Open Access

ARTICLE

Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm

Vin Cent Tai^1,*, Yong Chai Tan¹, Nor Faiza Abd Rahman¹, Yaw Yoong Sia², Chan Chin Wang², Lip Huat Saw³

1 Centre for Modelling and Simulation, Faculty of Engineering, Built Environment and Information Technology, SEGi University, Petaling Jaya, Selangor, 47810, Malaysia
2 Mechanical Engineering Department, Faculty of Engineering, Built Environment and Information Technology, SEGi University, Petaling Jaya, Selangor, 47810, Malaysia
3 Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Selangor, 43200, Malaysia

* Corresponding Author: Vin Cent Tai. Email:

(This article belongs to the Special Issue: The Role of Artificial Intelligence for Modeling and Optimizing the Energy Systems )

Energy Engineering 2021, 118(4), 1057-1068. https://doi.org/10.32604/EE.2021.014866

Received 04 November 2020; Accepted 18 March 2021; Issue published 31 May 2021

Abstract

Aircraft engine design is a complicated process, as it involves huge number of components. The design process begins with parametric cycle analysis. It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development, to shorten the design cycle for cost saving and man-hour reduction. To obtain a robust solution, optimisation program is often being executed more than once, especially in Reliability Based Design Optimisations (RBDO) with Monte-Carlo Simulation (MCS) scheme for complex systems which require thousands to millions of optimisation loops to be executed. This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm (LJ). A computer program called Turbo Jet Engine Optimiser v2.0 (TJEO-2.0) has been developed to perform the optimisation calculation. The program is made up of inner and outer loops, where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance. Latin-Hypercube-Sampling (LHS) technique is used to sample the design and model variations for uncertainty analysis. The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11% on average. The thrust obtained with uncertainty quantification was about 25% higher than the one without uncertainty quantification, at the expense of less than 3% of fuel consumption. The proposed algorithm can solve the turbofan RBDO problem within 3 min.

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