人工智能在空腔气动/声学特性预测与控制参数优化中的应用

多参数多条件下的精准气动特性数据是进行飞行器快速设计、系统完善、性能评估、指标考核的基本前提和根本保证。基于人工智能的深度学习技术与流体力学交叉融合已成为当前发展趋势，并在湍流模型改造、系统理论建模、气动数据预测、控制参数优化、复杂流场重构等方面得到成功应用。为最大限度发挥深度学习的强大表征能力，围绕内埋弹舱作战运用和智能优化设计需求，构建了弹舱空腔气动特性多场载荷数据库，采用基于数据驱动的深度学习方法，建立了耦合因素影响下的空腔气动/声学特性智能分析深度前馈神经网络模型，实现了有限约束条件下的空腔气动/声学特性快速预测，并引入随机搜索和贝叶斯超参数优化方法增强了模型鲁棒性，为空腔噪声有效控制模型快速优化设计提供了数据基础和方法途径。

Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft

Accurate aerodynamic characteristic data under different conditions is the prerequisite and fundamental guarantee for the fast design of a flight vehicle, the improvement of a control system, the evaluation of performances and performance appraisal. The cross synthesis between the machine learning technology （ML） based on deep neural network （DNN） and fluid mechanics is developing fast and has achieved remarkable progresses in the modification of turbulence models, modeling of systems, prediction of the aerodynamic and aeroacoustic characteristics, optimization of control parameters and reconstruction of the flow field. To effectively apply the powerful representative capability of DNN, according to the demand of intelligent optimization and design of weapon bays, this paper first established a database of aerodynamic loads for flows past cavities and then built deep forward neural network model for the prediction of aerodynamic loads. To enhance the robustness of the model, random search and Bayesian optimization are introduced during the training of the model. Numerical results show that the trained DNN model is able to predict the aerodynamic loads and aeroacoustic characteristics accurately and efficiently, which provides a useful tool for the prediction and control of the aeroacoustic characteristics of the cavity.