Helicopter gas turbine engine performance analysis: A multivariable approach. (March 2019)

• Record Type: Journal Article
• Authors: Arush, Ilan; Pavel, Marilena D
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 3(2019)
• Page Start: 837
• Page End: 850
• Publication Date: 2019-03
• Subjects: Gas turbine -- multivariable linear regression -- Karush–Khun–Tucker -- helicopter performance -- flight testing; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410017741329
• Languages: English
• ISSNs: 0954-4100

Abstract

Helicopter performance relies heavily on the available output power of the engine(s) installed. A simplistic single-variable analysis approach is often used within the flight-testing community to reduce flight-test data in order to predict the available output power under various atmospheric conditions. This simplistic approach often results in unrealistic predictions. This paper proposes a novel method for analyzing flight-test data of a helicopter gas turbine engine. The so-called "Multivariable Polynomial Optimization under Constraints" method is capable of providing an improved estimation of the engine maximum available power. The Multivariable Polynomial Optimization under Constraints method relies on optimization of a multivariable polynomial model subjected to equalities and inequalities constraints. The Karush–Khun–Tucker optimization method is used with the engine operating limitations serving as inequalities constraints. The proposed Multivariable Polynomial Optimization under Constraints method is applied to a set of flight-test data of a Rolls Royce/Allison MTU250-C20 gas turbine, installed on an MBB BO-105 M helicopter. It is shown that the Multivariable Polynomial Optimization under Constraints method can predict the engine output power under a wider range of atmospheric conditions and that the standard deviation of the output power estimation error is reduced from 13 hp in the single-variable method to only 4.3 hp using the Multivariable Polynomial Optimization under Constraints method (over 300% improvement).