Development and Performance evaluation of a Latent Heat Thermal Energy Storage (LHTES) unit coupled with a Nuclear Power Plant

Abstract

This thesis examines the growing requirement for improved flexibility in traditional power plants to manage the variable output of intermittent renewable energy sources and to enhance the efficiency of nuclear power plants, all while maintaining a secure and stable energy supply. The integration of thermal energy storage systems with nuclear power plants presents a viable approach to improve the continuous and efficient functioning of nuclear reactors. The implementation of thermal energy storage systems faces significant investment barriers, primarily stemming from insufficient understanding of their compatibility with nuclear power plants and the performance metrics required to assess their advantages and integration challenges. To bridge this knowledge gap, the thesis presents a detailed exploration of thermal energy storage advancements in nuclear power plants environments, emphasizing both theoretical and practical aspects of integration. Specifically, the behavior of phase change material melting kinetics is studied numerically under different gravity conditions to elucidate natural convection phenomena during melting, with potential applications for space and moon missions. To enhance the melting performance, passive techniques involving helical-shaped fins in vertical channels are proposed, and various configurations are analyzed to achieve uniform and accelerated melting in low-gravity conditions. Lower gravity conditions delayed the melting by 2 – 3 times for moon and 5 – 6 times for micro-gravity. Up to 23% enhancement in melting attained by introduction of helical fins. Two case studies (charging, discharging datasets) are then conducted on the deployment of thermal energy storage system for the APR1400 nuclear power plant to identify the optimal integration points for energy storage and retrieval. Computational fluid dynamics simulations and machine learning methodologies are employed to optimize system performance, with a focus on critical design parameters, thermophysical properties, and dynamic input conditions affecting charging and discharging behavior. For optimization, over 5,500 numerical simulations are conducted to evaluate phase change materials melting and solidification behavior under different heat transfer fluid injection conditions, thermal energy storage sizes, heat recovery dynamics, and charged temperature levels. Leveraging the extensive dataset, artificial neural networks are developed to predict thermal energy storage performance accurately. These artificial neural network models are then used in multi-objective genetic algorithm and Bayesian optimization strategies to optimize key objectives such as minimizing injection velocities, reducing charging durations, enhancing discharging efficiency, and maximizing stored energy under real-time demand fluctuations. The optimization frameworks identify critical thermal energy storage design and operational configurations tailored to nuclear power plant environments, demonstrating robust predictions for melting times, energy storage capacity, and efficiency improvements. Error metrics for machine learning prediction remain in very good acceptable range (approximately 6% for charging and 1% for discharging). Furthermore, novel design (frustum shape) with multistage configuration of thermal energy storage has been introduced. Multistage configurations save charging time up to 50% and frustum shape increased the round-trip efficiency by 8%. The findings of this thesis demonstrate the accuracy, reliability, and potential commercial applicability of the thermal energy storage techniques, providing scientific justification for their integration into future nuclear power plant energy systems. By establishing an optimized and scalable thermal energy storage integration strategy, this research contributes to enhancing the energy storage and utilization capabilities of nuclear power plants, supporting the broader transition toward more flexible and sustainable energy solutions in a decarbonized future.

Date of Award	5 May 2025
Original language	American English
Supervisor	Imran Afgan (Supervisor)