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A review on cooling performance enhancement for phase change materials integrated systems-flexible design and smart control with machine learning applications

机译：相变材料集成系统的冷却性能增强综述-灵活的设计和具有机器学习应用程序的智能控制

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Climate-adaptive design, smart control, latent thermal storages, multi-dimensional uncertainty analysis, and multi-objective optimisations are effective solutions for cooling performance enhancement of buildings through integrated techniques, such as hybrid ventilations, nocturnal sky radiation, radiative cooling and active PV cooling for the self-consumption. However, there is no systematic and in-depth analysis on this topic in the academia. In this study, a state-of-the-art review on novel PCMs based strategies to reduce cooling load of buildings has been presented. The investigated strategies include the structural configuration, systematic control and the multi-criteria for assessment. The roles of ventilations, radiative cooling and the underlying heat transfer mechanism have been characterized for the in-depth understanding. In order to realise the multivariable optimal design and robust operations under multi-level scenario uncertainties, parametric and uncertainty analysis, single- and multi-objective optimisations have been comprehensively reviewed, together with technical challenges for each solution. Research results show that, integrated passive and active systems with flexible transitions on operating modes are full of prospects for the multi-criteria performance improvement. Trade-off solutions along the multi-objective Pareto frontier are multi-diversified, dependent on the adopted approach and the studied scenario. Furthermore, machine learning methods are promising for the thermal and energy performances improvement, through the surrogate model development, the model predictive control and the optimisation function. Future studies and prospects have been demonstrated as avenues for future research. This study presents a systematic overview on novel PCMs based strategies, together with the application of machine-learning methods for cooling performance enhancement, which are critical for the promotion of novel PCMs based cooling strategies in buildings.
机译：气候适应性设计，智能控制，潜热存储，多维不确定性分析和多目标优化是通过集成技术（例如混合通风，夜空辐射，辐射冷却和主动PV）提高建筑物制冷性能的有效解决方案冷却为自耗。但是，学术界对此主题没有系统和深入的分析。在这项研究中，已提出了有关基于新型PCM的减少建筑物制冷负荷的策略的最新技术综述。研究的策略包括结构配置，系统控制和评估的多标准。对通风，辐射冷却和潜在的热传递机制的作用进行了深入了解。为了在多级场景不确定性，参数和不确定性分析下实现多变量最优设计和鲁棒性操作，已经对单目标和多目标优化进行了全面的审查，并对每种解决方案提出了技术挑战。研究结果表明，集成的被动和主动系统在操作模式上具有灵活的过渡，对于提高多标准性能具有广阔的前景。取决于所采用的方法和所研究的场景，沿着多目标Pareto边界的权衡解决方案是多种多样的。此外，通过替代模型开发，模型预测控制和优化功能，机器学习方法有望改善热和能源性能。未来的研究和前景已被证明是未来研究的途径。这项研究提供了有关基于PCM的新型策略的系统概述，以及机器学习方法在提高制冷性能方面的应用，这对于在建筑物中推广基于PCM的新型制冷策略至关重要。

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