Hybrid journal (It can contain Open Access articles) ISSN (Print) 2050-0467 - ISSN (Online) 2050-0475 Published by Inderscience Publishers[451 journals]
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Authors:Ahasanul Haque, Naila Anwar Chowdhury, Md Uzir Hossain Uzir, Mohammad Shamsuddoha Pages: 101 - 115 Abstract: During the operation of aerospace systems, the system's safety is an extremely important aspect. Therefore, safety considerations are a central aspect during the system's development. Several standard techniques like failure mode and effect analysis (FMEA), fault tree analysis (FTA) or reliability block diagrams are used to assess the safety aspects of the system under development. When the system is operational, it is supervised by a failure detection, identification, and recovery (FDIR) system. Unfortunately, even in today's development processes, there is no unified source of knowledge to support these tasks. This may lead to inconsistent results of safety assessments during development or even incorrect results during online supervision of the operational system. This paper describes an approach to use simulation models as a single and consistent source of knowledge for safety assessments during the system's development as well as for online supervision of the system during its operation. Keywords: simulation; fault tree analysis; FMEA; failure propagation; FDIR; visualisation Citation: International Journal of Sustainable Aviation, Vol. 8, No. 2 (2022) pp. 101 - 115 PubDate: 2022-04-19T23:20:50-05:00 DOI: 10.1504/IJSA.2022.122322 Issue No:Vol. 8, No. 2 (2022)
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Authors:Yuke Huang, Tatsuya Watanabe, Mai Bando, Shinji Hokamoto Pages: 116 - 135 Abstract: In the design of multi-rotor vehicles, it is very important to predesign the overall rotor layout from the perspectives of fluid dynamics, flight dynamics and control performance. However, there is currently no unified guiding principle. This paper discusses the parameter configuration of particle swarm optimisation (PSO) and optimal layout design of multi-rotor vehicles through PSO. First, a rotor dynamics model based on the blade element momentum theory is introduced. Next, the proper parameter settings of the PSO algorithm are discussed, mainly considering particle number and iteration number. Finally, by using the PSO algorithm, some results of proper multi-rotor layouts are shown according to different rotor numbers, take-off weights, blade numbers and rotor angles. The results show that the method proposed in this paper can be applied as a guideline for the layout design of multi-rotor vehicles. Keywords: particle swarm optimisation; PSO; parameter setting; multi-rotor vehicle; optimal layout design; guiding principle; blade element momentum theory; BEMT; particle number; iteration number; blade Citation: International Journal of Sustainable Aviation, Vol. 8, No. 2 (2022) pp. 116 - 135 PubDate: 2022-04-19T23:20:50-05:00 DOI: 10.1504/IJSA.2022.122324 Issue No:Vol. 8, No. 2 (2022)
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Authors:Shlok Misra, Ila Toppo, Flavio Antonio Coimbra Mendonca Pages: 136 - 151 Abstract: The objectives of this study were to identify the factors that are statistically associated with the probability of aircraft damage in the event of a bird strike and to develop classification models to predict aircraft damage in an event of a bird strike. The FAA National Wildlife Strike Database was used for the study to develop random forest, artificial neural network, logistic regression, support vector machine, extra gradient boost (XGBoost), and K-nearest neighbours classifier models. The random forest classifier, logistic regression, and XGBoost classifier exhibited the most robust predictive powers with accuracies of 78.81%, 78.51% and 78.35%, respectively. Based on the variable assessment scores for the random forest classifier, the size of the bird, height of impact, aircraft speed, and aircraft mass had the highest contributions towards predicting aircraft damage for the model. Keywords: machine learning in aviation; bird strikes; wildlife strikes; risk assessment; wildlife hazard management; aviation safety; classification models; FAA National Wildlife Strike Database; data mining Citation: International Journal of Sustainable Aviation, Vol. 8, No. 2 (2022) pp. 136 - 151 PubDate: 2022-04-19T23:20:50-05:00 DOI: 10.1504/IJSA.2022.122328 Issue No:Vol. 8, No. 2 (2022)
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Authors:à mer Seçgin, M. Ziya Sogut Pages: 152 - 161 Abstract: Aluminium alloys are widely used in aircraft construction. In this study, surface roughness optimisation was performed in the external turning process of AL7075 aluminium alloy, which is frequently used in aviation applications. Cutting speed, feedrate and depth of cut are considered as processing parameters. Response surface methodology was used to create the mathematical modelling of surface roughness. Variance analysis was used to determine the effects of parameters on surface roughness. The predicted model for surface roughness obtained from the analysis shows good agreement with the experiment results. Parameter levels that should be used for optimum results: 40 mm/min cutting speed, 0.08 mm/rev feedrate and 0.5 mm cutting depth. Keywords: AL7075; response surface methodology; RSM; surface roughness; turning; variance analysis Citation: International Journal of Sustainable Aviation, Vol. 8, No. 2 (2022) pp. 152 - 161 PubDate: 2022-04-19T23:20:50-05:00 DOI: 10.1504/IJSA.2022.122329 Issue No:Vol. 8, No. 2 (2022)
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Authors:Masoumeh Mirjafari, Alireza Rashidi Komijan, Ahmad Shoja Pages: 162 - 180 Abstract: This paper focuses on aircraft routing and crew rostering problems simultaneously considering the risk of COVID-19 infection. As airports are among high-risk places in COVID-19 pandemic, the crew prefer to spend less sit time in airports and come back to their home base at the end of each duty day. In this research, an integrated model is developed to assign crew and aircraft to flights in order to achieve a fair schedule for the crew. The objective function is minimisation of the difference between crew sit times. Moreover in this model, a framework including flight hours, number of days and number of take-offs is considered for maintenance requirements. Particle swarm optimisation (PSO) is used as the solution approach. To validate the solution approach, 20 test problems were solved using GAMS and PSO. The results show that PSO improved CPU time significantly (98.279% in average) in turn of 1.902% gap with GAMS in optimum solution. Keywords: crew scheduling; aircraft routing; mathematical modelling; particle swarm optimisation; PSO Citation: International Journal of Sustainable Aviation, Vol. 8, No. 2 (2022) pp. 162 - 180 PubDate: 2022-04-19T23:20:50-05:00 DOI: 10.1504/IJSA.2022.122330 Issue No:Vol. 8, No. 2 (2022)
Hybrid journal (It can contain Open Access articles)
