1208 船体中央断面の多段階最適設計に関する研究

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.

An expert system written in LISP is applied to a bulk carrier with emphasis on the structural design of the midship section. A optimisation technique is included to obtain the required longitudinal strength at the midship section in which the minimum weight is chosen to be an objective. This paper demonstrates the procedure of designing the midship section from requirements such as deadweight and ship's speed. In order to check the efficiency of the system, the midship section is compared with one having the same principal requirements. See also Abstract No. 89040812.

This paper presents the Generalized PopulationBased Sampling (GPBS) approach, which is a generalized version of the Population-Based Sampling (PBS) approach that was developed by the authors of this paper in earlier work. PBS is a Genetic Algorithm (GA) based approach that allows for discrete design optimization under uncertainty and requires a computational cost that is two orders of magnitude lower than the state-of-the-art method that couples the GA with Monte Carlo Sampling (MCS). The PBS approach uses the population-based search of the GA to provide samples that are used in the statistical evaluation of aggregate, uncertain fitness functions. In PBS, while large numbers of samples are accumulated to evaluate the fitness values of “good” designs, the computational cost spent on designs with “poor” fitness is minimal. The main assumption in PBS is that the uncertain parameters associated with the design variables all have Gaussian probability distributions; hence, the probability distributions of aggregate fitness functions are assumed to be Gaussian themselves. This valid assumption is used to impose constraints on estimated probabilities of success of expected values of aggregate fitness functions even when the number of samples accumulated is small, which is usually the case at the beginning of the optimization run. The suggested GPBS approach generalizes the PBS approach by allowing design variables’ uncertain parameters with non-Gaussian probability distributions and by eliminating the normality assumption on the probability distributions of aggregate fitness functions. The GPBS approach combines the concepts of MCS and PBS and imposes constraints on calculated, rather than estimated, probabilities of success of expected values of aggregate fitness functions. In this paper, the probabilistic approaches described above are implemented for reliability-based design of a communication satellite with uncertain component failure rates. NOMENCLATURE c penalty multiplier ( ) E expected value f fitness function g inequality constraint function G uncertain inequality constraint function samples N number of samples ( ) P probability of payload R total payload reliability spacecraft R total spacecraft reliability m std measured standard deviation of aggregate probabilistic function

Greenhouses should be designed to meet requirements of construction costs, durability against loads and the possibility of providing suitable environment to plant growth. To get guides to design an optimal greenhouse from the above-mentioned three viewpoints, one of the optimization techniques, which is called the steepest descent method, is applied in this study. The direct light transmission into the greenhouse in winter is regarded as one of the most important environmental factors.The steepest descent method gives the combination of the design variables which constitute the optimal (minimum or maximum) value in the objective function under some constraints. In this study the design variables were, for example, the shape of the house, the moment of inertia and crosssection features of each member and the ratch measure. The allowable stress or deflection of each member played a role of constraints in the course of the optimization. The objective function was the construction cost or the direct light transmission. In the steepest descent method, however, only one objective function is allowed. For this reason the following two steps were used. First the construction cost (Js) is chosen as the objective function and is minimized. Then the inequality (1) is added to the constraints and the light transmission is maximized, where k is the coefficient of construction cost allowance and larger than 1.0, and Jsopt is the minimum construction cost.Because in the steepest descent method all variables should be continuous and differentiable, some discontinuous variables, e.g. the number of purlins, were approximated with appropriate continuous functions. For the same reason any size and shape of steel member were assumed to exist. The crosssection features of the member are expressed with three independent variables on the basis of the empirical relations.The optimizations were carried out under the conditions of a snow load of 30cm and a wind speed of 50m/s. The span was fixed at 9m. The latitude and the coefficient of construction cost allowance in the optimization of the light transmission were 34°N and 1.10, respectively. The following points became clear by the optimizations.1) The steepest descent method can be applied to greenhouse design and the results show appreciable improvement both in the construction cost and in the light transmission in comparison with an example of commercial greenhouses.2) The results of the optimization of the construction cost (cost optimization, hereafter) and the light transmission of the N-S oriented greenhouse (N-S optimization, hereafter) show a symmetric shape, whereas the optimal E-W oriented greenhouse has a ridge nearer to the south wall, so that the south roof slope becomes steeper.3) Every optimization results in a ratch measuer of about 4m, which is wider than that of commercial greenhouses.4) The moment of inertia of the rafters and the posts ranges from 150 to 290cm4. In the cases of the cost and N-S optimizations, the rafters have a larger moment of inertia than the posts. In the E-W optimization, the north rafter has an appreciably larger moment of inertia than the three others.5) The cost optimization shows that members of the crosssection with larger depth and width are more suitable for the rafters and the posts, if the moment of inertia is the same. This holds true for the north members in the E-W optimization and for the posts in the N-S optimization. In the E-W oriented greenhouse, a member with shorter depth and larger section area and a member with shorter width are more suitable for the south rafter and for the south post, respectively.6) In this study the strength of the glass was not taken into consideration. This resulted in very wide purlin intervals, which seem to be unrealistic, but this still proves that larger purlins with wider intervals are more suitable

