scholarly journals Intelligent Extremum Surrogate Modeling Framework for Dynamic Probabilistic Analysis of Complex Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jia-Qi Liu ◽  
Yun-Wen Feng ◽  
Xiao-Feng Xue ◽  
Cheng Lu
Keyword(s):  
Reliability Analysis ◽  
Probabilistic Analysis ◽  
Research Effort ◽  
Surrogate Modeling ◽  
Reliability Estimation ◽  
Complex Mechanism ◽  
Modeling Framework ◽  
Reliability Design ◽  
Steering Mechanism ◽  
Complex Mechanisms

The reliability analysis of complex mechanisms involves time-varying, high-nonlinearity, and multiparameters. The traditional way is to employ Monte Carlo (MC) simulation to achieve the reliability level, but this method consumes too much computing resources and is even computationally intractable. To improve the efficiency and accuracy of dynamic probabilistic analysis of complex mechanisms, an intelligent extremum surrogate modeling framework (IESMF, short for) is proposed based on extremum response surface method (ERSM), combined with artificial neural network (ANN) method and an improved optimize particle swarm optimization (PSO) method. Hereinto, the ERSM is used to simplify the dynamic process of output response to the extremum value of transient analysis; ANN is applied to establish a mathematical model between input variables and response, and the improved PSO method is utilized in search of initial weights and thresholds of the model. The effectiveness of the IESMF is demonstrated to perform the Rack-and-pinion steering mechanism (RPSM) reliability analysis. The results show that when the allowable value of gear root stress is equal to 850 MPa, the RPSM has a reliability degree of 0.9971. Through the validation process, it is illustrated that IESMF is accurate and efficient in dynamic probabilistic analysis of complex mechanisms, and its comprehensive performance is better than the MC method and ERSM. The research effort offers new ideas for the reliability estimation of a complex mechanism, thus enriching the method and theory of mechanical reliability design.

Recent advances in reliability analysis of aeroengine rotor system: a review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue-Qin Li ◽  
Lu-Kai Song ◽  
Guang-Chen Bai
Keyword(s):  
Reliability Analysis ◽  
Surrogate Model ◽  
Engineering Application ◽  
Rotor System ◽  
Engineering Structures ◽  
Content Type ◽  
Reliability Design ◽  
Rotor Systems ◽  
Practical Engineering ◽  
Complex Engineering

PurposeTo provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.Design/methodology/approachIn this paper, recent researches on efficient reliability analysis and applications in complex engineering structures like aeroengine rotor systems are reviewd.FindingsThe recent reliability analysis advances of engineering application in aeroengine rotor system are highlighted, it is worth pointing out that the surrogate model methods hold great efficiency and accuracy advantages in the complex reliability analysis of aeroengine rotor system, since its strong computing power can effectively reduce the analysis time consumption and accelerate the development procedures of aeroengine. Moreover, considering the multi-objective, multi-disciplinary, high-dimensionality and time-varying problems are the common problems in various complex engineering fields, the surrogate model methods and its developed methods also have broad application prospects in the future.Originality/valueFor the strong demand for efficient reliability design technique, this review paper may help to highlights the benefits of reliability analysis methods not only in academia but also in practical engineering application like aeroengine rotor system.

Uncertainty Quantification in Metallic Additive Manufacturing Through Data-Driven Modelling Based on Multi-Scale Multi-Physics Models and Limited Experiment Data

2020 ◽  
Author(s):  
Zhuo Wang ◽  
Chen Jiang ◽  
Mark F. Horstemeyer ◽  
Zhen Hu ◽  
Lei Chen
Keyword(s):  
Temperature Field ◽  
Additive Manufacturing ◽  
Uncertainty Quantification ◽  
Surrogate Modeling ◽  
Data Driven ◽  
Modeling Framework ◽  
High Quality ◽  
Experimental Calibration ◽  
Multi Scale ◽  
Metallic Additive

