to help estimate the required area of plate-and-frame exchangers for preliminary sizing.
This document discusses the advantages of considering compact heat exchangers like plate-and-frame exchangers early in the process design stage. Plate-and-frame exchangers can be significantly smaller than traditional shell-and-tube exchangers while meeting the same heat transfer needs. Specifying design requirements without considering the characteristics of different exchanger types can lead to oversized and more expensive designs. Charts are provided to help estimate the required area of plate-and-frame exchangers for preliminary sizing.
This document discusses the advantages of considering compact heat exchangers like plate-and-frame exchangers early in the process design stage. Plate-and-frame exchangers can be significantly smaller than traditional shell-and-tube exchangers while meeting the same heat transfer needs. Specifying design requirements without considering the unique capabilities of different exchanger types can lead to oversized and more expensive designs. Charts are provided to help estimate the required area of plate-and-frame exchangers for preliminary sizing.
This document presents a rule-of-thumb design procedure for wet cooling towers that can be used for power plant cycle optimization. It begins with defining the design problem and specifying inlet/outlet water temperatures and ambient wet-bulb temperature. It then provides methods to calculate the outlet air temperature, tower characteristic, loading factor, and other key parameters. These include using the average of inlet/outlet water temperatures to approximate outlet air temperature, graphically integrating the Merkel equation to determine tower characteristic, and using graphs to determine the optimum loading factor based on design conditions. The goal is to provide simplified methods for estimating cooling tower dimensions, performance, costs and other details needed for power plant analysis without requiring detailed iterative design calculations.
The document discusses heat transfer in lithium-ion battery cells, specifically those used in Tesla electric vehicles. It provides background on battery heating during charging/discharging and the need for cooling. Both analytical and numerical methods are used to model the temperature distribution in a single cylindrical "18650" battery cell during cooling. While the analytical solution assumes uniform radial cooling, the numerical solution in COMSOL shows a more even internal temperature, indicating the full 3D modeling is needed to understand battery pack thermal management requirements.
I am Dave J. I am a Nuclear Engineering Exam Helper at liveexamhelper.com. I hold a Masters' Degree in Nuclear Physics from, University of Chicago, USA. I have been helping students with their exams for the past 12 years. You can hire me to take your exam in Nuclear Engineering.
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Mechanical engineering competitive exam previous year question paperdeepa sahu
The document contains 20 multiple choice questions related to topics in thermodynamics, heat transfer, refrigeration, and machining processes. Key topics covered include the Carnot cycle, vapor compression refrigeration cycle, heat exchangers, thermostatic expansion valves, and adiabatic processes. The questions assess understanding of concepts like efficiency, specific heat, latent heat of vaporization, and critical thickness of insulation.
Numerical_Analysis_of_Turbulent_Momentum_and_Heat_Transfer_in_a_Rectangular_H...Nate Werner
- The document analyzes turbulent momentum and heat transfer in a rectangular helical duct using water and freon-12 as working fluids.
- It develops a 3D model of the helical duct geometry based on prior research. Simulations are run using ANSYS Fluent and CFX to analyze flow, temperature, and turbulence fields.
- The results show secondary flows developing with higher velocities near the outer wall. While some aspects agree with prior work, the simulations produce higher velocities and different vortex structures than expected. Thermal development also takes longer than velocity development.
This document contains a 55 question multiple choice exam on mechanical engineering topics. The exam is divided into two sections, with Section A containing 70 marks worth of questions. The questions cover topics such as thermodynamics, heat transfer, fluid mechanics, mechanics, and materials. The format and content indicate this is a comprehensive exam assessing students' knowledge across various mechanical engineering subject areas.
Analysis of Heat Storage with a Thermocline Tank for Concentrated Solar Plant...Albert Graells Vilella
This document summarizes a simulation model developed to analyze heat storage in a thermocline tank for concentrated solar power plants. The model is based on solving convection-diffusion equations for heat transfer in the fluid and between the fluid and filler material. It is validated against experimental data and used to analyze the effects of tank geometry on storage efficiency and compare different heat transfer fluids. The model is then applied to design an alternative storage system for a solar plant called AndaSol I.
Analysis of Heat Storage with a Thermocline Tank for Concentrated Solar Plant...
adv_report_cfd
1. MAE598 Applied Computational Fluid
Dynamics
Fall 2016 Project#1
Aditya Vipradas
ASU ID: 1209435588
October 12, 2016
Nomenclature
Density: ρ(kg/m3
), Specific Heat: Cp(J/kg − K), Thermal Conductivity: K(W/m − K),
Viscosity: µ(kg/m − s), Thermal Expansion Coefficient: β(K−1
), Operating Temperature:
t(o
C), Water outlet temperature: Tout, non-uniform velocity at the outlet: νn, non-uniform
temperature at the outlet: T.
The hot water tank geometry is as shown in Fig.1.
Figure 1: Water tank geometry and dimensions
The second-order tetrahedral mesh parameters are kept consistent for all the cases
with relevance set to medium, advanced size function set to curvature and advanced
inflation set to program controlled. The material properties are set as shown in Table. 1.
