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

Abstract

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.

A Deterministic Calibration Method for the Thermodynamic Model of Gas Turbines

“…By employing elliptic curves to generate compressor maps, they refined gas turbine models with improved accuracy. Recently, Pang et al proposed a joint adaptation method that added a transient performance adaptation procedure following the steady-state counterpart [17]. After a two-step optimization process, engine models can be calibrated to have more accurate transient performance predictions.…”

“…The published work for multiple-point performance adaptations involves complex numerical searching and optimizations. Facing such problems, most researchers prefer to employ metaheuristics, such as genetic algorithms [11][12][13]15,16] and evolutionary computation [17]. The metaheuristics can often find good solutions over a huge set of feasible solutions with less computational effort than other optimization methods and proved to be useful approaches for optimization problems [18,19].…”

A data-driven modeling method for an aero gas turbine engine system based on the transient data

“…In a physics-based model, gas turbine performance is calculated using performance maps representing the characteristics of a component, and conservation equations for mass, energy, and momentum [10,11]. Many studies have been conducted to determine an effective method for calibrating a physics-based model using real measurement data so as to improve the prediction accuracy [12,13,14]. In gas turbine performance adaptation, there is a method for calibrating a performance map of compressors and turbines based on engine measurement data.…”

A dual-driven approach for modelling heavy-duty gas turbine based on operational data and intelligent genetic algorithm

“…Therefore, traditional modelling is not suitable when components' characteristic maps are missing. Some research improved traditional modelling method, where components' maps are obtained by introducing corrected factor or adaptive factor [6][7][8][9][10][11][12]. The characteristics of components can be obtained by this method, but only part of the actual operational data is considered for correction, and it is not directly related to the actual operational data.…”