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Ehsan Roohi

Publications and source records attributed to Ehsan Roohi.

At least 37 records · Page 2Linked to original sources
arXiv2026

Shock-Centered Low-Rank Structure and Neural-Operator Representation of Rarefied Micro-Nozzle Flows

We examine the structure of Direct Simulation Monte Carlo (DSMC)-resolved internal compression layers in rarefied micro-nozzle flows and show that their apparent parametric complexity is largely a registration and finite-thickness scaling effect. A density-gradient diagnostic identifies the compression-layer station \(x_s\), while a jump-based thickness \(δ_j=Δρ/\max|\partialρ/\partial x|\) defines a shock-centered coordinate \(ξ_j=(x-x_s)/δ_j\). In physical coordinates, the leading proper orthogonal decomposition (POD) mode of the centerline density profiles captures only \(83.33\%\) of the fluctuation energy, whereas the jump-scaled coordinate increases this value to \(98.33\%\). A two-dimensional shock-window POD further confirms that this compactness is not a centerline artifact: in the registered \((ξ_j,η)\) frame, the first density mode captures \(94.98\%\) and the first two modes capture \(99.05\%\) of the fluctuation energy. The same region is identified by density-gradient and gradient-length Knudsen-number diagnostics, linking the reduced representation to localized short-gradient-length rarefaction rather than to shock motion alone. We then use this structure as an inductive bias in a shock-aligned Fusion--Deep Operator Network (DeepONet) surrogate for density, velocity components, temperature, Mach number, and pressure. For held-out back-pressure cases, density, temperature, and pressure errors remain below \(6.8\%\), \(4.3\%\), and \(6.8\%\), respectively, and the hardest case reduces the shock-window mean error from \(9.75\%\)--\(22.27\%\) for standard baselines to \(4.51\%\). The results show that improved prediction follows from the reduced shock-centered structure of the DSMC fields rather than from network capacity alone.

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

AI-Accelerated Operator Learning Framework for Rarefied Microflows

The high computational cost of kinetic solvers such as DSMC remains a major challenge in rarefied flow simulations. This work presents a unified framework combining deep neural networks and neural operators to accelerate kinetic and hybrid solvers while preserving physical fidelity. GPU-native DNN surrogates eliminate costly moment-closure operations in Fokker Planck methods, achieving significant speedups without accuracy loss, while physics-guided and shock-aware DeepONet architectures enable accurate, data efficient modeling of multi regime micro nozzle, micro-step, and hypersonic flows. Extensions including ensemble uncertainty quantification and family-of-experts strategies further enhance robustness across wide Mach and Knudsen number ranges. Together, these results demonstrate a scalable and physics-consistent pathway toward real-time surrogate modeling in rarefied gas dynamics.

physics.flu-dyn↗
arXiv2025

Deep Learning Surrogates for Gas Dynamics: A Physics-Informed Pedagogical Approach

Compressible flow problems are characterized by highly nonlinear, implicit, and often transcendental governing equations. In undergraduate gas dynamics education, solving these equations traditionally relies on iterative numerical methods or extensive look-up tables, which can obscure the physical intuition of the solution space. This paper introduces a comprehensive framework using Deep Learning to generate high-fidelity surrogate models for five canonical problems: Rayleigh flow, Fanno flow, oblique shocks, convergent-divergent nozzles, and unsteady shock tubes. We detail the specific neural network architectures and physics-informed feature engineering strategies required for each problem, such as using logarithmic inputs for Fanno friction parameters or geometric anchors for oblique shocks. The resulting models achieve high accuracy and enable instantaneous visualization of complex design spaces, such as thermodynamic T s diagrams and unsteady x t wave interactions. This approach demonstrates how modern data-driven techniques can be integrated into the physics curriculum to enhance conceptual understanding.

physics.flu-dyn↗
arXiv2025

The 1979 Iranian Revolution and the Lost Decade of Science: A Counterfactual Scientometric Analysis

This paper presents a comprehensive scientometric analysis of the long-term impact of the 1979 Iranian Revolution on the nation scientific development. Using Scopus-indexed data from 1960 to 2024, we benchmark Iran publication trajectory against a carefully selected peer group representing diverse development models, established scientific leaders, Netherlands, stable regional powers, Israel, and high-growth, Asian Tigers, South Korea, Taiwan, Singapore alongside Greece and China. The analysis reveals a stark divergence, in the late 1970s, Iran scientific output surpassed that of South Korea, China and Taiwan. The revolution, however, precipitated a collapse, followed by a lost decade of stagnation, precisely when its Asian peers began an unprecedented, state driven ascent. We employ counterfactual models based on pre revolutionary growth trends to quantify the resulting knowledge deficit. The findings suggest that, in an alternate, stable timeline, Iran scientific output could have rivaled South Korea today. We further outline a research agenda to analyze normalized impact metrics, such as FWCI, and collaboration patterns, complementing our findings on publication volume. By contextualizing Iran unique trajectory, this study contributes to a broader understanding of the divergent recovery patterns exhibited by national scientific systems following profound political shocks, offering insights into the enduring consequences of historical disruptions on the global scientific landscape.

