Mathematical model of turbofan engine weight estimation taking into account the engine configuration and size

• Main Author: S. V. Avdeev
• Format: Article
• Language: English
• Published: Samara National Research University 2021-04-01
• Series: Вестник Самарского университета: Аэрокосмическая техника, технологии и машиностроение
• Subjects: gas turbine engine; mathematical modeling; correlation-regression model; weight; size
• Online Access: https://journals.ssau.ru/vestnik/article/viewFile/8632/8177

The paper presents a new correlation-regression model of estimating the turbofan engine weight considering the effect of the engines design schemes and dimensions. The purpose of this study was to improve the efficiency of the conceptual design process for aircraft gas turbine engines. Information on 183 modern turbofan engines was gathered using the available sources: publications, official websites, reference books etc. The statistic information included the values of the total engine air flow, the total turbine inlet gas temperature, the overall pressure ratio and the bypass ratio, as well as information on the structural layout of each engine. The engines and the related statistics were classified according to their structural layout and size. Size classification was based on the value of the compressor outlet air flow through the gas generator given by the parameters behind the compressor. Depending on the value of this criterion, the engines were divided into three groups: small-sized, medium-sized gas turbine engines, and large gas turbine engines. In terms of the structural layout, all engines were divided into three groups: turbofan engines without a mixing chamber, engines with a mixing chamber and afterburning turbofan engines. Statistical factors of the improved weight model were found for the respective groups of engines, considering their design and size. The coefficients of the developed model were determined by minimizing the standard deviations. Regression analysis was carried out to assess the quality of the developed model. The relative average error of approximation of the developed model was 8%, the correlation coefficient was 0,99, and the standard deviation was 10,2%. The model was found to be relevant and reliable according to Fisher's test. The obtained model can be used to assess the engine weight at the stage of conceptual design and for its optimization as part of an aircraft.