A Unified Pressure Correction Algorithm for Computing Complex Fluid Flows

1989 ◽  
pp. 135-147 ◽  
Author(s):  
Wei Shyy
Keyword(s):  
Fluid Flows ◽  
Correction Algorithm ◽  
Pressure Correction ◽  
Complex Fluid

On the role of helicity in complex fluid flows

1985 ◽  
Vol 113 (1) ◽  
pp. 32-37 ◽  
Author(s):  
Leonid Shtilman ◽  
Evgeny Levich ◽  
Steven A. Orszag ◽  
Richard B. Pelz ◽  
Arkady Tsinober
Keyword(s):  
Fluid Flows ◽  
Complex Fluid

scholarly journals The effects of luminal and trans-endothelial fluid flows on the extravasation and tissue invasion of tumor cells in a 3D in vitro microvascular platform

2020 ◽  
Author(s):  
Cynthia Hajal ◽  
Lina Ibrahim ◽  
Jean Carlos Serrano ◽  
Giovanni S. Offeddu ◽  
Roger D. Kamm
Keyword(s):  
Tumor Cells ◽  
Fluid Flows ◽  
Metastatic Potential ◽  
Migration Speed ◽  
Close Proximity ◽  
The Matrix ◽  
Luminal Flow ◽  
Complex Fluid ◽  
Key Steps

ABSTRACTThroughout the process of metastatic dissemination, tumor cells are continuously subjected to mechanical forces resulting from complex fluid flows due to changes in pressures in their local microenvironments. While these forces have been associated with invasive phenotypes in 3D matrices, their role in key steps of the metastatic cascade, namely extravasation and subsequent interstitial migration, remains poorly understood. In this study, an in vitro model of the human microvasculature was employed to subject tumor cells to physiological luminal, trans-endothelial, and interstitial flows to evaluate their effects on those key steps of metastasis. Luminal flow promoted the extravasation potential of tumor cells, possibly as a result of their increased intravascular migration speed. Trans-endothelial flow increased the speed with which tumor cells transmigrated across the endothelium as well as their migration speed in the matrix following extravasation. In addition, tumor cells possessed a greater propensity to migrate in close proximity to the endothelium when subjected to physiological flows, which may promote the successful formation of metastatic foci. These results show important roles of fluid flow during extravasation and invasion, which could determine the local metastatic potential of tumor cells.

scholarly journals Deep learning in fluid dynamics

2017 ◽  
Vol 814 ◽  
pp. 1-4 ◽  
Author(s):  
J. Nathan Kutz
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Turbulence Models ◽  
Fluid Flows ◽  
Navier Stokes ◽  
Complex Flows ◽  
Big Data Applications ◽  
Complex Fluid ◽  
Mining Tool ◽  
Performance Gains

It was only a matter of time before deep neural networks (DNNs) – deep learning – made their mark in turbulence modelling, or more broadly, in the general area of high-dimensional, complex dynamical systems. In the last decade, DNNs have become a dominant data mining tool for big data applications. Although neural networks have been applied previously to complex fluid flows, the article featured here (Ling et al., J. Fluid Mech., vol. 807, 2016, pp. 155–166) is the first to apply a true DNN architecture, specifically to Reynolds averaged Navier Stokes turbulence models. As one often expects with modern DNNs, performance gains are achieved over competing state-of-the-art methods, suggesting that DNNs may play a critically enabling role in the future of modelling complex flows.

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