Helicopter Gas Turbine Engine Performance Analysis: A Multivariable Approach

Ilan Arush, Marilena Pavel

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
25 Downloads (Pure)


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 raw flight-test data in order to predict the available output power under different atmospheric conditions. This simplistic analysis 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” (MPOC) method is capable of providing an improved estimation of engine performance and maximum available power. The MPOC method relies on optimization of a multivariable polynomial model subjected to equalities and inequalities constraints. The Karush-Khun-Tucker (KKT) optimization method is used with the engine operation limitations serving as inequalities constraints. The proposed MPOC method is applied to a set of flight-test data of a Rolls Royce/Allison MTU250-C20 gas turbine engine, installed on a MBB BO-105M helicopter. It is shown that the MPOC 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 13hp in the current single-variable method to only 4.3hp using the MPOC method (over 300% improvement).
Original languageEnglish
Number of pages22
JournalInstitution of Mechanical Engineers. Proceedings. Part G: Journal of Aerospace Engineering
Publication statusE-pub ahead of print - 2017


  • Gas turbine
  • multivariable linear regression
  • Karush–Khun–Tucker
  • helicopter performance
  • flight testing


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