The transformative potential of machine learning for experiments in fluid mechanics
The field of machine learning (ML) has rapidly advanced the state of the art in many fields of
science and engineering, including experimental fluid dynamics, which is one of the original …
science and engineering, including experimental fluid dynamics, which is one of the original …
[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
Turbulent skin-friction drag reduction by annular dielectric barrier discharge plasma actuator
B Zheng, D Lin, S Qi, Y Hu, Y Jin, Q Chen, D Bian… - Physics of …, 2023 - pubs.aip.org
Reducing turbulent skin friction drag is a fundamental goal for aircraft transportation to
conserve energy and decrease emissions. We introduce an annular dielectric barrier …
conserve energy and decrease emissions. We introduce an annular dielectric barrier …
From active learning to deep reinforcement learning: Intelligent active flow control in suppressing vortex-induced vibration
In the present work, an efficient active flow control strategy in eliminating vortex-induced
vibration of a cylinder at Re= 100 has been explored by two machine learning frameworks …
vibration of a cylinder at Re= 100 has been explored by two machine learning frameworks …
Flow control in wings and discovery of novel approaches via deep reinforcement learning
In this review, we summarize existing trends of flow control used to improve the aerodynamic
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …
efficiency of wings. We first discuss active methods to control turbulence, starting with flat …
Flows over periodic hills of parameterized geometries: A dataset for data-driven turbulence modeling from direct simulations
Computational fluid dynamics models based on Reynolds-averaged Navier–Stokes
equations with turbulence closures still play important roles in engineering design and …
equations with turbulence closures still play important roles in engineering design and …
Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems
Y Morita, S Rezaeiravesh, N Tabatabaei… - Journal of …, 2022 - Elsevier
Bayesian optimization (BO) based on Gaussian process regression (GPR) is applied to
different CFD (computational fluid dynamics) problems which can be of practical relevance …
different CFD (computational fluid dynamics) problems which can be of practical relevance …
Comparative analysis of machine learning methods for active flow control
Machine learning frameworks such as genetic programming and reinforcement learning
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …
(RL) are gaining popularity in flow control. This work presents a comparative analysis of the …
[HTML][HTML] Recent progress of machine learning in flow modeling and active flow control
In terms of multiple temporal and spatial scales, massive data from experiments, flow field
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …
measurements, and high-fidelity numerical simulations have greatly promoted the rapid …
[HTML][HTML] Xcompact3D: An open-source framework for solving turbulence problems on a Cartesian mesh
Xcompact3D is a Fortran 90–95 open-source framework designed for fast and accurate
simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the …
simulations of turbulent flows, targeting CPU-based supercomputers. It is an evolution of the …