Thermal Systems Modeling

Researchers develop computational models to understand and predict the behavior of thermal systems. This work involves simulating heat transfer, fluid flow, and phase changes in various applications, from engines and power generation to electronic cooling and building energy efficiency. Approaches include physics-based modeling, data-driven techniques, and the application of machine learning to improve model accuracy and computational efficiency. Development of open-source software tools is often a component, fostering wider adoption and collaboration.

This research has direct relevance to Arkansas's diverse economy. Industries such as manufacturing, agriculture (e.g., controlled environment agriculture, food processing), and advanced materials production rely on efficient thermal management. Understanding and optimizing thermal systems can lead to reduced energy consumption, improved product quality, and enhanced operational safety across these sectors. Furthermore, thermal systems are integral to infrastructure, impacting energy grids and building performance statewide.

The field draws on expertise in multiphase transport phenomena, energy system optimization, and machine learning. Research often involves collaborations with other institutions and the development of open-source software, contributing to a broader scientific community.