1
2. Material Properties
• ρ : 2719
Water container (aluminum) • Cp : 871
• K : 202.4
• ρ : 989.7576
• Cp : 4216
Water • K : 0.677
• µ : 8e−4
• β : 4.2827e−4
Table 1: Material Properties
The density of water is not constant. It is considered to vary with the water temperature
according to the Boussinesq approximation. This allows natural thermal convection due to
buoyancy. The Boussinesq approximation only updates the density terms associated with
gravitational acceleration g. Thus, gravity is set ”on” in ANSYS Fluent. It is expected that
hot water should rise and cold water should settle on the container base plate. The water
density and thermal expansion coefficient in Table. 1 is calculated at an operating temperature
of 318K (45o
C) using the Kell’s formulation in Eq. 1 and 2 respectively. These equations are
calculated at an operating pressure of 101.325 kPa.
ρ =
999.84 + 16.95t − 7.99 ∗ 10−3
t2
− 46.17 ∗ 10−6
t3
+ 105.56 ∗ 10−9
t4
− 280.54 ∗ 10−12
t5
1 + 16.9 ∗ 10−3t
(1)
β =
−1
ρ
dρ
dt
(2)
The material properties are set as shown in Figs.2 and 3. The Boussinesq approximation is
set by clicking: (Define – Operating Conditions).
Figure 2: Water material properties using Boussinesq implementation
2
3. Figure 3: Aluminum material properties
The boundary conditions are set as shown in Figs.4, 5 and 6. The tank base is externally
maintained at 65o
C or 338K. The container base plate roughness height is set to a standard
value of 1e − 6m.
Figure 4: Water inlet boundary conditions
Figure 5: Water outlet boundary conditions
3
4. Figure 6: Tank base boundary conditions
The water outlet temperature is calculated as the average of temperature weighted by
flow rate as in Eq.3. This is performed in each case as shown in Fig.7. Set the expressions
by clicking: (Define – Custom Field Functions) and integrate over the area by clicking:
(Reports – Surface Integrals).
Tout =
νnTdA
νndA
(3)
(a) Defining numerator in custom field function (b) Defining denominator in custom field
function
(c) Integrating numerator over water outlet area (d) Integrating denominator over water outlet
area
Figure 7: Calculating Tout using Eq. 3
4
5. The convergence residuals are set as shown in Table. 2.
Residual Absolute Criteria
continuity 0.001
x-velocity 0.001
y-velocity 0.001
z-velocity 0.001
energy 1e-06
k 0.001
epsilon 0.001
Table 2: Residual Criteria
Solution convergence is checked through the original residual plots and adapted mesh
residual plots. Temperature gradient based mesh adaptation can be performed in each case
as shown in Fig.8. The Refine Threshold is set at 10% of the maximum temperature gradient.
Procedure: (Adapt – Gradient).
Figure 8: Mesh adapt based on temperature gradient
Solution convergence is ensured by checking mass and energy balance of the system along
with the convergence in residual plots. Mass (M) and energy (H) equations are given in Eq.
4 and 5 respectively.
M = νnρdA (4)
H = νnρCpTdA (5)
5
6. The Fluxes and Custom Field Functions options are used to ensure the mass and
energy balance respectively as shown in Figs. 9 and 10.
Figure 9: Mass balance evaluation
Figure 10: Energy balance evaluation
6
7. 1 Task 1: Case A
In Case A: H = 1m, D = 0.5m, d = 0.04m, L = 0.1m, Z1 = 0.8m, Z2 = 0.2m. The
hot water tank is symmetrically model about ZX plane as shown in Fig. 11 to reduce the
computation cost .
(a) CAD model (b) Tetrahedral mesh
(c) CAD model (d) Tetrahedral mesh
Figure 11: Water tank model in ANSYS Workbench
The material properties and boundary conditions are set as explained previously. 858 cells
are marked for adaptation and the total number of cells increases from 40672 to 46678. The
solution converges with the scaled residuals chart as shown in Fig.12.
7
8. (a) Original scaled residuals after 472 iterations
(converges)
(b) Adapted scaled residuals after 516 iterations
(converges)
Figure 12: Solution convergence
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 3.
Water Inlet Water Outlet
Mass 0.0306 -0.0307
Energy 38425.91 40902.87
Table 3: Mass and energy balance
The Tout for Task1: Case A is calculated using Eq.3 and equals 311.0125 K.
The deliverables for this task are as follows:
Figure 13: Temperature contour plot on symmetry plane
8
9. (a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 14: Temperature contour plots
(a) Streamline on the symmetry plane (b) Streamline in the container
Figure 15: Streamline plots
2 Task 1: Case B
In Case B: H = 1m, D = 0.5m, d = 0.04m, L = 0.1m, Z1 = 0.2m, Z2 = 0.8m. The
hot water tank is symmetrically modeled about ZX plane as shown in Fig. 16 to reduce the
computation cost .
