With intensifying climate change, decarbonization of the global building sector has become a key priority. A substantial portion of a building’s energy demands consists of heating and cooling needs. Consequently, developing efficient thermal energy storage systems is a crucial part of this effort. Among available options, latent heat thermal energy storage (LHTES) systems that utilize phase change materials offer unique advantages. These include a high energy storage density and the ability to release large amounts of thermal energy at a near-constant temperature, which is crucial for stable thermal management. Indeed, some studies have shown that LHTES systems can reduce heating and cooling energy consumption by up to 45%.
Despite these advantages, accurately modelling and optimizing LHTES systems remains a major challenge. The coupled heat-transfer and fluid-flow processes involved are difficult to simulate accurately. Although physical experiments provide reliable ground-truth data, they are generally limited to laboratory-scale systems. Computational fluid dynamics (CFD) simulations, on the other hand, can capture these complex physical processes across different scales. However, they are computationally expensive and time-consuming, making large-scale design optimization impractical.
In a new study, a collaborative team of researchers from the Republic of Korea, led by Assistant Professor Joo Hyun Moon from the Department of Building Systems Engineering at Hanbat National University in South Korea, has developed a hybrid physics-informed neural network (PINN) framework for optimization of LHTES systems. “ The LHTES system utilizes a special wax-type material, called a phase change material, that soaks up a huge amount of heat when it melts and gives it back when it hardens, acting like a battery for warmth, ” explains Dr. Moon. “ Testing every new design using conventional computer simulations is slow and computationally intensive. In our PINN framework, we teach the AI model the governing laws of physics, enabling it to accurately reproduce the system's behavior and rapidly explore tens of thousands of possible designs. ” Their study was made available online on May 05, 2026, and published in Volume 167 of the Journal of Energy Storage on July 30, 2026.
To develop the data-driven PINN, the researchers first created a high-fidelity ground-truth dataset that captures the physics of the LHTES system. For this, they developed a laboratory-scale experimental LHTES setup, and a corresponding CFD model. Experimental measurements were then used to validate the CFD simulations, which showed excellent agreement with only minor deviations. Once validated, the CFD model was used to generate a sparse dataset of 15 high-fidelity simulations that served as training data for the PINN.
The proposed PINN framework employs a zero-dimensional physical model and embeds the system’s governing physical equations directly as loss functions. A key innovation is that the PINN learns case-specific effective heat-transfer coefficients, while geometric effects are captured through a response surface model. By focusing only on discovering the laws of energy conservation, the PINN avoids overfitting despite being trained on sparse data. The response surface model (RSM) then enables the framework to predict heat transfer properties for any arbitrary, unseen geometry and flow condition during the optimization process. Together, the PINN and RSM form a fast digital twin of the LHTES system.
The digital twin is then coupled with a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective design optimization. The optimization simultaneously searches for designs that maximize total discharged heat and average discharge power while minimizing pumping power.
In numerical experiments, the PINN reproduced the behavior predicted by the CFD simulations with excellent accuracy while enabling rapid, autonomous design optimization. The optimal design obtained using the multi-objective design optimization process performed similarly to the best-performing baseline design, while significantly reducing pumping power. Moreover, the process also shows that flatter pipes are more favorable.
Beyond buildings, the same physics-informed design approach could help improve thermal management in electric-vehicle batteries, data centers, cold-chain logistics, and solar thermal systems. Earlier studies also suggest that smarter control of latent heat storage can cut electricity costs by more than 70%, showing the broader potential of this technology for reducing energy use and emissions.
“Our approach moves the LHTES design process from manual evaluation of discrete cases to autonomous exploration of the entire design space, ” concludes Dr. Moon. “It provides engineers with a practical tool for developing more efficient thermal energy storage systems, helping reduce the energy consumption and carbon footprint of buildings while supporting a more sustainable energy future. ”
Reference
DOI: https://doi.org/10.1016/j.est.2026.122514
About the institute
Established in 1927, Hanbat National University (HBNU) is a university in Daejeon, South Korea. As a leading national university in the region, HBNU strives to take lead in solving problems in the local community and solidify its cooperation with industries. The university’s vision is to become a ‘Global industry-university cooperation university creating future values’. HBNU has been chosen for a variety of nationwide-level projects such as An Autonomous Improvement University Project and Leaders in INdustry-University Cooperation+(LINC+), among others. With its focus on practical education and regional impact, HBNU continually advances technological solutions grounded in creative thinking and real-world relevance.
Website: https://www.hanbat.ac.kr/eng/
About the author
Dr. Joo Hyun Moon is an Assistant Professor of Building Systems Engineering at Hanbat National University in Daejeon, Republic of Korea. He received his Ph.D. in Mechanical Engineering from Chung-Ang University in 2017. Before joining Hanbat National University, he served as an Assistant Professor at Sejong University and as a postdoctoral researcher at the University of Texas at Dallas. His research spans thermal system optimization, phase-change heat transfer, and physics-informed, machine learning-based models for real-time energy-system optimization.
Journal of Energy Storage
Computational simulation/modeling
Not applicable
Physics-informed neural networks for multi-objective design optimization of latent heat thermal energy storage systems
30-Jul-2026
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.