The necessity to diminish the heliostats’ cost so that central tower concentrating solar power (CSP) systems can stride to the forefront to become the technology of choice for generating renewable electricity is obliging the industry to consider innovative designs, leading to new materials being implemented into the development of heliostats. Honeycomb sandwich composites offer a lightweight but stiff structure that appear to be an ideal substitute for existing heliostat mirrors and their steel supporting trusses, avoiding large drive units and reducing energy consumption. However, realizing a honeycomb sandwich composite as a heliostat, among a multitude of possible combinations can be tailored from, that delivers the best trade-off between the panel’s weight reduction (broadly equates to cost) and structural integrity is cumbersome and challenging due to the complex nonlinear material behaviour, along with the large number of design variables and performance constraints. We herein offer a simulation–optimization model for behaviour prediction and structural optimization of lightweight honeycomb sandwich composite heliostats utilizing artificial neural network (ANN) technique and particle swarm optimization (PSO) algorithm. Considering various honeycomb core configurations and several loading conditions, a thorough investigation was carried out to optimally choose the training algorithm, number of neurons in the hidden layer, activation function in a network and the suitable swarm size that delivers the best performance for convergence and processing time. Carried out for three case scenarios, each with different design requirements, the results showed that the proposed integrated ANN-PSO approach provides a useful, flexible and time-efficient tool for heliostat designers to predict and optimize the structural performance of honeycomb sandwich composite-based heliostats as per desired requirements. Knowing that heliostats in the field are not all subjected to the same wind conditions, this method offers flexibility to tailor heliostats independently, allowing them to be made lighter depending on the local wind speed in the field. This could lead to reductions in the size of drive units used to track the heliostat, and the foundations required to support these structures. Such reductions would deliver real cost savings, which are currently an impediment to the wider spread use of CSP systems.

降低定日镜成本的必要性，以便中央塔式聚光太阳能 (CSP) 系统能够走在前沿，成为产生可再生电力的首选技术，这迫使该行业考虑创新设计，导致新材料被应用到定日镜的发展。蜂窝夹层复合材料提供了一种轻巧但坚硬的结构，似乎是现有定日镜及其钢支撑桁架的理想替代品，避免了大型驱动装置并降低了能耗。然而，实现蜂窝夹层复合材料作为定日镜，可以定制多种可能的组合，由于复杂的非线性材料行为，以及大量的设计变量和性能限制，在面板的重量减轻（广义上等同于成本）和结构完整性之间提供最佳权衡是繁琐和具有挑战性的。我们在此提供了一种模拟优化模型，用于利用人工神经网络 (ANN) 技术和粒子群优化 (PSO) 算法对轻质蜂窝夹层复合定日镜进行行为预测和结构优化。考虑到各种蜂窝芯配置和几种负载条件，进行了深入研究以优化选择训练算法、隐藏层神经元数量、网络中的激活函数和合适的群大小，提供最佳的收敛和处理时间性能。针对三种情况进行分析，每种情况都有不同的设计要求，结果表明，所提出的集成 ANN-PSO 方法为定日镜设计人员预测和优化基于蜂窝夹层复合材料的结构性能提供了一种有用、灵活且省时的工具。根据需要的定日镜。知道现场的定日镜并非都受到相同的风条件的影响，这种方法提供了独立定制定日镜的灵活性，允许根据现场当地的风速使它们更轻。这可能会导致用于跟踪定日镜的驱动单元尺寸减小，以及支撑这些结构所需的基础。这种减少将带来真正的成本节约，这目前是 CSP 系统更广泛使用的障碍。