Simulation Based Optimal Tolerancing for Multibody Systems

2007 ◽  
Author(s):  
Henry Arenbeck ◽  
Samy Missoum ◽  
Anirban Basudhar ◽  
Parviz E. Nikravesh
Keyword(s):  
Multibody Systems ◽  
Multibody System ◽  
Failure Detection ◽  
Optimization Method ◽  
Computational Effort ◽  
Efficient Estimation ◽  
Design Parameters ◽  
Support Vector ◽  
Manufacturing Cost ◽  
Analysis Scheme

This paper introduces a new methodology for probabilistic optimal design of multibody systems. Specifically, the effects of dimensional uncertainties on the behavior of a system are considered. The proposed reliability-based optimization method addresses difficulties such as high computational effort and non-smoothness of the system’s responses, for example, as a result of contact events. The approach is based on decomposition of the design space into regions, corresponding to either acceptable or non-acceptable system performance. The boundaries of these regions are defined using Support Vector Machines (SVMs), which are explicit in terms of the design parameters. A SVM can be trained based on a limited number of samples, obtained from a design of experiments, and allows a very efficient estimation of probability of failure, even when Monte Carlo Simulation (MCS) is used. A modularly structured tolerance analysis scheme for automatic estimation of system production cost and probability of system failure is presented. In this scheme, detection of failure is based on multibody system simulation, yielding high computational demand. A SVM-based replication of the failure detection process is derived, which ultimately allows for automatic optimization of tolerance assignments. A simple multibody system, whose performance usually shows high tolerance sensitivity, is chosen as an exemplary system for illustration of the proposed approach. The system is optimally designed for minimum manufacturing cost while satisfying a target performance level with a given probability.

Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

The Combined Use of Navier-Stokes Solvers and Optimization Methods for Decelerating Cascade Design

2000 ◽  
Author(s):  
Marcello Manna ◽  
Raffaele Tuccillo
Keyword(s):  
Optimization Method ◽  
Initial Guess ◽  
Computational Effort ◽  
Navier Stokes ◽  
Design Parameters ◽  
Analysis Tool ◽  
Optimization Strategy ◽  
Flow Solver ◽  
Geometrical Characteristics ◽  
Combined Use

The authors present an optimization strategy applied to decelerating cascades, with the combined use of a Navier-Stokes flow solver, correlation functions and a gradient based optimization method. In the initial stages of the searching the configurations proposed by the optimizer are mainly investigated with a semi-empirical analysis tool so to provide the flow solver with an improved initial guess. In the later stages the optimizer directly controls the flow solver. The objective of the optimization is two fold: determining the geometrical characteristics of the cascade yielding the best aerodynamic performances, and defining an appropriate cost function accounting for optimality conditions in a more general sense. The method is applied to blade profiles of the C4 type, whose geometrical characteristics are determined as a function of a few parameters (typically the camber angle, the maximum thickness to chord ratio, and the chord). By doing so the number of design parameters is substantially reduced, and the validity of the present methodology is correctly demonstrated, without loss of generality, with a limited computational effort. The examples deal with the design of decelerating cascades realizing considerable flow turning both in subsonic and transonic regimes and demonstrate the potential of the method.

scholarly journals Thermal optimization research of oil-immersed transformer winding based on the support machine response surface

2021 ◽  
pp. 264-264
Author(s):  
Fating Yuan ◽  
Wentao Yang ◽  
Bo Tang ◽  
Yue Wang ◽  
Fa Jiang ◽  
...  
Keyword(s):  
Response Surface ◽  
Low Voltage ◽  
Hot Spot ◽  
Optimization Method ◽  
Design Parameters ◽  
Support Vector ◽  
Structure Parameters ◽  
Transformer Winding ◽  
Polynomial Response Surface ◽  
Spot Temperature

In this paper, the CFD (computational fluid dynamics) model is established for the low voltage winding region of an oil-immersed transformer according to the design parameters, and the detailed temperature distribution within the region is obtained by numerical simulation. On this basis, the RSM (response surface methodology) is adopted to optimize the structure parameters with the purpose of minimizing the hot spot temperature. After a sequence of designed experiments, the second-order polynomial response surface and the SVM (support vector machine) response surface are established respectively. The analysis of their errors shows that the SVM response surface can be better used to fit the approximation. Finally, the PSO (particle swarm optimization) algorithm is employed to get the optimal structure parameters of the winding based on the SVM response surface. The results show that the optimization method can significantly reduce the hot spot temperature of the winding, which provides a guiding direction for the optimal design of the winding structure of transformers.

