Computational fluid dynamics (CFD) uses applied mathematics, physics and computational software to visualize how gases and liquids flow and interact with objects. CFD is based on solving the Navier-Stokes equations numerically using a software tool. The process involves defining a mathematical model, discretizing it, iteratively solving the resulting system of equations, running a simulation, and analyzing the results. CFD has various applications and can model different types of flows like laminar, turbulent, compressible and more. It has developed significantly since the 1940s with early computer-based calculations and is now widely used across industries.
The Powerpoint presentation discusses about the Introduction to CFD and its Applications in various fields as an Introductory topic for Mechanical Engg. Students in General.
The Powerpoint presentation discusses about the Introduction to CFD and its Applications in various fields as an Introductory topic for Mechanical Engg. Students in General.
Computational Fluid Dynamics (CFD) is the simulation of fluids engineering systems using modeling [mathematical physical problem formulation) and numerical methods (discretization methods, solvers, numerical parameters, and grid generations, etc]
The presentation gives glances of the importance of CFD analysis in engineering design with an illustration/ case study. For more details, follow the webinar "Role of CFD in Engineering Design" on https://www.learncax.com/knowledge-base/blog/by-author/ganesh-visavale
• In silico (literally alluding the mass use of silicon for semiconductor computer chips) is an expression used to performed on computer or via computer simulation
• In silico tools capable of identifying critical factors (i.e. drug physicochemical properties, dosage form factors) influencing drug in vivo performance, and predicting drug absorption based on the selected data set (s) of input factors.
Computational Fluid Dynamics (CFD) is the simulation of fluids engineering systems using modeling [mathematical physical problem formulation) and numerical methods (discretization methods, solvers, numerical parameters, and grid generations, etc]
The presentation gives glances of the importance of CFD analysis in engineering design with an illustration/ case study. For more details, follow the webinar "Role of CFD in Engineering Design" on https://www.learncax.com/knowledge-base/blog/by-author/ganesh-visavale
• In silico (literally alluding the mass use of silicon for semiconductor computer chips) is an expression used to performed on computer or via computer simulation
• In silico tools capable of identifying critical factors (i.e. drug physicochemical properties, dosage form factors) influencing drug in vivo performance, and predicting drug absorption based on the selected data set (s) of input factors.
Fluid dynamics, actually is the study of fluid under motion, governed with a certain set of conservation equations, wherein things are conserved, with reference to mass, momentum & energy.
If these three quantities i.e. mass, momentum & energy are solved entirely we can define any fluid flow. The conservation laws are formulated in the form of equations which we try to solve and that’s what simulation is all about. For my blogs kindly visit: https://www.learncax.com/knowledge-base/blog/by-author/ganesh-visavale
Heat Transfer & Periodic Flow Analysis of Heat Exchanger by CFD with Nano FluidsIJERA Editor
Many heat transfer applications such as steam generators in a boiler or air cooling coil of an air conditioner, can
be modelled in a bank of tubes containing a fluid flowing at one temperature that is immersed in a second fluid
in a cross flow at different temperature. CFD simulations are a useful tool for understanding flow and heat
transfer principles as well as for modelling these types of geometries. Both the fluids considered in the present
study are CUO Nano fluids, and flow is classified as laminar and steady with Reynolds number between 100-
600.The mass flow rate of the cross flow and diameter has been varied (such as 0.05, 0.1, 0.15, 0.20, 0.25, 0.30
kg/sec and 0.8, 1.0.1.2 &1.4cm) and the models are used to predict the flow and temperature fields that result
from convective heat transfer. Due to symmetry of the tube bank and the periodicity of the flow inherent in the
tube bank geometry, only a portion of the geometry will be modelled and with symmetry applied to the outer
boundaries. The inflow boundary will be redefined as a periodic zone and the outflow boundary is defined as the
shadow. The various static pressures, velocities, and temperatures obtained are reported.
In this present project tubes of different diameters and different mass flow rates are considered to examine the
optimal flow distribution. Further the problem has been subjected to effect of materials used for tubes
manufacturing on heat transfer rate. Materials considered are copper and Nickle Chromium alloys. Results
emphasize the utilization of alloys in place of copper as tube material serves better heat transfer with most
economical way.
