使用先进的计算技术优化木纤维增强PVC复合材料的导热性
Saksham Anand1, Venkatachalam Gopalan2, Shenbaga Velu Pitchumani3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, 600127, India.
Scientific reports
|May 14, 2025
概括
子搜索算法 (CSA) 有效地提高了子纤维增强PVC复合材料的导热性. 这种生物灵感的方法优化了高性能,环保材料的参数.
科学领域:
- 材料科学与工程 材料科学与工程
- 聚合物复合材料 聚合物复合材料
- 可持续材料 可持续材料
背景情况:
- 纤维提供可持续性和生物降解性,但在纳入聚乙烯 (PVC) 复合材料时,在热性能方面存在挑战.
- 优化导热性对于扩大这些环保复合材料的工业应用至关重要.
研究的目的:
- 为了提高子纤维增强PVC复合材料的导热性.
- 为了比较自然灵感优化算法的有效性,包括粒子群优化 (PSO),龙优化 (DFO) 和子搜索算法 (CSA),在实现这种增强方面.
主要方法:
- 使用响应表面方法 (RSM) 采用盒子-Behnken实验设计.
- 采用了用于样品制备的液压注射成型和用于测量导热性的防护热板装置.
- 应用PSO,DFO和CSA以优化纤维含量,颗粒大小和化学处理.
主要成果:
- 子搜索算法 (CSA) 实现了最高的导热率 (0.801 W/mK) 与最小的误差 (0.01-5.5%).
- 由CSA确定的最佳参数包括氧化处理,2%含量和75微米颗粒直径.
- DFO提供了一致的结果,错误率更高,而PSO显示了快速的趋同,但变化更大.
结论:
- CSA是最有效的优化方法,用于增强合金增强PVC复合材料的热性能.
- 这项研究开创了多种生物灵感算法的比较,以优化可持续复合材料的热性能.
- 这些发现为开发各种工业应用的高性能,环保材料提供了可行的途径.
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