arXiv subjects

Ahmad Shoja-Sani

Publications and source records attributed to Ahmad Shoja-Sani.

3 recordsLinked to original sources

arXiv2026

Rarefaction-induced inflation and similarity breakdown of hypersonic bow shocks over a circular cylinder

Rarefied hypersonic bow shocks over blunt bodies inflate as the Knudsen number increases, but it remains unclear whether this inflation is a simple shift and broadening of one common shock layer or a multi-scale change of the macroscopic and internal-energy fields. We address this question using direct simulation Monte Carlo (DSMC) data for Mach-10 flow over a circular cylinder in argon and nitrogen over \(Kn_\infty \approx 0.01\)--\(1\), together with a Mach-number sweep at \(Kn_\infty=0.01\). At low rarefaction, a ray-based density-gradient ridge gives a reproducible bow-shock location and agrees with an independent schlieren-based shock-wave-detection method. As \(Kn_\infty\) increases, this ridge is replaced by a broad kinetic compression layer, so the high-Knudsen cases are analysed using profile-based standoff and thickness metrics rather than by imposing a visual shock line. The Knudsen- and Mach-number sweeps separate two mechanisms. At fixed \(M_\infty\), the continuum normal-shock density ratio provides a useful low-rarefaction reference compression scale, whereas the measured standoff growth is governed primarily by the kinetic mean free path; the effective density thickness shows an intermediate minimum before increasing in the diffuse regime. At fixed low \(Kn_\infty\), changing \(M_\infty\) mainly changes compression strength and curvature, preserving a coherent attached-layer structure. Density-registered profiles and shock-attached proper orthogonal decomposition (POD) show that, within the present maximum-density-gradient registration, density becomes nearly rank one, whereas Mach number and thermal variables retain independent modal content. Rarefied bow-shock inflation is therefore a coupled compression--relaxation process, not a single-scale rescaling of a continuum-like shock.

physics.flu-dyn↗

arXiv2026

Physics Constrained Neural Collision Operators for Variable Hard Sphere Surrogates and Ab Initio Angle Prediction in Direct Simulation Monte Carlo

The Direct Simulation Monte Carlo (DSMC) method is the gold standard for non-equilibrium rarefied gas dynamics, yet its computational cost can be prohibitive, especially for near-continuum regimes and high-fidelity \emph{ab initio} potentials. This work develops a unified, physics-constrained neural-operator framework that accelerates DSMC while preserving physical invariants and stochasticity required for long-time kinetic simulations. First, we introduce a local neural collision kernel replacing the phenomenological Variable Hard Sphere (VHS) model. To overcome the variance suppression and artificial cooling inherent to purely deterministic regression surrogates, we augment inference with a physics-constrained stochastic layer. Controlled latent-noise injection restores thermal fluctuations, while cell-wise moment-matching strictly enforces momentum and kinetic-energy conservation. Remarkably, this operator exhibits zero-shot spatial and thermodynamic generalization: a model trained exclusively on 1D Couette flow accurately simulates a complex 2D lid-driven cavity, capturing high-order non-equilibrium moments without retraining.Second, to bypass the extreme cost of quantum-mechanical scattering, we develop a dedicated \emph{ab initio} neural operator for the Jäger interaction potential. Trained via a \emph{physics harvesting} strategy on large-scale collision pairs, it efficiently captures the high-energy scattering dynamics dominating hypersonic regimes. Validated on a Mach~10 rarefied argon flow over a cylinder, the framework reproduces transport behaviors and shock features with high fidelity, achieving an approximate 20\% cost reduction relative to direct numerical integration.

physics.comp-ph↗

arXiv2025

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks

This study develops and validates neural network frameworks with physics-based constraints for surrogate modeling of rarefied gas dynamics across different levels of complexity. As a baseline, we first examine the BGK kinetic relaxation problem and show that reformulating the learning task in terms of the perturbation from the Maxwell Boltzmann equilibrium ensures stability and accuracy. Building upon this foundation, we employ Deep Operator Networks, DeepONets, with physical constraints to address two more challenging problems. The first is the prediction of the one-dimensional structure of a standing shock wave in a rarefied polyatomic gas at Mach 5, where the incorporation of physical constraints avoids overshoot and yields accurate predictions even for unseen viscosity ratios. The second is the modeling of two-dimensional rarefied hypersonic flow over a cylinder, where an ensemble of DeepONets trained on a sparse dataset obtained from the direct simulation Monte Carlo, DSMC, approach, generalizes successfully to both interpolation and extrapolation cases up to M equal to 10. A custom weighted loss function improves the prediction of pressure, while ensemble-based uncertainty quantification correctly identifies regions of high gradients such as shock waves. The results demonstrate that embedding physical constraints into neural operator architectures enables accurate, physically consistent, and computationally efficient surrogates, paving the way for their application to multi-dimensional high-speed rarefied flow problems.

physics.flu-dyn↗