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A Smart Structures Concept for Truss and Tower Systems
T. Rengaraja,P. Devadas Manoharan
Journal of Engineering and Applied Sciences , 2012,
Abstract: Truss and tower are normally designed for specified loads. But in certain cases, they may be subjected to loads over the design values. The objective of this study, is to provide a smart control, which comes into effect only when the specified loads are exceeded by certain margins. To demonstrate the introduction of smartness, a three-dimensional, three-panel tower system is chosen. Actuators, which activate corrective control to externally applied forces at the nodes of the tower, are provided on the members of the tower. The control forces within an active control system are typically generated through actuators based on feedback information from the measured response of the structure. This study focuses on providing in-built smartness to handle both force and deformation when unanticipated loads up to 100% increase over a short duration act on these systems. The example highlights how suitable control forces are generated and how the system under combined action of unanticipated and control forces balanced.
演化算法求解桁架多目标拓扑优化的收敛速度研究
A convergence speed study on evolutionary algorithms for solving truss multi-objective topology optimization
 [PDF]

胡浩,李刚
- , 2015, DOI: 10.7511/jslx201503002
Abstract: 演化算法能够同时满足结构拓扑优化的前沿领域对全局优化、黑箱函数优化、组合优化和多目标优化的需求,但采用此类算法的可行性与必要性由其收敛性与计算效率决定。本文以应力约束桁架多目标拓扑优化问题为求解对象,致力于揭示在收敛性与计算效率两方面具有竞争力的算法。首先提出评估演化算法求解拓扑优化问题收敛性与计算效率的通用方法,采用穷举法严格推导了典型桁架多目标拓扑优化问题的全局最优解,并采用超体积指标定义了多层次收敛性能准则。最后通过比较研究得到不同收敛性需求下具有最快收敛速度的演化算法,并揭示了具有竞争力的算法机制。本研究为演化算法求解多目标拓扑优化问题的收敛速度奠定了理论基础,同时为高效求解实际工程拓扑优化问题提供算法支持。
Though evolutionary algorithms (EAs) are capable of satisfying the demands arising from the new advancements in structural topology optimization on global optimization,black-box function optimization,combinatorial optimization and multi-objective optimization,the necessity of applying them to this field still depends on their convergence and computational efficiency simultaneously.This paper aims to reveal competent algorithms on these two aspects for stress constrained truss multi-objective topology optimization (MOTO) problems.We first propose a general method tailor-made for examining the convergence and efficiency of EAs on solving MOTO.The global optima of typical MOTO problems are rigorously derived using enumeration.Then multi-level convergence criteria are defined using hypervolume metric.The comparative study reveals outstanding EAs with greatest convergence speeds under different convergence requirement and the corresponding algorithmic mechanism.This way,this paper not only contributes to the theoretical foundation of solving MOTO problems using EAs,but also provides support for high efficiently solving practical engineering topology optimization problems.
Actuator Location and Voltages Optimization for Shape Control of Smart Beams Using Genetic Algorithms  [PDF]
Georgia A. Foutsitzi,Christos G. Gogos,Evangelos P. Hadjigeorgiou,Georgios E. Stavroulakis
Actuators , 2013, DOI: 10.3390/act2040111
Abstract: This paper presents a numerical study on optimal voltages and optimal placement of piezoelectric actuators for shape control of beam structures. A finite element model, based on Timoshenko beam theory, is developed to characterize the behavior of the structure and the actuators. This model accounted for the electromechanical coupling in the entire beam structure, due to the fact that the piezoelectric layers are treated as constituent parts of the entire structural system. A hybrid scheme is presented based on great deluge and genetic algorithm. The hybrid algorithm is implemented to calculate the optimal locations and optimal values of voltages, applied to the piezoelectric actuators glued in the structure, which minimize the error between the achieved and the desired shape. Results from numerical simulations demonstrate the capabilities and efficiency of the developed optimization algorithm in both clamped?free and clamped?clamped beam problems are presented.
Robust Topology Optimization of Truss with regard to Volume  [PDF]
Daniel P. Mohr,Ina Stein,Thomas Matzies,Christina A. Knapek
Mathematics , 2011,
Abstract: A common problem in the optimization of structures is the handling of uncertainties in the parameters. If the parameters appear in the constraints, the uncertainties can lead to an infinite number of constraints. Usually the constraints have to be approximated by finite expressions to generate a computable problem. Here, using the example of the topology optimization of a truss, a method is proposed to deal with such uncertainties by using robust optimization techniques, leading to an approach without the necessity of any approximation. With adequately chosen load cases, the final expression is equivalent to the multiple load case. Simple numerical examples of typical problems illustrate the application of the method.
Vibration Control of Cantilever Smart Beam by using Piezoelectric Actuators and Sensors
K. B. Waghulde,,Dr. Bimleshkumar Sinha,Dr. S. Mishra,M. M. Patil
International Journal of Engineering and Technology , 2010,
Abstract: Vibration of a smart beam is being controlled. This smart beam setup is comprised of actuators and sensors placed at the root of a cantilever beam. Vibrations can be caused by various sources includinghuman activity and nearby motorized equipment. In this case, disturbance is produced using a white noise signal to the actuator. The piezoelectric sensors are used to detect the vibration. Simultaneously, feedback controller sends correction information to the actuator that minimizes the vibration. To optimize results, controllers were designed using Linear Quadratic Gaussian (LQG)theory. This theory generally results in high-order controllers. Additionally, optimal control theory is being used to directly optimize low-order controllers.
A comparative analysis of Piezoelectric and Magnetostrictive actuators in Smart Structures  [cached]
Pons, J. L.
Boletín de la Sociedad Espa?ola de Cerámica y Vidrio , 2005,