Heat Transfer & Periodic Flow Analysis of Heat Exchanger by CFD with Nano FluidsIJERA Editor
Many heat transfer applications such as steam generators in a boiler or air cooling coil of an air conditioner, can
be modelled in a bank of tubes containing a fluid flowing at one temperature that is immersed in a second fluid
in a cross flow at different temperature. CFD simulations are a useful tool for understanding flow and heat
transfer principles as well as for modelling these types of geometries. Both the fluids considered in the present
study are CUO Nano fluids, and flow is classified as laminar and steady with Reynolds number between 100-
600.The mass flow rate of the cross flow and diameter has been varied (such as 0.05, 0.1, 0.15, 0.20, 0.25, 0.30
kg/sec and 0.8, 1.0.1.2 &1.4cm) and the models are used to predict the flow and temperature fields that result
from convective heat transfer. Due to symmetry of the tube bank and the periodicity of the flow inherent in the
tube bank geometry, only a portion of the geometry will be modelled and with symmetry applied to the outer
boundaries. The inflow boundary will be redefined as a periodic zone and the outflow boundary is defined as the
shadow. The various static pressures, velocities, and temperatures obtained are reported.
In this present project tubes of different diameters and different mass flow rates are considered to examine the
optimal flow distribution. Further the problem has been subjected to effect of materials used for tubes
manufacturing on heat transfer rate. Materials considered are copper and Nickle Chromium alloys. Results
emphasize the utilization of alloys in place of copper as tube material serves better heat transfer with most
economical way
CFD and Artificial Neural Networks Analysis of Plane Sudden Expansion FlowsCSCJournals
It has been clearly established that the reattachment length for laminar flow depends on two non-dimensional parameters, the Reynolds number and the expansion ratio, therefore in this work, an ANN model that predict reattachment positions for the expansion ratios of 2, 3 and 5 based on the above two parameters has been developed. The R2 values of the testing set output Xr1, Xr2, Xr3, and Xr4 were 0.9383, 0.8577, 0.997 and 0.999 respectively. These results indicate that the network model produced reattachment positions that were in close agreement with the actual values. When considering the reattachment length of plane sudden-expansions the judicious combination of CFD calculated solutions with ANN will result in a considerable saving in computing and turnaround time. Thus CFD can be used in the first instance to obtain reattachment lengths for a limited choice of Reynolds numbers and ANN will be used subsequently to predict the reattachment lengths for other intermediate Reynolds number values. The CFD calculations concern unsteady laminar flow through a plane sudden expansion and are performed using a commercial CFD code STAR-CD while the training process of the corresponding ANN model was performed using the NeuroShellTM simulator.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
1. Cfd
Computational fluid dynamics (CFD) is the use of applied mathematics, physics and
computational software to visualize how a gas or liquid flows -- as well as how the gas
or liquid affects objects as it flows past. Computational fluid dynamics is based on the
Navier-Stokes equations. These equations describe how the velocity, pressure,
temperature, and density of a moving fluid are related.
Computational Fluid Dynamics became a commonly applied tool for generating
solutions for fluid flows with or without solid interaction. In a CFD analysis, the
examination of fluid flow in accordance with its physical properties such as velocity,
pressure, temperature, density and viscosity is conducted. To virtually generate a
solution for a physical phenomenon associated with fluid flow, without compromise on
accuracy, those properties have to be considered simultaneously.
A mathematical model of the physical case and a numerical method are used in a
software tool to analyze the fluid flow. For instance, the Navier-Stokes equations are
specified as the mathematical model of the physical case. This describes changes on all
those physical properties for both fluid flow and heat transfer. The mathematical model
varies in accordance with the content of the problem such as heat transfer, mass
transfer, phase change, chemical reaction, etc. Moreover, the reliability of a CFD
analysis highly depends on the whole structure of the process. The verification of the
mathematical model is extremely important to create an accurate case for solving the
problem. Besides, the determination of proper numerical methods to generate a path
through the solution is as important as a mathematical model. The software, which the
analysis is conducted with is one of the key elements in generating a sustainable
product development process, as the amount of physical prototypes can be reduced
drastically.
