The document discusses turbulence near walls and different approaches to modeling it in computational fluid dynamics (CFD). It explains that the boundary layer can be divided into different zones, and CFD requires different considerations depending on whether the viscous sub-layer is solved, the log-law layer is modeled, or the whole boundary layer is solved. It also discusses the use of non-dimensional variables to characterize the boundary layer and describes different near-wall treatments in CFD, including resolving the boundary layer with fine meshes or using wall functions with coarser meshes.
The document discusses reactive flow modeling using the eddy dissipation model (EDM) in ANSYS Fluent. EDM solves conservation equations for chemical species by predicting local mass fractions through a convection-diffusion equation. Reaction rates are assumed to be controlled by turbulence, ignoring chemical timescales. EDM gives the smaller of two expressions to calculate reaction rates, with the chemical reaction rate governed by the large eddy mixing timescale. EDM is computationally cheap but works best for one-two step global reactions, as it cannot capture detailed chemistry effects.
01 reactive flows - governing equations favre averaging Mohammad Jadidi
This document discusses reactive flow modeling in combustion chambers. It covers the equations governing reacting flows, including conservation equations for mass, momentum, molecular species, and energy. It also discusses the equation of state and turbulence transport. The document then covers statistical descriptions of turbulent flows using Reynolds decomposition and Favre averaging. Favre averaging is preferred for reacting flows with variable density as it leads to simpler expressions in the transport equations for continuity, momentum, species, and energy compared to Reynolds averaging. Various terms that arise in the averaged equations require turbulence modeling approaches.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
This document discusses turbulence modeling in computational fluid dynamics (CFD). It contains three main points:
1. Turbulence models used in CFD simulations like RANS and LES are introduced. Important turbulence concepts such as eddies, length scales, and the energy cascade are explained.
2. Reynolds-averaged Navier-Stokes (RANS) equations are presented along with Reynolds stress tensor and turbulent heat flux terms. Common RANS turbulence models and their governing equations are outlined.
3. Large eddy simulation (LES) is described as an alternative to RANS. Filtering operations in LES to separate large and small scales are discussed. Root-mean-square velocities are presented as a
Large eddy simulation (LES) is a computational fluid dynamics technique that resolves the larger turbulent scales in the fluid flow while modeling the smaller scales. LES aims to directly simulate the larger turbulent scales while parameterizing the effects of smaller scales through a subgrid scale model. LES requires significantly more computational resources than Reynolds-averaged Navier–Stokes (RANS) modeling but provides more detailed turbulent flow information.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
The document discusses turbulence near walls and different approaches to modeling it in computational fluid dynamics (CFD). It explains that the boundary layer can be divided into different zones, and CFD requires different considerations depending on whether the viscous sub-layer is solved, the log-law layer is modeled, or the whole boundary layer is solved. It also discusses the use of non-dimensional variables to characterize the boundary layer and describes different near-wall treatments in CFD, including resolving the boundary layer with fine meshes or using wall functions with coarser meshes.
The document discusses reactive flow modeling using the eddy dissipation model (EDM) in ANSYS Fluent. EDM solves conservation equations for chemical species by predicting local mass fractions through a convection-diffusion equation. Reaction rates are assumed to be controlled by turbulence, ignoring chemical timescales. EDM gives the smaller of two expressions to calculate reaction rates, with the chemical reaction rate governed by the large eddy mixing timescale. EDM is computationally cheap but works best for one-two step global reactions, as it cannot capture detailed chemistry effects.
01 reactive flows - governing equations favre averaging Mohammad Jadidi
This document discusses reactive flow modeling in combustion chambers. It covers the equations governing reacting flows, including conservation equations for mass, momentum, molecular species, and energy. It also discusses the equation of state and turbulence transport. The document then covers statistical descriptions of turbulent flows using Reynolds decomposition and Favre averaging. Favre averaging is preferred for reacting flows with variable density as it leads to simpler expressions in the transport equations for continuity, momentum, species, and energy compared to Reynolds averaging. Various terms that arise in the averaged equations require turbulence modeling approaches.
The document discusses the governing equations for reacting flows, including conservation of mass, momentum, molecular species, and energy. It outlines the continuity, momentum, species transport, and energy equations. The species transport equation accounts for convection, diffusion, and chemical reaction sources. The energy equation considers changes in enthalpy due to convection, diffusion, pressure work, and radiation. Simplifications are discussed under certain assumptions, such as a single diffusion coefficient and negligible pressure work/radiation terms, in which case enthalpy behaves as a passive scalar. Other relationships presented include the equation of state and definitions of specific heat capacity and density.
01 reactive flows - finite-rate formulation for reaction modelingMohammad Jadidi
This document discusses equations governing reacting flows as modeled in ANSYS Fluent. It describes how Fluent solves conservation equations for species mass fractions using a convection-diffusion equation, where the chemical source term Ri accounts for reaction rates. Finite-rate kinetics and turbulence-chemistry interaction models are discussed for determining Ri, including the eddy dissipation model. The Arrhenius equation is also presented for calculating forward reaction rate constants based on pre-exponential factors, temperature exponents, and activation energies specified in the kinetic mechanism.
The document discusses different types of multiphase flows. It defines multiphase flow as any fluid system with two or more distinct phases flowing simultaneously in mixture. Multiphase flows are classified into four main categories: gas-liquid flows, gas-solid flows, liquid-solid flows, and three-phase flows. Each category contains different flow regimes depending on factors like particle size and flow rates. Flow maps are used to characterize different flow patterns that can occur for a given system.
This document discusses turbulence modeling in computational fluid dynamics (CFD). It contains three main points:
1. Turbulence models used in CFD simulations like RANS and LES are introduced. Important turbulence concepts such as eddies, length scales, and the energy cascade are explained.
2. Reynolds-averaged Navier-Stokes (RANS) equations are presented along with Reynolds stress tensor and turbulent heat flux terms. Common RANS turbulence models and their governing equations are outlined.
3. Large eddy simulation (LES) is described as an alternative to RANS. Filtering operations in LES to separate large and small scales are discussed. Root-mean-square velocities are presented as a
Large eddy simulation (LES) is a computational fluid dynamics technique that resolves the larger turbulent scales in the fluid flow while modeling the smaller scales. LES aims to directly simulate the larger turbulent scales while parameterizing the effects of smaller scales through a subgrid scale model. LES requires significantly more computational resources than Reynolds-averaged Navier–Stokes (RANS) modeling but provides more detailed turbulent flow information.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.