The conventional roof contributes nearly 40% of the total heat that enters the building, resulting in higher interior temperatures. HVAC systems can be utilized to reduce interior temperatures but are not sustainable. Phase Change Materials can be employed as a part of the roofing system to reduce heat ingress through latent heat storage. The present work focuses on multiple-continuous factor optimization of PCM integration in roofs. The study was conducted in two climatic zones of India: composite (Hyderabad) and warm and humid (Cuttack). Continuous factors such as PCM type, position, and thickness were optimized using the Response Surface Methodology in Minitab software. The optimization used two responses: lag time (maximize) and decrement factor (minimize). The RSM required 20 design experiments to be performed, and the corresponding responses were used to optimize the continuous parameters. The optimized values were used to analyze the two responses using COMSOL Multiphysics v6.0 for subsequent validation. Overall, optimizing PCM continuous factors led to better responses, which were validated through simulations.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Optimization of Multiple Factors of PCM Integration in Building Roofs Using Response Surface Methodology

  • Rameshkumar Vighnesh,
  • Parol Viswanath,
  • Kalpathy Balakrishnan Anand

摘要

The conventional roof contributes nearly 40% of the total heat that enters the building, resulting in higher interior temperatures. HVAC systems can be utilized to reduce interior temperatures but are not sustainable. Phase Change Materials can be employed as a part of the roofing system to reduce heat ingress through latent heat storage. The present work focuses on multiple-continuous factor optimization of PCM integration in roofs. The study was conducted in two climatic zones of India: composite (Hyderabad) and warm and humid (Cuttack). Continuous factors such as PCM type, position, and thickness were optimized using the Response Surface Methodology in Minitab software. The optimization used two responses: lag time (maximize) and decrement factor (minimize). The RSM required 20 design experiments to be performed, and the corresponding responses were used to optimize the continuous parameters. The optimized values were used to analyze the two responses using COMSOL Multiphysics v6.0 for subsequent validation. Overall, optimizing PCM continuous factors led to better responses, which were validated through simulations.