physics.soc-ph↗
arXiv2025

Efficient Collision Algorithms in DSMC for Rarefied Gas Dynamics: Markovian NTC-Pre-Scan and Bernoulli-Trial Schemes

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. A series of collision algorithms, known as the Bernoulli-trials (BT) family schemes, have been proposed based on the Kac stochastic equation. The primary impetus of this paper is to rectify a long-standing theoretical flaw in the widely used no-time-counter (NTC) collision algorithm. We demonstrate that the standard NTC scheme is fundamentally non-Markovian, relying on a fixed majorant product that introduces a system 'memory' and leads to inaccuracies at low particle counts. We propose a new algorithm, NTC-Pre-Scan, which transforms the scheme into a fully Markovian process. When the repeated collisions are not crucial, our new NTC scheme, called NTC-Pre-Scan, could work accurately with a very low number of particles per cell (PPC), average PPC<1, i.e., PPC=0.01, which means with several empty cells in simulations. This contrasts with the standard NTC schemes, which typically require a PPC greater than 1. Then, a systematic evaluation of different BT-based collision partner selection schemes, including the simplified Bernoulli trials (SBT), generalized Bernoulli trials (GBT), symmetrized and simplified Bernoulli trials (SSBT), and the newly proposed symmetrized and generalized Bernoulli trials (SGBT), is conducted to treat some benchmark rarefied gas dynamics problems. The results show that the BT-based collision algorithms and NTC-Pre-scan successfully maintain the collision frequency as the number of particles per cell decreases.

physics.comp-ph↗
arXiv2025

Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows

We present a comprehensive, physics aware deep learning framework for constructing fast and accurate surrogate models of rarefied, shock containing micro nozzle flows. The framework integrates three key components, a Fusion DeepONet operator learning architecture for capturing parameter dependencies, a physics-guided feature space that embeds a shock-aligned coordinate system, and a two-phase curriculum strategy emphasizing high-gradient regions. To demonstrate the generality and inductive bias of the proposed framework, we first validate it on the canonical viscous Burgers equation, which exhibits advective steepening and shock like gradients.

cs.LG↗
arXiv2025

Analysis of the Rarefied Flow at Micro-Step using a DeepONet Surrogate Model with a Physics-Guided Zonal Loss Function

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 applications. To address this limitation, we introduce a Deep Operator Network (DeepONet) surrogate framework featuring an innovative, physics-guided zonal loss function. The zonal loss prioritizes physical fidelity in critical flow regions over global error metrics, leading to predictions with greater engineering relevance. It explicitly emphasizes accuracy in the recirculation zone, ensuring faithful reconstruction of separated flow features that are often under-resolved by conventional, globally averaged error metrics. Two operator-learning tasks are demonstrated: mapping the Knudsen number to the velocity field and mapping the step-height ratio to the flow solution. The results show excellent agreement with high-fidelity DSMC data. An ablation study highlights that, while global error metrics may suggest only marginal improvements, localized error analysis reveals the superior fidelity of the zonal loss in capturing vortex dynamics -- an aspect central to engineering relevance. The proposed surrogate reproduces detailed velocity fields with high physical fidelity and achieves predictions for unseen parameters in milliseconds, representing speedups of several orders of magnitude relative to DSMC. This capability enables the quantification of uncertainty, optimization, and design-space exploration that would otherwise be computationally intractable.

physics.flu-dyn↗
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↗
arXiv2025

Multi-Objective Aerodynamic Optimization of Ride Height and Rake Angle in a Sedan Car Using CFD and Machine Learning

This study investigates the aerodynamic performance of an Audi A4 sedan using CFD analysis. A 3D model was developed in SolidWorks and validated against DrivAer Notchback wind-tunnel data, showing only a 3.25 percent deviation in drag coefficient (Cd). Ride height varied from 1.336 to 1.536 m and rake angle from 0 to 5 degrees, across four Reynolds numbers. Gradient Boosting emerged as the most accurate predictive model (R square = 0.97 for Cd and 0.96 for lift coefficient, Cl), outperforming Random Forest and LightGBM. Differential Evolution optimization was performed under balanced, drag-focused, and downforce-focused conditions. Reynolds number had minimal impact on optimum location; therefore, detailed results are reported for one Reynolds number, with other Re showing similar trends. The baseline geometry exhibited Cd = 0.313 and Cl = 0.0288. Balanced optimization achieved Cd = 0.287 and Cl = - 0.0826. Minimum drag condition reached Cd = 0.285 with slight positive lift (Cl = 0.0142), while maximum downforce optimization reached Cl = - 0.1084 with a 6.71 percent drag penalty (Cd = 0.334). Near-optimal solutions were found within ride heights of 1.341 to 1.365 m and rakes of 0.158 to 4.610 degrees, indicating robust aerodynamic performance. Machine learning predictions were further validated against CFD with a small error in Cd.

