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闫盼盼, 牛青林, 高文强, 董士奎. 基于卷积神经网络的地面尾喷焰流场预测[J]. 力学学报.
引用本文: 闫盼盼, 牛青林, 高文强, 董士奎. 基于卷积神经网络的地面尾喷焰流场预测[J]. 力学学报.
PREDICTION OF GROUND EXHAUST PLUME FLOW FIELD BASED ON CONVOLUTIONAL NEURAL NETWORK[J]. Chinese Journal of Theoretical and Applied Mechanics.
Citation: PREDICTION OF GROUND EXHAUST PLUME FLOW FIELD BASED ON CONVOLUTIONAL NEURAL NETWORK[J]. Chinese Journal of Theoretical and Applied Mechanics.

基于卷积神经网络的地面尾喷焰流场预测

PREDICTION OF GROUND EXHAUST PLUME FLOW FIELD BASED ON CONVOLUTIONAL NEURAL NETWORK

  • 摘要: 针对采用传统计算流体力学(computational fluid dynamics,CFD)方法计算火箭发动机尾喷焰耗时较大的问题,提出了一种基于卷积神经网络方法的地面尾喷焰反应流场智能预测模型,用于实时、快速生成典型发动机型谱参数条件下的高精度喷焰流场结果。以固体推进剂弹道评估发动机(ballistic evaluation motor II,BEM-II)为研究对象,在一定发动机型谱参数范围内采用拉丁超立方抽样方法抽选样本空间,将基于CFD方法计算的40个尾喷焰稳态流场样本作为模型训练数据集。采用编码器-解码器结构,引入并行解码器、反卷积层和瓶颈层,使模型能够在数量较少的样本下取得良好的训练效果。采用试车实验数值结果验证和评估了不同欠膨胀状态下尾喷焰流场智能预测模型CNN-PLUME预测性能。结果表明:建立的尾喷焰智能预测模型回归系数接近于1,模型鲁棒性强;可实时生成尾喷焰反应流场结果,模型效率高;喷焰流场预测结果与试验校核数据高度一致,模型预测精度高;在不同欠膨胀状态下尾喷焰流场最大误差为14.17%,模型泛化能力较强。该模型可为地面试车尾喷焰流场规律以及发动机型谱参数设计提供方法支撑。

     

    Abstract: To address the time-consuming nature of traditional Computational Fluid Dynamics (CFD) methods for calculating rocket engine exhaust plumes, a ground exhaust plume reaction flow intelligent prediction model is proposed. This model utilizes Convolutional Neural Network (CNN) technology to rapidly generate accurate real-time exhaust flow field results based on typical engine spectral parameters. The study focuses on the BEM-II solid rocket engine and employs the Latin Hypercube Sampling (LHS) method to select a sample space within a specific range of engine spectral parameters. A total of 40 steady-state exhaust plume flow field samples, obtained through CFD computations, are used as training data for the model. The model employs an encoder-decoder structure with parallel decoders, deconvolutional layers, and bottleneck layers to achieve reliable training performance with a small sample size. Experimental numerical results are employed to validate and evaluate the performance of the intelligent exhaust plume flow field model, known as CNN-PLUME, under various under-expanded states. The results demonstrate that the developed intelligent prediction model exhibits high regression coefficients close to 1, indicating its robustness. The model is capable of generating real-time exhaust plume reaction flow field results efficiently, and the predicted flow field aligns well with the experimental verification data, illustrating the model's high prediction accuracy. The maximum error in the exhaust plume flow field under different under-expanded states is 14.17%, indicating strong generalization ability. This model provides valuable theoretical support for studying ground exhaust plume flow field characteristics and facilitates the design of rocket engine spectral parameters.

     

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