5 papers
Efficient Collision Algorithms in DSMC for Rarefied Gas Dynamics: Markovian NTC-Pre-Scan and Bernoulli-Trial Schemes
Ahmad Shoja-sani, Maryam Javani, Ehsan Roohi +1
The collision process is essential to the Direct Simulation Monte Carlo (DSMC) method, as it incorporates the fundamental principles of the Boltzmann and Kac stochastic equations.…
Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
Ehsan Roohi, Amirmehran Mahdavi
We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framewor…
Analysis of the Rarefied Flow at Micro-Step using a DeepONet Surrogate Model with a Physics-Guided Zonal Loss Function
Ehsan Roohi, Amirmehran Mahdavi
The Direct Simulation Monte Carlo (DSMC) method remains the gold standard for simulating rarefied gas flows but is prohibitively expensive for parametric and many-query application…
Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks
Ehsan Roohi, Ahmad Shoja-Sani, Bijan Goshayeshi +1
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…
Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks
Ehsan Roohi, Ahmad Shoja-sani
This study presents a deep neural network (DNN) framework that accelerates Direct Simulation Monte Carlo (DSMC) computations for rarefied-gas flows, while maintaining high physical…