Capturing flow in the human aorta requires high-resolution fluid models. In this case, the wireframe boxes indicate each computational bounding box describing the work assigned to an individual task. Image: Liam Krauss, Lawrence Livermore National Laboratory.
This project uses advanced data science techniques to drive analysis of extreme-scale fluid-structure-interaction simulations, providing insights to better understand the role biological parameters play in determining tumor-cell trajectory in the circulatory system.
One in four deaths in the United States is due to cancer, and metastasis is responsible for more than 90 percent of these death. The metastatic patterns of circulating tumor cells (CTCs) are strongly influenced by both a favorable microenvironment and mechanical factors such as blood flow. Advancing the use of data science to drive in situ analysis of extreme-scale fluid-structure-interaction (FSI) simulations, this work aims to leverage Aurora to model and analyze the movement of CTCs through the complex geometry of the human vasculature and thereby lay the groundwork for a predictive model of cancer metastasis. Simulating the rare cells, nearby red blood cells, and underlying fluid of the arterial network presents not only a computationally challenging simulation but a large data problem for posterior analysis. Scalable and in situ analysis of massively parallel FSI models, including cellular-level flow, will be critical for enabling new scientific insights into the mechanisms driving cancer progression.