The genetic algorithm(GA) is efficient method for topological optimization problem of truss structures because it is possible to obtain global optimum on non-convex design space. The bottleneck of this method is that a large number of function evaluations and structural analyses are necessarily. Performance of the GA depends on first arrangement of string positions and the crossover operator strongly when the simple GA apply to the topological optimization problems. If inadequate correspondence between design variables and the string positions is used, good solution is hardly obtained in spite of numerous calculations. The objective of this study is development a new genetic algorithm specialized to the topological optimization problem of the truss structure improving the calculation efficiency and reliability to obtain optimum solution. To overcome disadvantage of the simple GA, biased crossover operation with string position rearrangement is proposed. Highly fitted gene is crated and inherited by using this procedure. Minimum weight design of the truss structure consists of nine nodes are carried out by using simple G A and proposed GA, and calculation performance is compared. From the result, it is confirmed that the proposed GA is more effective and reliable in comparison with the simple GA.

FEM Computation of Dynamic Properties of a Structure Using Fuzzy Set Theory

In this paper is described a procedure to synthesize the optimal topology, shape, and size of compliant continua for a given nonlinear output path. The path is prescribed using a finite number of distinct precision points much in accordance with the synthesis for path generation in traditional kinematics. Geometrically nonlinear analysis is employed to model large displacements of the constituent members. It is also essential to employ nonlinear analysis to allow the output port to negotiate the prescribed path accurately. The topology synthesis problem is addressed in its original binary form in that the corresponding design variables are only allowed to assume values of “0” for no material and “1” for the material present at a site in the design region. Shape and size design variables are modeled using continuous functions. Owing to the discrete nature of topology design variables, since gradient based optimization methods cannot be employed, a genetic algorithm is used that utilizes only the objective values to approach an optimum solution. A notable advantage of a genetic algorithm over its gradient based counterparts is the implicit circumvention of nonconvergence in the large displacement analysis, which is another reason why a genetic algorithm is chosen for optimization. The least squared objective is used to compare the design and desired output responses. To allow a user to specify preference for a precision point, individual multiple least squared objectives, same in number as the precision points are used. The multiple objectives are solved using Nondominated Sorting in Genetic Algorithm (NSGA-II) to yield a set of pareto optimal solutions. Thus, multiple solutions for compliant mechanisms can be obtained such that a mechanism can traverse one or some precision points among those specified more precisely. To traverse the entire path, a solution that minimizes the sum of individual least square objectives may be chosen. Synthesis examples are presented to demonstrate the usefulness of the proposed method that is capable of generating a solution that can be manufactured as is without requiring any interpretation.

Multi-objective optimization of water-cooled pinfin heatsinks

Advantages of EPS module Reinforced concrete frame structure are enormous. For example, High precision, low cost, fast construction speed, low energy consumption. It has a wide application prospect in town building and post disaster reconstruction, cold region housing industrialization and other fields. It must have considerable economic benefit, if we can optimize the design reasonable on EPS module Reinforced concrete frame structure. As the design variables, constraint conditions, calculation and other reasons. In fact we are not on the EPS module Reinforced concrete frame structure to optimize the design of effective. This paper analyzed the EPS module of reinforced concrete frame structure and engineering optimization design theory development trend, research on EPS module of reinforced concrete frame structure optimization design problem and discusses the EPS module structure of reinforced concrete frame structure optimization design of the feasibility of using genetic algorithm.

In this paper, a hybrid automatic optimization strategy is proposed for the design of underwater robot lines. Isight is introduced as an integration platform. The construction of this platform is based on the user programming and several commercial software including UG6.0, GAMBIT2.4.6 and FLUENT12.0. An intelligent parameter optimization method, the particle swarm optimization, is incorporated into the platform. To verify the strategy proposed, a simulation is conducted on the underwater robot model 5470, which originates from the DTRC SUBOFF project. With the automatic optimization platform, the minimal resistance is taken as the optimization goal; the wet surface area as the constraint condition; the length of the fore-body, maximum body radius and after-body’s minimum radius as the design variables. With the CFD calculation, the RANS equations and the standard turbulence model are used for direct numerical simulation. By analyses of the simulation results, it is concluded that the platform is of high efficiency and feasibility. Through the platform, a variety of schemes for the design of the lines are generated and the optimal solution is achieved. The combination of the intelligent optimization algorithm and the numerical simulation ensures a global optimal solution and improves the efficiency of the searching solutions.

