structural parameters on the adsorption performance and optimization design, a magnetic wheel structure with a symmetric structure that can meet a variety of adsorption requir ements was obtained.Web
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Three methods i.e. Response surface, Taguchi, and improved Taguchi are used for multi-objective design optimization of the drilling permanent magnet synchronous motor (DPMSM) in this study.The optimization objectives are determined according to the performance requirements of the DPMSM aiming to maximize efficiency and minimize …Web
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tained by the optimization method in this paper; the optimal parameter values 𝑓 = 0.722 and 𝜃 = −0.157 are obtained by 𝐻 norm theory. When μ=2, the optimal pa-Web
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The optimal design of damping parameters for passive vibration control remains a challenge for both research and industrial applications. Here, we introduce a design methodology based on limit cycle analysis in concert with design optimization. A state-space representation is used to model the vibrational behavior converged to its limit …Web
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Design optimization involves the selection of design objectives, determination of constraints, and the set of design variables that optimize the objective(s) while satisfying all constraints. ... Design parameters that primarily affect the S/N ratio are identified, and the level of these parameters is found to minimize the response variations ...Web
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From the perspective of optimization design, there are uncertainties in the optimization model (objective function and constraint function), design variables, and …Web
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The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes (computational models) and iterative analysis methods. By employing object-oriented design to implement abstractions of the key components required for iterative systems …Web
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Abstract. The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with …Web
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Design optimization is the process of finding the best design parameters that satisfy project requirements. Engineers typically use design of experiments (DOE), statistics, …Web
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Element parameters and equations need to be specified for optimization. Under the properties for an element or for an equation, the Optimize field must be enabled. The variable limits can be specified by enabling the Constrain field and entering values for Lower bound, Upper bound and Step Size.Web
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A realistic industrial conceptual design optimization problem for commercial transport airplanes was formulated with reasonable fidelity and comprehensiveness by selecting appropriate design parameters, constraints, and objectives, in order to provide a baseline to facilitate research on developing robust and efficient optimization methods …Web
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@article{osti_991842, title = {DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's manual.}, author = {Eldred, Michael Scott and Dalbey, Keith R and Bohnhoff, William J and Adams, Brian M and Swiler, Laura Painton …Web
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Fig. 5 shows a scatter plot of the parameter values used in the optimization process regarding the outer hardpoint locations of the left lower control arm and the tierod. Parameters present a large parameter dispersion, which allows to sweep the entire design space during the optimization process and reach the global optimum.Web
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Background Knowledge for DOE and Optimization . We first need to define some terminologies for a design problem: Parameters: These include all available "nobs" for your design, e.g., geometry, topology, materials, and control gains.; Variables: These are the subset of design parameters that you want to tune (so the rest of the …Web
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The optimization of the integrated parameter design and tolerance design model typically results in a multi-objective optimization problem owing to the different objectives and constraints of the parameter design and tolerance design, the magnitudes, and conflicts between quality loss and tolerance cost (Wang et al., 2021).Web
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The elements of the design that were optimized include, but are not limited to: aesthetic form, structural form, mechanical performance, human performance (nurse walking …Web
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Simulink Design Optimization is an exciting toolbox since it allows you to use some optimization methods to minimize a cost function. This toolbox is commonly used for dynamical systems, as seen in control systems. It can help you in improving your system response and estimate the parameters of your model.Web
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parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogate-based optimization, mixed integer nonlinear …Web
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This manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities. The Dakota (Design Analysis Kit for Optimization and Terascale …Web
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follows: (1) The optimization objective of the LLC resonant converter is single. (2) The computation complexity is high. (3) The resonant tank parameter is discrete when searching for the optimal candidates of parameter scheme. In order to overcome these disadvantages, a design method of LLC resonant converter parameters with multi-objectiveWeb
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Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: ... and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-Web
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Powerful parameter-based optimization software will provide you with advanced techniques such as optimization, Design for Six Sigma (DFSS), approximations, and Design of Experiments (DOE), which ...Web
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Parameter optimization. After experiments are established and carried out, parameter optimization minimizes residuals between experiment and model. We define a least-squares minimization problem as in Eq. (49) where θ l and θ u are the lower and upper constraint vectors equivalent to ranges given in Table 6. The objective function is the sum ...Web
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Identify which parameters have the greatest impact on your model's behavior using the Sensitivity Analyzer app. Select better initial conditions for parameter estimation and …Web
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In parameter optimization, instead of searching for an optimum continuous function, the optimum values of design variables for a specific problem are obtained. Mathematical programming, optimality criteria (OC), and metaheuristic methods are some subsets of parameter optimization techniques. Figure 2.1 shows a classification of numerical ...Web
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The proposed robust parameter design and optimization approach utilizes the Taguchi method to find critical variables to be used in the optimization, and it utilizes robust optimization to immunize the obtained solution against uncertainty. To demonstrate our approach, we focus on design optimization of an injection molding product, a ...Web
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2. Parameter optimization, in some respects a more generalized version of geometry optimization, is a procedure for finding values for any parameter(s) in a design—not just geometric parameters—that are optimal in some defined respect, such as minimization of a specified objective function over a defined data set. 3.Web
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The system-level optimization solver only deals with design variables, where x represents a common design variable (that is, a variable required by all three subsystems; d is an input parameter), and xi (i = 1, 2, 3) represents a local design of subsystem f Variables (that is, only the input parameters required by subsystem f); f …Web
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parameters and material characteristics. This angle is typically about 45º from the normal to the powder bed. At OHA greater than this, the powder, which is melted several layers at a time to cool ... with the continuation line OVERHANG on the DTPL card for topology optimization design variables as shown in Figure 2, similar to other ...Web
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least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as …Web
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Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quanti cation with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods.Web
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Factorial Design (FD) Factorial experiment is an experiment whose design consist of two or more factor each with different possible values or "levels". FD technique introduced by "Fisher" in 1926. Factorial design applied in optimization techniques. Factors : Factors can be "Quantitative" (numerical number) or they are qualitative.Web
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The toolbox lets you perform design optimization tasks, including parameter estimation, component selection, and parameter tuning. It enables you to find optimal solutions in applications such as portfolio …Web
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