The current investigation reveals the need for combinatorial and geometric optimization for parabolic trough solar collectors (PTSCs) and proposes methods to perform them. An analytical model of PTSC was drafted, which emerged to be quite accurate when exhaustively validated using experimental results. The analysis reveals that superior properties of design components (solar selective absorber coatings (SSACs), heat transfer fluids (HTFs), etc.) cannot guarantee better performance, as there are many interacting factors. Also, a particular combination of components can perform better at a certain temperature while lagging at another. To acquire an optimal combination of components, combinatorial optimization is introduced and carried out for PTSCs, using genetic algorithm (GA). Six SSACs, three absorber materials, and five HTFs are considered, significant efficiency improvements of 8% at 150 °C and 6% at 300 °C are observed. This study discloses that geometrical parameters (length and width of collector, focal length, etc.) possess positive as well negative impacts on efficiency. By varying these in a reasonable range, optimal values that lead to improved efficiency can be obtained. Particle swarm optimization (PSO) is used to attain this geometric optimization, and improvement of ≥3% in efficiency is noticed by only ±5% variation in dimensions.

目前的调查揭示了对抛物槽太阳能集热器 (PTSC) 进行组合和几何优化的必要性，并提出了执行它们的方法。起草了一个 PTSC 的分析模型，当使用实验结果进行详尽验证时，该模型非常准确。分析表明，设计组件（太阳能选择性吸收涂层 (SSAC)、传热流体 (HTF) 等）的卓越性能并不能保证更好的性能，因为存在许多相互作用的因素。此外，组件的特定组合可以在某个温度下表现更好，而在另一个温度下则滞后。为了获得组件的最佳组合，使用遗传算法 (GA) 为 PTSC 引入和执行组合优化。考虑了六种 SSAC、三种吸收材料和五种 HTF，观察到在 150 °C 时效率提高了 8%，在 300 °C 时效率提高了 6%。这项研究揭示了几何参数（收集器的长度和宽度、焦距等）对效率具有积极和消极的影响。通过在合理范围内改变这些值，可以获得导致效率提高的最佳值。粒子群优化 (PSO) 用于实现这种几何优化，效率提高了 ≥3%，尺寸变化仅为 ±5%。