Integrated optimization of structural topology and control for piezoelectric smart trusses using genetic algorithm

The integrated optimization of structure and control systems is investigated for 2-D single structure using a typical planar frame taken from a building. The vibration control model is established for the plane frame, and the LQR algorithm is used to obtain equivalent stiffness and damping parameters of assigned passive controllers. The integrated optimization model, including design variables, the objective function and constraint function, is built. The design variables are the size parameters of structural elements, the parameters of the weighted matrix, the number and locations of the controllers. The maximal displacement of the controlled system in time domain is introduced as the objective function. Genetic algorithm is adopted to solve such a kind of optimization problem with discrete and continuous design variables. The results of a numerical example show that the proposed method is reasonable and effective for the integrated design of control/structural systems subjected to seismic load.

In this study, the optimization of logistics strategies for long-duration space-station operations is investigated; both the visit times and payload manifests of a series of coordinated flights of cargo vehicles are considered at the same time. An optimization model is established that employs both the launch time and manifesting mix of payload classes as design variables and considers the rendezvous launch window, onboard consumption demand, and vehicles’ carrying capabilities as constraints. Four metrics are defined to quantify the utilization benefit and operational robustness of a space-station operational scenario. Each metric is used as the optimization objective function, and a genetic algorithm is employed to obtain the optimal solutions. The approach is demonstrated with a notional one-year operational scenario of China’s future space station. The results indicate that the visit times of cargo vehicles have a considerable influence on logistics strategies, and each objective function of different metrics yields a different optimal logistics strategy.

During the last 20 years, structural optimization has become one of the most important topics of engineering applications. Design optimization of structure has been an interesting area of research in the field of engineering design for its ability to short the design cycle and to enhance product quality. Significant research activity has occurred in the area of structural optimization in the last decade. Especially for topology optimization of structure, many new theoretical, algorithmic, and computational contributions have resulted by researchers and engineers. Topology optimization is a powerful tool for global and multi-scale design of macrostructures, microstructures, and the cell of prescribed composite materials. The population based evolutionary algorithms have emerged as powerful mechanism for finding optimum solutions of complex optimization problems in engineering during the last two decades. Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation [1]. The thinking has wide application in various engineering fields, such as computer science, artificial intelligence, operations research. Genetic algorithm is another kind of bio-inspired optimization method and it is playing an increasingly important role in studies of complex adaptive systems. Its application ranges from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices and structures, such as aircraft turbines and integrated circuits [2]. Optimization of structures can be classified into three categories: sizing, shaping, and topology optimization. In the topology optimization, it is concerned with the structure members and connectivity between members. In general, it is easily represented by discrete variables rather than by those used for continuous optimization problems. Topology optimization is the most difficult and complex among three categories and it is special useful in developing innovative conceptual designs. Structural optimization, in particular the topology optimization, has been identified as one of the most challenging tasks in structural design. Various techniques and approaches have been established during the last two decades. Topology optimization usually referred to as layout optimization or general shape optimization [3]. It lets engineers get the optimal topology of structure or new configurations during product design phase, as they are implementing the design of the size

Structural topology optimization is a crucial approach for achieving lightweight design. An effective topology optimization algorithm must strike a balance between the objective functions, constraints, and design variables, which essentially reflects the symmetry and tradeoff between the objective and constraints. In this study, a topology optimization method grounded in load path theory is proposed. Element bearing capacity is quantified using the element birth and death method, with an explicit formulation derived via finite element theory. The effectiveness in evaluating structural performance is assessed through comparisons with stress distributions and topology optimization density maps. In addition, a novel evaluation index for element bearing capacity is proposed as the objective function in the topology optimization model, which is validated through thin plate optimization. Subsequently, sensitivity redistribution mitigates checkerboard patterns, while mesh filtering suppresses multi-branch structures and prevents local optima. The method is applied for the lightweight design of a triangular arm, with results benchmarked against the variable density method, demonstrating the feasibility and effectiveness of the proposed method. The element bearing capacity seeks to homogenize the load distribution of each element; the technique in this study can be extended to the optimization of symmetric structures.

