BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH X-WR-TIMEZONE:America/New_York PRODID:-//Apple Inc.//iCal 3.0//EN CALSCALE:GREGORIAN X-WR-CALNAME:Park School X-APPLE-CALENDAR-COLOR:#222222 BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT SEQUENCE:711 DTSTART;TZID=America/New_York:20200130T111500 SUMMARY:Surrogate-based Optimization and Analysis of Energy Harvesting Devices and Systems DESCRIPTION:by Dr. Shima Hajmirza, Assistant Professor of Mechanical Engineering and Director of Energy, Control and Optimization (ECO) lab at Texas A&M University Abstract New generations of solar cell devices heavily rely on the science and engineering of opto-electronics at nano-scale. In contrast to crystalline silicon cells, newer generation devices such as thin film solar cells have lower inherent efficiency. Therefore, efforts are dedicated to improving the opto-electrical performance through nano-texturing and light trapping mechanisms. The problem of designing structures with desirable efficiency improvement at nano-scale is complicated, due to the intertwined effects of many electromagnetic, solid state and quantum physical causes. Robust designs should therefore rely on efficient and smart numerical techniques. The first part of this talk is dedicated to computational and statistical techniques that we have developed to systematically learn and fine-tune the opto-electrical characteristics of semiconductors at nano-scale for photovoltaic applications. Particularly, I will first talk about the challenges in engineering design problems with extremely high cost functions, including joint opto-electrical modeling and optimization of nano-scale solar cells. I will demonstrate how we utilized novel geometrical parameterization techniques along with efficient numerical heuristic search algorithms for structural and topology optimization of a wide class of nano-textured thin film cells with significantly enhanced quantum efficiencies. I will then discuss the use of statistical machine learning techniques such as surrogate modeling and transfer learning that facilitate optimization of high cost functions and optimizations across various domains via surface approximation and other regression techniques. The use of these techniques has facilitated computational design of structures that would otherwise require a ginormous amount of time or resources. Some of our fabrication results, measurements and verification studies that have followed the computational design approaches will also be mentioned. The second part of this talk is dedicated to the generalization of surrogate modeling-based optimization and analysis to other related radiation or optics problems at multiple scales such as radiation heat transfer in porous media. Biography Dr. Shima Hajmirza is an assistant professor of Mechanical Engineering and the director of Energy, Control and Optimization (ECO) lab at Texas A&M University. She obtained her PhD in Mechanical Engineering from the University of Texas at Austin in August 2013 and then became a post-doctoral research scientist with the Institute for Computational Engineering and Science (ICES) at UT Austin, an Adjunct Professor with the Tennessee State University and an Assistant Professor of Engineering Technology at Cal State Pomona University. Prior to the doctoral education, she obtained a master’s degree in Mechanical Engineering from the Southern Illinois University and a master’s degree in Bioengineering from the California Institute of Technology in 2009 and 2010 respectively. Her research interests and expertise are in the general areas of thermal fluid sciences, radiation heat transfer, renewable energy technologies, nano-fabrication and measurement techniques, statistical analysis of dynamic systems, numerical optimization and mathematical modeling, specifically with application to nano-materials, nano-technology and bioengineering. She has published more than 45 peer-reviewed papers (22 journals) in reputable venues including Nature Scientific Reports, Journal of Solar Energy, IEEE Transactions on Sustainable Energy, etc.) and her research has been supported from National Science Foundation (NSF) and several industrial agencies. DTEND;TZID=America/New_York:20200130T121500 END:VEVENT END:VCALENDAR