Optimal Placement of Viscoelastic Dampers Represented by the Classical and Fractional Rheological Models

2013 ◽  
pp. 50-84
Author(s):  
Roman Lewandowski ◽  
Zdzislaw Pawlak
Keyword(s):  
Mathematical Formulation ◽  
Optimization Method ◽  
Sequential Optimization ◽  
Optimal Placement ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Rheological Models ◽  
Viscoelastic Dampers ◽  
Optimal Values

The problems of the optimal location of viscoelastic (VE) dampers and determination of the optimal values of parameters of dampers are considered in this chapter. The optimal distributions of dampers in buildings are found for various objective functions. The optimization problem is solved using the sequential optimization method and the particle swarm optimization method. The properties of VE dampers are described using the rheological models with fractional derivatives. These models have an ability to correctly describe the behaviour of VE dampers using a small number of model parameters. Moreover, generalized classical rheological models of VE dampers are also taken into account. A mathematical formulation of the problem of dynamics of structures with VE dampers, modelled by the classical and fractional rheological models is presented. The results obtained from numerical calculation are also discussed in detail.

scholarly journals Study on Optimal Placement and Reasonable Number of Viscoelastic Dampers by Improved Weight Coefficient Method

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Ji-ting Qu ◽  
Hong-nan Li
Keyword(s):  
Mathematical Model ◽  
Weight Coefficient ◽  
Optimal Placement ◽  
Optimal Method ◽  
Analogy Method ◽  
Numerical Examples ◽  
Viscoelastic Dampers ◽  
Reasonable Number ◽  
Coefficient Method

A new optimal method is presented by combining the weight coefficient with the theory of force analogy method. Firstly, a new mathematical model of location index is proposed, which deals with the determination of a reasonable number of dampers according to values of the location index. Secondly, the optimal locations of dampers are given. It can be specific from stories to spans. Numerical examples are illustrated to verify the effectiveness and feasibility of the proposed mathematical model and optimal method. At last, several significant conclusions are given based on numerical results.

scholarly journals Optimization of the Efficiency of a Michell–Banki Turbine Through the Variation of Its Geometrical Parameters Using a PSO Algorith

2021 ◽  
Vol 16 ◽  
pp. 37-46
Author(s):  
A. J. Perez-Rodriguez ◽  
J. Sierra-Del Rio ◽  
L. F. Grisales-Noreña ◽  
S. Galvis
Keyword(s):  
Processing Time ◽  
Mathematical Formulation ◽  
Optimization Method ◽  
Operating Conditions ◽  
Francis Turbine ◽  
Small Scale ◽  
Geometrical Parameters ◽  
Hydropower Generation ◽  
Swarm Optimization ◽  
Water Turbines

Small-scale hydropower generation can satisfy the needs of communities located near natural sources of flowing water. The operating conditions of a Michell–Banki Turbine (MBT) are relatively easier to meet than those of other types of turbine, making it useful in places where other devices are not suitable. Moreover, MBT efficiency is almost invariable with respect to flow rate conditions. Nevertheless, such efficiency commonly ranges between 70% and 85%, which is lower than that of other water turbines like Turgo, Pelton, or Francis turbine. The objective of this work is to determine the maximum theoretical efficiency of an MBT and its associated geometrical parameters by implementing Particle Swarm Optimization. The results show a higher effectiveness of the mathematical formulation compared with other cases from literature and show the performance of the optimization method proposed in this study in terms of solution and processing time. Finally, a maximum MBT efficiency of 93.3% was achieved

scholarly journals Inversion of residual gravity anomalies using tuned PSO

2017 ◽  
Vol 6 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Ravi Roshan ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Gravity Anomalies ◽  
Optimization Method ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Residual Gravity ◽  
Amplitude Coefficient ◽  
Improved Technique

