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Turbulence modeling of 3D high-speed flows with upstream-informed corrections
Turbulence modeling has the potential to revolutionize high-speed vehicle design by serving as a co-equal partner to costly and challenging ground...
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Validation and analysis of turbulence modeling in pipe elbow under secondary flow conditions
The present work proposes to analyze the performance of five eddy-viscosity turbulence models in predicting an internal single-phase incompressible...
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The Turbulence Dam** Effect on the Slug Flow Modeling
The slug flow is one of the most complex flow patterns due to the unstable behavior of phase distribution. This pattern occurs in a wide range of...
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Magnetohydrodynamics with Application to the Study of Electrolysis and Turbulence
AbstractThe equations of magnetohydrodynamics (MHD) are presented as continual modeling for slow motions. The original equations of the MHD...
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Methods of Statistical Modeling of Atmospheric Turbulence
The paper studies the methods of statistical modeling of Gaussian random processes used in the modeling of atmospheric turbulence. The need to model...
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Coherent structure-turbulence interaction studied via a vortex column embedded in fine-scale turbulence
The coupling of large-scale coherent structures (CSs) with fine-scale turbulence and its possible relevance to cascade are explored via numerical...
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Representing the Small Scales of Turbulence by Periodic Box Homogeneous Isotropic Turbulence Simulations
Large Eddy Simulations (LESs) use Sub-Grid Scale (SGS) models to account for the effects of the unresolved scales of turbulence. The complex...
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Advances in modeling boundary layer turbulence and its effects on motion independent aerodynamic force of bridge decks
Wind tunnel tests remain crucial to solving the wind-induced issues, such as buffeting. The turbulence impacts on the aerodynamic forces is vital to...
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Artificial Neural Network Modeling Small-Scale Turbulence of Isotropic Turbulent Flows
Small-scale fluid motions play an important role in the relative dispersion and clustering of inertial particles in turbulent flows. In this paper,...
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Signal Processing Methods Related to Models of Turbulence
Turbulence deals with the complex motions in fluid at high velocity and/or involving a large range of length-scales. Understanding turbulence is...
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Stochastic Modeling of Partially Stirred Reactor (PaSR) for the Investigation of the Turbulence-Chemistry Interaction for the Ammonia-Air Combustion
The Partially Stirred Reactor (PaSR) model is carried out for the ammonia-air combustion system by means of stochastic modeling, namely by solving...
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Turbulence in Multiphase Flows
An overview of the fundamental modeling aspects related to disperse multiphase flow is provided. For clarity and accessibility, the discussion is...
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Progress in physical modeling of compressible wall-bounded turbulent flows
Understanding, modeling and control of the high-speed wall-bounded transition and turbulence not only receive wide academic interests but also are...
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Modeling Reynolds stress anisotropy invariants via machine learning
The presentation and modeling of turbulence anisotropy are crucial for studying large-scale turbulence structures and constructing turbulence models....
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Simulation of Turbulent Natural Convection in Photovoltaic Solar Panels Based on the Spalart–Allmares (SA) Turbulence Model
AbstractIn this study, the efficiency of air velocity on solar panels during cooling was studied based on temperature and solar radiation in the...
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Advances in Turbulence Selected Papers from the XII Spring School on Transition and Turbulence
This book presents selected papers from the 12thedition of the Spring School of Transition and Turbulence which took place in 2020. The papers cover...
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Overview of Outfall Discharge Modeling with a Focus on Turbulence Modeling Approaches
A better understanding of flow discharge behavior can aid in the optimum design of outfall systems while adhering to regulatory demands. Improvements...
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An Analytical View on Data-Driven Turbulence Modeling and a Realization via a regularized Newton Method
Field Inversion and Machine Learning is an active field of research in Computational Fluid Dynamics (CFD). This approach can be leveraged to obtain a...
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Deep learning to develop zero-equation based turbulence model for CFD simulations of the built environment
This study aims to improve the accuracy and speed of predictions for thermal comfort and air quality in built environments by creating a coupled...
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Numerical simulation of bluff body turbulent flows using hybrid RANS/LES turbulence models
Many engineering applications involve turbulent flows around bluff bodies. Because of their intrinsically unsteady dynamics, bluff body...