History of Computational Fluid Dynamics
From antiquity to present, humankind has been eager to discover phenomena based on
fluid flow. So, how old is CFD? Experimental studies in the field of computational fluid
dynamics have one big disadvantage: if they need to be accurate, they consume a
significant amount of time and money. Consequently, scientists and engineers wanted
to generate a method that enabled them to pair a mathematical model and a numerical
method with a computer for faster examination.
The brief story of Computational Fluid Dynamics can be seen below:
Until 1910: Improvements on mathematical models and numerical methods.
2. 1910 - 1940: Integration of models and methods to generate numerical solutions
based on hand calculations11.
1940 - 1950: Transition to computer-based calculations with early computers
(ENIAC)33. Solution for flow around cylinder by Kawaguti with a mechanical desk
calculator in 195388.
1950 - 1960: Initial study using computers to model fluid flow based on the Navier-
Stokes equations by Los Alamos National Lab, US. Evaluation of vorticity - stream
function method44. First implementation for 2D, transient, incompressible flow in the
world66.
1960 – 1970: First scientific paper “Calculation of potential flow about arbitrary
bodies” was published about computational analysis of 3D bodies by Hess and Smith
in 196755. Generation of commercial codes. Contribution of various methods such k-
ε turbulence model, Arbitrary Lagrangian-Eulerian, SIMPLE algorithm which are all
still broadly used66.
1970 – 1980: Codes generated by Boeing, NASA and some have unveiled and
started to use several yields such as submarines, surface ships, automobiles,
helicopters and aircrafts4,64,6.
1980 – 1990: Improvement of accurate solutions of transonic flows in three-
dimensional case by Jameson et. al. Commercial codes have started to implement
through both academia and industry77.
1990 – Present: Thorough developments in Informatics: worldwide usage of CFD
virtually in every sector.
The following steps are going to explain the mathematical approach behind a CFD simulation. For
you to understand it more easily, they are categorized into 7 steps.
Applications of Computational Fluid Dynamics
Where there is fluid, there is CFD. the initial stage to conduct a CFD simulation is
specifying an appropriate mathematical model of reality. Rapprochements and
assumptions give direction through solution processes to examine the case in the
computational domain. For instance, fluid flow over a sphere / cylinder is a repetitive
issue that has been taught by the lecturer as an example in fluid courses. The same
phenomenon is virtually available in the movement of clouds in the atmosphere which is
indeed tremendous
3. Incompressible and Compressibleflow
If compressibility becomes a non-negligible factor, this type of analysis helps you to find
solutions in a very robust and accurate way. One example would be a Large Eddy
Simulation of flow around a cylinder.
Laminar and Turbulentflow
Different turbulence models play a role in this type of analysis. A lot of computing power
is required to solve turbulence simulations and its complex numerical models. The
difficulty of turbulence is the simulation of changes over time. The entire domain where
the simulation takes place needs to be recalculated after every time step.
The analysis of a ball valve is one possible application of a turbulent flow analysis.
Mass and Thermal transport
Mass transport simulations include smoke propagation, passive scalar transport or gas
distributions. To solve these kinds of simulations, OpenFOAM solvers are used.
Heat exchanger simulations are one possible application.
Different Types of CFD Applications
Computational Fluid Dynamics tools diversify in accordance with mathematical models,
numerical methods, computational equipment and post-processing facilities. As a
physical phenomenon could be modeled with completely different mathematical
approaches, it would also be integrated with unlike numerical methods simultaneously.
Thus, a conscious rapprochement is the essential factor on the path to developing CFD
tools. There are several license-required commercial software solutions, though there
are also open source projects available. One of the most used open-source solvers for
CFD is OpenFOAM1818.
4. CFD ANALYSIS PROCESS
First step:
Problem Statement:
The first step of the simulation is to gather information about the simulation process in general.
What is the most convenient way of solving this problem in an economic way:
o Cheap solution: No high computational costs
o Fast solution: Fast solution possible without giving up much information of the solution
o Uncomplicated solution: Simplify the problem as much as possible without restating a new problem
Modelling:
o Laminar or Turbulent - if turbulent → +turbulence model + near-wall treatment
o Combustion
o Other Physical Models
o Is the flow steady or unsteady?
o Are there any problems about the flow simulation that others have dealt with in the past?
o Will physical phenomena influence the simulation?
o What is the goal of the CFD simulation?
