Improved Kriging with extremum response surface method for structural dynamic reliability and sensitivity analyses

Feng

et al. 2019

Applied Sciences

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The emergence extension system (a mechanical system) of nose landing gear (NLG) seriously influences the reliability, safety and airworthiness of civil aircrafts. To efficiently realize the NLG emergence extension, a promising driving plan of emergence extension is proposed in respect of the reliability sensitivity analyses with a mixture of models. The working principle, fault tree analysis and four reliability models are firstly discussed for NLG emergence extension. In respect of the mixture of models, the reliability sensitivity analyses of emergence extension are then performed under different flight speeds (270 Kts, 250 Kts, 220 Kts, and 180 Kts). We find dimpling torque and aerodynamic torques of forward and after doors are the top three failure factors and the start reliability is the most in emergence extension failures. Regarding the results, feasible driving plans of NLG emergence extension are developed by adjusting the aerodynamic torque of NLG forward door, and are validated by the aerodynamic torque experiment of forward door with regard to strut rotational angle under the flight speed 270 Kts. It is indicated that (1) the adverse torque generated by the new driving mechanism obviously reduces by about 24.8% from 1462.8 N·m to 1099.6 N·m, and the transmission ratio of aerodynamic torque (force) is greatly improved when the NLG strut is lowered near to 100 • ; (2) under different flight speeds (180 Kts, 220 Kts, 250 Kts, and 270 Kts), the new driving mechanism realizes the lower tasks of emergence extension which cannot be completed by the initial driving mechanism; and (3) the lowering time of the new driving mechanism shortens with the increasing flight speed. The proposed new driving mechanism is verified to be reliable for emergence extension of aircraft NLG besides normal extension and to be a promising feasible driving plan with high lowering reliability. The efforts of the paper provide an efficient driving mechanism for the design of NLG in civil and military aircrafts.

Section: Reliability Methods and Mixture Of Modelsmentioning

confidence: 99%

Efficient Driving Plan and Validation of Aircraft NLG Emergency Extension System via Mixture of Reliability Models and Test Bench

Feng

et al. 2019

Applied Sciences

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“…To improve SR modeling accuracy, the ABC technique [29] was employed to optimize the parameters θ � (c, σ, ε) in SR modeling. e coefficients of ISRM for turbine blade deformation are shown in equation (5). In the next section, we will employ the established ISRM to complete the reliability optimization of turbine blade deformation.…”

Section: Turbine Blade Optimization With Improved Supportmentioning

confidence: 99%

“…e reliability optimization model of turbine blade deformation is shown in equation (6). Herein, the upper and lower limits of constraints are revealed in Table 2, where the values of upper and lower limits are determined by the 3 sigma levels [3,5].…”

Section: Turbine Blade Optimization With Improved Supportmentioning

confidence: 99%

“…With the requirement of design analysis for advanced mechanical systems, many reliability optimization methods have emerged in engineering practice [4][5][6][7][8][9]. Surrogate model method (also called response surface method) is a promising way to ensure efficiency and accuracy in structural reliability optimization, instead of the direct simulation method with the FE model [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model

Advances in Materials Science and Engineering

Feng

et al. 2020

Self Cite

Deformation is one important failure mode of turbine blades. e quality of a model seriously influences the reliability optimization of turbine blades in turbo machines. To improve the reliability optimization of turbine blades, this paper proposes a novel machine learning-based reliability optimization approach, named improved support vector regression (SR) model (ISRM) method, by fusing artificial bee colony (ABC), traditional SR model, and multipopulation genetic algorithm (MPGA). In this proposed method, the ABC algorithm was applied to find the optimal parameters in the SR model to establish accurate ISRM, following the thought of the surrogate model method and the randomness of constraints. en the reliability optimization model and procedure with the ISRM method were resolved by the MPGA. Regarding many design parameters (i.e., rotor speed, temperature, and aerodynamic pressure), design objective (i.e., blade deformation), and the randomness constraints of reliability degree and boundary conditions, we performed the reliability optimization of a turbine blade deformation. From the optimization results, we find that the turbine blade deformation is reduced by 0.09329 mm, and the ISRM learning method can improve the reliability optimization design of complex structures with the emphasis on modeling precision and optimization efficiency. e works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods.

“…erefore, it is required to investigate the probabilistic analysis of the complex structure to improve the performance and safety of the mechanical system with the consideration of the effects of dynamic loads. Probabilistic analysis generally includes reliability analysis and sensitivity analysis [3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Reliability Design of Multicomponent Structure with Improved Weighted Regression Distributed Collaborative Surrogate Model Method

Dong

Advances in Materials Science and Engineering

et al. 2018

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For dynamic reliability design of complex structures with multiple components, an improved weighted regression distributed collaborative surrogate model method (IWRDCSMM) is developed from the extremum response surface method (ERSM), decomposed-coordinated thought, and improved weighted regression principle. e ERSM is used to address the dynamic reliability and sensitivity analyses of multicomponent structures and enhance the computing efficiency. e decomposedcoordinated thought is applied to handle the relationship among multiple components. e improved weighted regression method is used to find the efficient samples with smaller errors to improve the modeling accuracy. e proposed method is first introduced for dynamic probabilistic analysis (including reliability analysis and sensitivity analysis) of multicomponent structures. e method is then mathematically modeled by adopting the efficient samples selected based on the improved weighted regression method. Finally, the radial deformation dynamic probabilistic analysis of an aeroengine turbine blisk assembled by blade and disk is accomplished, in respect of the IWRDCSMM, fluid-thermal-structure interaction, and the randomness of input parameters within the time domain [0, T]. e results illustrate that the reliability degree of turbine blisk radial deformation is 0.9951 when the allowable value u allow is 2.30 × 10 −3 m, and all the input parameters affecting the turbine blisk radial deformation are gas temperature, angular speed, inlet velocity, outlet pressure, material density, and inlet pressure, successively. As revealed by the comparison of different methods, the IWRDCSMM has high fitting speed and simulation efficiency with the guarantee of accuracy. e efforts of this study provide a promising dynamic probabilistic analysis technique for complex structures with multiple components and enrich mechanical reliability theory.