A preliminary evaluation of an F100 engine parameter estimation process using flight data

Maine, Trindel A.; Gilyard, Glenn B.; Lambert, Heather H.

doi:10.2514/6.1990-1921

Cited by 9 publications

(9 citation statements)

References 2 publications

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“…Control modes included maximum thrust, minimum specific fuel consumption, and minimum temperature for constant thrust. Initially, engine P680063 was used to evaluate the Kalman filter and its ability to identify engine component performance with changes in engine bleed (Maine et al, 1990 A reduced-temperature mode at constant thrust was also tested (Orme and Gilyard, 1993).…”

Section: Fig 10 Results For Engine P680068 Adecs (1987)mentioning

confidence: 99%

Flight Research Using F100 Engine P680063 in the NASA F-15 Airplane

Burcham

Conners

Maxwell

1995

Volume 2: Aircraft Engine; Marine; Microturbines and Small Turbomachinery

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The value of flight research in developing and evaluating gas turbine engines is high. NASA Dryden Flight Research Center has been conducting flight research on propulsion systems for many years. The F100 engine has been tested in the NASA F-15 research airplane in the last three decades. One engine in particular, S/N P680063, has been used for the entire program and has been flown in many pioneering propulsion flight research activities. Included are detailed flight-to-ground facility tests; tests of the first production digital engine control system, the first active stall margin control system, the first performance-seeking control system; and the first use of computer-controlled engine thrust for emergency flight control. The flight research has been supplemented with altitude facility tests at key times. This paper presents a review of the tests of engine P680063, the F-15 airplanes in which it flew, and the role of the flight test in maturing propulsion technology.

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Section: Fig 10 Results For Engine P680068 Adecs (1987)mentioning

confidence: 99%

Flight Research Using F100 Engine P680063 in the NASA F-15 Airplane

Burcham

Conners

Maxwell

1995

Volume 2: Aircraft Engine; Marine; Microturbines and Small Turbomachinery

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“…J matrix and u b and y b are functions of P t 4 and P t 6 as shown in Figure 2. 16–18 In this article, the high pressure turbine inlet pressure P t 4 is unmeasurable. Therefore, P t 3 is chosen as scheduled variable.…”

Section: Improve Steady-state Perturbation Modelmentioning

confidence: 99%

“…The Kalman estimates the engine health parameters such as η f , η c , η HT , and η LT . 16,20,21 J matrix is as function of P t 3 and P t 6 . Δ y can be calculated by equation (1).…”

Section: Improve Steady-state Perturbation Modelmentioning

confidence: 99%

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Research on aero-engine steady model based on an improved compact propulsion system model

Zheng

Yong

Sun

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

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The aero-engine steady model is the basis of the modern advanced control method such as performance seeking control. An improved compact propulsion system model is proposed to improve the steady model accuracy. The improved compact propulsion system model mainly contains linear model, such as steady-state variable model, and physical-based models, such as inlet model, nonlinear model, and nozzle model. The improved compact propulsion system model applied to full envelop by parameter corrections. The basepoint control vector and basepoint output vector of improved compact propulsion system model are four-dimensional interpolation instead of two-dimensional interpolation as conventional compact propulsion system modeling does. The improved compact propulsion system model not only considers the change of engine state but also take the flight parameter into account. The simulations of the conventional compact propulsion system modeling and the improved compact propulsion system model are conducted in subsonic and transonic flight envelop. The simulations show that, compared with the conventional compact propulsion system modeling, the relative testing errors of the improved compact propulsion system model decrease greatly. Moreover, the testing time of the conventional compact propulsion system modeling and the improved compact propulsion system model are both almost equal to 0.027 ms.

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“…Hence, the performance seeking control (PSC) was proposed and researched by National Aeronautics and Space Administration (NASA) in the 1990s. [5][6][7][8][9][10] The main idea of PSC was seeking optimal control variables to get some kind of performance, such as maximum thrust, minimum fuel consumption, and minimum turbine temperature for different flying tasks, while the engine satisfied all operating limits, such as surge margin, maximum rotors speed, and minimum turbine inlet temperature. To achieve the PSC, there are two key factors: on-board model and optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on hybrid optimization and deep learning modeling method in the performance seeking control

Zheng

Chen

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part G:

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A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

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A preliminary evaluation of an F100 engine parameter estimation process using flight data

Cited by 9 publications

References 2 publications

Flight Research Using F100 Engine P680063 in the NASA F-15 Airplane

Flight Research Using F100 Engine P680063 in the NASA F-15 Airplane

Research on aero-engine steady model based on an improved compact propulsion system model

Research on hybrid optimization and deep learning modeling method in the performance seeking control

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