Abstract One of significant challenges in the metallic additive manufacturing (AM) is the presence of many sources of uncertainty that leads to variability in microstructure and properties of AM parts. Consequently, it is extremely challenging to repeat the manufacturing of a high-quality product in mass production. A trial-and-error approach usually needs to be employed to attain a product with high quality. To achieve a comprehensive uncertainty quantification (UQ) study of AM processes, we present a physics-informed data-driven modeling framework, in which multi-level data-driven surrogate models are constructed based on extensive computational data obtained by multi-scale multi-physical AM models. It starts with computationally inexpensive metamodels, followed by experimental calibration of as-built metamodels and then efficient UQ analysis of AM process. For illustration purpose, this study specifically uses the thermal level of AM process as an example, by choosing the temperature field and melt pool as quantity of interest. We have clearly showed the surrogate modeling in the presence of high-dimensional response (e.g. temperature field) during AM process, and illustrated the parameter calibration and model correction of an as-built surrogate model for reliable uncertainty quantification. The experimental calibration especially takes advantage of the high-quality AM benchmark data from National Institute of Standards and Technology (NIST). This study demonstrates the potential of the proposed data-driven UQ framework for efficiently investigating uncertainty propagation from process parameters to material microstructures, and then to macro-level mechanical properties through a combination of advanced AM multi-physics simulations, data-driven surrogate modeling and experimental calibration.

The Iterative Method for Reliability Estimation of Fatigue Life

2012 ◽  
Vol 249-250 ◽  
pp. 628-631
Author(s):  
Xin Li Bai ◽  
Peng Xu ◽  
Jiang Yan Li
Keyword(s):  
Fatigue Life ◽  
Design Method ◽  
Estimation Method ◽  
Engineering Application ◽  
Reliability Estimation ◽  
Fatigue Reliability ◽  
Reliability Design ◽  
Fatigue Design ◽  
Machine Parts ◽  
Stress Cycles

The expression of reliability estimation method for fatigue life of machine parts was derived, and two kinds of stress cycles (reversed cycle and un-symmetric reversed cycle) were considered. An iteration method is presented and the corresponding computer program named STRENGTH-2 is developed for estimating reliable life of machine parts. The engineering application results show that the calculated results are close to experimental results. The proposed method can be convenient to carry out the fatigue reliability design for machine parts under the action of uni-axial and multi-axial loadings, and promote the popularization and application of existing anti-fatigue design method. It has the high value of engineering application.

Inverse surrogate modeling for low-cost geometry scaling of microwave and antenna structures

2016 ◽  
Vol 33 (4) ◽  
pp. 1095-1113 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Surrogate Modeling ◽  
High Fidelity ◽  
Modeling Framework ◽  
Simulation Accuracy ◽  
Content Type ◽  
Scaling Process ◽  
Variable Fidelity ◽  
Em Simulation

Purpose – The purpose of this paper is to investigate strategies for expedited dimension scaling of electromagnetic (EM)-simulated microwave and antenna structures, exploiting the concept of variable-fidelity inverse surrogate modeling. Design/methodology/approach – A fast inverse surrogate modeling technique is described for dimension scaling of microwave and antenna structures. The model is established using reference designs obtained for cheap underlying low-fidelity model and corrected to allow structure scaling at high accuracy level. Numerical and experimental case studies are provided demonstrating feasibility of the proposed approach. Findings – It is possible, by appropriate combination of surrogate modeling techniques, to establish an inverse model for explicit determination of geometry dimensions of the structure at hand so as to re-design it for various operating frequencies. The scaling process can be concluded at a low computational cost corresponding to just a few evaluations of the high-fidelity computational model of the structure. Research limitations/implications – The present study is a step toward development of procedures for rapid dimension scaling of microwave and antenna structures at high-fidelity EM-simulation accuracy. Originality/value – The proposed modeling framework proved useful for fast geometry scaling of microwave and antenna structures, which is very laborious when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted dimension scaling of microwave components at the EM-simulation level.

