Venue: Zoom Event
Bio: Anil Yildirim is a PhD candidate in Aerospace Engineering and Scientific Computing. His research focuses on the development and application of robust computational tools in the context of multidisciplinary design optimization for aircraft configurations.
ROBUST AND HIGH-PERFORMANCE TOOLS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION: The development of future sustainable aircraft heavily relies on the design and integration of advanced propulsion systems. However, the design of these systems are challenging due to the tightly coupled interactions between the aerodynamic and the propulsion disciplines. My research focuses on enabling these advanced technologies using aeropropulsive design optimization, in which the aerodynamic and propulsion system designs are optimized in a coupled manner. In this process, I use multiple robust and high-performance computational tools including the computational fluid dynamics (CFD) solver we have been developing in the MDO Lab at the University of Michigan. In this talk, I will cover some recent advancements in the field of CFD-based aeropropulsive design optimization and the computational methodologies we have been using for this work.
JIALE TAN, GRADUATE STUDENT, EPIDEMIOLOGY & SCIENTIFIC COMPUTING
Bio: Jiale is a second year Phd student working with Prof. Rafael Meza in Epidemiology. His interest is to apply computational skills to public health challenges so that he can develop and apply modeling techniques for infectious and noninfectious diseases, including for viral infections like HIV and HCV, and eventually use them for modeling non-communicable diseases that disproportionately affect global health like cancer.
MARKOV MULTISTATE TRANSITION MODEL ON ELECTRONIC NICOTINE DELIVERY SYSTEMS AND TRADITIONAL CIGARETTES: Electronic nicotine delivery systems (ENDS) have dramatically changed the landscape of tobacco products patterns in the USA since 2011. The impact of ENDS use on traditional cigarettes smoking remains a topic of considerable debate. A Markov multistate transition model was used to estimate transition rates (Hazard rate) between ENDS and cigarette use states (25 use states); never user, non-current experimental user, non-current regular user, current experimental user, and current regular user for each product. A 25×25 transition matrix was generated from this model. Parallel computations using 150 processors was used to estimate the transition rates. The Population Assessment of Tobacco and Health study, which includes longitudinal data from 11,475 youth of ages 12 to 24 years from 2013-2018 was used to calibrate the model. The hazard estimates show the patterns of ENDS and cigarette use experimentation and transition to regular use. Next steps will assess the impact of different sociodemographic covariates (age, sex, race, education, household income) on the estimated transition rates.
This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This webinar was not recorded for public distribution.
Questions? Email MICDEemail@example.com
Additional research image from Anil Yildirim.