[CCoE Notice] Defense Announcement: Kripa Adhikari, "Heat and mass transport in bio-inspired vascular systems and porous media: Applications to microvascular composites and geothermal energy"

[Greenwell, Stephen J]

[Dissertation Defense Announcement at the Cullen College of Engineering]
Heat and mass transport in bio-inspired vascular systems and porous media: Applications to microvascular composites and geothermal energy
Kripa Adhikari
April 23rd, 2025, 10:30 a.m. to 12:30 p.m. (CST)
Location: Dean's Office Conference Room, E421, 4th floor, Engineering Building 2.
Committee Chair:
Dr. Kalyana Babu Nakshatrala, Ph.D.
Committee Members:
Dr. Yi-Lung Mo, Ph.D. | Dr. Keh-Han Wang, Ph.D. | Dr. Ruda Zhang, Ph.D. |
Dr. Dong Liu, Ph.D. | Dr. Sailendra P Joshi, Ph.D.
Abstract
Heat and mass transport are fundamental to natural and engineering processes, playing a crucial role in energy systems, environmental applications, and material design. This dissertation focuses on two primary themes: bio-inspired thermal regulation and mass transport in porous media. Bio-inspired thermal regulation, mimicking blood circulation, is crucial in aerospace and geothermal applications exposed to extreme temperatures. Prior active cooling models often neglect the temperature dependence of thermophysical properties in fiber-reinforced composites (FRCs). This dissertation integrates temperature-dependent material properties, specifically the thermal conductivity and specific heat capacity, into an active cooling reduced-order model (ROM). The study demonstrates that while qualitative properties, such as minimum and maximum principles, remain unchanged, quantitative aspects-such as mean surface temperature-show slight variations. Second, an in-depth analysis of advanced geothermal systems (AGS) leveraging bio-inspired thermal regulation is conducted. To efficiently model such systems, a geothermal reduced-order model (GROM) is developed that effectively captures the intricate vascular geometry while providing fast and accurate temperature predictions crucial for assessing power production over time. The modeling framework and the predictions reported in this dissertation offer valuable insights to designers and stakeholders for geothermal system's performance evaluation and their optimization.
The final focus is mass transport in porous media, particularly for fast bimolecular reactions involving advection, diffusion, and chemical reactions. Understanding these mechanisms is vital for subsurface applications, including contaminant transport. This dissertation formulates the problem using a mixed formulation for flow and a tensorial diffusion equation for transport. To solve fast bimolecular reactions efficiently, physics-informed neural networks (PINNs) is introduced, offering a robust framework for rapid and reliable predictions, particularly in data-scarce environments. PINNs successfully addresses the heterogeneous flow subproblem, the diffusion process, and ultimately, the fast bimolecular reaction, accurately predicting the spatial evolution of the chemical product plume. These insights are essential for assessing contaminant risks and improving predictive modeling in subsurface fluid transport applications.
By integrating physics-informed neural networks (PINNs) and finite element method (FEM), this dissertation advances predictive capabilities in thermal regulation and mass transport, offering novel tools for designing efficient cooling systems, optimizing geothermal energy, and assessing environmental risks in subsurface applications.
[Engineered For What's Next]


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