Design of Optimally Stiff Structures Using Shape Density Function

1999 ◽  
Author(s):  
Ashok V. Kumar ◽  
Aaron Wood
Keyword(s):  
Optimal Design ◽  
Density Function ◽  
Material Properties ◽  
Optimization Techniques ◽  
Optimal Designs ◽  
And Topology

Abstract Optimization techniques for the design of the shape and topology of structures is presented where the geometry is represented using contours of a piece-wise bilinear shape density function. The relation between the material properties and the density function is studied to understand the influence of this relation on the optimal design obtained. Relations that would yield homogeneous and fully dense optimal designs are sought.

Investigations on Optimal Design of a U-Tube Steam Generator

2010 ◽  
Author(s):  
Hui-min Qin ◽  
Chang-qi Yan ◽  
Meng Wang ◽  
Shi-jing He
Keyword(s):  
Optimal Design ◽  
Steam Generator ◽  
Optimization Techniques ◽  
Point Of View ◽  
Geometric Constraints ◽  
Optimal Designs ◽  
Optimal Method ◽  
Obvious Effect ◽  
Pressurized Water ◽  
Performance Point

Steam generator is one of the key equipments in the pressurized water reactor, from the performance point of view, it is necessary to apply optimization techniques to the design of the steam generator. In this work, the optimal designs of a U-tube steam generator (UTSG), taking minimization of the total volume and net weight as objective respectively, are carried out by considering thermohydraulic and geometric constraints using a complex-genetic algorithm (CGA). And the sensitivities of some parameters which influence the total volume and net weight of UTSG are also analyzed. Under the condition of constant secondary thermalhydraulic parameters of the steam generator, the optimal design indicates an obvious effect taking either the overall volume or the total weight of the steam generator as the objective. The optimization results show that the proposed optimal method is feasible and effective. And the results of optimal designs and sensitivity analysis would provide guidance in the engineering design of UTSG.

Q-optimal experimental designs and close to them experimental designs for polynomial regression on the interval

2020 ◽  
Vol 86 (5) ◽  
pp. 65-72
Author(s):  
Yu. D. Grigoriev
Keyword(s):  
Optimal Design ◽  
Polynomial Regression ◽  
Direct Consequence ◽  
Experimental Designs ◽  
Optimal Designs ◽  
Software Systems ◽  
General Character ◽  
Support Points ◽  
Optimal Weights ◽  
Universal Expression

The problem of constructing Q-optimal experimental designs for polynomial regression on the interval [–1, 1] is considered. It is shown that well-known Malyutov – Fedorov designs using D-optimal designs (so-called Legendre spectrum) are other than Q-optimal designs. This statement is a direct consequence of Shabados remark which disproved the Erdős hypothesis that the spectrum (support points) of saturated D-optimal designs for polynomial regression on a segment appeared to be support points of saturated Q-optimal designs. We present a saturated exact Q-optimal design for polynomial regression with s = 3 which proves the Shabados notion and then extend this statement to approximate designs. It is shown that when s = 3, 4 the Malyutov – Fedorov theorem on approximate Q-optimal design is also incorrect, though it still stands for s = 1, 2. The Malyutov – Fedorov designs with Legendre spectrum are considered from the standpoint of their proximity to Q-optimal designs. Case studies revealed that they are close enough for small degrees s of polynomial regression. A universal expression for Q-optimal distribution of the weights pi for support points xi for an arbitrary spectrum is derived. The expression is used to tabulate the distribution of weights for Malyutov – Fedorov designs at s = 3, ..., 6. The general character of the obtained expression is noted for Q-optimal weights with A-optimal weight distribution (Pukelsheim distribution) for the same problem statement. In conclusion a brief recommendation on the numerical construction of Q-optimal designs is given. It is noted that in this case in addition to conventional numerical methods some software systems of symbolic computations using methods of resultants and elimination theory can be successfully applied. The examples of Q-optimal designs considered in the paper are constructed using precisely these methods.

Structural Analysis and Optimal Design of Lightweight Skate for Loading and Unloading

2013 ◽  
Vol 785-786 ◽  
pp. 1258-1261
Author(s):  
In Pyo Cha ◽  
Hee Jae Shin ◽  
Neung Gu Lee ◽  
Lee Ku Kwac ◽  
Hong Gun Kim
Keyword(s):  
Topology Optimization ◽  
Structural Analysis ◽  
Optimal Design ◽  
Optimization Techniques ◽  
Normal Operation ◽  
Stress And Strain ◽  
Initial Model ◽  
Design Variables ◽  
Model Set ◽  
Main Frame

Topology optimization and shape optimization of structural optimization techniques are applied to transport skate the lightweight. Skate properties by varying the design variables and minimize the maximum stress and strain in the normal operation, while reducing the volume of the objective function of optimal design and Skate the static strength of the constraints that should not degrade compared to the performance of the initial model. The skates were used in this study consists of the main frame, sub frame, roll, pin main frame only structural analysis and optimal design was performed using the finite element method. Simplified initial model set design area and it compared to SM45C, AA7075, CFRP, GFRP was using the topology optimization. Strength does not degrade compared to the initial model, decreased volume while minimizing the stress and strain results, the optimum design was achieved efficient lightweight.

scholarly journals Structural optimization in ESAC: annals 2011

2014 ◽  
Vol 5 ◽  
pp. A09
Author(s):  
Ji-Hong Zhu ◽  
Wei-Hong Zhang
Keyword(s):  
Topology Optimization ◽  
Structural Optimization ◽  
Material Properties ◽  
Numerical Results ◽  
Multiphase Materials ◽  
And Topology ◽  
Effective Material Properties

