Machine learning-assisted highly efficient thermal management in function-oriented thermochromic smart windows

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Abstract Windows contribute significantly to energy inefficiency in electric vehicles and buildings, and the development of smart windows with customized optical responses catering to various purposes presents a significant challenge. Vanadium dioxide (VO 2 ) Fabry-Perot resonator has emerged as an effective device to modulate heat transfer passively to enhance energy-saving performance, with its efficacy highly limited by the complex interrelationship between the structure and multispectral selectivity. This study introduces a physics-guided neural network to design energy-efficient thermochromic smart windows for directional privacy protection (DPP). Guided by machine learning predictions, the VO 2 nanoparticle size- and spacer-controlled DPP smart window exhibits a targeted luminous transmission (<0.15) with both high modulation of near-infrared transmittance of 0.12 and longwave infrared radiation emissivity of 0.56, surpassing both commercial and the best-reported windows. When applied in electric vehicles and building envelopes, the DPP smart window demonstrated superior thermal management compared to commercial counterparts. This work provides a framework for the inverse design of function-oriented smart windows, advancing energy-efficient solutions for real-world applications.

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