9
10. (a) CAD model (b) Tetrahedral mesh
(c) CAD model (d) Tetrahedral mesh
Figure 16: Water tank model in ANSYS Workbench
The material properties and boundary conditions are set as explained previously. 1705
cells are marked for adaptation and the total number of cells increases from 52418 to 64353.
The solution converges with the scaled residuals chart as shown in Fig.17.
(a) Original scaled residuals after 818 iterations
(converges)
(b) Adapted scaled residuals after 1093
iterations (converges)
Figure 17: Solution convergence
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 4.
10
11. Water Inlet Water Outlet
Mass 0.0306 -0.0306
Energy 38425.97 40442.35
Table 4: Mass and energy balance
The Tout for Task1: Case B is calculated to be 310.0846 K.
The deliverables for this task are as follows:
Figure 18: Temperature contour plot on symmetry plane
(a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 19: Temperature contour plots
11
12. (a) Streamline on the symmetry plane (b) Streamline in the container
Figure 20: Streamline plots
Thus, for Task 1,
Case A Case B
Temperature (K) 311. 0125 310.0846
Table 5: Water outlet temperatures (K)
As seen in Table. 5, the Tout in Case A is greater than that in Case B . Even though the
difference is not that significant, some rationales can be drawn to explain this difference.
1. As observed from the streamline plots of both the cases, water from the inlet in Case A
is in contact with the heating base over a larger area (almost complete) as compared to
that in Case B. In Case B, the inlet water does not come in contact with a portion of
heating base area near the inlet as seen in Fig.20. The hot base is the primary source
of temperature increase in water through conduction.
2. In Case A, as seen from Figs.15 and 13, water vortex as seen in the streamline plots
is formed in the region where the temperature is considerably higher (more than 310
K) whereas Figs.20 and 18 show that the water vortex forms in the region where the
temperature is relatively lower (about 308 K). This results into more heat transfer in
Case A due to buoyant natural convection.
3. Water in Case A falls from a greater height than that in Case B. Thus, it has more
energy as compared to Case B which eventually results in a larger heat dissipation and
temperature increase as it strikes the container base. This is a minor detail but it
contributes to the small temperature difference in both the cases.
4. Also, heat transfer at the base in Case B should be more due to larger temperature
gradient. This is because the water inlet in Case B is closer to the base as compared to
Case A. But the three reasons mentioned above seem predominant resulting in a larger
water outlet temperature in Case A.
12
13. 3 Task 2: Case C
In Case C: H = 1m, D1 = 0.625m, D2 = 0.4m, d = 0.04m, L = 0.1m, Z1 = 0.2m, Z2 = 0.8m.
Total volume of main tank remains the same as that of the design in Task 1 as shown in Fig.
21. The hot water tank is symmetrically modeled about ZX plane as shown in Fig. 22 to
reduce the computation cost.
Figure 21: Elliptical cross section of the water tank
(a) CAD model (b) Tetrahedral mesh
Figure 22: Water tank model in ANSYS Workbench
13
14. The material properties and boundary conditions are set as explained previously. 749 cells
are marked for adaptation and the total number of cells increases from 35724 to 40967. The
solution converges with the scaled residuals chart as shown in Fig.23.
(a) Original scaled residuals after 399 iterations
(converges)
(b) Adapted scaled residuals after 499 iterations
(converges)
Figure 23: Solution convergence
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 6.
Water Inlet Water Outlet
Mass 0.0306 -0.02930
Energy 38425.99 40308.45
Table 6: Mass and energy balance
The Tout for Task2: Case C is calculated to be 311.3155 K.
The deliverables for this task are as follows:
Figure 24: Temperature contour plot on symmetry plane
14
15. (a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 25: Temperature contour plots
(a) Streamline on the symmetry plane (b) Streamline in the container
Figure 26: Streamline plots
The given container being a heat pump, the efficiencies of elliptical and circular designs
are measured in terms of their coefficients of performance as given in Eq.6.
COP =
TH
TH − TC
(6)
Here, TH and TC are water outlet and inlet temperatures respectively. The COPs obtained
are as shown in Table. 7. Here, given that the cross-sectional areas are the same in cases
Case B (circular) Case C (elliptical)
COP 25.67 23.38
Table 7: Coefficients of Performance
B and C, for the given mesh, the heat flux from the base should be equal. But as seen in
the simulation, it is 1560 W for Case B and 1735 W for Case C. This is due to different
15
16. mesh at the container base. The work consumed i.e. water inlet height is same in both the
cases. Therefore, ideally, COP and water outlet temperatures for cases B and C should be
the same. Here, COPB is larger than COPC due to the different heat flux values. The outlet
temperature in Case C is larger as seen in Table. 8.
Case B (circular) Case C (elliptical)
Outlet Temperature (K) 310.0846 311.3155
Table 8: Outlet Temperatures
4 Task 2: Case D
In Case D: H = 1m, D1 = 0.4m, D2 = 0.625m, d = 0.04m, L = 0.1m, Z1 = 0.2m,
Z2 = 0.8m. Total volume of the main tank remains the same as the design in Task 1 as shown
in Fig. 21. The hot water tank is symmetrically modeled about ZX plane as shown in Fig.
