Modeling Techniques for a Computational Efficient Dynamic Turbofan Engine Model

Roberts, Richard A.; Eastbourn, Scott M.

doi:10.1155/2014/283479

Cited by 35 publications

(15 citation statements)

References 16 publications

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“…This whole process could have also been performed by fixing the altitude instead of the TLA; the principle is the same. By combining Equations (18) and (19), it is possible to express an output based on the three inputs by using an estimation algorithm. This polynomial 'Black Box' approach was performed for each output (FPR, EPR, ITT, F n and Wf).…”

Section: Black Boxmentioning

confidence: 99%

“…According to Suraweera (17) , component level modelling (CLM) plays a fundamental role in engine modelling. It has largely been used to model engine outputs (17)(18)(19)(20)(21)(22)(23) , performance deterioration (24) and fault diagnosis (25) . This CLM approach consists in modelling each engine component based on thermodynamical assumptions and equations (26) .…”

Section: Introductionmentioning

confidence: 99%

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Cessna Citation X simulation turbofan modelling: identification and identified model validation using simulated flight tests

Botez¹,

Bardela²,

Bournisien³

2019

Aeronaut. j.

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The aviation industry relies on accurate models. These models are used to predict an aircraft system’s outputs, and thus allow an understanding of the parameters involved, which could lead to system improvements. This study focuses on the engine modelling of an aircraft, and on its experimental validation using the Cessna Citation X Research Aircraft Simulator designed by CAE Inc., equipped with a level D Flight Dynamics toolbox. Level D is the highest rank attributed by the Federal Aviation Administration FAA certification authorities for flight dynamics. The proposed model aims to predict the thrust and the fuel consumption for various altitudes, Mach numbers and throttle lever angles (TLA). Different generic static models, which correspond to their steady state, from the literature, were used in this study; however, most of them were validated under restricted hypotheses. An optimisation algorithm was used in order to tune the static model parameters with the set of identification flight test data. Another set of data was then used in order to validate the identified model. Furthermore, a dynamic model corresponding to the transient operations was identified. TLA steps, impulses and ramp perturbations were performed in order to identify the system response, and to validate system dynamic model with other flight tests than the identification tests.

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Section: Black Boxmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cessna Citation X simulation turbofan modelling: identification and identified model validation using simulated flight tests

Botez¹,

Bardela²,

Bournisien³

2019

Aeronaut. j.

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“…Aero-engine mathematical models play important roles in the R&D stage, and they are used widely in performance simulation [1], [2], control and health management system design [3]- [6] and so on. Among many types of models, component level models (CLMs) are preferred because they are applicable to large envelopes and can calculate unmeasurable performance parameters [7]- [10].…”

Section: Introductionmentioning

confidence: 99%

Joint Steady State and Transient Performance Adaptation for Aero Engine Mathematical Model

Pang

Feng

et al. 2019

IEEE Access

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In the field of aero-engine control, it is valuable to build highly accurate component level models to meet the requirements of controller validation and model-based control. The accuracy of current performance map generation method is limited by the test data that always cannot cover the full envelope. The scaling-based performance map correction methods only focus on the adjustment of the steady-state performance. Therefore, a performance map segmentation-based joint steady-state and transient performance adaptation technique is proposed. Both the idle point and the design point are taken as precision references to scale the performance maps with large deviations. A scaling factor domain determination method is provided based on the characters of the speed lines. The performance map is first optimized with the steady state calculation based on the steady-state test data, and further optimized with the transient calculation based on the transient test data. Both the steady-state and transient performance adaptations are achieved by adjusting the performance maps. The proposed method was applied to a turbofan engine model. For continuous acceleration case, the average error is less than 1% at steady state points and less than 2% during transient operation. For large magnitude acceleration and rapid deceleration cases, the maximum errors for parameters around the idle point decrease by about one time (from 8.3% to 4.1%) and three and a half times (from 28% to 8%), respectively, by comparing with the model without transient performance adaptation. Thus, the effectiveness of the proposed method is demonstrated.

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“…These models are used to generate performance numerical dynamic models [2][3][4][5] which are used to optimize flight trajectories [5][6][7][8][9][10][11][12][13][14][15][16] for flight cost reduction. The most common approach for engine modeling is the Component Level Modeling (CLM) [17][18][19][20][21][22][23][24]. In this approach, each component of the turbofan is modelled in order to simulate the entire engine.…”

Section: Introductionmentioning

confidence: 99%

Cessna Citation X Engine Model Experimental Validation

Bardella¹,

Botez²,

Pageaud³

2017

Modelling, Identification and Control

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The aviation industry is motivated to develop and validate new aircraft models for the prediction of engine performance. These models are used in the preliminary aircraft design in order to predict its engines performance. The purpose of this study is to design an accurate model of the fan and compressor engine components. This model will then be integrated in a full engine model based on a component modeling approach. Several methods already exist to model compressing components. Among them, the stage-stacking method is used in this paper. This method can be used to predict the compressor performance but also its deterioration (ex: "fouling"). In both cases, the principle is the same. Each stage is separated, and the first stage outputs are used for the next stage as inputs until the last stage is reached, when the final outputs are acquired. A Cessna Citation X Level D Research Flight Simulator designed by CAE Inc. is used to sample the data needed to identify and validate the engine models elaborated for the whole flight envelope. Level D is the highest level of certification given by the FAA for the flight dynamics. Thus the simulator is used as real aircraft flight dynamics data. Different flight tests were performed to mesh the flight envelope for different flight conditions (Mach numbers from 0 to 0.92; altitude from 0 to 50000ft and Throttle Lever Angle,TLA, from "idle" to "max" in degrees). Nonetheless, the stage stacking method needs information which is not always available, such as the blade angle. A "grey box" approach was chosen. The unmeasurable parameters were identified in order to tune the model, and thus to reduce the global error between the model and the simulation data. A "black box" approach was also with an optimisation algorithm to approximate the outputs as polynomial functions of the inputs. In both cases the results were found to be accurate.

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Modeling Techniques for a Computational Efficient Dynamic Turbofan Engine Model

Cited by 35 publications

References 16 publications

Cessna Citation X simulation turbofan modelling: identification and identified model validation using simulated flight tests

Cessna Citation X simulation turbofan modelling: identification and identified model validation using simulated flight tests

Joint Steady State and Transient Performance Adaptation for Aero Engine Mathematical Model

Cessna Citation X Engine Model Experimental Validation

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