Showing papers on "Optimal design published in 2013"

A Modified High-Efficiency LLC Converter With Two Transformers for Wide Input-Voltage Range Applications

Abstract: This paper proposed a modified LLC converter with two transformers in series, which has four operation configurations, covering the range of four times the minimum input voltage. To optimize the proposed LLC converter in an attempt to achieve good efficiency, a numerical method is developed based on the LLC converter's steady-state equations. In order to minimize the magnetizing current and thus minimize the conduction and core losses, an optimal objective is proposed to find the maximum magnetizing inductance. An optimization procedure and a design example are given. A 250-W 210-V output prototype with input voltage ranging from 25 to 100 V is built to verify the developed numerical model and optimal design method. The dc gain obtained from experimental data agrees pretty well with that from the developed numerical model. Two conventional LLC converters are designed using fundamental harmonic approximation and the proposed optimal design, respectively, to make comparison with the proposed LLC converter and validate the proposed optimal design. Experimental results show that the proposed converter with proposed optimal design can achieve the peak efficiency up to 98%, while maintaining a very wide input voltage range.

Optimal Design for Nonlinear Response Models

Abstract: Regression Models and Their Analysis Linear Model, Single Response More about Information Matrix Generalized Versions of Linear Regression Model Nonlinear Models Maximum Likelihood and Fisher Information Matrix Generalized Regression and Elemental Fisher Information Matrices Nonlinear Regression with Normally Distributed Observations Convex Design Theory From Optimal Estimators to Optimal Designs Optimality Criteria Properties of Optimality Criteria Continuous Optimal Designs Sensitivity Function and Equivalence Theorems Equivalence Theorem, Examples Optimal Designs with Prior Information Regularization Optimality Criterion Depends on Estimated Parameters or Unknown Constants Response Function Contains Uncontrolled and Unknown Independent Variables Response Models with Random Parameters Algorithms and Numerical Techniques First-Order Algorithm: D-Criterion First-Order Algorithm: General Case Finite Sample Size Other Algorithms Optimal Design under Constraints Single Constraint Multiple Constraints Constraints for Auxiliary Criteria Directly Constrained Design Measures Nonlinear Response Models Bridging Linear and Nonlinear Cases Mitigating Dependence on Unknown Parameters Box and Hunter Adaptive Design Generalized Nonlinear Regression: Use of Elemental Information Matrices Model Discrimination Locally Optimal Designs in Dose Finding Binary Models Normal Regression Models Dose Finding for Efficacy-Toxicity Response Bivariate Probit Model for Correlated Binary Responses Examples of Optimal Designs in PK/PD Studies Introduction PK Models with Serial Sampling: Estimation of Model Parameters Estimation of PK Metrics Pharmacokinetic Models Described by Stochastic Differential Equations Software for Constructing Optimal Population PK/PD Designs Adaptive Model-Based Designs Adaptive Design for Emax model Adaptive Designs for Bivariate Cox Model Adaptive Designs for Bivariate Probit Model Other Applications of Optimal Designs Methods of Selecting Informative Variables Best Intention Designs in DoseFinding Studies Useful Matrix Formulae Symbols and Notation Definitions Matrix Derivatives Partitioned Matrices Kronecker Products Equalities Inequalities Bibliography Index

Design Optimization of a Surface-Mounted Permanent-Magnet Motor With Concentrated Windings for Electric Vehicle Applications

