In this study, aviation, energy, exergy, environmental, exergoeconomic, and exergoenvironmental analyses are performed on a CFM56-3 series high by-pass turbofan engine fueled with Jet-A1 fuel. Specific fuel consumption and specific thrust of the engine are found to be 0.01098 kg/kN.s and 0.3178 kN/kg/s, respectively. Engine's energy efficiency is calculated as 35.37%, while waste energy ratio is obtained as 64.63%. Exergy efficiency, waste exergy rate, and fuel exergy waste ratio are forecasted as 33.32%, 33175.03 kW, and 66.68%, respectively. Environmental effect factor and ecological effect factor are computed as 2.001 and 3.001, while ecological objective function and its index are taken into account of −16597.22 kW and −1.001, respectively. Exergetic sustainability index and sustainable efficiency factor are determined as 0.5 and 1.5 for the CFM56-3 engine, respectively. Environmental damage cost rate is determined as 519.753 $/h, while the environmental damage cost index is accounted as 0.0314 $/kWh. Specific exergy cost of the engine production is found as 40.898 $/GJ from exergoeconomic analysis, while specific product exergy cost is expressed as 49.607 $/GJ from exergoenvironmental analysis. From exergoenvironmental economic analysis, specific exergy cost of fuel is computed as 10.103 $/GJ when specific exergy cost of production is determined as 40.898 $/GJ.
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This work focuses on humidity effects of turbofan engines, in order to identify the magnitude of the error in operational conditions and the implications on maintenance decision support. More specifically, this paper employs a set of different methods, including semi-empirical corrections used in engine test beds, performance simulation models and analysis of historical data, in order to investigate the effects of humidity. We show that varying humidity can have a noticeable influence on the performance of the engine. These discrepancies cannot be currently quantified by health monitoring systems. Simulation and test bed correlations indicate a decrease of EGT of 0.35% per 1wt% of absolute humidity, which varies worldwide between 0 and 3wt%. Consequently, deviations in EGTM can be up to 1%, a figure which can be up to 12K for a modern civil turbofan. In practice, variations in ambient humidity have the potential to conceal possible deterioration in engine components. Following, the flight historical data were corresponded to historical humidity data. The two methods were identified to provide comparable results, indicating a higher EGTM for increasing ambient humidity. Overall, it was concluded that EGTM corrections for ambient humidity is an area of significant interest, especially for newer engine types where accurate diagnostics are of increasing importance.
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Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.
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