Dynamic Analysis of Planar Multibody Systems Considering Contact Characteristics of Ball Bearing Joint

2021 ◽  
pp. 2150159
Author(s):  
Yu Chen ◽  
Kailei Liu ◽  
Rui Qiu ◽  
Chengtao Yu ◽  
Xianfei Xia ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Dynamic Behavior ◽  
Multibody Systems ◽  
Ball Bearing ◽  
Multibody System ◽  
Design Parameters ◽  
Continuous Contact ◽  
Coulomb’S Friction ◽  
Driving Speed ◽  
Selection Of

A comparative study of dynamic analysis for planar multibody systems with ball bearing joints is conducted in this study. The transmission mechanism is used as the exemplar case for illustrating the effect of ball bearing joints on the dynamic behavior of multibody systems. To reflect the energy loss, the models of continuous contact force and modified Coulomb’s friction are considered in the kinematic equations for the multibody system with ball bearing joint. With this, the dynamic characteristics of the mechanism are studied. Meanwhile, an experimental platform is built to generate the test data for demonstrating the effectiveness and correctness of the proposed method. Moreover, the effects of driving speed and clearance size on the dynamic behavior of the multibody system are investigated. The numerical results indicate that the dynamic behavior of the mechanical system is sensitive to the variation of the design parameters and the selection of parameters can affect greatly the accuracy of the mechanism with clearance joints.

scholarly journals Multi-Objective Optimization Design of Permanent Magnet Torque Motor

2021 ◽  
Vol 12 (3) ◽  
pp. 131
Author(s):  
Jiawei Chai ◽  
Tianyi Zhao ◽  
Xianguo Gui
Keyword(s):  
Permanent Magnet ◽  
Optimization Method ◽  
Torque Ripple ◽  
Numerical Control ◽  
Design Parameters ◽  
Support Vector ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Output Torque ◽  
Low Torque

Permanent magnet torque motor (PMTM) is widely used in aerospace, computer numerical control (CNC) machine tools, and industrial robots with many advantages such as high torque density, strong overload capacity, and low torque ripple. With the upgrading of industrial manufacturing, the requirements for the performance of torque motors have become more stringent. At present, how to achieve high output torque and low torque ripple has become a research hotspot of torque motors. In the optimization process, it is necessary to increase the output torque while the torque ripple can be reduced, and it is difficult to get a good result with the single-objective optimization. In this paper, a multi-objective optimization method based on the combination of design parameter stratification and support vector machine (SVM) is proposed. By analyzing the causes of torque ripple, the output torque, efficiency, cogging torque, and total harmonic distortion (THD) of back electromotive force (EMF) are selected as the optimization objectives. In order to solve the coupling problem between the motor parameters, the calculation formula of Pearson correlation coefficient is used to analyze the relationship between the design parameters and the optimization objectives, and the design parameters are layered ac-cording to the sensitivity. In order to shorten the optimization cycle of the motor, SVM is used as a fitting method of the mathematical model. The performance between initial and optimal motors is compared, and it can be found that the optimized motor has a higher torque and lower torque ripple. The simulation results verify the effectiveness of the proposed optimization method.

Dynamics of Multibody Systems With Variable Kinematic Structure

1986 ◽  
Vol 108 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Y. A. Khulief ◽  
A. A. Shabana
Keyword(s):  
Multibody Systems ◽  
Multibody System ◽  
Kinematic Structure ◽  
The Finite Element Method ◽  
Flexible Bodies ◽  
Generalized Coordinates ◽  
Structure Changes ◽  
Dynamics Of Multibody Systems ◽  
Analysis Scheme ◽  
Deformation Modes

The problem of predicting the dynamic behavior of a general multibody system subject to kinematic structure changes is addressed using a mixed set of Lagrangian coordinates. Changes in the kinematic structure may occur smoothly or accompanied by a change in the system momenta. The finite element method is employed to estimate the modal characteristics of flexible bodies. An automated pieced-interval computational scheme that accounts for the change in the dynamic characteristics due to the imposition of new sets of constraints on the boundaries of flexible components is developed. The resulting change in the deformation modes and the associated change in basis of the configuration space requires a new set of generalized coordinates for each subinterval of the analysis. A numerical example is used to demonstrate the analysis scheme developed in this paper.

