Session: Rising Stars of Mechanical Engineering Celebration & Showcase

Paper Number: 150319

150319 - Understanding Radiation Heat Transfer in Complex Porous Media Through Analytical Renewal Theory 

This research aims to develop advanced mathematical and computational models for accurately estimating radiation heat transfer (RHT) in complex media, focusing on a novel dual abstraction-regression framework. This framework will characterize and solve macro radiative quantities in heterogeneous media. Current computational methods face challenges when applied to complex applications, while experimental approaches are costly and very inefficient. Emerging machine learning techniques show promise but often lack post-training accuracy and flexibility. The project's primary objectives include establishing a robust theoretical foundation using adapted Powers-Gerber-Shiu risk surplus theories to better characterize radiative properties in heterogeneous media. This involves developing dual abstraction-regression models that integrate macro-configuration abstraction with precise regression models for predicting macro radiative responses. The framework will be validated through comprehensive experimental studies, ensuring its reliability and accuracy. One key innovation of the work is the incorporation of renewal and ruin theories to enhance predictions of macro radiative responses. This approach leverages non-machine-learning computational models based on expert-designed algorithms and solutions. By doing so, the framework aims to overcome gaps in current RHT solutions, offering more reliable and efficient tools compared to existing methods such as Monte Carlo ray tracing. The significance of this research lies in its potential to advance RHT modeling. The research objectives are threefold: first, to explore the application of Renewal, Ruin, and Powers-Gerber-Shiu risk surplus theories for precise radiation heat transfer estimations, improving the accuracy of predicting radiative behavior in complex media. Second, to construct dual models integrating abstraction models with point-wise radiative features and regression models for macro property estimation, providing a comprehensive understanding of radiative properties in heterogeneous materials. Third, to assess the efficacy and reliability of the proposed framework across various media complexities, ensuring wide applicability. Accurate RHT modeling in porous media can benefit industries like solar-driven processes, energy storage, and biomedical engineering, enhancing design and analysis software for geothermal systems, fuel cells, nuclear reactors, and many more. Beyond immediate applications, this research contributes to broader scientific disciplines such as climatology and biomedical engineering, offering new approaches to inverse problems and dynamic systems. The framework's validation and evaluation will ensure robustness and applicability across different media, revolutionizing RHT modeling in both industrial and scientific contexts. This comprehensive approach aims to advance modeling techniques, improving efficiency and accuracy in thermal management and energy utilization, and promising breakthroughs in predicting and managing heat transfer in complex systems, enhancing energy efficiency and sustainability across multiple domains.

Presenting Author: Shima Hajimirza Stevens Institute of Technology

Presenting Author Biography: Dr. Shima Hajmirza is an Associate Professor of Mechanical Engineering and the director of Energy, Control and Optimization (ECO) lab at Stevens Institute of Technology. She obtained her Ph.D. in Mechanical Engineering from the University of Texas at Austin in August 2013 and then became a post-doctoral research scientist with the Oden Institute for Computational Engineering and Science. Prior to her doctoral education, she obtained a Master’s degree in Mechanical Engineering from Southern Illinois University and a Master’s degree in Bioengineering from the California Institute of Technology in 2009 and 2010 respectively.
Her expertise lies in the broader domains of thermal fluid sciences, radiation heat transfer, data-driven modeling and design, and the modeling of energy transfer in intricate media. Additionally, she specializes in Machine Learning/AI-based modeling and optimization, particularly applied to nanomaterials, nanotechnology, and bioengineering. With a portfolio of over 80 peer-reviewed papers (published in esteemed journals such as Nature Scientific Reports, International Journal of Heat and Mass Transfer, Journal of Solar Energy, IEEE Transactions on Sustainable Energy, etc.), her research has garnered support from US National Science Foundation (NSF), US Department of Defense (DOD), US Department of Energy (DOE), as well as industrial partners such as Qualcomm, Emerson, Chevron, and others. Notably, she has been honored with the NSF CAREER award for her contributions to the computational modeling of radiation heat transfer in porous media.

Authors:

Shima Hajimirza Stevens Institute of Technology