Second step:
Mathematical Fundamental:
The Initial Boundary Value Problem consists of the Partial Differential Equation the Initial Conditions
as well as the Boundary Conditions:
IBVP = PDE + IC + BC
Choose flow model that fits your simulation:
o Spalart-Allmaras
o k-epsilon
o k-omega
o L-VEL & yPlus
Identify the forces which cause and influence the motion of the fluid.
Define the Computational Domain of the problem.
Formulate conservation laws for mass, momentum and energy.
If possible, simplify the equations:
o Check for Symmetry
o Check for dominant flow directions (1D/2D).
o Terms that have no influence on the solution can be neglected.
5. o Incorporate knowledge that you’ve had beforehand (CFD results, measurement data).
Add constitutive relations:
o Shear Stress
o Viscosity
Dynamic Viscosity
Kinematic Viscosity
Add Boundary Conditions and Initial Conditions.
Third step:
Discretization:
The system of Partial Differential Equations is transformed into algebraic equations.
The discretion process is divided into three parts.
1. Mesh generation - Nodes and Cells
Structured Mesh / Unstructured Mesh / Hybrid Mesh.
Mesh adaption in “critical” regions and set size:
o r-Refinement
o h-Refinement
o p-Refinement
2. Space discretization - Coupled Ordinary Differential Equation/ Differential algebraic equation
systems
Finite-Difference-Method / Finite-Volume-Method / Finite-Element-Method.
High-Order-Approximation / Low-Order-Approximation.
3. Time discretization - Algebraic System (Ax=b).
Explicit Schemes / Implicit Schemes
Fourth step:
Iterative solution of the algebraic equation:
Solving systems of linear equations:
o Direct Methods: Gaussian elimination, LU decomposition.
o Iterative Methods: Strongly Implicit Procedure (SIP) , Alternating Direction Implicit (ADI) ,
Tridiagonal Matrix Algorithm (TDMA), Runge-Kutta method, Multigrid method.
Coupled systems of equations.
Nonlinear Equations
Methods for transient problems: Linear multistep method etc.
6. Convergence: Check if the iterations converge.
Residuals (Decrease by three orders of magnitude indicate at least qualitative convergence).
Mass, Momentum, Energy, and Scalar balances are achieved.
Fifth step:
Simulation Run:
Once the problem is well defined with the boundary conditions, and if necessary with initial
conditions, the problem is solved with a software. Open∇FOAM is a popular option for a solver
which is used by several companies that provide CFD software. SimScale is among them.
Sixth step:
Post-Processing:
Looking at the solutions from the the computed flow.
Post-Processing of integral parameters (Drag, Lift etc.)
Visualization in different dimensions:
o 1-D: Straight lines
o 2-D: Contour plots, Streamlines
o 3-D: Isosurfaces, Isovolumes, Streamtracer
o Animation of the flow
Statistical analysis
Seventh step:
According to AIAA (1998) & Oberkampf and Trucano (2002) the following terminology is widely used
and accepted:
Verification (“Are we solving the equations right?”) :
→ Quantification of errors
Compare results with analytical solutions if possible.
If we ignore the fact that there might be coding errors and user errors, we can examine the
following:
Roundoff Error
7. Iterative Convergence Error
Discretization Error
Validation (“Are we solving the right equations?”) :
→ Quantification of input & physical model uncertainty
Input uncertainty
Physical uncertainty
General tips
Influencing parameters for computation times in CFD
Code used in order to solve the flow (→ MPI, Vectorization)
Hardware (CPU, RAM, etc.)
Mesh size / Mesh Quality
Algorithms
Solvers
REFRENCES
Literature References:
Laurien & Oertel: Numerische Strömungsmechanik - Grundgleichungen und Modelle -
Lösungsmethoden - Qualität und Genauigkeit
Versteeg & Malalasekera: An Introduction to Computational Fluid Dynamics - The Finite
Volume Method - 2nd Edition
http://www.mathematik.uni-dortmund.de/~kuzmin/Transport.pdf 12
http://www.mathematik.uni-dortmund.de/~kuzmin/cfdintro/lecture1.pdf