基于NARX神经网络的飞机货舱模拟烟雾近似模型

APPROXIMATE MODEL OF SIMULATED SMOKE IN AIRCRAFT CARGO HOLD BASED ON NARX NEURAL NETWORK

  • 摘要: 为解决飞机货舱模拟烟雾流场扩散规律研究中CFDComputational Fluid Dynamics仿真手段对研究资源的过度依赖和耗费过多的问题。提出一种新的NARX神经网络模型对CFD模型,将时间因素和流场边界条件当作影响条件对烟雾流场的扩散规律作出预测。以CFD模型中某点的烟雾浓度和流场的边界条件作为神经网络模型的输入对其进行训练,得到神经网络代理模型。模型训练和测试结果表明,该模型可以有效代替CFD模型进行相关研究,且近似计算效果好,仿真时间大大减少。

     

    Abstract: To solve the problem of over-dependence on research resources and excessive consumption of computational fluid dynamics CFD simulation tools in the study of the diffusion law of the simulated smoke flow field in the cargo hold of an aircraft, a new NARX neural network model is proposed to the CFD model, and the time factor and the boundary conditions of the flow field are used as the influencing conditions to make predictions on the diffusion law of the smoke flow field. The smoke concentration at a point in the CFD model and the boundary conditions of the flow field were used as the input of the neural network model to train it and obtain the neural network agent model. The model training and testing results show that the model can effectively replace the CFD model for related studies, and the approximation calculation is effective and the simulation time is greatly reduced.

     

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