27 to reduce the computation cost.
(a) CAD model (b) Tetrahedral mesh
Figure 27: Water tank model in ANSYS Workbench
The material properties and boundary conditions are set as explained previously. 886 cells
are marked for adaptation and the total number of cells increases from 41317 to 47519. The
solution converges with the scaled residuals chart as shown in Fig.28.
16
17. (a) Original scaled residuals after 187 iterations
(converges)
(b) Adapted scaled residuals after 254 iterations
(converges)
Figure 28: Solution convergence
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 12.
Water Inlet Water Outlet
Mass 0.0306 -0.0307
Energy 38452.99 41951.39
Table 9: Mass and energy balance
The Tout for Task2: Case D is calculated to be 311.3222 K.
The deliverables for this task are as follows:
Figure 29: Temperature contour plot on symmetry plane
17
18. (a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 30: Temperature contour plots
(a) Streamline on the symmetry plane (b) Streamline in the container
Figure 31: Streamline plots
The COPs obtained for this case from Eq.6 are as shown in Table. 10. Here, given that
Case B (circular) Case D (elliptical)
COP 25.67 23.37
Table 10: Coefficients of Performance
the cross-sectional areas are the same in cases B and D, for the given mesh, the heat flux from
the base should be equal. But as seen in the simulation, it is 1560 W for Case B and 1730
W for Case D. This is due to different mesh at the container base. The work consumed i.e.
water inlet height is same in both the cases. Therefore, ideally, COP and outlet temperature
for cases B and D should be the same. Here, COPB is larger than COPD due to the different
heat flux values. The outlet temperature in Case D is larger as seen in Table. 11.
18
19. Case B (circular) Case D (elliptical)
Outlet Temperature (K) 310.0846 311.3222
Table 11: Outlet Temperatures
5 Task 3
The geometry in this task is the same as that in Task 1: Case A.
The material properties and boundary conditions are set as explained previously except
that the water density is set constant and gravity effects are turned ”off”. 836 cells are
marked for adaptation and the total number of cells increases from 40672 to 46524. The
solution converges with the scaled residuals chart as shown in Fig.32.
(a) Original scaled residuals after 644 iterations
(converges)
(b) Adapted scaled residuals after 656 iterations
(converges)
Figure 32: Solution convergence
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 12.
Water Inlet Water Outlet
Mass 0.0306 -0.0306
Energy 38425.91 38694.71
Table 12: Mass and energy balance
The Tout for Task3 is calculated to be 300.8424 K.
The deliverables for this task are as follows:
19
20. Figure 33: Temperature contour plot on symmetry plane
(a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 34: Temperature contour plots
(a) Streamline on the symmetry plane (b) Streamline in the container
Figure 35: Streamline plots
20
21. When the density is set constant and gravity is turned ”off”,
1. There is no buoyancy driven natural convection in the fluid due to the Boussinesq
approximation.
2. Thus, motion of hot and cold water is not driven due to density difference in them.
3. Heat transfer only takes place due to heat conduction from the bottom plate and thus
major portion of the fluid in the container is close to 298 K (temperature of cold water).
4. The water outlet temperature is significantly lower (300.84 K) as compared to that in
tasks 1 and 2.
5. Water flow is driven only by the inlet velocity and not by gravity.
6 Task 4
The geometry in this task is the same as that in Task 1: Case B.
The heat flow from the bottom plate into the tank is obtained by using the Total Heat
Transfer Rate option in the Fluxes menu as shown in Fig.36.
Figure 36: Heat transfer rate estimation
Thus, the total heat flow rate from the bottom plate into the tank is 1559.466 W.
To obtain the average heat flux, the total heat flow rate obtained is divided by half the
cross-sectional area of the bottom plate as shown in Eq.7.
HeatFluxavg =
1559.466
π
8
0.52
= 15884.59W/m2
(7)
The constant temperature condition on the bottom plate is replaced with that of the
constant heat flux (15884.59W/m2) as shown in Fig.37.
21
22. Figure 37: Applying constant heat flux condition
The material properties and boundary conditions are set as explained previously. The
solution converges with the scaled residuals chart as shown in Fig.38.
Figure 38: Scaled residuals (converges)
Mass (kg/s) and energy (J/s) for this case are conserved as seen in Table. 13.
Water Inlet Water Outlet
Mass 0.0306 -0.0306
Energy 38425.97 40476.43
Table 13: Mass and energy balance
The Tout for Task4 is calculated to be 310.0598 K.
This temperature is exactly equal to the outlet temperature obtained in Task 1: Case B
as seen in Table. 14.
22
23. Task 4 Task 1: Case B
Outlet Temperature (K) 310.0598 310.0846
Table 14: Outlet Temperatures
The reason for this is that the constant temperature condition of 338K generates the heat
flux of 15884.59W/m2
. Therefore, providing the constant temperature condition of 338K or
heat flux condition of 15884.59W/m2
gives the same results and eventually the same water
outlet temperature.
