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Boundary Layer Theory - Part V: Development

Intrduction In the previous posts of the “Boundary Layer Series,” we explored how separation is determined by the interplay between the viscous shear-stress gradient ∂τ/∂y and the pressure gradient dp/dx. However, you may wonder how separation in laminar flow can be unaffected by the Reynolds number. After all, doesn’t a change in Reynolds number alter the viscous stress? Indeed, a change in…

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What a DRAG - Collection

It is important to keep in mind that unless one or more of the flowfield’s processes responsible for creating pressure drag are impacted, modifications to the surface pressure distribution will not alter the pressure drag. Therefore, it may be quite deceptive to consider only the surface pressures itself in order to comprehend how a change in the surface pressures can impact the pressure…

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The integration of machine learning (ML) into CFD – Part VII: Flow Optimization And Control Using Nachine Learning

Introduction Learning algorithms are highly suitable for optimizing the flow and controlling problems that involve blackbox or multimodal cost functions. These algorithms follow an iterative approach and often require a significantly higher number of evaluations of the cost function compared to gradient-based algorithms (Bewley et al., 2001). However, it is important to note that learning…

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Understanding The Spalart-Allmaras Turbulence Model

“It’s easy to explain how a rocket works, but explaining how a wing works takes a rocket scientist…” – Philippe Spalart Most of nowadays CFD simulations are conducted with the Reynolds Averaging approach. Reynolds-Averaged Navier-Stokes (RANS) simulation  is based on the Reynolds decomposition according to which a flow variable is decomposed into mean and fluctuating quantities. When the…

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All About CFD Blog - Discography

We believed it would be a great idea to create a collection of links featuring the most successful posts from “All About CFD”. The blog has already attracted over a million viewers and due to the plethora of posts some of them have been buried among the newly published ones. Therefore, we have put together a compilation of posts for you to choose from.We kindly request you to notify us of any…

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Some Fundamental Thoughts About Emergent Theories and Fluid Dynamics

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Aerodynamic physics of the Delta Wing - POST #100

Reference: ResearchGate Delta wings are known as highly swept wings and that’s what makes them aerodynamically more efficient at a high angle of attack. Supersonic aircraft are equipped with such wing configurations. The high-speed aircraft have to fly at low speed for takeoff and landing. Therefore it is important to study their aerodynamic characteristics at low speed. Delta wings are able to…

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Theory and Implementation of Implicit Large-Eddy Simulation

INTRODUCTION One of the most important methods for numerically simulating complicated turbulent flows is large-eddy simulation, particularly in situations where more approachable alternatives, such statistically averaged The Navier-Stokes the Reynolds-averaged Navier-Stokes equations, or RANS, are not successful. This is especially true when non-turbulent temporal or spatial scales are…

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The integration of machine learning (ML) into CFD – Part IV

Conclusions Machine learning presents many chances to further the subject of computational fluid dynamics and is quickly emerging as a fundamental tool for scientific computing. We highlight some of the areas with the greatest potential influence in this perspective, such as enhancing turbulence closure modeling, speeding up direct numerical simulations, and to create improved lower-order…

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The integration of machine learning (ML) into CFD – Part III

Introduction Machine learning presents many chances to further the subject of computational fluid dynamics and is quickly emerging as a fundamental tool for scientific computing. We highlight some of the areas with the greatest potential influence in this perspective, such as enhancing turbulence closure modeling, speeding up direct numerical simulations, and to create improved lower-order…

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The integration of machine learning (ML) into CFD – Part II

Introduction Machine learning presents many chances to further the subject of computational fluid dynamics and is quickly emerging as a fundamental tool for scientific computing. We highlight some of the areas with the greatest potential influence in this perspective, such as enhancing turbulence closure modeling, speeding up direct numerical simulations, and to create improved lower-order…