小型飞机客舱对气味敏感的乘客舒适度

C. Nasoulis, S. Mantziou, V. Gkoutzamanis, A. Kalfas
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引用次数: 1

摘要

这项工作提出了一项数值研究,旨在模拟小型飞机机舱的切片作为受控环境的实验设施,以评估暴露于高挥发性有机化合物(VOC)浓度时的乘客舒适度。利用计算流体动力学方法对800 s舱内实际流动时间内化学物质的混合和输运进行了评估,每10 s从靠近乘客鼻子的选定计算节点进行测量。结果用于创建一个数据集,该数据集训练四种不同的机器学习分类器,即随机森林,支持向量机,逻辑回归和朴素贝叶斯,并比较它们的性能。此外,还对机舱进行了额外的模拟,以评估化合物分子量对其停留时间的影响,并将其与没有过滤器的模拟进行比较。结果表明,该模型对进气质量流量变化不敏感,监测计算节点测量的VOCs质量相对不受影响,说明空调系统设置的影响较小。此外,使用Boruta特征选择算法来确定模拟的每个测量的重要性,并形成一个将训练四个机器学习分类器的数据集。此外,两种模拟(有和没有过滤器的模拟)的比较表明,化合物的停留时间(RT)与它们的分子量无关,因为它们都显示出等效的百分位数减少,萘和苯乙烯分别比没有过滤器的模拟减少了28.5%和28.3%。最后,客舱内的流动不规则,破坏了流动的对称性,表明并非所有乘客都有相同的旅行体验。
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Odour-sensitive passenger comfort in small aircraft cabins
This work presents a numerical investigation targeting to simulate the slice of a small aircraft cabin as an experimental facility with a controlled environment, to assess passenger comfort when exposed to high volatile organic compound (VOC) concentrations. The mixing and transport of chemical species are evaluated using computational fluid dynamics for 800 s of in-cabin actual flow time and measurements are taken every 10 s from selected computational nodes close to the passengers’ noses. The results are used to create a dataset that trains four different machine learning classifiers, namely, the Random Forest, Support Vector Machine, Logistic Regression and Naive Bayes, and their performance is compared. Moreover, an additional simulation of the cabin with a filtering system utilising high-efficiency particulate air and activated carbon filters is conducted, to evaluate the impact of the molecular weight of the compounds on their residence time, and compare it to the simulation without the filters. Results indicate that the model is insensitive to the inlet air mass flow variation and that the mass of the VOCs measured in the monitored computational nodes remains relatively unaffected, meaning that the impact of the air-conditioning system setting is minor. Additionally, a Boruta feature selection algorithm is used to determine the importance of each measurement of the simulation and to form a dataset that will train the four machine learning classifiers. Furthermore, the comparison of the two simulations, the one with and the one without the filters, indicates that the residence time (RT) of the compounds is independent of their molecular weight, as they all show equivalent percentile reductions, with the naphthalene and styrene showing a 28.5% and 28.3% reduction respectively, compared to the simulation without the filters. Finally, in-cabin flow irregularities are present, disrupting the flow symmetry and suggesting that not all passengers share the same traveling experience.
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