arXiv subjects

Qiuxiang Huang

Publications and source records attributed to Qiuxiang Huang.

3 recordsLinked to original sources

arXiv2026

Lattice Boltzmann methods for simulating non-Newtonian fluids: A comprehensive review

Non-Newtonian fluids encompass a large family of fluids with additional nonlinear material properties, contributing to non-trivial flow behaviour that cannot be captured through a single constant viscosity term. Common non-Newtonian characteristics include shear-thinning, shear-thickening, viscoplasticity, and viscoelasticity, commonly encountered in everyday fluids, such as ketchup, blood, toothpaste, mud, etc., as well as practical applications involving porous media, cosmetics, food processing, and pharmaceuticals. Due to the complex nature of these fluids, accurate computational fluid dynamics simulations are essential for predicting their behaviour under various flow conditions. Recent advancements have highlighted the growing trend of using the lattice Boltzmann method to solve such complex flows, owing to its ability to handle intricate boundary conditions, ease of including additional multiphysics, and providing computationally efficient parallel simulations. Since the initial review over a decade ago [Phillips & Roberts, IMA J. Appl. Math. 76, 790-816 (2011)], significant advancements have been made to the lattice Boltzmann method to simulate non-Newtonian fluids. Here, we present a comprehensive review of different lattice Boltzmann techniques used to solve non-Newtonian fluid systems, specifically dealing with shear-dependent viscosity, viscoplasticity, and viscoelasticity. In addition, we discuss various benchmark cases that validate these approaches and highlight their growing application to realistic and challenging complex flow problems. We further address outstanding issues in current lattice Boltzmann models, as well as future directions for numerical advancement and application.

physics.flu-dyn↗

arXiv2025

LatentFlow: Cross-Frequency Experimental Flow Reconstruction from Sparse Pressure via Latent Mapping

Acquiring temporally high-frequency and spatially high-resolution turbulent wake flow fields in particle image velocimetry (PIV) experiments remains a significant challenge due to hardware limitations and measurement noise. In contrast, temporal high-frequency measurements of spatially sparse wall pressure are more readily accessible in wind tunnel experiments. In this study, we propose a novel cross-modal temporal upscaling framework, LatentFlow, which reconstructs high-frequency (512 Hz) turbulent wake flow fields by fusing synchronized low-frequency (15 Hz) flow field and pressure data during training, and high-frequency wall pressure signals during inference. The first stage involves training a pressure-conditioned $β$-variation autoencoder ($p$C-$β$-VAE) to learn a compact latent representation that captures the intrinsic dynamics of the wake flow. A secondary network maps synchronized low-frequency wall pressure signals into the latent space, enabling reconstruction of the wake flow field solely from sparse wall pressure. Once trained, the model utilizes high-frequency, spatially sparse wall pressure inputs to generate corresponding high-frequency flow fields via the $p$C-$β$-VAE decoder. By decoupling the spatial encoding of flow dynamics from temporal pressure measurements, LatentFlow provides a scalable and robust solution for reconstructing high-frequency turbulent wake flows in data-constrained experimental settings.

cs.LG↗

arXiv2025

Residual U-Net for accurate and efficient prediction of hemodynamics in two-dimensional asymmetric stenosis

This study presents residual U-Net (U-ResNet), a deep learning surrogate model for predicting steady hemodynamic fields in two-dimensional asymmetric stenotic channels at Reynolds numbers ranging from 200 to 800. By integrating residual connections with multi-scale feature extraction, U-ResNet achieves exceptional accuracy while significantly reducing computational costs compared to computational fluid dynamics (CFD) approaches. Comprehensive evaluation against U-Net, Fourier Neural Operator (FNO), and U-Net enhanced Fourier Neural Operator (UFNO) demonstrates U-ResNet superior performance in capturing sharp hemodynamic gradients and complex flow features. For pressure prediction, U-ResNet achieves a normalized mean absolute error (NMAE) of 1.10%. Similarly, the performance of U-ResNet for wall shear stress (NMAE: 0.56%), velocity (NMAE: 1.06%), and vorticity (NMAE: 0.69%) consistently surpasses alternative architectures. Notably, U-ResNet demonstrates robust generalization to interpolated Reynolds numbers without retraining - a capability rarely achieved in existing models. From a computational perspective, U-ResNet delivers a 180-fold acceleration over CFD, reducing simulation time from approximately 30 minutes to 10 seconds per case. The model with non-dimensional formulation ensures scalability across vessel sizes and anatomical locations, enhancing its applicability to diverse clinical scenarios. These advances position U-ResNet as a promising auxiliary tool to complement CFD simulations for real-time clinical decision support, treatment planning, and medical device optimization. Future work will focus on extending the framework to three-dimensional geometries and integrating it with patient-specific data.

physics.flu-dyn↗