physics.flu-dyn↗
arXiv2025

Wall Wettability Control of Cavitation Patterns and Stability

This study investigates the role of wall wettability, characterized by the wall contact angle (WCA), in controlling cavitation dynamics and stability around a Clark Y hydrofoil. High-fidelity Large Eddy Simulations (LES) coupled with a dynamic contact angle model were employed within the OpenFOAM framework to explore WCAs ranging from hydrophilic ($0^\circ$) to superhydrophobic ($160^\circ$) under distinct cavitation numbers ($σ= 1.6$, $0.8$, and $0.4$), representing incipient, cloud, and supercavitation regimes, respectively. The results show that increasing WCA consistently promotes earlier cavitation inception, thicker cavity development, and greater flow unsteadiness. For $σ= 1.6$, higher WCAs led to smaller, detached vapor bubbles and localized pressure fluctuations. At $σ= 0.8$, superhydrophobic surfaces caused more extensive vapor structures, intensified shedding dynamics, and stronger pressure fluctuations. For $σ= 0.4$, high WCAs facilitated stable, wall-adhered cavities that suppressed re-entrant jet activity and reduced unsteady loading. These findings demonstrate that surface wettability serves as an effective passive control mechanism for tailoring cavitation behavior and optimizing flow stability in engineering applications.

physics.flu-dyn↗
arXiv2025

Data-Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks

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 fidelity. First, a fully connected deep neural network is trained on high-quality DSMC data for seven temperatures (200-650 K) to reproduce the Maxwell-Boltzmann speed distribution of argon. Injecting the physical boundary point into the training set enforces the correct low-speed limit. It reduces the mean-squared error to below 10^-5, thereby decreasing inference time from tens of minutes per DSMC run to milliseconds. For one-dimensional shock waves, a multi-output network equipped with learnable Fourier features learns the complete profiles of density, velocity, and temperature. Trained only on Mach numbers 1.4-1.9, it predicts a Mach 2 and 2.5 case with near-perfect agreement to DSMC, demonstrating robust out-of-training generalization. In a lid-driven cavity, the large parametric spread in Knudsen number is handled by a "family-of-experts" strategy: separate specialist models are trained at discrete Knudsen (Kn) values, and log-space interpolation fuses their outputs. This hybrid surrogate recovers the full 2-D velocity and temperature fields at unseen Kn with less than 2% spatial error. Key innovations include (i) explicit injection of physical constraints during data preprocessing, (ii) learnable Fourier feature mapping to capture steep shock gradients, and (iii) a modular expert-interpolation scheme to cover wide Knudsen ranges. Together, they establish a general recipe for trustworthy, rapid surrogate models that can be extended to non-equilibrium phenomena, gas mixtures, and design optimization workflows

physics.comp-ph↗
arXiv2025

A Computational Approach for Modeling Platelet Adhesion Dynamics on Thrombogenic Surfaces

Platelet adhesion and aggregation are essential for primary hemostasis, forming a clot that quickly stops initial bleeding. Despite this critical role, the dynamic interactions of platelet receptors with exposed collagen and von Willebrand factor (vWF) at the injury site and how these interactions influence thrombus formation under varying blood flow conditions are not fully understood. This study aimed to investigate the mechanisms of platelet adhesion and aggregation on collagen- or vWF-coated surfaces numerically. We combined the stochastic Bell's law with a deterministic elastic force featuring a time-dependent coefficient within the context of a dissipative particle dynamics (DPD) model to simulate thrombosis formation numerically. Our simulation results revealed that the numerically predicted platelet adhesion patterns closely matched experimental observations reported in the literature, demonstrating accurate replication of platelet behavior on collagen- and vWF-coated surfaces. Consequently, our deterministic/stochastic force model in DPD provides valuable insights into platelet adhesion dynamics under different flow conditions. These results contribute to a deeper understanding of platelet dynamics and potential therapeutic targets for managing hemostatic disorders.

physics.bio-ph↗
arXiv2023

Computational Study of Rarefied Gas Flow and Heat Transfer in Lid-driven Cylindrical Cavities

