Energy-Environment evaluation and Forecast of a Novel Regenerative turboshaft engine combine cycle with DNN application

TL;DR

This study evaluates a regenerative turboshaft engine with inlet air cooling, analyzing performance impacts and developing a deep neural network model to accurately predict thermal efficiency and NOx emissions.

Contribution

It introduces a novel integrated energy-environment analysis of a regenerative turboshaft engine and develops a DNN model for precise performance prediction.

Findings

DNN model accurately predicts thermal efficiency and NOx emissions.

Performance parameters are significantly affected by flight and engine variables.

Model validation shows high correlation coefficients close to 1.

Abstract

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.