基于BSO-ELM的涡轴发动机加速过程性能参数预测

Prediction of turboshaft engine acceleration process performance parameters based on BSO-ELM

建立精度和实时性均满足要求的航空发动机性能参数预测模型是实现发动机性能优化和实时监控的基础。极限学习机(extreme learning machine,ELM)对复杂的非线性航空发动机系统具有良好的适应性,本文提出了利用头脑风暴优化算法(brain storm optimization,BSO)优化ELM的网络参数以提高其性能。并提出以发动机的台架试车加速过程数据为训练和验证样本,利用BSO-ELM算法回归辨识得到涡轴发动机加速过程性能参数预测模型。结果表明预测参数燃气发生器转速ng、燃气发生器出口温度T4和增压比πc的两项精度指标均优于BSO算法优化的反向传播神经网络和粒子群优化算法优化的ELM方法得到的预测模型,表明了BSO-ELM预测模型的可行性与优越性;在相同仿真环境下,BSO-ELM算法可大幅提高计算效率使预测模型的实时性更优。

It is the basis of engine performance optimization and real-time monitoring to establish the prediction model of aeroengine performance parameters which can meet the requirements of both accuracy and real-time.Extreme learning machine(ELM)has good adaptability to the complex nonlinear aeroengine system.In this paper,a brain storm optimization(BSO)algorithm is proposed to optimize the network parameters of ELM for improving its performance.The acceleration process data of the engine on the bench test are used as training and verification samples,and the performance parameter prediction model of the turboshaft engine acceleration process is obtained by regression identification using BSO-ELM algorithm.The results show that the prediction parameters of output parameter gas generator speed ng,gas generator outlet temperature T4 and pressure ratioπc are better than the prediction models obtained by backpropagation neural network optimized with BSO algorithm and ELM method optimized with particle swarm optimization,which indicates the feasibility and superiority of the BSO-ELM prediction model.In the same simulation environment,the BSO-ELM algorithm can greatly improve the computational efficiency and improve the real-time performance of the prediction model.