Advanced Control Algorithm for FADEC Systems in the Next Generation of Turbofan Engines to Minimize Emission Levels

Majid Aghasharifian Esfahani, Mohammadmehdi Namazi, Theoklis Nikolaidis and Soheil Jafari
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Majid Aghasharifian Esfahani: School of Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
Mohammadmehdi Namazi: Department of Mechanical Engineering, Iranian Research Organization for Science and Technology (IROST), Tehran 3313193685, Iran
Theoklis Nikolaidis: Centre for Propulsion and Thermal Power Engineering, School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UK
Soheil Jafari: Centre for Propulsion and Thermal Power Engineering, School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Cranfield MK43 0AL, UK

Mathematics, 2022, vol. 10, issue 10, 1-15

Abstract: New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO 2 , NO x , and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines.

Keywords: emission reduction; gas turbine aero engines; artificial neural networks; adaptive network-based fuzzy inference system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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