arXiv:2607. 09582v1 Announce Type: cross Abstract: We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows.
By Okezzi Ukorigho, Opeoluwa Owoyele
arXiv:2606. 14729v1 Announce Type: cross Abstract: Turbulent combustion simulations are crucial for many scientific and engineering systems.
By Nicolas J. Tricard, Benjamin C. Koenig, Sili Deng
arXiv:2609.21590v1 Announce Type: cross
Abstract: Accurate prediction of unsteady aerodynamic loads remains a major challenge in turbomachinery design. High-fidelity Computational Fluid Dynamics (CFD...
By Lionel Salesses, Joachim Dominique, Tariq Benamara, Th\'eo Flament, Franck Mastrippolito
arXiv:2604. 19465v3 Announce Type: replace-cross Abstract: Understanding how complex systems respond to perturbations, such as whether they will remain stable or what their most sensitive patterns are, is a fundamental challenge across science and engineering.
By Chengyun Wang, Liwei Chen, Nils Thuerey
The paper presents a method to enhance one‑dimensional rotating detonation engine (RDE) models by integrating data assimilation and machine learning. Continuous data assimilation (nudging) aligns the low‑order Koch‑Kutz solver with high‑fidelity temperature data, while a Jacobian‑regularized closure is trained on the recorded correction forces. The resulting corrected model, once the observation term is removed, autonomously reproduces the temperature spectrum and key statistical properties of the full high‑fidelity simulation.
By Ashwin Suriyanarayanan, Romit Maulik
This chapter reviews recent advances in Scientific Machine Learning (SciML) for modeling coupled fluid flow and transport phenomena governed by the incompressible Navier-Stokes and scalar transport equations. Such systems, found in applications like turbidity currents and thermal convection, feature strong nonlinear coupling and multiscale behavior that make high-fidelity simulations computationally expensive.