Presentation Abstract
Image-based Computational Fluid Dynamics (ICFD) has emerged as a transformative tool, enabling the precise modeling of fluid dynamics within intricate geometries extracted from images. This technology offers unprecedented insights into the 4-dimensional (space+time) hemodynamic abnormalities present in diseased human vessels, driving innovation in medical device design, revascularization planning, and tissue engineering for cardiovascular diseases. By unveiling the complexities of fluid flow within these vessels, ICFD significantly contributes to the advancement of clinical sciences, propelling us toward the era of precision medicine in cardiovascular care. In engineering, the study of pore-scale porous media flows (PSPMFs) has garnered increasing attention due to its wide-ranging applications, from underground reservoirs to biological tissues. ICFD plays a pivotal role in this domain by accurately capturing the heterogeneity, complex pore interconnectivity, and morphologies of porous media. This capability marks a significant advancement, paving the way for heterogeneous porous media flow to become a standard practice across various industries. However, the widespread adoption of ICFD is hindered by the considerable computational expenses it entails. To address this challenge, we have developed a unique ICFD solver with two distinctive advantages. Firstly, it integrates image segmentation and CFD modeling seamlessly through the volumetric lattice Boltzmann method (VLBM), fostering a cohesive approach to multidisciplinary research. Secondly, its ideal compatibility with GPU parallel computing delivers significant speed enhancements, reducing computation times by orders of magnitude. Through collaborative efforts with physicians, medical researchers, experimentalists, and material scientists, we have applied our ICFD solver to a diverse array of research projects. These projects encompass the quantification of 4-dimensional fluid dynamics in human arteries for various cardiovascular conditions, as well as investigations into human choriocapillaris, nuclear waste forms, digital rocks, and eye drug implants. Our work has consistently demonstrated the reliability and applicability of ICFD in diverse real-world scenarios, underscoring its potential to revolutionize both clinical practice and engineering solutions.
Biography
Dr. Huidan (Whitney) Yu is a Professor of Mechanical Engineering at Purdue University in Indianapolis and a Research Professor in Vascular Surgery at Indiana University School of Medicine. With dual Ph.D. degrees in Physics from Peking University, China, and Aerospace Engineering from Texas A&M University, USA, she has enriched her academic journey with postdoctoral research at Los Alamos National Laboratory and The Johns Hopkins University. Dr. Yu’s expertise lies in Computational Fluid Dynamics (CFD), particularly in modeling and simulating complex flows. Her research focuses on employing kinetic-based and GPU-parallelized lattice Boltzmann methods to tackle intricate flow systems across multiple scales and physics domains. With over 100 papers contributed to esteemed journals and numerous impactful talks, she is a recognized authority in the field. Dr. Yu's recent endeavors concentrate on pioneering engineering technologies using image based CFD. She has addressed two primary applications: addressing hemodynamic irregularities for precise diagnostics and therapeutics in cardiovascular diseases; and resolving pore-scale porous media flows (PSPMFs) for various engineering domains such as nuclear waste management, drug delivery, and energy extraction. Her innovative research has resulted in the issuance of two US patents, offering potential breakthroughs in noninvasive cardiovascular disease assessment and predictive therapeutic modeling. She has fostered close collaborations with experts from national laboratories, universities, and medical practitioners, enriching her interdisciplinary approach to research and innovation.
讲座时间：2024年5月20日 09:30-11:00（GMT+08:00）
讲座地点： 东南大学机械楼南高厅
主办单位：东南大学机械工程学院
