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首页> 外文期刊>Building and Environment >Multi-objective optimization for designing of high-speed train cabin ventilation system using particle swarm optimization and multi-fidelity Kriging
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Multi-objective optimization for designing of high-speed train cabin ventilation system using particle swarm optimization and multi-fidelity Kriging

机译：使用粒子群优化和多保真克里格设计高速列车机舱通风系统的多目标优化

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摘要

Maintaining a high level of thermal comfort and indoor air quality for occupants while minimizing the system energy consumption is crucial for the long-haul High-speed train cabins. The traditional way of handling the multi-objective problem relies on the "trial and error" design which involves lengthy manual design parameter adjustment and performance evaluation based on on-site measurements or analytical and empirical models. To shorten design optimization process, a multi-objective optimization platform has been developed using the nondominated sorting-based particle swarm optimization (NSPSO) algorithm for searching the trade-off optimal design of the ventilation system in a fully occupied high-speed train (HST) cabin. A computational model of the HST cabin occupied by four full rows of passengers was constructed using ANSYS Fluent. To ensure the accuracy of the CFD model, high resolution computational thermal manikins were adopted to simulate the thermal and pollutant dispersion under influence of the passengers. Different combinations of ventilation operation parameters were evaluated against its performance in terms of thermal comfort, air quality and energy consumption. Furthermore, to reduce the computational cost of constructing the training sample, a Multi-fidelity Kriging technique is also proposed a surrogate method in replacing the time-consuming CFD simulations while maintaining acceptable accuracy. The result demonstrates that the presented approach is capable to perform a multi-objective optimization for indoor ventilation system design and yield accurate Pareto-front result with up to 35.61% saving of computational time.
机译：为乘客保持高水平的热舒适度和室内空气质量，同时最大限度地减少系统能源消耗对于长途高速列车舱来说至关重要。处理多目标问题的传统方式依赖于“试验和错误”设计，这涉及冗长的手动设计参数调整和性能评估，基于现场测量或分析和经验模型。为了缩短设计优化过程，采用NondoMinated分类的粒子群优化（NSPSO）算法开发了一种多目标优化平台，用于搜索完全占用的高速列车通风系统的权衡最佳设计（HST ）小屋。使用ANSYS流畅的ANSYS占据四排乘客的HST小屋的计算模型。为了确保CFD模型的准确性，采用高分辨率计算热马尼克斯来模拟乘客影响下的热和污染物分散。在热舒适性，空气质量和能耗方面，评估了通风操作参数的不同组合。此外，为了降低构建训练样本的计算成本，还提出了一种多保真kriging技术在保持耗时的CFD仿真时，在保持可接受的精度的同时提出了一种代理方法。结果表明，所提出的方法能够对室内通风系统设计进行多目标优化，并得到准确的静脉前线结果，可节省高达35.61％的计算时间。

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