scholarly journals Inversion of Thermal Conductivity in Two-Dimensional Unsteady-State Heat Transfer System Based on Finite Difference Method and Artificial Bee Colony

2019 ◽  
Vol 9 (22) ◽  
pp. 4824 ◽  
Author(s):  
Liangliang Yang ◽  
Bojun Sun ◽  
Xiaogang Sun
Keyword(s):  
Finite Difference ◽  
Finite Difference Method ◽  
Convergence Rate ◽  
Difference Method ◽  
Measurement Errors ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Unsteady State ◽  
Bee Colony ◽  
Heat Transfer System

Based on the finite difference method and the artificial bee colony algorithm, the thermal conductivity in the two-dimensional unsteady-state heat transfer system is deduced. An improved artificial bee colony algorithm (IABCA), that artificial bee colony algorithm (ABCA) coupled with calculated deviation feedback, is proposed to overcome the shortcomings of insufficient local exploitation capacity and slow convergence rate in the late stage of the artificial bee colony algorithm (ABCA). For the forward problems, the finite difference method (FDM) is used to calculate the required temperature value of a discrete point; for the inverse problems, the IABCA is applied to minimize the objective function. In the inversion problem, the effects of colony size, number of measuring points, and the existence of measurement errors on the results are studied, and the inversion convergence rate of IABCA and ABCA is compared. The results demonstrate that the methods adopted in this paper had good effectiveness and accuracy even if colony sizes differ and measurement errors exist; and that IABCA has a more efficient convergence rate than ABCA.

Reducing the Impact of Measurement Errors When Reconstructing Spatial Dynamic Forces

2006 ◽  
Vol 128 (5) ◽  
pp. 586-593 ◽  
Author(s):  
Yi Liu ◽  
W. Steve Shepard
Keyword(s):  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
Regularization Method ◽  
Measurement Errors ◽  
Tikhonov Method ◽  
Dynamic Forces ◽  
The Finite Difference Method ◽  
The Impact ◽  
Modal Method

Inferring external spatially distributed dynamic forces from measured structural responses is necessary when direct measurement of these forces is not possible. The finite difference method and the modal method have been previously developed for reconstructing these forces. However, the accuracy of these methods is often hindered due to the amplification of measurement errors in the computation process. In order to analyze these error amplification effects by using the singular value decomposition approach, the mathematic expressions for these two force reconstruction methods are first transformed into a certain linear system of equations. Then, a regularization method, the Tikhonov method, is applied to increase computational stability. In order to achieve a good regularized result, the L-curve method is used in conjunction with the Tikhonov method. The effectiveness in reducing the influence of the measurement errors when applying the regularization method to the finite difference method and the modal method is investigated analytically and numerically. It is found that when the regularization method is appropriately applied, reliable computational results for the reconstructed force can be achieved.

scholarly journals Solving of Two-Dimensional Unsteady-State Heat-Transfer Inverse Problem Using Finite Difference Method and Model Prediction Control Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shoubin Wang ◽  
Rui Ni
Keyword(s):  
Heat Transfer ◽  
Inverse Problem ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Difference Method ◽  
Measurement Errors ◽  
Control Method ◽  
Two Dimensional ◽  
Measurement Point ◽  
Prediction Control

The Inverse Heat Conduction Problem (IHCP) refers to the inversion of the internal characteristics or thermal boundary conditions of a heat transfer system by using other known conditions of the system and according to some information that the system can observe. It has been extensively applied in the fields of engineering related to heat-transfer measurement, such as the aerospace, atomic energy technology, mechanical engineering, and metallurgy. The paper adopts Finite Difference Method (FDM) and Model Predictive Control Method (MPCM) to study the inverse problem in the third-type boundary heat-transfer coefficient involved in the two-dimensional unsteady heat conduction system. The residual principle is introduced to estimate the optimized regularization parameter in the model prediction control method, thereby obtaining a more precise inversion result. Finite difference method (FDM) is adopted for direct problem to calculate the temperature value in various time quanta of needed discrete point as well as the temperature field verification by time quantum, while inverse problem discusses the impact of different measurement errors and measurement point positions on the inverse result. As demonstrated by empirical analysis, the proposed method remains highly precise despite the presence of measurement errors or the close distance of measurement point position from the boundary angular point angle.

scholarly journals A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Wen Liu
Keyword(s):  
Convergence Rate ◽  
Learning Strategies ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Population Diversity ◽  
Local Optimum ◽  
Simulation Experiments ◽  
Bee Colony ◽  
Search Mechanism ◽  
Improved Algorithm

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

Artificial bee colony algorithm with dynamic multi-population

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740087 ◽  
Author(s):  
Ming Zhang ◽  
Zhicheng Ji ◽  
Yan Wang
Keyword(s):  
Convergence Rate ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Space ◽  
Experimental Results ◽  
Search Process ◽  
Competitive Performance ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Fcm Clustering

To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

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