From thermal physics
to practical systems.
Thermal research across energy systems, components, and working fluids.
Research projects
Image-Based Analysis of Flow Boiling for High-Heat-Flux Electronics Cooling
PI: Prof. Haotian Liu
This project investigates flow boiling as an advanced thermal-management approach for high-heat-flux electronics. By combining experimental visualization with computer-vision techniques, the project aims to relate observable bubble dynamics to present and future heat-transfer performance and critical operating conditions. The broader goal is to support the development and control of efficient and reliable two-phase cooling systems for applications such as high-performance computing and data centers.
Students: Yash Trivedi, Liam Groves
Development of A Flexible Heat Pump System Testing Platform with Refrigerant Injection
PI: Prof. Haotian Liu
This project aims to develop a flexible heat pump testing platform with refrigerant injection to characterize system and component performance under controlled operating conditions. Following commissioning and experimentation, the resulting data will support component and system model development and validation. Expected outputs include performance maps, experimental datasets, and technical reports documenting the platform design, development, and experimental results.
Students: Muhammad Shummas Humayun, Liam Groves
Generating Performance Data and Semi-Physics Tunable Models for Injected Compressors using Low-GWP Refrigerants
Co-PI: Prof. Haotian Liu, with Prof. Christian K. Bach
This project investigates the performance of refrigerant-injected compressors using low-GWP refrigerants. By combining experimental testing with semi-physics-based modeling, the project aims to generate reliable compressor performance data and develop tunable models that can accurately represent compressor behavior under different operating conditions. The broader goal is to improve the understanding, modeling, and performance of injected compression systems for next-generation refrigeration and HVAC applications using low-GWP refrigerants.
Student: Muhammad Hasanat Khan
Generating Performance Data and Semi-Physics Tunable Models for Injected Compressors using Low-GWP Refrigerants
Co-PI: Prof. Haotian Liu, with Prof. Christian K. Bach
This project combines experimental testing and modeling to investigate the performance of injected scroll compressors using refrigerants with low global warming potential. It examines baseline operation, vapor injection, and two-phase injection, using experimental data to develop and validate semi-physics-based models for predicting compressor performance. The broader goal is to support more efficient and reliable injected compressor heat pump systems. Rasheed Shittu supports the project by helping develop data analysis scripts to process experimental measurements and by assisting with experimental data collection.
Student: Rasheed Shittu
Optimal Redesign and Multi-Objective Optimization of Unitary HVAC Systems for Next-Generation Refrigerants
PI: Prof. Haotian Liu
Co-PI: Prof. Jeffrey Spitler
This project investigates the optimal redesign of unitary HVAC systems for next-generation refrigerants. Through system-level modeling and multi-objective optimization, the project aims to explore what changes are needed to maintain and improve thermal performance. The broader objective is to support the transition to next-generation refrigerants in commercial HVAC applications.
Student: Tajwar Haque
Additional Benefits of Secondary Loop Systems: Thermal Storage and Demand Response
Co-PI: Prof. Haotian Liu, with Prof. Jeffrey Spitler and Prof. Christian K. Bach
This project evaluates the potential benefits of thermal energy storage (TES) in residential buildings, including improved system performance, reduced electricity consumption and costs, and electricity grid benefits such as peak load shaving and load shifting. Experimentally validated, physics-based models are used to simulate a TES-integrated building over a full year under control strategies ranging from simple rule-based controllers to optimization-based model predictive controllers. The goal is to develop guidance for system and controller designs that benefit homeowners, utilities, and grid operators.
Student: Pouria Moghimi Ghadikolaei
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Build what comes next.
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Questions about research or collaboration?
haotian.liu@okstate.edu