The gas flow characteristics in lid-driven cavities are influenced by several factors, such as cavity geometry, gas properties, and boundary conditions. In this study, the physics of heat and gas flow in cylindrical lid-driven cavities with various cross-sections, including fully or partially rounded edges, is investigated through numerical simulations using the direct simulation Monte Carlo (DSMC) and the discrete unified gas kinetic scheme (DUGKS) methods. The thermal and fluid flow fields are systematically studied for both constant and oscillatory lid velocities, for various degrees of gas rarefaction ranging from the slip to the free-molecular regimes. The impact of expansion cooling and viscous dissipation on the thermal and flow fields, as well as the occurrence of counter-gradient heat transfer (also known as anti-Fourier heat transfer) under non-equilibrium conditions, are explained based on the results obtained from numerical simulations. Furthermore, the influence of the incomplete tangential accommodation coefficient on the thermal and fluid flow fields is discussed. A comparison is made between the thermal and fluid flow fields predicted in cylindrical cavities and those in square-shaped cavities. The present work contributes to the advancement of micro/nano-electromechanical systems (MEMS/NEMS) by providing valuable insights into rarefied gas flow and heat transfer in lid-driven cavities.

physics.flu-dyn↗
arXiv2021

Pressure-driven Nitrogen Flow in Divergent Microchannels with Isothermal Walls

Gas flow and heat transfer in confined geometries at micro and nano scales differ considerably from those at macro-scales, mainly due to nonequilibrium effects such as velocity slip and temperature jump. The nonequilibrium effects enhance with a decrease in the characteristic length-scale of the fluid flow or the gas density, leading to the failure of the standard Navier-Stokes-Fourier (NSF) equations in predicting thermal and fluid flow fields. The direct simulation Monte-Carlo (DSMC) method is employed in the present work to investigate pressure-driven nitrogen flow in divergent microchannels with various divergence angles and isothermal walls. The thermal fields obtained from numerical simulations are analysed for different inlet-to-outlet pressure ratios (1.5 $\leq Π\leq$ 2.5), tangential momentum accommodation coefficients and Knudsen numbers (0.05 $\leq \mathrm{Kn} \leq$ 12.5), covering slip to free-molecular rarefaction regimes. The thermal field in the microchannel is predicted, heat-lines are visualised, and the physics of heat transfer in the microchannel is discussed. Due to the rarefaction effects, the direction of heat flow is largely opposite to that of the mass flow. However, the interplay between thermal and pressure gradients, which are affected by geometrical configurations of the microchannel and applied boundary conditions, determines the net heat flow direction. Additionally, the occurrence of thermal separation and cold-to-hot heat transfer (also known as anti-Fourier heat transfer) in divergent microchannels is explained.

physics.flu-dyn↗
arXiv2018

Numerical study of liquid flow and heat transfer in rectangular microchannel with longitudinal vortex generators

The liquid flow and conjugated heat transfer performance of single-phase laminar flow in rectangular microchannels equipped with longitudinal vortex generators (LVGs) are numerically investigated. Deionized-water with temperature-dependent thermo-physical properties is employed to conduct the simulations. Three-dimensional simulations are performed using an open-source flow solver based on finite volume approach and SIMPLEC algorithm. Five different configurations of the microchannel with different angles of attack of the LVGs are considered. Simulation results are compared with available experimental data and a deviation below 10% is achieved. The results show that there is a 2-25% increase in the Nusselt number for microchannels with LVGs, while the friction factor increased by 4-30%, for Reynolds number ranged from 100 to 1100. Except one at Re=100, the overall performance of the all configurations of microchannels with LVGs is higher than one.

physics.flu-dyn↗
arXiv2018

Numerical study of flow patterns and heat transfer in mini twisted oval tubes

Flow patterns and heat transfer inside mini twisted oval tubes (TOTs) heated by constant-temperature walls are numerically investigated. Different configurations of tubes are simulated using water as the working fluid with temperature-dependent thermo-physical properties at Reynolds numbers ranging between 500 and 1100. After validating the numerical method with the published correlations and available experimental results, the performance of TOTs is compared to a smooth circular tube. The overall performance of TOTs is evaluated by investigating the thermal-hydraulic performance and the results are analyzed in terms of the field synergy principle and entropy generation. Enhanced heat transfer performance for TOTs is observed at the expense of a higher pressure drop. Additionally, the secondary flow generated by the tube-wall twist is concluded to play a critical role in the augmentation of convective heat transfer, and consequently, better heat transfer performance. It is also observed that the improvement of synergy between velocity and temperature gradient and lower irreversibility cause heat transfer enhancement for TOTs.

physics.flu-dyn↗