  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.
