Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion
J Le, M Yang, M Guo, Y Tian, H Zhang - Progress in Aerospace Sciences, 2024 - Elsevier
Due to the significant improvement in computing power and the rapid advancement of data
processing technologies, artificial intelligence (AI) has introduced new tools and …
processing technologies, artificial intelligence (AI) has introduced new tools and …
Improvement of turbulence model for predicting shock-wave–boundary-layer interaction flows by reconstructing Reynolds stress discrepancies based on field …
D Tang, F Zeng, T Zhang, C Yi, C Yan - Physics of Fluids, 2023 - pubs.aip.org
ABSTRACT Reynolds-averaged Navier–Stokes (RANS) models have been the mainstay of
engineering applications in recent years, and this trend will likely persist in the coming …
engineering applications in recent years, and this trend will likely persist in the coming …
Data-driven Reynolds-averaged turbulence modeling with generalizable non-linear correction and uncertainty quantification using Bayesian deep learning
H Tang, Y Wang, T Wang, L Tian, Y Qian - Physics of Fluids, 2023 - pubs.aip.org
The past few years have witnessed a renewed blossoming of data-driven turbulence
models. Quantification of the concomitant modeling uncertainty, however, has mostly been …
models. Quantification of the concomitant modeling uncertainty, however, has mostly been …
Aerodynamic evaluation of cascade flow with actual geometric uncertainties using an adaptive sparse arbitrary polynomial chaos expansion
Z Guo, W Chu, H Zhang, C Liang, D Meng - Physics of Fluids, 2023 - pubs.aip.org
In this paper, an adaptive sparse arbitrary polynomial chaos expansion (PCE) is first
proposed to quantify the performance impact of realistic multi-dimensional manufacturing …
proposed to quantify the performance impact of realistic multi-dimensional manufacturing …
Field inversion for transitional flows using continuous adjoint methods
Transition modeling represents one of the key challenges in computational fluid dynamics.
While numerical efforts were traditionally devoted to either improving Reynolds-averaged …
While numerical efforts were traditionally devoted to either improving Reynolds-averaged …
[HTML][HTML] Structural uncertainty quantification of Reynolds-averaged Navier–Stokes closures for various shock-wave/boundary layer interaction flows
Z Fanzhi, T Zhang, T Denggao, LI Jinping… - Chinese Journal of …, 2024 - Elsevier
Abstract Accurate prediction of Shock-Wave/Boundary Layer Interaction (SWBLI) flows has
been a persistent challenge for linear eddy viscosity models. A major limitation lies in the …
been a persistent challenge for linear eddy viscosity models. A major limitation lies in the …
[HTML][HTML] Improved self-consistency of the Reynolds stress tensor eigenspace perturbation for uncertainty quantification
M Matha, C Morsbach - Physics of Fluids, 2023 - pubs.aip.org
The limitations of turbulence closure models in the context of Reynolds-averaged Navier–
Stokes (RANS) simulations play a significant part in contributing to the uncertainty of …
Stokes (RANS) simulations play a significant part in contributing to the uncertainty of …
Multi-fidelity Deep Learning-based methodology for epistemic uncertainty quantification of turbulence models
M Chu, W Qian - arXiv preprint arXiv:2310.14331, 2023 - arxiv.org
Computational Fluid Dynamics (CFD) simulations using turbulence models are commonly
used in engineering design. Of the different turbulence modeling approaches that are …
used in engineering design. Of the different turbulence modeling approaches that are …
A Deep Learning Approach For Epistemic Uncertainty Quantification Of Turbulent Flow Simulations
M Chu, W Qian - arXiv preprint arXiv:2405.08148, 2024 - arxiv.org
Simulations of complex turbulent flow are part and parcel of the engineering design process.
Eddy viscosity based turbulence models represent the workhorse for these simulations. The …
Eddy viscosity based turbulence models represent the workhorse for these simulations. The …
Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
M Chu, W Qian - arXiv preprint arXiv:2408.06864, 2024 - arxiv.org
Complex turbulent flow simulations are an integral aspect of the engineering design
process. The mainstay of these simulations is represented by eddy viscosity based …
process. The mainstay of these simulations is represented by eddy viscosity based …