This paper presents the population-based sampling genetic algorithm, which allows for discrete design optimization under uncertainty. The population-based sampling approach uses the genetic algorithm population to provide samples for the probabilistic evaluation of aggregate uncertain constraint or objective functions. In population-based sampling, large numbers of samples are accumulated to evaluate the fitness values of good designs during the genetic algorithm run, whereas the computational cost spent on designs with poor fitness is minimal. Using Monte Carlo sampling with a genetic algorithm for optimization under uncertainty is a currently accepted approach; however, this approach incurs a large computational cost. In this paper, the genetic algorithm with population-based sampling generates solutions to a discrete optimization problem under uncertainty associated with a commercial satellite design that was solved in previous work via a genetic algorithm with Monte Carlo sampling. The genetic algorithm with population-based sampling and genetic algorithm with Monte Carlo sampling approaches are compared in terms of efficiency (computational cost) and effectiveness (solution quality). The comparison also examines the scalability of the algorithms' performance when solving three additional problem sizes. Furthermore, two population-based sampling variants are introduced, namely, the variable population-based sampling approach, which combines the concepts of population-based sampling and Monte Carlo sampling, and the generalized population-based sampling approach, which removes the restriction in population-based sampling that the uncertain parameters associated with the design variables all have Gaussian probability distributions.

Analysis on the effect of novel topological optimization fin structures considering eccentricity on the heat storage and release characteristics of shell and tube phase change heat accumulator

Preference-based optimum design of an integrated structural control system using genetic algorithms

Topology optimization (TO) is currently a focal point for researchers in the field of structural optimization, with most studies concentrating on single-loading conditions. However, real engineering structures often have to work under various loading conditions. Approaches addressing multiple-loading conditions often necessitate subjective input in order to determine the importance of each loading condition, aiming for a compromise between them. This paper proposes a so-called bisection constraint method (BCM), offering a unique, user-preference-independent solution for TO problems amidst multiple-loading conditions. It is well-known that minimizing the system’s compliance is commonly used in TO as the objective. Generally, compliance is not as sufficient as stress to be used as a response to evaluate the performance of structures. However, formulations focusing on minimizing stress levels usually pose significant difficulties and instabilities. On the other hand, the compliance approach is generally simpler and more capable of providing relatively sturdy designs. Hence, the formulation of min–max compliance is used as the target problem formulation of the proposed method. This method attempts to minimize compliance under only one loading condition while compliances under the remaining loading conditions are constrained. During the optimization process, the optimization problem is automatically reformulated with a new objective function and a new set of constraint functions. The role of compliance under different loading conditions, i.e., whether it is to be treated as an objective or constraint function, might be changed throughout the optimization process until convergence. Several examples based on the solid isotropic material with penalization (SIMP) approach were conducted to illustrate the validity of the proposed method. Furthermore, the general effectiveness of the compliance approach in terms of stress levels is also discussed. The calculation results demonstrated that while the compliance approach is effective in several cases, it proves ineffective in certain scenarios.

This paper discusses a geometrical optimization method for thermoelectric generators. Our method provides the optimized geometry in accord with various arbitrary conditions such as types of materials, volume of materials, temperatures of installation position, and shape of installation position. It has the potential to contribute to improving thermoelectric devices in supporting design stages. By establishing the coupled equations of state for the thermoelectric problem, an analytical model subject to these equations is introduced that mimics the closed circuit composed of the thermoelectric materials, electrodes and a resistor. The total electric power lost through the resistor is formulated as an objective function for optimization. The proposed optimization method for thermoelectric generators is implemented as a geometrical optimization method using the solid isotropic material with penalization (SIMP) method used in topology optimizations. Simple relations are formulated between the density function of SIMP method and physical properties of thermoelectric material. Sensitivity analysis for the objective function is formulated with respect to the density function and the adjoint equations required for calculating it. Depending on the sensitivity, the density function is updated using the method of moving asymptotes, which has numerous benefits in various optimization problems by virtue of its combination with topology optimization. Finally, the numerical examples are provided to demonstrate the validity of our method.

Free-mounted cooling plate multi-objective topology optimization method towards precision machine tool heat dissipation: An experimental and numerical study

In topology optimization, the optimal layout of the structure is obtained through an iterative update of the geometry. Since this update procedure utilizes the gradient of the objective function, it is important to select an appropriate objective function in order to obtain a meaningful geometry. From this point of view, the mathematical definition and physical meaning of the objective functions for topology optimization of heat sinks are reviewed in this article. For this, studies from conductive heat transfer problems to recent computational fluid dynamics (CFD)-based conjugate heat transfer problems are covered. In most of the studies, the objective functions are defined in order to minimize the thermal resistance of the heat sink or to minimize the maximum temperature of the heat source. In place of thermal resistance, thermal compliance is commonly used as the objective function. However, it is worth noting that minimization of the thermal compliance may lead to considerable deviation of the design from that based on the minimum thermal resistance. This is because the thermal compliance is linearly proportional to the average temperature of the heat source in the computational domain, and the average temperature of the heat source is usually different from the maximum temperature of the heat source. In heat conduction problems, the difference between the maximum temperature and the average temperature becomes large when the thermal conductivity of the solid material becomes low. In convection problems, the difference becomes large when the streamwise fluid temperature variation becomes large due to the low heat capacity rate of the fluid.