Outlet Temperature Results
Case A Case B Case C Case D Task 3 Task 4
Outlet Temperature (K) 311.0125 310.0846 311.3155 311.3222 300.8424 310.0598
Table 15: Outlet Temperatures
23
24. 7 Challenge 1 [Aditya Vipradas (1209435588)]
Collaborated with Abhijeet Durgude (1209679741)
The geometry in this task is the same as that in Task 1: Case B. The temperature imposed
on the bottom plate is non-uniform time-independent as given in Eq. 8.
T(r) = 65exp(−r/D) (8)
This temperature is in o
C, r is the radial distance at the bottom plate. The non-uniform
temperature is imposed using a User-Defined Function (UDF) in Fluent. The UDF is programmed
in Notepad as shown in Fig. 39.
Figure 39: UDF for non-uniform temperature
As seen in the C Program, the F CENTROID function in udf.h outputs the centroids of
each cell of a given face f. These centroids are used to calculate the radial distance r which
is used to define the temperature profile in F PROFILE function. 273 is added to convert the
temperature in K. The .c programme file is interpreted in Fleunt (Procedure: Defined -
User-Defined - Functions - Interpreted). The boundary condition is applied by selecting
the respective programme file in the Temperature section of the bottom plate boundary
condition.
Figure 40: Impose the non-uniform temperature boundary condition
24
25. The temperature profile obtained on the bottom plate is as shown in Fig.41.
Figure 41: Temperature profile on the bottom plate
The Tout for Challenge 1 is calculated to be 304.8164 K.
The deliverables for this task are as follows:
Figure 42: Temperature contour plot on symmetry plane
25
26. (a) Temperature contour plot on the c/s at Z =
0.2 m
(b) Temperature contour plot on the c/s at Z =
0.8 m
Figure 43: Temperature contour plots
(a) Streamline on the symmetry plane (b) Streamline in the container
Figure 44: Streamline plots
Challenge 1 Task 1: Case B
Outlet Temperature (K) 304.8164 310.0846
Table 16: Outlet Temperatures
As seen, the outlet temperature in challenge 1 is lower than that in Task 1: Case B as
expected.
26
27. 8 Challenge 2 [Aditya Vipradas (1209435588)]
No Collaboration
The geometry in this task is the same as that in Task 1: Case A. The dependence of
temperature on varying inlet velocity is evaluated in this task. The ParametricDesign tool is
used for this purpose. The inlet velocity input parameter is defined as shown in Fig. 45. The
numerator and denominator surface integrals over the outlet and mid-plane areas are defined
as the output parameters
Figure 45: Defining input and output parameters
As seen in this figure, the X-velocity in the inlet is defined as the input parameter, whereas,
the numerator and denominator surface integrals from Eq.3 over outlet and mid-plane areas
are defined as the output parameters. The Fluent box in ANSYS changes as shown in Fig.46.
Clicking the Parametric Set option and solving for all the design points yields the output
as shown in Fig.47.
Figure 46: Parametric Design
27
28. Figure 47: Parametric Design Output
As seen in Fig.47, P2: Numerator surface integral over outlet area, P3: Denominator
surface integral over outlet area, P4: Numerator surface integral over mid-plane area, P5:
Denominator surface integral over mid-plane area. Thus,
Tout =
P2
P3
(9)
Tmid =
P4
P5
(10)
The outlet and mid-plane temperatures are obtained by exporting the parametric data in
Excel as shown in Fig.48.
Figure 48: Tout and Tmid temperature calculations
The line plots for Tout and Tmid are as shown in the Figs. 49 and 50.
28
29. Figure 49: Tout vs Vin plot
Figure 50: Tmid vs Vin plot
As seen in Fig.49, the outlet temperature is lower for 0.01m/s inlet velocity because of
increase in the back-flow due to low inlet velocity. The Tmid increases with decrease in Vin as
seen in Fig.50.
29
30. MAE598 Applied Computational Fluid
Dynamics
Fall 2016 Project#2
Aditya Vipradas
ASU ID: 1209435588
November 9, 2016
1 Common Simulation Procedure
Create the respective geometry in DesignModeler and mesh it according to the given
criteria in ANSYS Workbench. Use Pressure-Based solver and turn Gravity on. Select
the Multiphase model with 2 Volume of Fluids. Set Implicit Body Force on. Use the
required material properties from the Fluent Database. In Solution Methods, select the
Pressure-Velocity Coupling to PISO given the transient simulations with large time steps.
Use PRESTO! pressure spatial discretization scheme. Change the Pressure Under-Relaxation
Factor to 0.9.
The entire domain is occupied by Phase-1 by default. In order to add Phase-2 in it, adapt
the region using Adapt - Region - Mark by specifying the corresponding coordinates as
shown in Fig.1. Once, the region is marked, it is patched to the domain by selecting the
Patch option after initializing the solution. Patch button is present near the Initialize button
in Solution Initialization.
(a) Mark the phase-2 region (b) Patch the phase-2 region
Figure 1
In the patch window, make sure to select phase-2 option in Phase and input the value of
1 to include that phase in the simulation domain.
