开放式泡沫塑料混合通风空舱的CFD模拟与验证

IF 0.6 Q3 ENGINEERING, MULTIDISCIPLINARY
Shahliza Azreen Sarmin, Azli Abd Razak, Fauziah Jerai, Mohd Khir Harun
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引用次数: 0

摘要

为了查明新冠病毒在飞机机舱等密闭空间内的传播机制,有必要进行调查。然而,这是耗时且范围有限的,因此使用计算流体动力学(CFD)模拟来代替。因此,在进行模拟之前,需要进行事先的研究并选择合适的湍流模型。本研究的主要目的是通过与文献中使用粒子图像测速(PIV)测量的实验数据进行比较,验证和评估开源现场操作与操作(OpenFOAM)软件预测的结果。选择了三种不同的reynolds -average Navier-Stokes湍流模型;重新归一化组k - ε (RNG)、可实现k - ε (RLZ)和低雷诺数(LRN),模拟空舱比例模型的混合通风系统。在RNG和LRN模型情况下,在代表乘客区的模型的左右两侧观察到相当大的循环流量。结果还使用两次观测因子(FAC2)对速度分量和湍流动能(TKE)进行了定量评估,前者具有均方根误差(RMSE),后者具有归一化均方根误差(NMSE)。仿真结果表明,RNG和LRN能够较好地预测流场。然而,对于TKE的预测,LRN优于RNG,这表明LRN是模拟本研究流场的合适湍流模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CFD SIMULATION AND VALIDATION FOR MIXING VENTILATION SCALED-DOWN EMPTY AIRCRAFT CABIN USING OPENFOAM
An investigation into the spread of the COVID-19 virus within a confined space including an aircraft cabin is essential in order to find out the mechanism. However, this is time-consuming and limited in scope, so a computational fluid dynamics (CFD) simulation is used instead. Therefore, a prior study and an appropriate choice of turbulence model are required before the simulation. The main objective of this study is to validate and evaluate the results predicted by the Open Source Field Operation and Manipulation (OpenFOAM) software through comparison with the experimental data from the literature which was conducted using particle image velocimetry (PIV) measurement. Three different Reynolds-averaged Navier-Stokes turbulence models were selected; Re-normalisation Group k - ɛ (RNG), Realizable k - ɛ (RLZ) and Low Reynold Number (LRN) to simulate a mixing ventilation system of a scaled-down model of empty aircraft cabin. In the RNG and LRN model cases, a fairly large circulation flows were observed on the right and left sides of the model representing the passenger area. The results were also evaluated quantitatively using the factor of two of observations (FAC2) for the velocity components and turbulent kinetic energy (TKE) with root mean square error (RMSE) for the former and normalised mean square errors (NMSE) for the latter.    The simulation results showed that RNG and LRN are capable of predicting the flow field well. However, for TKE prediction LRN performed better than RNG which concluded that LRN is the suitable turbulence model in simulating flow fields in investigated case.  
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来源期刊
Jurnal Teknologi-Sciences & Engineering
Jurnal Teknologi-Sciences & Engineering ENGINEERING, MULTIDISCIPLINARY-
CiteScore
1.30
自引率
0.00%
发文量
96
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