When globally optimal designs are important and required sensitivity analysis is complicated, genetic algorithms can be effective alternatives to gradient-based optimization methods. Genetic algorithms are being used more widely in various engineering fields. However, when the design variables become large, standard genetic algorithms become difficult to use because of excessive computation time needed to obtain converged solutions. In this investigation, we consider two-dimensional structural topology optimizations based on finite element models as a large-size optimization problem. To expedite the solution convergence by orders of magnitude, the genetic algorithm in the multiscale multiresolution setting is proposed. By the multiscale setting, standard single-scale binary design variables are represented in multiple scales by the so-called Binary Wavelet Transform. Note that all genetic operators such as crossover and mutation must be redefined or modified for multiscaled design variables. A key advantage of using multiscale design variables is that the exploration of solution becomes more effective with them. If the multiresolution setting based on the multiscale variables is used, the design optimization can proceed from low to high resolution over several resolution stages where the information passing over resolutions is very effective. In this multiresolution approach, a converged population at a certain resolution level is reused as a part of the initial population at the next higher resolution level. In fact, we form the initial population in three groups: the converged population in the previous resolution, a randomly generated population and the mixture of the converged population and the random population. The detailed technique to generate the three groups is presented, which is an important part of the developed multiscale multiresolution genetic algorithm. The convergence improvement by the multiscale multiresolution approach was verified numerically with several numerical case studies.

GA\SQP optimization for the dimensional synthesis of a delta mechanism based haptic device design

Population-based (gradient-free) methods have become attractive for solving optimization problems since they have been known to locate "optimal" (best) positions within a search space. In this study, we investigate the use of three different gradient-free optimization methods namely: Iterative Latin Hypercube Sampling (ILHS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) for well placement and well controls optimization problem of the CO2 underground storage in a 2D saline aquifer with bound constraints on the design variables. We also consider sequential optimization of well locations and well controls using the gradient-free methods. To evaluate the performance of these algorithms, we test the methods on well-known test (or toy) problems such as the multi-dimensional (40 design variables) Rosenbrock function, as well as the Traveling Salesman Problem (TSP) with 20 cities (20 design variables). We further extend the study to a more complex problem which involves finding the optimal well types, well locations, or well controls for a CO2 injection problem in a synthetic saline aquifer compositional model built in a commercial simulator. The objective function formulated for the CO2 injection problem is the net present environmental value (NPEV) involving discrete design variables (well types and well locations) and real design variables (well controls). Results obtained from the performance comparison of these gradient-free methods on the toy problems demonstrate the superior performance of the ILHS over PSO and GA for the Rosenbrock test function, while GA outperforms ILHS and PSO in solving the TSP. We also observe that for the simple well locations optimization problems involving one or two wells, all three algorithms obtained comparable results, given the same number of samples and number of iterations. However, for more complex cases such as the sequential or simultaneous optimization problems, when multiple wells which may include injectors and producers are present, we observe significant differences in the selection of the optimal well types and well locations among the three optimizers. In the simultaneous optimization of well types, well locations, and well controls, when both injectors and producers are BHP controlled, the results obtained showed that it is more optimal for at least one producer to be present, and for the injectors to be operated at the upper bound of the injector BHP, while the producers operated at the lower bound of the producer BHP to maximize the NPEV.

Multi-objective optimization of a typical parallel tracking mechanism considering parameter uncertainty is carried out in this paper. Both dimensional and sectional parameters are regarded as design variables. Workspace, kinematic, stiffness, and dynamic performances are simultaneously considered in formulating optimal objectives and constraint conditions. Considering manufacturing and assembling errors, parameter uncertainty is modeled and evaluated to minimize their effects on the optimized performances. Analytical models between objectives and design variables are established to improve the efficiency of optimization while its accuracy is assured. The study of parameter uncertainty and analytical mapping model is incorporated in the optimization of the parallel tracking mechanism. With the aid of particle swarm algorithm, a cluster of solutions, called Pareto frontier, are obtained. By proposing an index, a cooperative equilibrium point representing the balance among objectives is selected and the optimized parameters are determined. The present study is expected to help designers build optimized parallel tracking mechanism in an effective and efficient manner.

The elevator mechanism in roller module rapid replacement system had been designed and an optimal math model had been established aiming at the maximum thrust demanded for hydraulic cylinder of the elevator mechanism is too large in this paper. In the model, installation positional parameters of the hydraulic cylinder are used as design variables; the minimum installation distance of the hydraulic cylinder, triangle position relations formed by hydraulic cylinder and design variables, and so on are used as constraint conditions; the maximum thrust of hydraulic cylinder is used as objective functions. And the model had been solved by using the MATLAB software. The case optimization result shows: the maximum thrust of hydraulic cylinder is lessened. And design purpose has been realized.