1
31. 2 Task 1
Initial Conditions: The domain is filled with kerosene and engine-oil as shown in Fig.2.
At t>0: Kerosene and engine-oil rearrange under the effect of gravity
Model: Multiphase viscous laminar and inviscid
Mesh: Relevance center: Fine, Nodes: 1980
(a) 2-D domain
(b) Mesh
Figure 2
The contour plots obtained after the simulation are as shown in the figures below.
(a) Phase-2 contour plot at t = 1s (b) Phase-2 contour plot at t = 5s
Figure 3: Viscous laminar model
2
32. (a) Phase-2 contour plot at t = 10s for laminar
model
(b) Phase-2 contour plot at t = 1s for
inviscid model
Figure 4
(a) Phase-2 contour plot at t = 5s (b) Phase-2 contour plot at t = 10s
Figure 5: Viscous inviscid model
The potential energy of the mixture in the rectangular domain when t tends to infinity
(PEo) is calculated to be 7761.2953 J or 3880.6477 J/m3
. This is the potential energy of the
mixture when the engine-oil settles at the bottom. The available potential energy (APE) is
defined as the difference between the potential energy of the mixture at a given time (PE)
and the potential energy when t tends to infinity.
APE = PE − PEo (1)
The APE, KE and APE +KE are plotted for the simulation for both the cases when the
viscous model is laminar and inviscid. The plot extends from t = 0s to t = 25s and consists
of 250 data points at the interval of 0.1s.
3
33. (a) APE (White), KE (Red), APE+KE (Green)
plots for the laminar model
(b) APE (White), KE (Red), APE+KE
(Green) plots for the inviscid model
Figure 6: Energy plots
The effect of viscosity is present in the laminar model and it is absent in the inviscid model.
As observed in Fig.6, the effect of viscosity in the laminar model dissipates energy and results
in the convergence of the available kinetic and potential energies to zero at a faster rate.
Whereas, in the inviscid model, the absence of viscosity does not result in the dissipation of
energy and therefore, the available kinetic and potential energies converge to zero at a slower
rate. As seen in the figure, the energies have not converged to zero in 25s in the inviscid model
whereas in the laminar model, they have almost converged to zero in 25s.
4
34. 3 Task 2
The 2-D chamber has a geometry as shown in the Fig.7a below.
Initial Conditions: Chamber is filled with air
At t>0: Water is injected through the inlet
Model: Multiphase viscous k-epsilon model
Mesh: Relevance center: Fine, Nodes: 1886
The contour plots obtained after the simulation are as shown in the figures below.
(a) 2-D chamber (b) Mesh
(a) Phase-2 contour plot at t = 2s (b) Phase-2 contour plot at t = 4s
Figure 8: Vinlet = 0.3 m/s
(a) Phase-2 contour plot at t = 6s (Vinlet = 0.3
m/s)
(b) Phase-2 contour plot at t = 1s (Vinlet
= 0.6 m/s)
5
35. (a) Phase-2 contour plot at t = 2s (b) Phase-2 contour plot at t = 3s
Figure 10: Vinlet = 0.6 m/s
4 Task 3
The 2-D chamber has a geometry as shown in the Fig.11 below.
Initial Conditions: Chamber is filled with air
At t>0: Methane is injected in the domain at 5 m/s and normal air is blown from the left
velocity inlet
Model: Multiphase viscous k-epsilon model
Mesh: Relevance center: Fine, Nodes: 1842
The contour plots obtained after the simulation are as shown in the figures below.
(a) 2-D chamber
(b) Mesh
Figure 11
6
36. (a) Phase-2 contour plot at t = 5s (b) Phase-2 contour plot at t = 10s
Figure 12: Left inlet velocity = 0.2 m/s
(a) Phase-2 contour plot at t = 5s (b) Phase-2 contour plot at t = 10s
Figure 13: Left inlet velocity = 2 m/s
5 Task 4
The 2-D system has a geometry with an inclined plate that forms a 15o
angle with the ground
as shown in the Fig.14 below.
Figure 14: 2-D system
Initial Conditions: A spherical drop of engine-oil of radius 1 cm is placed on the plate
At t>0: The engine oil drop evolves due to gravity
Model: Multiphase viscous laminar
7
37. Mesh for 3-D system: Relevance center: Fine, Element size: 0.12 cm, Nodes: 89376
Boundary Conditions: A small air domain with outflow boundary conditions on all the
sides except the ground.
(a) Mesh for 2-D system (b) Mesh for 3-D system
Figure 15: Mesh
The contour plots for 2-D and 3-D systems obtained after the simulation are as shown in
the figures below.
