Abstract: Ever-increasing energy demands, amidst the rapid transition to renewable energy solutions, are driving the quest for novel and efficient thermal solutions. To contend with these demands, thermal designs must be continuously improved and optimized while ensuring their reliability. With the modern design process becoming increasingly complex and computationally driven, direct optimization and reliability analysis of these designs has become virtually intractable. In an effort to alleviate the computational cost associated with these design routines, surrogate models can be employed as proxies for the original model, as they are efficiently evaluated. Moreover, several models, each with a varying level of cost and fidelity, are often available to describe a system of interest. In such situations, multi-fidelity procedures can combine information from different levels of fidelity to accelerate these design routines. In this talk, several surrogate-based multi-fidelity methods for optimization and reliability analysis will be presented with applications on concentrated solar receivers and heat exchangers.
Bio: Bharath Pidaparthi is a Ph.D. candidate in the Department of Aerospace and Mechanical Engineering at the University of Arizona. He has been at the University of Arizona since 2017 in the Computational Optimal Design of Engineering Systems (CODES) Lab under the guidance of Dr. Samy Missoum and completed his master's degree in 2019. His research interests are geared toward machine learning and uncertainty quantification approaches for computational design and analysis of complex thermal systems.