In this video from the 2017 HPC Advisory Council Stanford Conference, Mahdi Esmaily from Stanford presents: Best Practices: Multi-Physics Methods, Modeling, Simulation & Analysis.
“The cycle of modeling high impact applications to finding new solutions is completed by the use of high-performance computing. I this talk, I will discuss two particular applications which have highly benefitted from HPC. The surgical operation performed on single ventricle heart patients has not been modified in last few decades despite a high rate of mortality. Through multiscale simulation of the circulatory system, it is now possible to model this surgery and optimize it using the state of the art optimization techniques. In-silico analysis has allowed us to test new surgical design without posing any risk to patient’s life. I will show the outcome of this study, which is a novel surgical option that may revolutionize current clinical practice. The second application that I will discuss in this talk is related to renewable energy. The particle-based solar receivers operate by collecting radiative energy volumetrically through dispersed particles rather than the conventional approach of absorption via a surface. I will discuss our recent work on the investigation of the operating modes of these devices, where we are exploring the interaction of particles with turbulence, solid boundaries and radiation.”
Mahdi Esmaily-Moghadam received his B.S. and M.Sc. in Mechanical Engineering from the Sharif University of Technology, Tehran, Iran, and his Ph.D. from the University of California, San Diego, working on the development of multiscale methods for optimization of surgical techniques for single ventricle heart patients. He is currently a postdoctoral scholar at the Center for Turbulence Research at Stanford University, studying particle-based solar receivers. He has an interdisciplinary background in areas of computational and cardiovascular mechanics, particle-laden flows, finite-element analysis, shape optimization methods, high-performance computing, and linear algebraic solvers.