ENGINEERING PROBABILISTIC ANALYSIS USING MODEL REDUCTION AND AUGMENTED RADIAL BASIS FUNCTIONS

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Qian Wang
Keyword(s):  
Model Reduction ◽  
Reliability Analysis ◽  
Radial Basis Functions ◽  
Probabilistic Analysis ◽  
Basis Functions ◽  
Engineering System ◽  
Performance Function ◽  
First Order ◽  
Engineering Problems ◽  
Radial Basis

Probabilistic analysis of practical engineering problems has long been based on traditional sampling-based approaches, such as Monte Carlo Simulations (MCS) and gradient-based first-order and second-order methods. Since the finite element (FE) or other numerical methods are required to evaluate engineering system responses, such as forces or displacements, it is not efficient to directly integrate FE and sampling-based analysis approaches. Over the years, various approximate methods have been developed and applied to the reliability analysis of engineering problems. In this study, an efficient model reduction technique based on high-dimensional model reduction (HDMR) method has been developed using augmented radial basis functions (RBFs). The basic idea is to use augmented RBFs to construct HDMR component functions. The first-order HDMR model only requires sample points along each variable axis. The HDMR model, once created and used to explicitly express a performance function, is further combined with MCS to perform probabilistic calculations. As test problems, a mathematical problem and a 10-bar truss example are studied using the proposed reliability analysis approach. The proposed method works well, and accurate reliability analysis results are found with a small number of original performance function evaluations, i.e., FE simulations.

A Direct Method for Analyzing Accelerations in Complex Mechanisms

1972 ◽  
Vol 39 (1) ◽  
pp. 266-271
Author(s):  
C. H. Chiang
Keyword(s):  
Direct Method ◽  
New Method ◽  
Complex Mechanism ◽  
Diagram Method ◽  
Acceleration Analysis ◽  
Complex Mechanisms ◽  
A Direct Method

Complex mechanism containing four-bar loops may be treated as simpler compound mechanisms in acceleration analysis by introducing the relative center of curvature of the relative path of a certain point on one link with respect to the opposite link. By means of a new method of acceleration analysis recently suggested by the author, together with the relative center of curvature, it is possible to analyze accelerations of complex mechanisms in a simple and direct way. Results thus obtained are obviously more accurate than those found by using the conventional acceleration diagram method.

Reliability Estimation of Fatigue Life for Machine Parts under Uniaxial Constant Amplitude Load

2012 ◽  
Vol 249-250 ◽  
pp. 632-635
Author(s):  
Yu He Li ◽  
Xin Li Bai ◽  
Ying Fang Zhang
Keyword(s):  
Fatigue Life ◽  
Design Method ◽  
Engineering Application ◽  
Reliability Estimation ◽  
Estimation Methods ◽  
Fatigue Reliability ◽  
Constant Amplitude ◽  
Reliability Design ◽  
Fatigue Design ◽  
Machine Parts

Two methods acquiring p-S-N curve for machine parts are given, namely directly searching out the p-S-N curve of the material from material database and using the idealized p-S-N curve. Reliability estimation methods of fatigue life of machine parts are derived under uniaxial constant amplitude load. Two kinds of circumstances (fixed stress and probabilistic stress) and two kinds of stress cycles (reversed cycle and unsymmetric reversed cycle) are considered. An iteration method is presented and the corresponding computer program is developed for estimating reliable life of machine parts. The engineering application results show that the calculated results are closer to experimental results. The suggested method can be convenient to fatigue reliability design of machine parts. It has good stimulative effect on popularization and application of existing anti-fatigue design method for machine parts, and high value of engineering application.

Network System Reliability and Unit Probability Importance Based on Monte Carlo Method

2014 ◽  
Vol 1049-1050 ◽  
pp. 1458-1462
Author(s):  
Rao Bin
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Network Reliability ◽  
Approximate Algorithm ◽  
Network System ◽  
Reliability Design ◽  
Precise Calculation ◽  
Improve Calculation ◽  
Factor Decomposition ◽  
State Enumeration

The network system reliability research contains a number of problems, such as: Reliability analysis and reliability design, reliability, maintenance and a lot of problems so on. The calculation of reliability of the network is the important area of network reliability analysis, State enumeration method and principle of a class, don't pay the product and method, the factor decomposition method is a classic accurate algorithm of computing network reliability. Due to the difficulty of precise calculation, in the method, appeared and bound method, Monte carol method, the reliability of the approximate algorithm. Compared with the accurate algorithm, approximate algorithm is still under development. So far, no recognized classic algorithms, so the method to improve calculation accuracy, reduce the complexity of the target of the researchers.

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