Deep learning approach for prediction of exergy and emission parameters of commercial high by-pass turbofan engines - Dursun, Omer Osman; Toraman, Suat; Aygun, Hakan (Springer Heidelberg, 2023)
Aviation emissions originated from the fuel burn have been hot topics by engineers and policy-makers due to their harmful effects on the environment and thereby human health as well as sustainability. In this study, it is tried that several emission indexes (EIs) involving CO, HC and NOx as well as fuel flow of several commercial aircraft engines (CAEs) are predicted using support vector regression (SVR) and long short-term memory (LSTM) approaches for take-off phase. Moreover, exergo-environmental parameters involving exergy efficiency (ExEFF), wasted exergy ratio (WExR) and environmental effect factor (EEF) pertinent to CAEs are computed employing thermodynamics laws. While establishing the models, rated thrust, by-pass ratio, overall pressure ratio and combustion type of the CAEs are utilized as the model inputs. According to the findings of emission modelling, the coefficient of determination (R-2) of EI NOx and EI CO of the CAEs is found as 0.929074 and 0.960277 with SVR, whereas their R-2 values are elevated to 0.954878 and 0.989283 with LSTM approach, respectively. However, R-2 of EI HC is determined lower with 0.632280 (by SVR) and 0.651749 (by LSTM). On the other hand, exergo-environmental parameters for the CAEs are estimated with high correctness at both models. Namely, R-2 of ExEFF and EEF regarding the CAEs are computed as 0.991748 and 0.989067 by SVR; however, these are calculated as 0.994785 and 0.992797 by LSTM method. To model these parameters with low error by using significant design variables as model inputs could help in predicting emission and environmental metrics for new engine designs.
Keywords: Emission modelling; Aircraft emission; Exergy modelling; Support vector regression; Long short-term memory