Abstract: This paper describes design techniques for electric vehicle (EV) traction machines to achieve high efficiency against a defined driving cycle such as the New European Drive Cycle (NEDC) while satisfying the required torque-speed operating range. A fractional-slot concentrated-winding (FSCW) surface-mounted permanent-magnet (SPM) machine has been identified as a suitable candidate for EV applications due to its high power/torque density, high efficiency, and good flux-weakening capability compared with other competing machine topologies. Based on the vehicle characteristics and the reference driving cycle, the motor specifications are established, and the design constraints for the SPM machine to satisfy the peak torque and flux-weakening capabilities are derived. Furthermore, the influence of the key parameters, such as slot-pole number combination, machine inductance, axial length, and number of turns, on the machine copper and iron losses over the NEDC is evaluated. Optimizations were carried for these parameters to minimize the total energy losses over the driving cycle. It has been shown that conventional design methodologies that aim to maximize efficiency in the region close to the rated operating condition may lead to less optimal designs and higher energy losses over the NEDC. A prototype motor for a front- and rear-wheel-driven EV has been designed, manufactured, and tested. The experimental results validate the proposed design methodology.

Banki-Michell Optimal Design by Computational Fluid Dynamics Testing and Hydrodynamic Analysis

Abstract: In hydropower, the exploitation of small power sources requires the use of small turbines that combine efficiency and economy. Banki-Michell turbines represent a possible choice for their simplicity and for their good efficiency under variable load conditions. Several experimental and numerical tests have already been designed for examining the best geometry and optimal design of cross-flow type machines, but a theoretical framework for a sequential design of the turbine parameters, taking full advantage of recently expanded computational capabilities, is still missing. To this aim, after a review of the available criteria for Banki-Michell parameter design, a novel two-step procedure is described. In the first step, the initial and final blade angles, the outer impeller diameter and the shape of the nozzle are selected using a simple hydrodynamic analysis, based on a very strong simplification of reality. In the second step, the inner diameter, as well as the number of blades and their shape, are selected by testing single options using computational fluid dynamics (CFD) simulations, starting from the suggested literature values. Good efficiency is attained not only for the design discharge, but also for a large range of variability around the design value.

Optimal Design, Fabrication, and Control of an $XY$ Micropositioning Stage Driven by Electromagnetic Actuators

Abstract: This paper presents the optimal design, fabrication, and control of a novel compliant flexure-based totally decoupled XY micropositioning stage driven by electromagnetic actuators. The stage is constructed with a simple structure by employing double four-bar parallelogram flexures and four noncontact types of electromagnetic actuators to realize the kinematic decoupling and force decoupling, respectively. The kinematics and dynamics modeling of the stage are conducted by resorting to compliance and stiffness analysis based on matrix method, and the parameters are obtained by multiobjective genetic algorithm (GA) optimization method. The analytical models for electromagnetic forces are also established, and both mechanical structure and electromagnetic models are validated by finite-element analysis via ANSYS software. It is found that the system is with hysteresis and nonlinear characteristics when a preliminary open-loop test is conducted; thereafter, a simple PID controller is applied. Therefore, an inverse Preisach model-based feedforward sliding-mode controller is exploited to control the micromanipulator system. Experiments show that the moving range can achieve 1 mm t 1 mm and the resolution can reach ±0.4 μm. Moreover, the designed micromanipulator can bear a heavy load because of its optimal mechanical structure.

On Optimal Designs for Nonlinear Models: A General and Efficient Algorithm

Abstract: Finding optimal designs for nonlinear models is challenging in general. Although some recent results allow us to focus on a simple subclass of designs for most problems, deriving a specific optimal design still mainly depends on numerical approaches. There is need for a general and efficient algorithm that is more broadly applicable than the current state-of-the-art methods. We present a new algorithm that can be used to find optimal designs with respect to a broad class of optimality criteria, when the model parameters or functions thereof are of interest, and for both locally optimal and multistage design strategies. We prove convergence to the optimal design, and show in various examples that the new algorithm outperforms the current state-of-the-art algorithms.

