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%
The process of empirical models evaluation is at the core business of experimental flight-testing data analysis. Accurate and convenient flight-testing of helicopter engine(s) available power is crucial for predicting the total helicopter performance. Common practice in estimation of in-flight helicopter gas turbine engine power consists of a reduction of flight-test data into simplistic single-variable analysis approach. While such an approach is convenient for practical use, it often results in unrealistic predictions of the available engine(s) power. A novel approach for the helicopter available power problem is the so-called Multivariable Polynomial Optimization under Constraints method. In this method, 18 regressors, constructed from the engine non-dimensional parameters, are used to define empirical polynomial models. This paper is intended to complement the Multivariable Polynomial Optimization under Constraints method and answer the question of which multivariable polynomial can be generally used in representing helicopter gas-turbine engine performance? In this sense, a variety of seven gas-turbine engines installed on different helicopters are analyzed, each one giving 512 possible polynomial models to be used for available-power calculations. While conventional statistical methods of hypothesis-testing failed in providing the answer to the question stated above of which the best general empirical model for representing engine performance is, an alternative approach based on the Singular-Value-Decomposition theorem, was proven successful in providing the answer. Moreover, this approach presented in the paper yielded a short list of 10 simple and convenient multivariable polynomials, best representing the performance of all seven engines analyzed as a group.
Evaluation of the power required in level flight is essential to any new or modified helicopter performance flight-testing effort. The conventional flight-test method is based on an overly simplification of the induced and profile power components required for a helicopter in level flight. This simplistic approach incorporates several drawbacks that not only make execution of flight sorties inefficient and time consuming, but also compromise the level of accuracy achieved. This paper proposes an alternative flight-test method for evaluating the level-flight performance of a conventional helicopter while addressing and rectifying all identified deficiencies of the conventional method. The proposed method, referred to as the corrected-variables screening using dimensionality reduction (CVSDR), uses an original list of 36 corrected variables derived from basic dimensional analysis principles. This list of 36 corrected variables is reduced using tools of dimensionality reduction to keep only the most effective level-flight predictors. The CVSDR method is demonstrated and tested in this paper using flight-test data from a MBB BO-105 helicopter. It is shown that the CVSDR method predicts the power required for level flight about 21% more accurately than the conventional method while reducing the required flight time by an estimate of at least 60%. Unlike the conventional method, the CVSDR is not bounded by the high-speed approximation associated with the induced power estimation, therefore it is also relevant to the low airspeed regime. This low-airspeed relevancy allows the CVSDR method to bridge between the level-flight regime and the hover. Although demonstrated in this paper for a specific type of helicopter, the CVSDR method is applicable for level-flight performance flight testing of any type of conventional helicopter.
The power required to hover a helicopter is fundamental to any new or modified performance flight-testing effort. The conventional method of relating two nondimensional variables (coefficients of power and weight) is overly simplified and neglects compressibility effects in the power required to hover under a wide range of gross weights and atmospheric conditions. An alternative flight-test method for assessing hover performance while addressing this deficiency of the conventional method is proposed. The method uses an original list of 15 corrected variables derived from fundamental dimensional analysis, which is further reduced by means of dimensionality reduction to include only the most essential and effective predictors. The method is demonstrated using data of a Bell Jet-Ranger and shows that at the 95% confidence level; the averaged prediction error is only 0.9 hp (0.3% of the maximum continuous power). Using the same data, the conventional method yields a much larger averaged prediction error of 1.7 hp.
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