The purpose of this paper is to give an overall introduction of the structural optimization research works in ESAC group in 2011. Four main topics are involved, i.e. 1) topology optimization with multiphase materials, 2) integrated layout and topology optimization, 3) prediction of effective material properties and 4) composite design. More detailed techniques and some numerical results are also presented and discussed here.

scholarly journals A Highly D‑efficient Spring Balance Weighing Design for an Even Number of Objects

2019 ◽  
Vol 5 (344) ◽  
pp. 17-27
Author(s):  
Małgorzata Graczyk ◽  
Bronisław Ceranka
Keyword(s):  
Optimal Design ◽  
Sufficient Conditions ◽  
Optimality Criteria ◽  
Point Of View ◽  
Necessary And Sufficient Conditions ◽  
Optimal Designs ◽  
Efficient Design ◽  
Spring Balance ◽  
Experimental Errors ◽  
Necessary And Sufficient

The problem of determining unknown measurements of objects in the model of spring balance weighing designs is presented. These designs are considered under the assumption that experimental errors are uncorrelated and that they have the same variances. The relations between the parameters of weighing designs are deliberated from the point of view of optimality criteria. In the paper, designs in which the product of the variances of estimators is possibly the smallest one, i.e. D‑optimal designs, are studied. A highly D‑efficient design in classes in which a D‑optimal design does not exist are determined. The necessary and sufficient conditions under which a highly efficient design exists and methods of its construction, along with relevant examples, are introduced.

Optimal Design of Plastic Structures for Fixed and Shakedown Loadings

1973 ◽  
Vol 40 (2) ◽  
pp. 595-599 ◽  
Author(s):  
M. Z. Cohn ◽  
S. R. Parimi
Keyword(s):  
Optimal Design ◽  
Loading Condition ◽  
Minimum Weight ◽  
Optimal Solution ◽  
Failure Criteria ◽  
Optimal Designs ◽  
Design Solution ◽  
Variable Loading ◽  
Framed Structures ◽  
A Value

Optimal (minimum weight) solutions for plastic framed structures under shakedown conditions are found by linear programming. Designs that are optimal for two failure criteria (collapse under fixed loads and collapse under variable repeated loads) are then investigated. It is found that these designs are governed by the ratio of the specified factors defining the two failure criteria, i.e., for shakedown, λs and for collapse under fixed loading, λ. Below a certain value (λs/λ)min the optimal solution under fixed loading is also optimal for fixed and shakedown loading. Above a value (λs/λ)max the optimal design for variable loading is also optimal under the two loading conditions. For intermediate values of λs/λ the optimal design that simultaneously satisfies the two criteria is different from the optimal designs for each independent loading condition. An example illustrates the effect of λs/λ on the nature of the design solution.

scholarly journals Testing parallelism for the four-parameter logistic model with D-optimal design

2020 ◽  
Vol 15 (3) ◽  
pp. 273-284
Author(s):  
Lin Ying ◽  
Hyun Seung Won
Keyword(s):  
Optimal Design ◽  
Logistic Model ◽  
Test Preparation ◽  
Test Sample ◽  
Optimal Designs ◽  
Model Parameters ◽  
Reference Standard ◽  
Response Curves ◽  
Classical Design ◽  
Intersection Union Test

In order to determine the potency of the test preparation relative to the standard preparation, it is often important to test parallelism between a pair of dose-response curves of reference standard and test sample. Optimal designs are known to be more powerful in testing parallelism as compared to classical designs. In this study, D-optimal design was implemented to study the parallelism and compare+ its performance with a classical design. We modified D-optimal design to test the parallelism in the four-parameter logistic (4PL) model using Intersection-Union Test (IUT). IUT method is appropriate when the null hypothesis is expressed as a union of sets, and by using this method complicated tests involving several parameters are easily constructed. Since D-optimal design minimizes the variances of model parameters, it can bring more power to the IUT test. A simulation study will be presented to compare the empirical properties of the two different designs.

scholarly journals Pyrolysis modeling, sensitivity analysis, and optimization techniques for combustible materials: A review

2019 ◽  
Vol 37 (4-6) ◽  
pp. 377-433
Author(s):  
Tatenda Nyazika ◽  
Maude Jimenez ◽  
Fabienne Samyn ◽  
Serge Bourbigot
Keyword(s):  
Sensitivity Analysis ◽  
Material Properties ◽  
Input Parameter ◽  
Fire Behavior ◽  
Sensitivity Study ◽  
Optimization Techniques ◽  
Decomposition Reactions ◽  
Input Parameters ◽  
Gradient Based ◽  
Complete Set

Over the past years, pyrolysis models have moved from thermal models to comprehensive models with great flexibility including multi-step decomposition reactions. However, the downside is the need for a complete set of input data such as the material properties and the parameters related to the decomposition kinetics. Some of the parameters are not directly measurable or are difficult to determine and they carry a certain degree of uncertainty at high temperatures especially for materials that can melt, shrink, or swell. One can obtain input parameters by searching through the literature; however, certain materials may have the same nomenclature but the material properties may vary depending on the manufacturer, thereby inducing uncertainties in the model. Modelers have resorted to the use of optimization techniques such as gradient-based and direct search methods to estimate input parameters from experimental bench-scale data. As an integral part of the model, a sensitivity study allows to identify the role of each input parameter on the outputs. This work presents an overview of pyrolysis modeling, sensitivity analysis, and optimization techniques used to predict the fire behavior of combustible solids when exposed to an external heat flux.

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