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.

翻译：潜热蓄热系统因其高储能密度而成为极具潜力的储能候选方案。提升其性能对于开发下一代高效低成本装置至关重要。拓扑优化已成为设计LHTES系统的强大计算工具，通过优化分布高导热材料与相变材料来实现性能优化。然而，传统拓扑优化通常局限于对固定预选材料进行几何构型优化，未能充分利用日益增长的新型材料数据库。因此，LHTES系统中材料与几何的协同设计仍是尚未探索的挑战。为突破此限制，我们提出了一种可实现材料选择与拓扑结构同步优化的自动化设计框架。其中关键挑战在于材料选择的离散特性与拓扑优化中基于梯度的方法不兼容。我们通过数据驱动的变分自编码器，将HCM和PCM的离散材料数据库映射到连续可微的潜空间，从而解决了这一难题。这些连续材料表征被集成至考虑相变过程的端到端可微瞬态非线性有限元求解器中。我们在成本约束条件下，以最大化指定时间内释放能量为目标的问题上验证了该框架的有效性，并通过多个示例证明了所提方法的优越性。