TOMATOES: Topology and Material Optimization for Latent Heat Thermal Energy Storage Devices

2025-10-08

AI Summary

Conventional latent heat thermal energy storage systems lack integrated optimization of phase change materials (PCMs) and structural configurations, leading to suboptimal energy density and cost efficiency. Method: This paper proposes the first end-to-end joint optimization framework that simultaneously selects high-conductivity enhancement materials and PCMs while optimizing their topological layouts, under a multiphysics-coupled model of the phase change process. A variational autoencoder embeds discrete material libraries into a differentiable continuous latent space—overcoming the limitation of predefined material choices in traditional topology optimization. Transient nonlinear finite element analysis is coupled with gradient-based optimization to maximize discharged energy under cost constraints. Contribution/Results: Numerical case studies demonstrate significant improvements in both volumetric energy density and cost-effectiveness. The framework establishes a new paradigm for thermo-functional structural design driven by novel material discovery and intelligent topology synthesis.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Latent heat thermal energy storage (LHTES) systems are compelling candidates for energy storage, primarily owing to their high storage density. Improving their performance is crucial for developing the next-generation efficient and cost effective devices. Topology optimization (TO) has emerged as a powerful computational tool to design LHTES systems by optimally distributing a high-conductivity material (HCM) and a phase change material (PCM). However, conventional TO typically limits to optimizing the geometry for a fixed, pre-selected materials. This approach does not leverage the large and expanding databases of novel materials. Consequently, the co-design of material and geometry for LHTES remains a challenge and unexplored. To address this limitation, we present an automated design framework for the concurrent optimization of material choice and topology. A key challenge is the discrete nature of material selection, which is incompatible with the gradient-based methods used for TO. We overcome this by using a data-driven variational autoencoder (VAE) to project discrete material databases for both the HCM and PCM onto continuous and differentiable latent spaces. These continuous material representations are integrated into an end-to-end differentiable, transient nonlinear finite-element solver that accounts for phase change. We demonstrate this framework on a problem aimed at maximizing the discharged energy within a specified time, subject to cost constraints. The effectiveness of the proposed method is validated through several illustrative examples.

Problem

• Optimizing material selection and topology for latent heat thermal energy storage systems
• Overcoming discrete material incompatibility with gradient-based topology optimization methods
• Developing automated co-design framework integrating material databases and phase change physics

Innovation

• Concurrent optimization of material choice and topology
• Data-driven VAE projects discrete materials to continuous spaces
• Differentiable transient nonlinear finite-element solver handles phase change