Helicopter performance flight-testing is an expensive activity that requires efficient testing techniques and appropriate data analysis for good performance prediction. Regarding the flight testing techniques used to evaluate the available power of a Turboshaft engine, current methodologies involve a simplistic singlevariable polynomials analysis of the flight test data. 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’ (MPOC) method is proven capable of providing an improved estimation of the engine maximum available power. The MPOC method relies on maximization of a multivariable polynomial subjected to both equalities and inequalities constraints. The Karush-Khun-Tucker (KKT) optimization technique is used with the engine operating limitations serving as inequalities constraints. The proposed MPOC method is implemented to a set of flight-test data of a Rolls Royce/Allison MTU250-C20 gas turbine, installed on a MBB BO-105M helicopter. It is shown that the MPOC method can realistically 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 single-variable method to only 4.3hp using the MPOC method (over 300% improvement).

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).