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现代发动机在进行优化时参数较多,而且每个参数是多级别的,因此参数优化的工作量非常大,即使只在计算机上仿真优化也是如此.为了提高概念设计阶段的参数优化效率和精度,本文基于过程仿真模拟技术和先进的统计学方法对一台4缸4冲程、风冷、自然吸气发动机性能进行多参数优化研究.利用一维热力学仿真模拟软件GT-Power来模拟发动机性能,产生统计学分析所需要的基本数据.在模拟数据的基础上,利用实验设计方法(Do E)和响应面法(RSM)建立统计学模型对发动机性能进行优化.结果显示,RSM模型能准确预测发动机性能,并且通过该方法优化发动机的主要设计及运行参数,使发动机的最大输出功率提高了10%.
In modern engines, there are many parameters in optimization and each parameter is multi-level, so the workload of parameter optimization is very large, even if it is only simulated and optimized in computer.In order to improve the parameter optimization efficiency and precision in the conceptual design stage, In this paper, based on process simulation and advanced statistical methods, the performance of a 4-cylinder 4-stroke, air-cooled and naturally aspirated engine is studied by multi-parameter optimization.The engine performance is simulated using one-dimensional thermodynamic simulation software GT-Power Based on the simulation data, a statistical model was established by using the experimental design method (Do E) and response surface methodology (RSM) to optimize the engine performance.The results show that the RSM model can accurately predict the engine Performance, and through this method to optimize the main engine design and operating parameters, the maximum output power of the engine increased by 10%.