A Probabilistic Design Methodology for a Turboshaft Engine Overall Performance Analysis

Chen, Min; Zhang, Kun; Tang, Huazhong

doi:10.1155/2014/976853

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…In recent years, tremendous research has been donated to the cumulative effect of uncertainty on aeroengine overall performance. Based on Monte-Carlo probabilistic analysis method, Chen et al proposed a feasible methodology to quantify the impacts of uncertainty in component performance on the overall performance of conventional gas turbine engine [17]. Their later research has studied the impact of component performance deviation (CPD) on adaptive cycle engine in multiple operating conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the detailed statistical data from aircraft engine manufacturers is unavailable, the uncertainty (or noise) parameters of interest for this study are given at Table 2, according to reference [17]. On-design point studies compare gas turbines of different geometry, which is a necessary preparation for off-design point calculations.…”

Section: Uncertainty Component Performance Modulementioning

confidence: 99%

Section: Off-design Point Calculation Modulementioning

confidence: 99%

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A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability

Cao

Bai

2020

Applied Sciences

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Owing to the realization of multi-mission adaptability requires more complex mechanical structure, the candidates of future aviation propulsion are confronted with more overall reliability problems than that of the conventional gas turbine engine. This situation is challenging to a traditional aeroengine deterministic design method. To overcome this challenge, the Reliability-based Multi-Design Point Methodology is proposed for aeroengine conceptual design. The presented methodology adopted an unconventional approach of engaging the reliability prediction by artificial neural network (ANN) surrogate models rather than the time-consuming Monte Carlo (MC) simulation. Based on the Adaptive Particle swarm optimization, the utilization of the pre-training technique optimizes the initial network parameters to acquire better-conditioned initial network, which is sited closer to designated optimum so that contributes to the convergence property. Moreover, a new hybrid algorithm is presented to integrate the pre-training technique into neural network training procedure in order to enhance the ANN performance. The proposed methodology is applied to the cycle design of a turbofan engine with uncertainty component performance. The testing results certify that the prediction accuracy of pre-trained ANN is improved with negligible computational cost, which only spent nearly one-millionth as much time as the MC-based probabilistic analysis (0.1267 s vs. 95,262 s, for 20 testing samples). The MC simulation results substantiate that optimal cycle parameters precisely improve the engine overall performance to simultaneously reach expected reliability (≥98.9%) in multiple operating conditions without unnecessary performance redundancy, which verifies the efficiency of the presented methodology. The presented efforts provide a novel approach for aeroengine cycle design, and enrich reliability design theory as well.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Uncertainty Component Performance Modulementioning

confidence: 99%

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A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability

Cao

Bai

2020

Applied Sciences

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“…Due to the absence of turbomachinery monitoring data from airline company, this investigation bounds the numerical range of uncertainty performance parameters based on the 3-σ principle [20], as shown in Table 1.…”

Section: Uncertainty Turbomachinery Performance Modulementioning

confidence: 99%

DNN-Based Surrogate Modeling-Based Feasible Performance Reliability Design Methodology for Aircraft Engine

Cao

Bai

2020

IEEE Access

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The risks and costs of developing a new aeroengine are fundamentally depending on the performance design final proposal. Thus, this paper presents a novel aeroengine performance design methodology that is committed to managing the effectiveness and economic availability of the design proposal. To reach such a target, the presented methodology formulates the traditional thermal cycle design problem as a reliability-based fuzzy optimization. The performance reliability is predicted by the deep neural network (DNN)-based surrogate models while a hyperparameter tuning technique is proposed to find the optimal DNN topology for better implementing a particular deep learning task. The testing results imply the DNN models with optimized topology possess remarkable function approximation capability in global so that achieves significantly higher prediction accuracy. Moreover, the DNN-based surrogate models only cost nearly 0.003% as much computing time as MC simulation (2.3591 sec vs 64746 sec, for 20 samples). Such kind of remarkably higher computational efficiency facilitates the optimization for reliability-based fitness calculation. The efficiency of the presented methodology can be further verified by abundant feasible cycle proposals. The obtained cycle solutions can achieve expected reliability (>95%) in all reference states without unnecessary performance redundancy. Besides, the diversity of feasible cycle solutions contributes to the selection of best proposal associated with engineering situation. The presented effort is favorable to acquire a more cost-efficient design proposal and enrich thermodynamic system design theory.

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“…Gas turbine power of the engine is usually not measurable. At present, calculations of gas turbine power for different types of engines at home and abroad basically adopt the methods of aerodynamic thermal modeling [6][7][8][9][10][11][12][13] and simulation modeling [14,15], among which the typical ones are the following: Coban et al [6] combined the aerodynamic performance calculation method with bench test data to evaluate the energy and dynamic characteristics of a military turbo-shaft engine. Zhu [7] applied a differential evolution algorithm to optimize the computational performance of 2 Mathematical Problems in Engineering the aerodynamic thermal model with the consideration of machining error and component performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

Zhao

et al. 2018

Mathematical Problems in Engineering

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The installed positions of three domestic turbo-shaft engines mounted on a certain type of ship-borne helicopter interfere with the intake air flow of the engines, resulting in a decline of engine performance after initial installation. Due to the difference of load and adjustment method under the bench and installed conditions, it is necessary to study the change in gas turbine power rather than output shaft power of the engine before and after installation to evaluate the engine initial installed power loss. In this paper, quantum-behaved particle swarm optimization (QPSO) is applied to optimize the calculation of gas turbine power at different steady states based on the component-level aerodynamic thermal model of gas generator. Then, extreme learning machine (ELM) is adopted for regressive identification of the established gas generator state assessment model based on data mining and the identification model is applied to engine installed condition. Finally, statistical analysis of engine initial installed gas turbine power loss at three installed positions is carried out, respectively. Results show that the values of engine initial installed gas turbine power loss at three installed positions all conform to the normal distribution, the mean values are 1.658%, 9.828%, and 5.089%, respectively, and a confidence interval with 95% confidence level of the mean values are (1.388%, 1.928%), (9.178%, 10.478%) and (4.308%, 5.870%), which can provide references for determining the power monitoring thresholds after engine installation.

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A Probabilistic Design Methodology for a Turboshaft Engine Overall Performance Analysis

Cited by 11 publications

References 15 publications

A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability

A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability

DNN-Based Surrogate Modeling-Based Feasible Performance Reliability Design Methodology for Aircraft Engine

Research on Initial Installed Power Loss of a Certain Type of Turbo-Shaft Engine Using Data Mining and Statistical Approach

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