Optimization of parameters of pulse current gas tungsten arc welding using non conventional techniques

Nonperiodic metamaterials can have better vibration attenuation than periodic metamaterials with properly designed resonator distributions. In this study, we propose optimal design routes for rainbow metamaterials. The rainbow metamaterials are nonperiodic structures composed of Π-shaped beams as backbones and attached spatially varying cantilever-mass resonators. The main optimization objective is to obtain good vibration attenuation within frequency of interests. Frequency response functions of the rainbow metamaterials are first calculated by an analytical model that is built on the basis of the displacement transfer matrix method. Two optimization objective functions are developed with the maximum and average receptance values within a prescribed frequency range, respectively. The mass of resonators acts as design variables. The objective functions are later solved by virtue of the genetic algorithm method. The optimized rainbow metamaterials show good vibration attenuation in the prescribed optimization frequency range.KeywordsRainbow metamaterialOptimizationVibration attenuationGenetic algorithm

PurposeThis paper aims to present a topology optimization (TopO) method for designing heat exchangers (HEx) with two working fluids to be kept apart. The introduction of cut–cells gives rise to the cut-cell TopO method, which computes the optimal distribution of an artificial impermeability field and successfully overcomes the weaknesses of the standard density-based TopO (denTopO) by computing the fluid–solid interface (FSI) at each cycle. This allows to accurately solve the flow and conjugate heat transfer (CHT) problem by imposing exact boundary conditions on the computed FSI and results to correct performances computed without the need to re-evaluate the optimized solutions on a body-fitted grid.Design/methodology/approachThe elements of an artificial impermeability distribution field defined on a background grid act as the design variables and allow topological changes to take place. Post-processing them yields two fields indicating the location of the two flow streams inside the HEx. At each TopO cycle, the FSIs computed based on these two fields are used as the cutting surfaces of the cut-cell grid. On the so-computed grid, the incompressible Navier–Stokes equations, coupled with the Spalart–Allmaras turbulence model, and the temperature equation are solved. The derivatives of the objective and constraint functions with respect to the design variables of TopO are computed by the continuous adjoint method, using consistent discretization schemes devised thanks to the “Think Discrete – Do Continuous” (TDDC) adjoint methodology.FindingsThe effectiveness of the cut-cell–based TopO method for designing HEx is demonstrated in 2D parallel/counter flow and 3D counter flow HEx operating under both laminar and turbulent flow conditions. Compared to the standard denTopO, its ability to compute FSIs along which accurate boundary conditions are imposed, increases the accuracy of the flow solver, which usually leads to optimal, rather than sub-optimal, solutions that truly satisfy the imposed constraints.Originality/valueThis work proposes a new/complete methodology for the TopO of two-fluid systems including CHT that relies on the cut-cell method. This successfully combines aspects from both TopO and Shape Optimization (ShpO) in a single framework thus overcoming the well-known downsides of standard denTopO regarding its accuracy or the need for a follow-up ShpO after TopO. Instead of adding the well-known Brinkman penalization terms into the flow equations, it computes the FSIs at each optimization cycle allowing the solution of the CHT problem on a cut-cell grid.

A linear network is a type of wireless sensor network in which sparse nodes are deployed along a virtual line; for example, on streetlights or columns of a bridge, tunnel, and pipelines. The typical deployment of Linear Wireless Sensor Net-work (LWSN) creates an energy hole around the sink node since nodes near the sink nodes deplete their energy faster than others. Optimal network topology is one of the key factors that can help improve LWSN performance and lifetime. Finding optimal topology becomes tough in large network where total possible combinations is very high. We propose an Optimal Topology Generation (OpToGen) framework based on genetic algorithm for LWSN. Network deployment tools can use OpToGen to configure and deploy LWSNs. Through a discrete event simulator, we demonstrate that the use of genetic algorithm accomplishes fast convergence to optimal topologies as well as less computational overhead as compared to brute force search for optimal topology. We have evaluated OpToGen on the number of generations it took to achieve the best topology for various sized LWSNs. The trade-off between energy consumption and different network sizes is also reported.