Compressive strength of hollow microlattices: Experimental characterization, modeling, and optimal design

Abstract: Recent advances in multiscale manufacturing enable fabrication of hollow-truss based lattices with dimensional control spanning seven orders of magnitude in length scale (from ∼50 nm to ∼10 cm), thus enabling the exploitation of nano-scale strengthening mechanisms in a macroscale cellular material. This article develops mechanical models for the compressive strength of hollow microlattices and validates them with a selection of experimental measurements on nickel microlattices over a wide relative density range (0.01–10%). The limitations of beam-theory-based analytical approaches for ultralight designs are emphasized, and suitable numerical (finite elements) models are presented. Subsequently, a novel computational platform is utilized to efficiently scan the entire design space and produce maps for optimally strong designs. The results indicate that a strong compressive response can be obtained by stubby lattice designs at relatively high densities (∼10%) or by selectively thickening the nodes at ultra-low densities.

Optimization of Fractional-Order RLC Filters

Abstract: This paper introduces some generalized fundamentals for fractional-order RL β C α circuits as well as a gradient-based optimization technique in the frequency domain. One of the main advantages of the fractional-order design is that it increases the flexibility and degrees of freedom by means of the fractional parameters, which provide new fundamentals and can be used for better interpretation or best fit matching with experimental results. An analysis of the real and imaginary components, the magnitude and phase responses, and the sensitivity must be performed to obtain an optimal design. Also new fundamentals, which do not exist in conventional RLC circuits, are introduced. Using the gradient-based optimization technique with the extra degrees of freedom, several inverse problems in filter design are introduced. The concepts introduced in this paper have been verified by analytical, numerical, and PSpice simulations with different examples, showing a perfect matching.

Simple cell mapping method for multi-objective optimal feedback control design

Abstract: This paper introduces the simple cell mapping (SCM) method for the multi-objective optimal time domain design of feedback controls for linear systems with or without time delay. The SCM method is originally developed for the global analysis of nonlinear dynamical systems, and is extended to the multi-objective optimal design problem of feedback controls in this paper. We consider two feedback control design problems to demonstrate the method: a linear quadratic regulator based approach with the weighting matrices as design parameters, and a direct optimization with feedback control gains as design parameters. The Pareto set and Pareto front consisting of the peak time, overshoot and integrated absolute tracking error are obtained for two linear control systems, one of which has a control time delay. It is interesting to note that for the second order linear system, we have found a structure of the Pareto front, which has been very difficult to obtain using stochastic search algorithms. This study suggests that the SCM method is an effective method that can provide global and fine-structured solutions of MOPs for complex dynamical systems.

A-optimal design of experiments for infinite-dimensional Bayesian linear inverse problems with regularized $\ell_0$-sparsification

Abstract: We present an efficient method for computing A-optimal experimental designs for infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs). Specifically, we address the problem of optimizing the location of sensors (at which observational data are collected) to minimize the uncertainty in the parameters estimated by solving the inverse problem, where the uncertainty is expressed by the trace of the posterior covariance. Computing optimal experimental designs (OEDs) is particularly challenging for inverse problems governed by computationally expensive PDE models with infinite-dimensional (or, after discretization, high-dimensional) parameters. To alleviate the computational cost, we exploit the problem structure and build a low-rank approximation of the parameter-to-observable map, preconditioned with the square root of the prior covariance operator. This relieves our method from expensive PDE solves when evaluating the optimal experimental design objective function and its derivatives. Moreover, we employ a randomized trace estimator for efficient evaluation of the OED objective function. We control the sparsity of the sensor configuration by employing a sequence of penalty functions that successively approximate the $\ell_0$-"norm"; this results in binary designs that characterize optimal sensor locations. We present numerical results for inference of the initial condition from spatio-temporal observations in a time-dependent advection-diffusion problem in two and three space dimensions. We find that an optimal design can be computed at a cost, measured in number of forward PDE solves, that is independent of the parameter and sensor dimensions. We demonstrate numerically that $\ell_0$-sparsified experimental designs obtained via a continuation method outperform $\ell_1$-sparsified designs.

