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.

中文翻译：

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新型再生涡轴发动机联合循环与 DNN 应用的能源环境评估与预测

在这项综合研究中，基于能量环境分析，通过添加进气冷却和再生冷却来评估涡轮轴发动机。一、飞行马赫数、飞行高度、主循环压气机1压缩比、主循环涡轮1涡轮入口温度、涡轮2温度分数、附件压缩比的影响循环，进气冷却系统进气温度变化对配备进气冷却系统的再生涡轮轴发动机循环的一些功能性能参数，例如功率比燃料消耗，功率输出，热效率和氮化物质量流量（ NOx) 包括 NO 和 NO2 已通过使用氢气作为燃料进行研究。因此，根据分析，开发了一个模型来预测配备冷却空气冷却系统的再生涡轮轴发动机循环的能量环境性能，该冷却系统基于具有 2 个隐藏层的深度神经网络 (DNN)，每个隐藏层有 625 个神经元。该模型旨在预测含有 NO 和 NO2 的氮氧化物 (NOx) 的热效率量和质量流量。结果证明了集成 DNN 模型的准确性，具有适当数量的 MSE、MAE 和 RMSD 成本函数，用于两个预测输出以验证测试和训练数据。此外，对于热效率和 NOx 排放质量流量的验证，其训练和测试数据的热效率和 NOx 排放质量流量预测值的 R 和 R^2 明显接近 1。