This paper presents a summary of the computational flutter results associated with the AIAA
third Aeroelastic Prediction Workshop (AePW-3) High-Angle Working Group (HAWG). The …

Thanks to the computational power of modern cluster machines, numerical simulations can
provide, with an unprecedented level of details, new insights into fluid mechanics. However …

The physics-informed neural network (PINN) method is extended to learn and predict
compressible steady-state aerodynamic flows with a high Reynolds number. To better learn …

In recent years, CFD has proven to be a very useful asset to help with predicting complex
flows in a wide range of situations, including multiphase and gas-particle flows. On this track …

The present work describes the development of heterogeneous GPGPU implicit CFD
coupled solvers, encompassing both density-and pressure-based approaches. In this setup …

Numerical simulations express surface roughness as equivalent sand grain roughness
values, so the correlation is required to convert realistic roughness into equivalent sand …

Ice roughness modeling is crucial for accurately predicting iced airfoils, as it involves
predicting the enhancement of convective heat transfer on rough surfaces. The challenge of …

With one of the major contributors to climate change being air travel, future gas turbine
designs are striving towards higher efficiencies by achieving greater turbine entry …

Анотація Спеціальний програмний модуль до OpenFoam додано достатньо давно,
більш ніж 10 років тому, й достатньо велика кількість робіт присвячена його …

The aerodynamic performance of industrial aircraft is strongly coupled with the structural
deformation under aerodynamic load. Consequently, multi-physics simulations represent an …