计算流体动力学和机器学习集成用于评估太阳能热收集器效率 - - 基于参数分析
Xiaoyu Hu1, Lanting Guo2, Jiyuan Wang3
1Stevens Institute of Technology, Hoboken, USA.
Scientific reports
|July 8, 2025
概括
这项研究介绍了一种混合框架,将计算流体动力学 (CFD) 和机器学习结合起来,以优化太阳能热收集器与微热管阵列 (MHPA) 进行食品脱水,实现高精度和效率.
科学领域:
- 可再生能源工程可再生能源工程
- 计算科学 计算科学
- 热力工程是热力工程中的一个.
背景情况:
- 优化太阳能热收集器用于食物脱水对于有效利用可再生能源至关重要.
- 在热系统设计中,平衡计算效率和预测准确度是关键的挑战.
- 微热管阵列 (MHPA) 提供了太阳能集热器中增强热性能的潜力.
研究的目的:
- 开发和验证一个新的混合计算框架,集成CFD和机器学习.
- 用MHPA优化太阳能热收集器,用于食品脱水应用.
- 在热系统设计中提高计算效率和预测准确性.
主要方法:
- 一个经过验证的CFD模型生成了935个数值案例用于训练和评估机器学习算法 (LR,SVR,ANN).
- 用透分析量化信息传输并确定有影响力的设计参数.
- 解读性分析确定了关键收集器参数的最佳操作范围.
主要成果:
- 与LR (R2 = 0.61) 相比,优化的SVR和ANN模型实现了较高的预测准确性 (R2 = 0.96和0.94).
- 确定MHPA导热率,空气入口温度和空气速度是最有影响力的参数.
- 超过600个数据样本实现了最佳性能,在100-300个样本之间观察到关键过渡.
结论:
- 混合CFD-ML框架为优化太阳能热收集器设计提供了一种高效的方法.
- 该方法为改善太阳能食品干燥系统提供了有价值的见解,并降低了计算成本.
- 确定关键参数和最佳操作范围可以指导未来的太阳能收集器设计和开发.
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