Multi-objective tradeoffs in the design optimization of a brushless permanent magnet machine with fractional-slot concentrated windings

Abstract: In this paper, a robust parametric model of a brushless (BL) permanent magnet (PM) machine with fractional-slot concentrated windings (FSCW), which was developed for automated design optimization is presented. A computationally efficient-finite element analysis (CE-FEA) method was employed to estimate the dq-axes inductances, the induced voltage and torque ripple waveforms, and losses of the machine. A method for minimum effort calculation of the torque angle corresponding to the maximum torque per ampere (MTPA) load condition was developed. A differential evolution (DE) algorithm was implemented for the global design optimization with two concurrent objectives of minimum losses and minimum material cost. An engineering decision process based on the Pareto-optimal front for 3,500 candidate designs is presented together with discussions on the tradeoffs between cost and performance. One optimal design was finally selected, prototyped and tested.

Regularization of shape optimization problems using FE-based parametrization

Abstract: This paper introduces a general fully stabilized mesh based shape optimization strategy, which allows for shape optimization of mechanical problems on FE-based parametrization. The well-known mesh dependent results are avoided by application of filter methods and mesh regularization strategies. Filter methods are successfully applied to SIMP (Solid Isotropic Material with Penalization) based topology optimization for many years. The filter method presented here uses a specific formulation that is based on convolution integrals. It is shown that the filter methods ensure mesh independency of the optimal designs. Furthermore they provide an easy and robust tool to explore the whole design space with respect to optimal designs with similar mechanical properties. A successful application of optimization strategies with FE-based parametrization requires the combination of filter methods with mesh regularization strategies. The latter ones ensure reliable results of the finite element solutions that are crucial for the sensitivity analysis. This presentation introduces a new mesh regularization strategy that is based on the Updated Reference Strategy (URS). It is shown that the methods formulated on this mechanical basis result in fast and robust mesh regularization methods. The resulting grids show a minimum mesh distortion even for large movements of the mesh boundary. The performance of the proposed regularization methods is demonstrated by several illustrative examples.

Simultaneous process synthesis and control design under uncertainty: A worst-case performance approach

Abstract: A new methodology that includes process synthesis and control structure decisions for the optimal process and control design of dynamic systems under uncertainty is presented The method integrates dynamic flexibility and dynamic feasibility in a single optimization formulation, thus, reducing the costs to assess the optimal design A robust stability test is also included in the proposed method to ensure that the optimal design is stable in the presence of magnitude-bounded perturbations Since disturbances are treated as stochastic time-discrete unmeasured inputs, the optimal process synthesis and control design specified by this method remains feasible and stable in the presence of the most critical realizations in the disturbances The proposed methodology has been applied to simultaneously design and control a system of CSTRs and a ternary distillation column A study on the computational costs associated with this method is presented and compared to that required by a dynamic optimization-based scheme © 2013 American Institute of Chemical Engineers AIChE J, 59: 2497–2514, 2013

Optimal free-defect synthesis of four-bar linkage with joint clearance using PSO algorithm

Abstract: This paper presents the design of planar four-bar linkages free of order, branch and circuit defects, for the purpose of path generation, having clearances at one, two, three or all of its joints. Joint clearance is treated as a massless virtual link and its direction is known by the direction of the joint force. A Particle Swarm Optimization based algorithm is given here to solve this highly nonlinear optimization problem with some constraints, namely; the Grashof’s and free of the foregoing defects conditions. An example is included in which the optimal problem is solved for different cases; namely planar four-bar linkage having clearances at one, two, three, all of the joints and without clearance. For all the designs, the generated paths, the errors and the directions of the virtual links are plotted and are compared. Finally, we compare the optimal designs with reality.