(a) Phase-2 contour plot at t = 0s (b) Phase-2 contour plot at t = 0.1s
Figure 16: 2-D system phase-2 contour plots
(a) Phase-2 contour plot at t = 1s (b) Phase-2 contour plot at t = 10s
Figure 17: 2-D system phase-2 contour plots
8
38. (a) 0.9 VF iso-surface at t = 0s (b) 0.9 VF iso-surface at t = 0.1s
Figure 18: 3-D system phase-2 contour plots
(a) 0.9 VF iso-surface at t = 1s (b) 0.9 VF iso-surface at t = 10s
Figure 19: 3-D system phase-2 contour plots
9
39. 6 Challenge 3 [Aditya Vipradas (1209435588)]
Initial Conditions: Chamber is filled with air
At t>0: Methane is injected in the domain at 5 m/s according to the graph in Fig.20 and
normal air is blown from the left velocity inlet at 2 m/s
Model: Multiphase viscous k-epsilon model
Mesh: Relevance center: Fine, Nodes: 1842
(a) Temporal variation of methane
(b) UDF defined for the temporal variation (udf sporadic)
Figure 20
10
40. The boundary condition specified by the UDF is input to the methane inlet boundary as
shown in Fig.21.
(a) Select phase-2 in Phase (b) Select UDF for the phase-2 volume
fraction
Figure 21
The contour plots obtained after the simulation are as shown in the figures below.
(a) Phase-2 contour plot for t=5s (b) Phase-2 contour plot for t=10s
Figure 22: Phase-2 contour plots
(a) Phase-2 contour plot for t=15s (b) Phase-2 contour plot for t=20s
Figure 23: Phase-2 contour plots
11
41. 7 Challenge 4 [Aditya Vipradas (1209435588)]
(a) 2-D geometry (b) Mesh
Figure 24: Phase-2 contour plots
Initial Conditions: Two 2-D open containers filled with water connected in the bottom by
a pipe as shown in the Fig.24
At t>0: Water levels in the two containers oscillate before settling down
Model: Multiphase viscous laminar
Mesh: Relevance center: Fine, Refinement: 3, Nodes: 24717
Boundary Conditions: Inlet vent at the top openings of both the containers to allow the
inflow and outflow of air
The plots obtained after the simulation are as shown in the figures below. The water level is
tracked by defining an iso-surface for the phase-2 volume fraction value of 1. A custom-field
function of mesh y-coordinate is then defined. The area-weighted average of the defined
custom-field function is monitored on this iso-surface and the results for the left and right
water levels in plotted in the same window. The procedure is described briefly in figures
below.
(a) Mark the region (Adapt-Region-Mark) (b) Separate the marked region
(Mesh-Separate-Cells)
Figure 25: Split the domain in two parts
12
42. Patch phase-2 as explained in the common simulation procedure and run the simulation
for a single small time step of 0.001s. This shall aid us in creating the iso-surfaces.
(a) Create the left and right iso-surfaces with
phase-2 value as 1 (Contours-New Surface)
(b) Generated left and right iso-surfaces
Figure 26: Create iso-surfaces
(a) Create the custom-field function (b) Monitor the custom-field function on
both the iso-surfaces and plot
Figure 27: Monitor the field function on the iso-surfaces
Figure 28: Line plot of water levels in the two containers. Right container (white) and left
container (red)
13
43. As seen from the plot in Fig.28, the period of oscillation is approximately 2s. The water
levels become equal at t1 = 0.5s, t2 = 1.5s, t3 = 2.5s, t4 = 3.5s and so on.
Figure 29: Contour plot of water when the level in the right container peaks for the first time
(t = 1s)
(a) Velocity vector field at t1 = 0.5s (b) Velocity vector field at t2 = 1.5s
Figure 30: Velocity vector fields
7.1 Both the openings are set as wall
(a) Line plot of water levels in the two containers.
Right container (white) and left container (red)
(b) Contour plot of water at t = 1s
Figure 31: Plots
14
44. The simulation is run for 1s. The water level plots obtained is as shown in the figure. As
seen in Fig.31, the water levels remain the same at any given time instant because of the wall
boundary conditions on both the openings. There is no opening for air to flow inwards or
outwards.
7.2 Wall on the left opening and inlet vent on the right opening
(a) Line plot of water levels in the two containers.
Right container (white) and left container (red)
(b) Contour plot of water at t = 1s
Figure 32: Plots
The simulation is run for 1s. The water level plots obtained is as shown in the figure. As
seen in Fig.32, the water levels remain the same at any given time instant because of the
wall boundary condition on the left opening. No work is done to expand or compress the air
trapped in the left container. Hence, water level remains the same.
7.3 Outlet vents on both the openings
(a) Line plot of water levels in the two containers.
Right container (white) and left container (red)
(b) Contour plot of water at t = 1s
Figure 33: Plots
The simulation is run for 1s. The water level plots obtained is as shown in the figure. As
seen in Fig.33, the water levels oscillate as the outlet vent condition allows inflow and outflow
of air. Thus as time proceeds, the levels will keep on oscillating before they settle at equal
heights.
15
45. MAE598 Applied Computational Fluid Dynamics Project #3
Aditya Vipradas (1209435588)
Task 1
The 2D domain is as follows. The circle is assumed to be fixed at the given location. In Part a, liquid kerosene
with an inlet velocity of 0.006 m/s and in Part b, water with inlet velocity of 0.0003 m/s flows through the inlet.
