Please cite this article in press as: B. Blais et al., A conservative lattice Boltzmann model for the volume-averaged Navier-Stokes equations based on a novel collision operator, J. Comput. Phys. (2015), http://dx.
AbstractThe volume-averaged Navier-Stokes (VANS) equations are at the basis of numerous models used to investigate flows in porous media or systems containing multiple phases, one of which is made of solid particles. Although they are traditionally solved using the finite volume, finite difference or finite element method, the lattice Boltzmann method is an interesting alternative solver for these equations since it is explicit and highly parallelizable. In this work, we first show that the most common implementation of the VANS equations in the LBM, based on a redefined collision operator, is not valid in the case of spatially varying void fractions. This is illustrated through five test cases designed using the so-called method of manufactured solutions. We then present a LBM scheme for these equations based on a novel collision operator. Using the Chapman- * Enskog expansion and the same five test cases, we show that this scheme is second-order accurate, explicit and stable for large void fraction gradients.
Fibrous filter media are commonly used to remove airborne particles that are harmful to human health and the environment. Although filter media are often multilayered for various reasons, no systematic study of the impact of multilayering on filter media performance has been reported. In this paper, direct numerical simulations with the lattice Boltzmann method are used in order to shed light on the impact of multilayering on the performance of clean bimodal fibrous filter media in a Stokes flow regime. Virtual model clean filter media with up to eight layers and various fibre formulations are compared in terms of permeability or pressure drop, capture efficiency, and quality factor. A careful analysis of the results revealed that multilayering had no statistically significant impact on the performance of the clean filter media. At best, the impact of multilayering was similar to that of the inherent variability of such random structures. Fibre formulation was found to be a more efficient way of
Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.
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