Randomised Response-Adaptive Designs in Clinical Trials

Abstract: Introduction: Stories and Data Scope and Limits Two-Treatment Trials with a Binary Response Equal Randomisation Adaptive Allocation Urn Model Some Motivating Clinical Trials Adaptive Design: Controversies and Progress Why Adaptive? How Adaptive? Criticism What Next? Randomised Balanced Sequential Treatment Allocation Introduction Balance with Two Treatments Designs with Three or More Treatments Designs with Covariates The Distribution of Loss and of Bias Heteroscedastic Models More about Biased-Coin Designs Further Reading Response-Adaptive Designs for Binary Responses Introduction Urn Designs Play-the-Winner Rule Randomised Play-the-Winner Rule Generalised Polya Urn Success Driven Design (SDD) Failure-Driven Design (FDD) Birth and Death Urn (BDU) Birth and Death Urn with Immigration Drop-the-Loser Rule Odds Ratio-Based Adaptive Designs Delayed Response in the RPW Rule Prognostic Factors in Urn Designs Targeting an Allocation Proportion Adaptive Designs for Categorical Responses Comparisons and Recommendations Response-Adaptive Designs for Continuous Responses Motivation Some Trials with Continuous Responses Doubly Adaptive Biased-Coin Designs (DBCD) Nonparametric Designs Adaptive Designs for Survival Data Link Function-Based Adaptive Design (BB) Multi-Treatment Multivariate Design DL Rule for Continuous Responses (CDL) Response-Adaptive Designs for Longitudinal Responses Repeated Responses Binary Longitudinal Responses (SLPW) Design and Analysis for the PEMF Data Longitudinal Categorical Responses Longitudinal Multivariate Ordinal Responses Models with Covariates Continuous Longitudinal Responses Random Number of Responses Numerical Illustrations Optimum Biased-Coin Designs with Covariates Modelling and Design Biased-Coin DA-Optimum Designs Numerical Comparisons for Two Treatments Designs for Three Treatments Distribution of Loss Skewed Allocations Skewed Allocation - Numerical Heteroscedastic Normal Models Allocation Rules for Heteroscedastic Models Generalized Linear Models Binary Data Allocation Rules for Binomial Models Gamma Data Loss, Power, Variability Further Reading: Skewed Designs Optimum Response-Adaptive Designs with Covariates Introduction Link-Function-Based Adaptive Design Adaptive Designs Maximising Utility Power Comparisons for Four Rules Redesigning a Trial: Fluoxetine Hydrochloride Extensions Further Reading Optimal Response-Adaptive Designs with Constraints Optimal Designs Subject to Constraints Design of Jennison and Turnbull RSIHR Design Maximising Power: Neyman Allocation Other Designs BM Design ZR Design A General Framework: BBZ Design Two Normal Populations with Unknown Variances Two-Sample Nonparametric Design BM Design for More Than Two Treatments Optimal Designs with More than One Constraint Designs for Survival Times Covariates Implementation Adaptive Constraints Back to Chapter 7 Adaptive Design: Further Important Issues Bayesian Adaptive Designs Two-Stage Adaptive Design Group Sequential Adaptive Design Optimal Design for Binary Longitudinal Responses Inverse Sampling Robustness in Adaptive Designs Missing Data in Response-Adaptive Designs Asymptotic Results for CARA Designs How to Bridge Theory and Practice Appendix: Optimum Design Bibliography Index

Optimal Design of Composite Plates with Discrete Variable Stiffness

Abstract: Abstract A new optimization technique of composite plates with discrete varying stiffness is proposed. It consists of three main steps. Analysis of optimal continuous varying bending and shear stiffness of plate is done in the first step by minimizing structural compliance and stress field differences. Size optimization of discrete domains is done in the second step by solving minimization problem with a Genetic Algorithm. Dimension optimization of plates internal structure is done by previously trained feed-forward artificial neural network in the third step. A simply supported rectangular plate with uniformly distributed load and multispan plate with concentrated loads are optimized by the using of proposed method. The deflection of optimized plate could be reduced up to 50%, and average increase of strength to mass ratio is about 20%.

Multiple zone multifocal phase designs.

