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).
|Number of pages||22|
|Journal||Institution of Mechanical Engineers. Proceedings. Part G: Journal of Aerospace Engineering|
|Publication status||E-pub ahead of print - 2017|
- Gas turbine
- multivariable linear regression
- helicopter performance
- flight testing