An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Xu, Huachao; Qin, Datong; Liu, Changzhao; Zhang, Yang

doi:10.1109/access.2021.3053395

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The largest source of errors in CFD numerical simulation comes from the turbulence model, of which the closure coefficients need to be specified before the simulation [5]. Their values may significantly impact the CFD numerical simulation results.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Correction method of CFD based on the Approximate Bayesian Computation technique

Zhang,

Li,

Wang

et al. 2023

J. Phys.: Conf. Ser.

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Numerical simulation and modeling techniques are becoming the primary research tools for aerodynamic analysis and design. However, various uncertainties in physical modeling and numerical simulation seriously affect the credibility of Computational Fluid Dynamics (CFD) simulation results. Therefore, CFD models need to be adjusted and modified with consideration of uncertainties to improve the prediction accuracy and confidence level of CFD numerical simulations. This paper presents a parameter correction method of CFD for aerodynamic analysis by making full use of the advantages of the Approximate Bayesian Computation (ABC) technique in dealing with the analysis and inference of complex statistical models, in which the parameters of turbulence models for CFD are inferenced. The proposed parameter correction method is applied to the aerodynamic prediction of the NACA0012 airfoil. The results show the feasibility and effectiveness of the proposed approach in improving CFD prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

A Parameter Correction method of CFD based on the Approximate Bayesian Computation technique

Zhang,

Li,

Wang

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…In general, dynamic model-updating methods can be classified into matrix updating methods, also called direct updating methods [2][3][4], and parameter updating methods, also called iterative updating methods [5][6][7]. In matrix updating methods, the mass, stiffness, and damping matrices of the FE model are directly updated to generate an accurate model by one-step procedure calculation, which is a very efficient approach.…”

Section: Introductionmentioning

confidence: 99%

Structural Dynamic Model Updating with Automatic Mode Identification Using Particle Swarm Optimization

Fang

Sun

et al. 2022

Applied Sciences

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Dynamic model-updating methods are a useful tool for obtaining high-precision finite element (FE) models. However, when using such methods to update a model, there will be problems with incompleteness and mode switching. To overcome these problems, this paper proposes a structural dynamic model-updating with an automatic mode-identification method. In this method, a mode-identification index is established based on image-similarity recognition to identify the consistency between FE and experimental mode shapes, and particle swarm optimization is introduced to update the model. In addition, to reduce the computational time, Latin hypercube sampling is employed to perform probability statistics of the switching range of the concerned mode orders, and the orders of mode identification are reduced according to the statistics results. In this paper, the proposed method was validated by model-updating of a square plate. The natural frequencies and mode shapes of the plate were obtained by experimental modal analysis and used as the updating objectives of the FE model. In addition, the boundary condition of the plate was simplified by a series of springs, which were used as updating parameters along with material properties and dimensions. Finally, the FE model of the plate was updated by the present method, and the results indicate that the objective function error of the updated FE model was successfully reduced from 14.31% to 1.05%, which proves that the proposed model-updating method is effective and feasible.

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Model validation method by considering uncertainty for numerical simulation

REN,

XIONG,

WANG

et al. 2023

西北工业大学学报

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There is a great amount of uncertainties in the whole life cycle of a flight vehicle, which can induce anon-negligible uncertainty in the numerical simulation output, then the model validation of the numerical simulation is the premise of design optimization. Lots of researches on the uncertainty quantification (UQ) of numerical simulation has been carried out. However, literature has rarely seen works about performing scientific model parameter correction by using the results of UQ and establishing a closed-loop procedure of numerical simulation model validation in a systematic way. Therefore, a numerical simulation model validation method by considering uncertainties is proposed, and a model parameter correction approach based on high-quality small samples is developed, and then a closed-loop model validation process composed by uncertainty quantification, global sensitivity analysis, and parameter correction strategy to provide a scientific and systematic procedure for constructing high-fidelity numerical simulations is established. The effectiveness and advantages of the model validation method is verified through airfoil simulation.

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An Improved Dynamic Model Updating Method for Multistage Gearbox Based on Surrogate Model and Sensitivity Analysis

Cited by 4 publications

References 29 publications

A Parameter Correction method of CFD based on the Approximate Bayesian Computation technique

A Parameter Correction method of CFD based on the Approximate Bayesian Computation technique

Structural Dynamic Model Updating with Automatic Mode Identification Using Particle Swarm Optimization

Model validation method by considering uncertainty for numerical simulation

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