Mesh relevance center is ‘fine’ and the dashed region is adapted.
Deliverables:
1. Reynold’s number of the system (Re) = uL/ν, where u is the inlet velocity, L is the cylinder diameter (0.2
m) and ν is the kinematic viscosity of the fluid.
Figure 1. 2D domain
Figure 2. Velocity magnitude plot (liquid kerosene) Figure 3. Velocity magnitude plot (water)
Figure 4. y-velocity plot (liquid kerosene) Figure 5. y-velocity plot (water)
Fluid Re
Liquid kerosene 390
Water 59.71
46. Figure 6. Static pressure (liquid kerosene) Figure 7. Static pressure (water)
Figure 8. x-velocity along x = 50cm (liquid kerosene)
Figure 9. x-velocity along x = 150cm (kerosene)
47. Figure 10. x-velocity along x = 50cm (water)
Figure 11. x-velocity along x = 150cm (water)
Task 2
The 2D domain is shown as follows. Air flows at the inlet with 10 m/s. Model: Viscous-turbulence k-epsilon.
Solution: Steady-state.
Figure 12. 2D domain with half-fish
49. Force Total (N) Pressure (N) Viscosity (N)
Lift -7.412 -7.420 0.008
Drag 2.312 2.266 0.047
Table 1. Lift and drag on half-fish
Task 3 (3D version of Task 2.) Deliverables:
Figure 16. Mesh along the symmetry plane
Figure 17. Velocity magnitude on x-y plane
Figure 18. Static pressure on the x-y plane
50. Figure 19. x-velocity on plane parallel to YZ at x=25cm
Force Total (N) Pressure (N) Viscosity (N)
Drag 0.179 0.166 0.013
Task 4
UDF implementation to apply parabolic inlet velocity. The velocity function written in the UDF is as follows:
Inlet velocity = Vmax(1 – (r/R)2
), where r varies from 0 to R, R = 0.6 m, Vmax = 2 * 10 m/s
Figure 20. 3D fish in cylinder
Figure 21. Parabolic velocity UDF implementation
51. Figure 22. x-velocity at the inlet
Figure 23. Velocity magnitude on the XY plane
Figure 24. Static pressure on the XY plane
52. Figure 25. x-velocity parallel to YZ plane at x=25cm
Force Total (N) Pressure (N) Viscosity (N)
Drag 0.757 0.708 0.049
53. Challenge 5 (Aditya Vipradas – 1209435588)
Comparison of lift coefficient amplitude and time period for circular, x-elongated and y-elongated cylinder geometries.
Lift is calculated at an interval of 5s.
Figure 26. Change in cylinder cross-sections
Figure 27. Lift coefficient plot for circle (10,10)
Figure 28. Lift coefficient plot for x-elongated ellipse (11.11,9)
54. Figure 29. Lift coefficient plot for y-elongated ellipse (5,20)
Figure 30. Combined lift coefficients plots
Cross-section Lift coefficient amplitude Lift coefficient period (sec)
circle (10,10) 0.595 160
x-elongated ellipse (11.11,9) 0.325 155
y-elongated ellipse (5,20) 1.05 260
Table 2. Lift coefficient amplitude and time period for different cross-sections
As observed from this table, the lift coefficient amplitude increases with the cross-section axis perpendicular to the flow.
The lift coefficient time period increases with the amplitude.
55. Challenge 6 (Aditya Vipradas – 1209435588)
Similar to Task 3 but the fish is tilted.
Figure 31. Tilted 3D fish
Procedure for tilting the fish:
1. Design Modeler -> Concept -> 3D Curve -> Import the geometry coordinates
Figure 32. 3D Curve geometry import
2. Revolve about the lower edge
Figure 33. Revolve about the lower edge
3. Create -> Body Transformation -> Rotate (about Z-component)
Enter 1 in Z Component and enter the desired tilt angle (here 15) in the Angle box.
56. Figure 34. Fish tilted by 15 deg about Z component Figure 35. Enter 1 in Z Component and enter tilt angle in Angle
Figure 36. Velocity magnitude on XY plane for 15 deg fish tilt
Figure 37.Velocity magnitude on XY plane for 30 deg fish tilt
57. Figure 38. Velocity magnitude on XY plane for 45 deg fish tilt
Figure 39. Lift and drag force variation with fish tilt angle(in degree)
58. MAE598 Applied Computational Fluid Dynamics Project #4
Aditya Vipradas (1209435588)
Task 1
A 2D nozzle has a surface profile given by the following equation:
Simulation parameters:
1. Inviscid model 2. Density-based solver 3. Air as ideal gas,
4. Gauge total (stagnation) pressure = 101360 Pa
5. Supersonic/Initial gauge pressure = 98910 Pa
6. Outlet gauge pressure = 5000 Pa
Results:
1. Contour plot of density
2. Contour plot of static pressure
59. 3. Contour plot of x-velocity
4. Line plot of Mach number
5. Mesh plot