Abstract. Many kinds of particle swarm optimization (PSO) techniques are now available and various efforts have been made to solve linear and non-linear problems as well as one-dimensional and multi-dimensional problems of geophysical data. Particle swarm optimization is a metaheuristic optimization method that requires intelligent guesswork and a suitable selection of controlling parameters (i.e. inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique, tuned PSO, is an improved technique of PSO, in which efforts have been made to choose the controlling parameters, and these parameters have been selected after analysing the responses of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and validated using synthetic gravity anomalies over various source geometries. Finally, tuned PSO is applied over field residual gravity anomalies of two different geological terrains to find the model parameters, namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also that the strategy has enhanced the exploration capability of the proposed method. Thus tuned PSO is an efficient and more robust technique to achieve an optimal solution with minimal error.

scholarly journals AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT

SINERGI ◽  
2020 ◽  
Vol 24 (3) ◽  
pp. 177
Author(s):  
Heru Suwoyo ◽  
Yingzhong Tian ◽  
Muhammad Hafizd Ibnu Hajar
Keyword(s):  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Comparative Results ◽  
The Stability ◽  
Wall Following ◽  
The Membership Function

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

scholarly journals Inversion of Residual Gravity Anomalies using Tuned-PSO Technique

2016 ◽  
Author(s):  
Ravi Roshan ◽  
Upendra Kumar Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Gravity Anomalies ◽  
Optimization Method ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Residual Gravity ◽  
Amplitude Coefficient ◽  
Improved Technique

Abstract. Many kinds of particle swarm optimization (PSO) technique are now available and various efforts have been made to solve linear and non linear problems as well as one dimensional and multidimensional problem of geophysical data. Particle swarm optimization is a Meta heuristic optimization method that requires the intelligent guess and suitable selection of controlling parameters (i.e. Inertia weight and acceleration coefficient) for better convergence at global minima. The proposed technique Tuned–PSO is an improved technique of PSO, in which effort has been made for choosing the controlling parameters and these parameters have selected after analysing the response of various possible exercises using synthetic gravity anomalies over various geological sources. The applicability and efficacy of the proposed method is tested and also validated using synthetic gravity anomalies over various source geometries. Finally Tuned-PSO is applied over field residual gravity anomalies of two different geological terrains to find out the model parameters namely amplitude coefficient factor (A), shape factor (q) and depth (z). The analysed results have been compared with published results obtained by different methods that show a significantly excellent agreement with real model parameters. The results also show that the proposed approach is not only superior to the other methods but also shows that the strategy has enhanced the exploration capability of proposed method. Thus Tuned–PSO is an efficient and more robust technique to achieve optimal solution with minimal error.

scholarly journals Automated Floodway Determination Using Particle Swarm Optimization

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1420 ◽  
Author(s):  
Huidae Cho ◽  
Tien Yee ◽  
Joonghyeok Heo
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Cross Sections ◽  
Particle Swarm ◽  
Optimization Method ◽  
The United States ◽  
Swarm Optimization ◽  
Analysis System ◽  
Automated Tool

The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area because encroachment areas may be permitted for human activities. However, manual determination of the floodway can be time-consuming and subjective depending on the modeler’s knowledge and judgments, and may not necessarily produce a small floodway especially when there are many cross sections because of their correlation. Very little work has been done in terms of floodway optimization. In this study, we propose an optimization method for minimizing the floodway area using the Isolated-Speciation-based Particle Swarm Optimization algorithm and the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). This method optimizes the floodway by defining an objective function that considers the floodway area and hydraulic requirements, and automating operations of HEC-RAS. We used a floodway model provided by HEC-RAS and compared the proposed, manual, and default HEC-RAS methods. The proposed method consistently improved the objective function value by 1–40%. We believe that this method can provide an automated tool for optimizing the floodway model using HEC-RAS.

scholarly journals Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural Area

2017 ◽  
Vol 7 (4) ◽  
pp. 2125
Author(s):  
Messaoud Garah ◽  
Houcine Oudira ◽  
Lotfi Djouane ◽  
Nazih Hamdiken
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Path Loss ◽  
Optimization Method ◽  
Performance Criteria ◽  
Model Parameters ◽  
Loss Reduction ◽  
Swarm Optimization ◽  
Loss Models ◽  
Propagation Prediction

In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.

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