arXiv subjects

Amirmehran Mahdavi

Publications and source records attributed to Amirmehran Mahdavi.

5 recordsLinked to original sources

arXiv2026

Chordwise micro-jet placement reveals a trade-off between mean hydrodynamic performance and unsteady loading under cloud cavitation

Prescribed tangential micro-jet injection can modify cloud-cavitation dynamics, but the chordwise location that improves mean hydrodynamic performance may differ from the location that minimizes unsteady loading. This study compares five injection locations (x/c = 0.15, 0.30, 0.45, 0.60, and 0.70) on a Clark-Y hydrofoil at Re = 7 x 10^5 and cavitation number 0.8. Transient large-eddy simulation with a Volume-of-Fluid formulation and the Schnerr-Sauer cavitation model is used in a two-dimensional parametric framework, with one representative three-dimensional case included to illustrate spanwise cavity deformation. The analysis considers vapor topology, turbulent kinetic energy, velocity and pressure fields, cycle-averaged surface pressure, hydrodynamic forces, force fluctuations, spectra, and cavity-thickness histories. Injection at x/c = 0.15 gives the highest cycle-averaged lift-to-drag ratio among the tested cases: drag decreases from 0.137 to 0.107 and the lift-to-drag ratio increases from 5.693 to 6.261, while lift decreases from 0.78 to 0.67. In contrast, x/c = 0.60 gives the lowest recorded force-fluctuation RMS, with lift- and drag-coefficient RMS values of about 0.112 and 0.0165. The preferred injection location is therefore objective-dependent: the location favored by mean hydrodynamic performance does not coincide with the location favored by unsteady-load reduction under the present conditions.

physics.flu-dyn↗

arXiv2026

Prescribed Wall-Heat-Flux Control of Blockage and Impulse in a Rarefied Micro-Nozzle

Prescribed wall heat flux provides an active route for controlling rarefied micro-nozzle flows, but its effect is governed by the coupled wall--bulk thermal response rather than by the imposed flux alone. This work uses direct simulation Monte Carlo (DSMC) simulations to study nitrogen flow in a converging--diverging micro-nozzle with cooling, adiabatic, and heating applied on the diverging wall. The imposed heat flux is scaled by the inlet kinetic-energy flux, $E=0.5\rho_i U_i^3$, giving $Q_w/E$ from $-10.5\%$ to $97.3\%$; this range spans moderate cooling, weak-to-intermediate heating, and a near-unity thermal-forcing regime. Wall and mass-flux-weighted bulk temperature profiles, film-temperature-based Nusselt and local-viscosity Brinkman-type diagnostics, gradient-length Knudsen indicators, mass-flux thickness, thrust decomposition, and proper orthogonal decomposition (POD) of signed numerical schlieren are analyzed. The results show that heating creates strong wall--bulk stratification: the wall temperature exceeds five times the inlet value, while the bulk temperature responds more gradually. Cooling cases contain locations where $T_w-T_b$ changes sign, making the local Nusselt-type response singular; the raw singular behavior is retained for diagnosis and a validity mask is used only for comparative plotting. Heating contracts the effective mass-carrying core, increasing aerodynamic blockage and reducing mass flow rate. However, strong heating increases the specific impulse from $156$ s to $201$ s because thermal and pressure-thrust augmentation outweigh the mass-flow penalty. The internal compression feature evolves into a finite viscous--thermal compression zone, and its heat-flux-parametric response remains low-dimensional, with the first two POD modes capturing more than $97\%$ of the fluctuation energy.

physics.flu-dyn↗

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 $\delta_j=\Delta\rho/\max|\partial\rho/\partial x|$ defines a shock-centered coordinate $\xi_j=(x-x_s)/\delta_j$. 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 $(\xi_j,\eta)$ 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↗

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↗