4 papers
A few-shot and physically restorable symbolic regression turbulence model based on normalized general effective-viscosity hypothesis
Ziqi Ji, Penghao Duan, Gang Du
Turbulence is a complex, irregular flow phenomenon ubiquitous in natural processes and engineering applications. The Reynolds-averaged Navier-Stokes (RANS) method, owing to its low…
Learning Non-Ideal Vortex Flows Using the Differentiable Vortex Particle Method
Ziqi Ji, Gang Du, Penghao Duan
Vortex flows are ubiquitous in both natural processes and engineering applications, including phenomena such as typhoons, water currents, and aerospace fluid dynamics. The vortex p…
A symbolic regression-based implicit algebraic stress turbulence model: incorporating the production of non-dimensional Reynolds stress deviatoric tensor
Ziqi Ji, Penghao Duan, Gang Du
Turbulence constitutes an exceptionally complex and irregular flow phenomenon that manifests in liquids, gases, and plasma, making it ubiquitous in both natural processes and engin…
Enhancing generalizability of machine learning general effective-viscosity turbulence model via tensor basis normalization
Ziqi Ji, Penghao Duan, Gang Du
With the rapid advancement of machine learning techniques, the development and study of machine learning turbulence models have become increasingly prevalent. As a critical compone…