scholarly journals Parameter Identification of the Yoshida-Uemori Hardening Model for Remanufacturing

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1859
Author(s):  
Xuhui Xia ◽  
Mingjian Gong ◽  
Tong Wang ◽  
Yubo Liu ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Inverse Analysis ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Hardening Model ◽  
Mechanical Parts ◽  
Variable Weight ◽  
Strain Relationship ◽  
Local Solutions ◽  
Local Fitting ◽  
Fitting Accuracy

The deformation of plastics during production and service means that retired parts often possess different mechanical states, and this can directly affect not only the properties of remanufactured mechanical parts, but also the design of the remanufacturing process itself. In this paper, we describe the stress-strain relationship for remanufacturing, in particular the cyclic deformation of parts, by using the particle swarm optimization (PSO) method to acquire the Yoshida-Uemori (Y-U) hardening model parameters. To achieve this, tension-compression experimental data of AA7075-O, standard PSO, oscillating second-order PSO (OS-PSO) and variable weight PSO (VW-PSO) were acquired separately. The influence of particle numbers on the inverse analysis efficiency was studied based on standard PSO. Comparing the results of PSO variations showed that: 1) standard PSO is able to avoid local solutions and obtain Y-U model parameters to the same degree of precision as the OS-PSO; 2) by adjusting section weight, the VW-PSO could improve local fitting accuracy and adapt to asymmetric deformation; 3) by reducing particle numbers to a certain extent, the efficiency of analysis can be improved while also maintaining accuracy.

Analysis of the Deformation Characteristics of Loess Based on Thermal–Mechanical Coupling under an Energy Internet

2021 ◽  
Author(s):  
Zengle Li ◽  
Bin Zhi ◽  
Enlong Liu
Keyword(s):  
Mechanical Properties ◽  
Temperature Change ◽  
Clean Energy ◽  
Model Parameters ◽  
Stress Strain ◽  
Energy Internet ◽  
Influence Of Temperature ◽  
Medium Model ◽  
Strain Relationship ◽  
Natural Loess

In response to the major challenges faced by China’s transition to green low-carbon energy under the dual-carbon goal, the use of energy Internet cross-boundary thinking will help to develop research on the integration of renewable clean energy and buildings. Energy piles are a new building-energy-saving technology that uses geothermal energy in the shallow soil of the Earth’s surface as a source of cold (heat) to achieve heating in winter and cooling in summer. It is a complex thermomechanical working process that changes the temperature of the rock and soil around the pile, and the temperature change significantly influences the mechanical properties of natural loess. Although the soil temperature can be easily and quickly obtained by using sensors connected to the Internet of Things, the mechanical properties of natural loess will change greatly under the influence of temperature. To explore the influence of temperature on the stress–strain relationship of structural loess, the undrained triaxial consolidation tests were carried out under different temperatures (5, 20, 50 and 70∘C) and different confining pressures (50, 100, 200 and 400[Formula: see text]kPa), and a binary-medium model was introduced to simulate the stress–strain relationship. By introducing the damage rate under temperature change conditions, a binary-medium model of structural loess under variable temperature conditions was established, and the calculation method of the model parameters was proposed. Finally, the calculated results were compared with the test results. The calculation results showed that the established model has good applicability.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

scholarly journals Short-term power load forecasting based on support vector machine and particle swarm optimization

2018 ◽  
Vol 13 ◽  
pp. 174830181879706 ◽  
Author(s):  
Song Qiang ◽  
Yang Pu
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Influencing Factors ◽  
Load Forecasting ◽  
Particle Swarm ◽  
Forecast Accuracy ◽  
Support Vector ◽  
Model Parameters ◽  
Short Term ◽  
Swarm Optimization

In this work, we summarized the characteristics and influencing factors of load forecasting based on its application status. The common methods of the short-term load forecasting were analyzed to derive their advantages and disadvantages. According to the historical load and meteorological data in a certain region of Taizhou, Zhejiang Province, a least squares support vector machine model was used to discuss the influencing factors of forecasting. The regularity of the load change was concluded to correct the “abnormal data” in the historical load data, thus normalizing the relevant factors in load forecasting. The two parameters are as follows Gauss kernel function and Eigen parameter C in LSSVM had a significant impact on the model, which was still solved by empirical methods. Therefore, the particle swarm optimization was used to optimize the model parameters. Taking the error of test set as the basis of judgment, the optimization of model parameters was achieved to improve forecast accuracy. The practical examples showed that the method in the work had good convergence, forecast accuracy, and training speed.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

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