Abstract: New multifocal phase designs aiming at expanding depth of focus in the presbyopic eye are presented. The designs consist of multiple radial or angular zones of different powers or of combined low- and high-order aberrations. Multifocal performance was evaluated in terms of the dioptric range for which the optical quality is above an appropriate threshold, as well as in terms of the area under the through-focus optical quality curves. For varying optical power designs optimal through-focus performance was found for a maximum of three to four zones. Furthermore adding more zones decreased the optical performance of the solution. Angular zone designs provided better multifocal performance (1.95 times on average) than radial zone designs with identical number of zones and the same power range. The optimal design (angular design with three zones) surpassed by 33% the multifocal performance of a bifocal angular zone design and by 32% a standard multifocal phase plate with induced spherical aberration only. By using combinations of low- and high-order aberrations the through-focus range can be extended further by another 0.5 D beyond that of the best design of varying optical power. These designs can be implemented in adaptive optics systems for testing their visual performance in subjects and converted into multifocal contact lenses, intraocular lenses, or presbyopic corneal laser ablation profiles.

Sizing and Parametric Analysis of a Stand-Alone Photovoltaic Power Plant

Abstract: A hybrid approach, combining analytical sizing equations with long-term performance, for an optimal design of a stand-alone photovoltaic (PV)-battery system is proposed in this paper. This study explicitly examines the interaction of hourly variation of the load on the energy supplied from a stand-alone PV-generating source keeping the system 100% reliable, and a modification is proposed in the sizing algorithm to include the effect of loading profile. The correctness of the methodology is validated using an experiment exercise and by comparing it with other existing models. An adaptive feedback iteration technique, for fast convergence, is presented to obtain the best optimum combination for PV-battery configuration. A parametric analysis is carried out to examine the effect of load duration and charge controller low-voltage disconnect (LVD) on system sizing. A significant reduction of system requirements, i.e., up to 14%, is observed when the load is operating between 6 a.m. and 12 p.m. For a given condition, the optimum LVD of the charge controller is reported to be 11.4 V. The results of this study will serve any PV design engineer to decide the optimum system requirements and charge controller settings without compromising system reliability.

Optimal design formulas for viscous tuned mass dampers in wind‐excited structures

Abstract: SUMMARY Optimal design for tuned mass dampers (TMDs) with linear or nonlinear viscous damping is formulated in order for design practitioners to directly compute the optimal parameters of a TMD in a damped structure subjected to wind excitations. The optimal TMD tuning frequency ratio and damping coefficient for a viscous TMD system installed in a damped structure under 10 white noise excitations are determined by using the time-domain optimization procedure, which minimizes the structural response. By applying a sequence of curve-fitting schemes to the obtained optimal values, design formulas for optimal TMDs are then derived. These are expressed as a function of the mass ratio and damping power-law exponent of the TMD as well as the damping ratio of the structure. The feasibility of the proposed optimal design formulas is verified in terms of formulary accuracy and of comparisons with existing formulas from previous research works. In addition, one numerical example of the Taipei 101 building with a nonlinear TMD, which is redesigned according to the proposed optimal formulas, is illustrated in effort to describe the use of the formulas in the TMD design procedure and to investigate the effectiveness of the optimal TMD. The results indicate that the proposed optimal design formulas provide a convenient and effective approach for designing a viscous TMD installed in a wind-excited damped structure. Copyright © 2011 John Wiley & Sons, Ltd.

Optimal Design of Large Permanent Magnet Synchronous Generators

Abstract: High power machine has become a large market for wind power and ship propulsion electric, among other applications. Since the size of these machines is much larger than conventional industrial ones, optimum design must be considered in order to reduce the material cost and increase profitability. In this paper, a simple analytic optimization algorithm is used to maximize the apparent airgap power transferred under tangential stress constraint. In this approach, close related expressions between the main design variables, operational restrictions, and external dimensions are derived to build the mathematical structure of the optimization process. To improve the torque capacity estimation of the designed machine, a correction procedure, based on the previous result, is used to remove the idealizations considered for the initial design. Close agreement with the finite element analysis results are found with this approach, which is based on analytical method.