Multipoint Design Optimization of a Transonic Compressor Blade by Using an Adjoint Method

2013 ◽  
Vol 136 (5) ◽  
Author(s):  
Jiaqi Luo ◽  
Chao Zhou ◽  
Feng Liu
Keyword(s):  
Design Optimization ◽  
Adjoint Method ◽  
Pressure Ratio ◽  
Single Point ◽  
Optimization Method ◽  
Computational Effort ◽  
Operating Conditions ◽  
Design Parameters ◽  
Transonic Compressor ◽  
Wide Range

This paper presents the application of a viscous adjoint method to the multipoint design optimization of a rotor blade through blade profiling. The adjoint method requires about twice the computational effort of the flow solution to obtain the complete gradient information at each operating condition, regardless of the number of design parameters. NASA Rotor 67 is redesigned through blade profiling. A single point design optimization is first performed to verify the effectiveness and feasibility of the optimization method. Then in order to improve the performance for a wide range of operating conditions, the blade is redesigned at three operating conditions: near peak efficiency, near stall, and near choke. Entropy production through the blade row combined with the constraints of mass flow rate and total pressure ratio is used as the objective function. The design results are presented in detail and the effects of blade profiling on performance improvement and shock/tip-leakage interaction are examined.

scholarly journals Design Optimization of a Passive Building with Green Roof through Machine Learning and Group Intelligent Algorithm

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 192
Author(s):  
Yaolin Lin ◽  
Luqi Zhao ◽  
Xiaohong Liu ◽  
Wei Yang ◽  
Xiaoli Hao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Green Roof ◽  
Back Propagation ◽  
Latin Hypercube Sampling ◽  
Optimization Method ◽  
Design Parameters ◽  
Support Vector ◽  
Visual Discomfort

This paper proposed an optimization method to minimize the building energy consumption and visual discomfort for a passive building in Shanghai, China. A total of 35 design parameters relating to building form, envelope properties, thermostat settings, and green roof configurations were considered. First, the Latin hypercube sampling method (LHSM) was used to generate a set of design samples, and the energy consumption and visual discomfort of the samples were obtained through computer simulation and calculation. Second, four machine learning prediction models, including stepwise linear regression (SLR), back-propagation neural networks (BPNN), support vector machine (SVM), and random forest (RF) models, were developed. It was found that the BPNN model performed the best, with average absolute relative errors of 3.27% and 1.25% for energy consumption and visual comfort, respectively. Third, six optimization algorithms were selected to couple with the BPNN models to find the optimal design solutions. The multi-objective ant lion optimization (MOALO) algorithm was found to be the best algorithm. Finally, optimization with different groups of design variables was conducted by using the MOALO algorithm with the associated outcomes being analyzed. Compared with the reference building, the optimal solutions helped reduce energy consumption up to 34.8% and improved visual discomfort up to 100%.

Multi-Point Design Optimization of a Transonic Compressor Blade by Using an Adjoint Method

2013 ◽  
Author(s):  
Jiaqi Luo ◽  
Feng Liu ◽  
Chao Zhou
Keyword(s):  
Design Optimization ◽  
Adjoint Method ◽  
Pressure Ratio ◽  
Single Point ◽  
Optimization Method ◽  
Computational Effort ◽  
Operating Conditions ◽  
Design Parameters ◽  
Transonic Compressor ◽  
Wide Range

This paper presents the application of a viscous adjoint method to the multi-point design optimization of a rotor blade through blade profiling. The adjoint method requires about twice the computational effort of the flow solution to obtain the complete gradient information at each operating condition, regardless of the number of design parameters. NASA Rotor 67 is redesigned through blade profiling. A single point design optimization is first performed to verify the effectiveness and feasibility of the optimization method. Then in order to improve the performance for a wide range of operating conditions, the blade is redesigned at three operating conditions: near peak efficiency, near stall, and near choke. Entropy production through the blade row combined with the constraints of mass flow rate and total pressure ratio is used as the objective function. The design results are presented in detail and the effects of blade profiling on performance improvement and shock/tip-leakage interaction are examined.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

2019 ◽  
Vol 13 ◽  
Author(s):  
Yan Zhang ◽  
Ren Sheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Optimization Method ◽  
Support Vector ◽  
Optimal Parameters ◽  
Instantaneous Power ◽  
Practical Usefulness ◽  
Sample Data ◽  
Diagnosis Method ◽  
Fault Characteristic

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

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