arXiv subjects

Arshad Kamal

Publications and source records attributed to Arshad Kamal.

3 recordsLinked to original sources

arXiv2026

Multilevel radial basis function surrogates for noise-robust DSMC-CFD coupling

Hybrid methods for simulating rarefied gas flows reduce computational cost by coupling a particle-based model, typically the direct simulation Monte Carlo (DSMC) method, to a continuum-based solver, i.e. a computational fluid dynamics (CFD) code. However, widespread adoption of these methods is hindered by numerical instabilities caused by statistical noise and difficulties in applying them to complex, arbitrary geometries. To be effective, a hybrid framework must be robust to noise, reliable in not introducing errors to the flow physics, automated, and flexible enough for general spatial domains. Previous iterations of the micro-macro-surrogate-sparse (MMS-Sparse) method successfully addressed the first three requirements using Bayesian surrogate models to provide smooth, constitutive corrections to the CFD. However, they relied on global basis functions, limiting their applicability to relatively simple geometries. In this work, we address the fourth requirement - flexibility - by introducing a set of multilevel radial basis functions (RBFs) to represent the smooth corrections within the MMS-Sparse framework. Unlike global polynomials, multilevel RBFs can resolve broad and fine flow details locally, allowing the method to be applied to complex geometrical systems. We couple this approach with a finite-volume CFD solver (OpenFOAM) and validate it using the rarefied lid-driven cavity flow problem. This serves as a rigorous test case for spatially two-dimensional coupling. Our results demonstrate that this enhanced MMS-Sparse method produces estimates in good agreement with benchmarks while retaining the noise-robust and automated benefits of the Bayesian approach.

physics.flu-dyn↗

arXiv2023

A DSMC-CFD coupling method using surrogate modelling for low-speed rarefied gas flows

A new Micro-Macro-Surrogate (MMS) hybrid method is presented that couples the Direct Simulation Monte Carlo (DSMC) method with Computational Fluid Dynamics (CFD) to simulate low-speed rarefied gas flows. The proposed MMS method incorporates surrogate modelling instead of direct coupling of DSMC data with the CFD, addressing the limitations CFD has in accurately modelling rarefied gas flows, the computational cost of DSMC for low-speed and multiscale flows, as well as the pitfalls of noise in conventional direct coupling approaches. The surrogate models, trained on the DSMC data using Bayesian inference, provide noise-free and accurate corrections to the CFD simulation enabling it to capture the non-continuum physics. The MMS hybrid approach is validated by simulating low-speed, force-driven rarefied gas flows in a canonical parallel-plate system and shows excellent agreement with DSMC benchmark results. A comparison with the typical domain decomposition DSMC-CFD hybrid method is also presented, to demonstrate the advantages of noise-avoidance in the proposed approach. The method also inherently captures the uncertainty arising from micro-model fluctuations, allowing for the quantification of noise-related uncertainty in the predictions. The proposed MMS method demonstrates the potential to enable multiscale simulations where CFD is inaccurate and DSMC is prohibitively expensive.

physics.flu-dyn↗

arXiv2018

Enhanced locomotion, effective diffusion, and trapping of undulatory micro-swimmers in heterogeneous environments

Swimming cells and microorganisms must often move though complex fluids that contain an immersed microstructure such as polymer molecules, or filaments. In many important biological processes, such as mammalian reproduction and bacterial infection, the size of the immersed microstructure is comparable to that of the swimming cells. This leads to discrete swimmer-microstructure interactions that alter the swimmer's path and speed. In this paper, we use a combination of detailed simulation and data-driven stochastic models to examine the motion of a planar undulatory swimmer in an environment of spherical obstacles tethered via linear springs to random points in the plane of locomotion. We find that depending on environmental parameters, the interactions with the obstacles can both enhance swimming speeds, as well as prevent the swimmer from moving at all. We also show how the discrete interactions produce translational and angular velocity fluctuations that over time lead to diffusive behaviour primarily due to the coupling of swimming and rotational diffusion. Our results demonstrate that direct swimmer-microstructure interactions can produce changes in swimmer motion that may have important implications to the spreading of cell populations in, or the trapping of harmful pathogens by complex fluids.

physics.flu-dyn↗