CFD SIMULATION AND VALIDATION FOR MIXING VENTILATION SCALED-DOWN EMPTY AIRCRAFT CABIN USING OPENFOAM

• Published in: Jurnal Teknologi
• Main Author: Sarmin S.A.; Razak A.A.; Jerai F.; Harun M.K.
• Format: Article
• Language: English
• Published: Penerbit UTM Press 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168925109&doi=10.11113%2fjurnalteknologi.v85.19423&partnerID=40&md5=403b81625d79c9eafe3201e4eb2594a4

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 the investigated case. © 2023 Penerbit UTM Press. All rights reserved.