论文标题
能源环境的评估和新型再生涡轮轴发动机的预测与DNN应用结合了
Energy-Environment evaluation and Forecast of a Novel Regenerative turboshaft engine combine cycle with DNN application
论文作者
论文摘要
在这项综合研究中，通过基于能量环境分析添加入口空气冷却和再生冷却来评估涡轮轴发动机。首先，飞行机器数量，飞行高度，压缩机1在主周期中的压缩比，涡轮1在主周期中的涡轮机入口温度，涡轮机2的温度分数，辅助周期的压缩比以及入口空气温度在某些燃油式启动空气稳定系统上的进气速度速度稳定型涡轮上的功能性能的进气速度的进气温度变化，例如通过使用氢作为燃料工作，已经研究了包括NO和NO2在内的氮化物氧化物（NOX）的热效率和质量流量。因此，基于分析，开发了一个模型来预测带有冷却空气冷却系统的再生涡轮移动发动机周期的能量环境性能，该系统基于深神经网络（DNN）（DNN），其中2个隐藏层，每个隐藏层具有625个神经元。该模型提出了预测含有NO和NO2的氮化物氧化物（NOX）的热效率和质量流量的量。结果证明了综合DNN模型的准确性，具有适当的MSE，MAE和RMSD成本函数，用于验证测试和培训数据。同样，对于热效率和NOX发射质量流量，对于热效率的验证和NOX发射质量流量率预测值及其测试数据的验证，R和R^2非常接近1。
In this integrated study, a turboshaft engine was evaluated by adding inlet air cooling and regenerative cooling based on energy-environment analysis. First, impacts of flight-Mach number, flight altitude, the compression ratio of compressor-1 in the main cycle, the turbine inlet temperature of turbine-1 in the main cycle, temperature fraction of turbine-2, the compression ratio of the accessory cycle, and inlet air temperature variation in inlet air cooling system on some functional performance parameters of Regenerative turboshaft engine cycle equipped with inlet air cooling system such as power-specific fuel consumption, Power output, thermal efficiency, and mass flow rate of Nitride oxides (NOx) including NO and NO2 has been investigated via using hydrogen as fuel working. Consequently, based on the analysis, a model was developed to predict the energy-environment performance of the Regenerative turboshaft engine cycle equipped with a cooling air cooling system based on a deep neural network (DNN) with 2 hidden layers with 625 neurons for each hidden layer. The model proposed to predict the amount of thermal efficiency and the mass flow rate of nitride oxide (NOx) containing NO and NO2. The results demonstrated the accuracy of the integrated DNN model with the proper amount of the MSE, MAE, and RMSD cost function for both predicted outputs to validate both testing and training data. Also, R and R^2 are noticeably calculated very close to 1 for both thermal Efficiency and NOx emission mass flow rate for both validations of thermal efficiency and NOx emission mass flow rate prediction values with its training and its testing data.
