Abstract
The modeling of the engine starting process is vital to ensure the successful start of the engine. However, the engine starting process is very complicated and challenging to model. To optimize the start model performance, an improved teaching-learning based optimization (ITLBO) algorithm is proposed. In ITLBO, a collective lesson preparation phase is increased to enhance the teaching ability of the teacher. The random learning phase is replaced by S-shape group learning, and students learn from the top students of their groups. Also the deterministic sampling selection phase is introduced to ITLBO, and the students with higher evaluation have more possibility to advance in class. The improved algorithm is tested on 18 benchmark functions. The results indicate that the proposed ITLBO algorithm performs much better in terms of convergence speed and accuracy than standard TLBO. When applied to the model adaptation of the turbofan engine starting process, ITLBO is used to optimize the speed line of the rotation components gradually from the lower speed line to the idle speed line. The weighted sum of relative errors between the model outputs and the start test data is taken as the fitness function. After adaptation, the maximum relative errors of model outputs to start test data are significantly decreased, which shows the effectiveness of the ITLBO in model adaption.
Highlights
The aero-engine starting process is a very complex nonequilibrium and non-linear aero-thermodynamic process
The maximum relative error of N1 decreases from 17.2% to 7.8%
The maximum relative error of N2 decreases from 14.2% to 5.75%
Summary
The aero-engine starting process is a very complex nonequilibrium and non-linear aero-thermodynamic process. The commonly used method to enhance the accuracy is performance map adaptation based on the start test data. With the development of evolutionary algorithms, many parameter identification problems are solved by intelligent optimizations such as particle swarm optimization [13], bat algorithm [14], differential evolution algorithm [15] and other heuristic optimization algorithms [16] Different from all these evolutionary algorithms, Rao proposed an efficient optimization method called TeachingLearning-Based Optimization (TLBO) in 2011 [17]. An improved TLBO (ITLBO) algorithm is proposed to enhance its searching ability and the model accuracy.
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