Abstract: This paper introduces a comparative analysis of Piezoelectric (PZ) and Magnetostrictive (MS) actuators as components in smart structures. There is an increasing interest in functional structures which are able to adapt to external or internal perturbations, i.e. changes in loading conditions or ageing. Actuator technologies must perform concomitantly as sensors and actuators to be applicable in smart structures. In this paper we will comparatively analyze the possibility of using PZ and MS actuators in smart structures and in so doing their capability to act concomitantly as sensors and of modifying their material characteristics. We will also focus on the analysis of how them can be integrated in structures and on the analysis of the most appropriate structures for each actuator. The operational performance of PZ (Stacks) and MS actuators will be compared and eventually some conclusions will be drawn. Este artículo presenta un estudio comparativo de actuadores Piezoeléctricos (PZ) y Magnetoestrictivos (MS) como elementos integrantes de estructuras inteligentes. Existe un interés creciente en estructuras activas que puedan adaptarse a perturbaciones tanto internas como externas, por ejemplo, ante cambios en carga estructural o ante su envejecimiento. Para que un actuador forme parte de una estructura inteligente, debe poder actuar también como sensor. Este artículo presenta un estudio comparativo del uso de actuadores PZ y MS en estructuras inteligentes y, como consecuencia, de su habilidad para actuar y medir simultáneamente así cómo para modificar sus características mecánicas. Nos centraremos también en el análisis de como pueden integrase en estructuras y cuales son las más indicadas para cada actuador. Se compararán las características operacionales de los actuadors PZ multicapa y los MS.
Particle Swarm Optimization Algorithm for Smart Antenna System
Falih M. Mousa
Journal of Mobile Communication , 2012, DOI: 10.3923/jmcomm.2011.6.10
Abstract: This study presents a particle swarm optimization algorithm to optimize the performance of the smart antenna system. All particles of the population are assessed to cost function chosen to be equal to the mean square error between the array output signal and a reference signal considered to be similar to the desired signal. The results obtained show that particle swarm optimization algorithm has a small mean square error and improved output signal resolution than those of two well known adaptive algorithms namely, Recursive Least Square (RLS) and Sample Matrix Inversion (SMI) algorithms.
Using Optimization to Solve Truss Topology Design Problems
Bastos,Fernando; Cerveira,Adelaide; Gromicho,Joaquim;
Investiga??o Operacional , 2005,
Abstract: the design of truss structures is an important engineering activity which has traditionally been done without optimization support. nowadays we witness an increasing concern for efficiency and therefore engineers seek aid on mathematical programming to optimize a design. in this article, we consider a mathematical model where we maximize the stiffness with a volume constraint and bounds in the cross sectional area of the bars, [2]. the basic model is a large-scale non-convex constrained optimization problem but two equivalent problems are considered. one of them is a minimization of a convex non-smooth function in several variables (much less than in the basic model), being only one non-negative. the other is a semidefinite programming problem. we solve some instances using both alternatives and we present and compare the results.
Multi-Objective Two-Dimensional Truss Optimization by using Genetic Algorithm  [cached]
Harun Alrasyid,Pujo Aji
IPTEK : The Journal for Technology and Science , 2011, DOI: http://dx.doi.org/10.12962/j20882033.v22i2.62
Abstract: During last three decade, many mathematical programming methods have been develop for solving optimization problems. However, no single method has been found to be entirely efficient and robust for the wide range of engineering optimization problems. Most design application in civil engineering involve selecting values for a set of design variables that best describe the behavior and performance of the particular problem while satisfying the requirements and specifications imposed by codes of practice. The introduction of Genetic Algorithm (GA) into the field of structural optimization has opened new avenues for research because they have been successful applied while traditional methods have failed. GAs is efficient and broadly applicable global search procedure based on stochastic approach which relies on “survival of the fittest” strategy. GAs are search algorithms that are based on the concepts of natural selection and natural genetics. On this research Multi-objective sizing and configuration optimization of the two-dimensional truss has been conducted using a genetic algorithm. Some preliminary runs of the GA were conducted to determine the best combinations of GA parameters such as population size and probability of mutation so as to get better scaling for rest of the runs. Comparing the results from sizing and sizing– configuration optimization, can obtained a significant reduction in the weight and deflection. Sizing–configuration optimization produces lighter weight and small displacement than sizing optimization. The results were obtained by using a GA with relative ease (computationally) and these results are very competitive compared to those obtained from other methods of truss optimization.
Smart Grid and Optimization  [PDF]
Murat Ahat, Soufian Ben Amor, Marc Bui, Alain Bui, Guillaume Guérard, Coralie Petermann
American Journal of Operations Research (AJOR) , 2013, DOI: 10.4236/ajor.2013.31A019
Abstract:

With urging problem of energy and pollution, smart grid is becoming ever important. By gradually changing the actual power grid system, smart grid may evovle into different systems by means of size, elements and strategies, but its fundamental requirements and objectives will not change such as optimizing production, transmission and consumption. Studying the smart grid through modeling and simulation provides us with valuable results which can not be obtained in real world due to time and cost related constraints. However, due to the complexity of the smart grid, achieving optimization is not an easy task, even using computer models. In this paper, we propose an complex system based approach to the smart grid modeling, accentuating on the optimization by combining game theoretical and classical methods in different levels. Thanks to this combination, the optimization can be achieved with flexibility and scalability, while keeping its generality.

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