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<p>Accelerated urbanization and modernization have augmented urban heat island effects, driving up cooling energy demand in buildings and exerting pressure on ecological sustainability. In this study, the application of phase change material (PCM) in building roof top slab, sandwiched between brick-mortar and concrete layers, is investigated as a sustainable passive cooling strategy. A numerical model based on finite difference method is employed to analyze the thermo-physical behavior of PCM under realistic boundary conditions. A case study of New Delhi (28.6139° N, 77.2088° E) is conducted using measured solar radiation and ambient temperature data in a typical day across all seasons. The simulation results of reduced energy consumption are further integrated with an advanced artificial intelligence model (AI) - artificial neural network (ANN) for optimization. Sensitivity analysis using ANN identifies the PCM thickness as the most influential factor in minimizing energy consumption. The optimized value of PCM thickness is determined to be 40&#xa0;mm for minimum energy consumption 0.0176 kWh, against target values of maximum ambient temperature and solar radiation in New Delhi. This work not only presents a framework combining numerical model and ANN based optimization, replicable for different climatic zones, but also quantifies relevant sustainability indicators that confirm the alignment of this study with the United Nations Sustainable Development Goals (SDGs).</p>

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Phase change material integrated building envelope toward SDG aligned sustainable solutions: Numerical and artificial intelligence driven analysis

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Mrinmoy Dhar,
  • Palash Dey

摘要

Accelerated urbanization and modernization have augmented urban heat island effects, driving up cooling energy demand in buildings and exerting pressure on ecological sustainability. In this study, the application of phase change material (PCM) in building roof top slab, sandwiched between brick-mortar and concrete layers, is investigated as a sustainable passive cooling strategy. A numerical model based on finite difference method is employed to analyze the thermo-physical behavior of PCM under realistic boundary conditions. A case study of New Delhi (28.6139° N, 77.2088° E) is conducted using measured solar radiation and ambient temperature data in a typical day across all seasons. The simulation results of reduced energy consumption are further integrated with an advanced artificial intelligence model (AI) - artificial neural network (ANN) for optimization. Sensitivity analysis using ANN identifies the PCM thickness as the most influential factor in minimizing energy consumption. The optimized value of PCM thickness is determined to be 40 mm for minimum energy consumption 0.0176 kWh, against target values of maximum ambient temperature and solar radiation in New Delhi. This work not only presents a framework combining numerical model and ANN based optimization, replicable for different climatic zones, but also quantifies relevant sustainability indicators that confirm the alignment of this study with the United Nations Sustainable Development Goals (SDGs).