High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engineering design, parameter studies, and control. We develop neural-operator surrogates for temporal prediction of the hydrodynamic fields generated by planar and volumetric eel swimmers. The surrogates are trained on regular-grid fields exported from adaptive fluid--structure simulations and are conditioned on swimmer geometry and Reynolds number. The planar model jointly predicts two velocity components, scalar vorticity, and pressure. On five held-out high-Reynolds-number trajectories, its full-domain global relative L^2 error is 3.51 %. The volumetric formulation uses three target-specific models with a common multichannel input: one model predicts three-dimensional velocity, one predicts vorticity, and one predicts pressure. Their full-domain global relative L^2 errors on five held-out within-range trajectories are 3.44 %, 5.58 %, and 19.2 %. Together, the results demonstrate the feasibility of field-resolved neural surrogates for moving-boundary swimmer flows while identifying pressure accuracy and physical consistency as priorities for further development.
The transient dynamics of the micrometer-scale slipper-pair oil film in axial piston pumps are governed by multi-factor coupling effects, rendering traditional numerical methods inadequate for real-time simulation and rapid design optimization. This paper proposes a physics-enhanced neural operator (PENO) to predict tr...
Hanyu Gao, Hua Fang, Hong Pan et al.· The Physics of Fluids· 0 citations
This study investigates the passive control of backward-facing step flows using rigid and elastic cantilevered plates, with emphasis on flow reattachment, pressure recovery, and fluid–structure interaction effects. The numerical methodology was first validated against an experimental benchmark, yielding a reattachment-...
B. Gungordu, Matin Hasanli· Applied Sciences· 1 citation
A fast-converging and asymptotic-preserving adjoint shape optimization method is proposed for drag reduction of multiscale gas flows in vibrating micro-electro-mechanical systems. The convergence of the Boltzmann kinetic equation is accelerated by macroscopic synthetic equations, whose constitutive relations integrate...
Plasolver, a physics-informed neural operator framework that combines the efficiency of operator learning with the accuracy and robustness of classical numerical solvers, provides an efficient, accurate, and discretization-invariant computational framework for nonlinear, path-dependent elastoplastic problems.
Yi-Zheng Wang, M. Eshaghi, Hua-Dong Zhang et al.· 0 citations
This study focuses on inertial fusion burn via a novel hybrid surrogate model. The proposed hybrid model delivers computational speedup of several orders of magnitude compared with conventional radiation-hydrodynamic simulations, while maintaining high predictive fidelity. Traditional Fourier Neural Operators (FNOs)...
Si-Han Shen, Ming-Quan Feng, Yang-Yi Lei et al.· Plasma Science and Technolog...· 0 citations
Data-driven turbulence closures are usually calibrated by inverse methods that embed a CFD solver in the loop, tying the model to a particular discretization and requiring every iterate to yield a convergent solve. We instead train the closure inside a physics-informed neural network (PINN): the Reynolds-averaged Navie...
Zhen Zhang, T. Kaufer, Louise Ronglan et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
