Abstract
You This study offers a metaheuristic design for primary parameters and architectures of two models of artificial neural network (ANN) in predicting a business jet aircraft’s exergo-emission parameters, such us exergy destruction ratio (rex,dest) and waste exergy ratio (rwex), at different flight stages. In consideration of this, the development of hybrid genetic algorithm (GA)-ANN models has been achieved by considering real databases of rex, dest and rwex at various power levels. Implementing a metaheuristics-based optimization on the generated multilayer perceptron (MLP) ANN models has produced the most favorable initial network weights, step-size, biases as well as training algorithm’s back-propagation (BP) momentum rate in addition to optimal quantity of neurons in the hidden layer(s) with regard to the topology design. In accordance with an error assessment approach, there exists a close fit linking the reference real data and rwex (linear correlation ratio, R, value of 0.999851) as well as rex,dest (R value of 0.999985) predicted values.
Highlights
With the growth of air travel around the world, environmental impacts and energy consumption of the aviation industry has increasing rapidly
When energy efficiency of the engine increases and wasted energy decreases, fuel usage decreases at same power level
Latest trends in improving specific fuel consumption and overall efficiency show that aircraft entering aviation sector are around 80% more fuel-efficient using turboprop and high bypass ratio turbofan engines [18-22]
Summary
With the growth of air travel around the world, environmental impacts and energy consumption of the aviation industry has increasing rapidly. When energy efficiency of the engine increases and wasted energy decreases, fuel usage decreases at same power level. The objective of this study is to accomplish two models of artificial neural network (ANN) in predicting a business jet aircraft’s exergo-emission parameters, such us exergy destruction ratio (rex,dest) and waste exergy ratio (rwex), at different flight stages and five input parameters (fuel and air mass flows, power and torque levels, gas generator shaft speed). There is no study on a detailed measure exergetic emission of turboprop power system using metaheuristics optimized machine learning modeling. Lack of this makes the paper original for aircraft energy systems and during typical flight phases
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
