Showing papers on "Optimal design published in 2018"

Optimal Design of Experiments in the Presence of Interference

Abstract: We formalize the optimal design of experiments when there is interference between units, that is, an individual’s outcome depends on the outcomes of others in her group. We focus on randomized saturation designs, two-stage experiments that first randomize treatment saturation of a group, then individual treatment assignment. We map the potential outcomes framework with partial interference to a regression model with clustered errors, calculate standard errors of randomized saturation designs, and derive analytical insights about the optimal design. We show that the power to detect average treatment effects declines precisely with the ability to identify novel treatment and spillover effects.

Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning.

Abstract: Making kirigami-inspired cuts into a sheet has been shown to be an effective way of designing stretchable materials with metamorphic properties where the 2D shape can transform into complex 3D shapes. However, finding the optimal solutions is not straightforward as the number of possible cutting patterns grows exponentially with system size. Here, we report on how machine learning (ML) can be used to approximate the target properties, such as yield stress and yield strain, as a function of cutting pattern. Our approach enables the rapid discovery of kirigami designs that yield extreme stretchability as verified by molecular dynamics (MD) simulations. We find that convolutional neural networks, commonly used for classification in vision tasks, can be applied for regression to achieve an accuracy close to the precision of the MD simulations. This approach can then be used to search for optimal designs that maximize elastic stretchability with only 1000 training samples in a large design space of ∼4×10^{6} candidate designs. This example demonstrates the power and potential of ML in finding optimal kirigami designs at a fraction of iterations that would be required of a purely MD or experiment-based approach, where no prior knowledge of the governing physics is known or available.

Particle Swarm Optimization Algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

Abstract: In this study, we propose a modified particle swarm optimization (PSO) algorithm, which is an improved version of the conventional PSO algorithm. To improve the performance of the conventional PSO, a novel method is applied to intelligently control the number of particles. The novel method compares the cost value of the global best (gbest) in the current iteration to that of the gbest in the previous iteration. If there is a difference between the two cost values, the proposed algorithm operates in the exploration stage, maintaining the number of particles. However, when the difference in the cost values is smaller than the tolerance values assigned by the user, the proposed algorithm operates in the exploitation stage, reducing the number of particles. In addition, the algorithm eliminates the particle that is nearest to the best particle to ensure its randomness in terms of the Euclidean distance. The proposed algorithm is validated using five numerical test functions, whose number of function calls is reduced to some extent in comparison to conventional PSO. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the total harmonic distortion (THD) of the back electromotive force (back EMF). Considering the performance constraint, an optimal design is attained, which reduces back EMF THD and satisfies the back EMF amplitude. Finally, we build and test an experimental model. To validate the performance of the optimal design and optimization algorithm, a no-load test is conducted. Based on the experimental result, the effectiveness of the proposed algorithm on optimal design of an electric machine is validated.

Demand-based optimal design of oscillator with parallel-layout viscous inerter damper

Abstract: Summary In this study, a demand-based optimal design method is proposed for an oscillator (a single-degree-of-freedom system) with a parallel-layout viscous inerter damper (PVID) The proposed design method overcomes some deficiencies of the existing method, which is based on the fixed-point theory and is mainly suitable for tuned mass dampers Moreover, for the fixed-point method, the inherent damping of the primary structure is neglected, and the global optimal solution cannot be obtained The proposed method can obtain a more rational and practical design for the actual design by minimizing both the response and the cost The design problem of a PVID-equipped oscillator is transformed into a multi-objective optimization problem that can be solved using the e-constraint approach, which is consistent with the concept of demand-based design The dynamic response of the oscillator and the force of the PVID (ie, the cost factor) are evaluated according to theories of random vibration to reduce the number of calculations required A computer program is developed to perform demand-based parametric design of a PVID-equipped oscillator Several design cases were examined under different excitation conditions using the computer program, and dynamic time history analyses were then conducted to verify the designs obtained The results show that the proposed optimal design method identifies satisfactory designs more effectively than the existing method by obtaining PVID design parameter values that better meet the performance demand and simultaneously minimize the cost

Optimization of tensile strength in TIG welding using the Taguchi method and analysis of variance (ANOVA)

Abstract: The main criteria discussed in this paper concern the welding optimization parameters and tensile strength of duplex stainless steel 2205 by tungsten inert gas welding based on Taguchi method and analysis of variance Taguchi method of orthogonal L9 design experiment is carried out using orthogonal array for defining the problem occur on welding process and to reduce the error occurred in the neural network for the prediction of output The neural network is a mathematical prediction model for the optimization process using back propagation algorithm Analysis of variance (ANOVA) is a decision tool for detecting the variation of process parameters, it is a statistical technique for find out the optimal level of factors for the verification of the optimal design parameters through confirmation experiments The purpose of this paper to increase the tensile strength, hardness and depth of weld by varying the parameters such as current, time, speed, variation of oxide fluxes, electrode diameter and gas flow rate The Mat lab software is used for analyzing results and it shows that neural network coupled with Taguchi method and Anova is an effective method for optimizing the weld quality of material

Optimal a priori balance in the design of controlled experiments

Abstract: Summary We develop a unified theory of designs for controlled experiments that balance baseline covariates a priori (before treatment and before randomization) using the framework of minimax variance and a new method called kernel allocation. We show that any notion of a priori balance must go hand in hand with a notion of structure, since with no structure on the dependence of outcomes on baseline covariates complete randomization (no special covariate balance) is always minimax optimal. Restricting the structure of dependence, either parametrically or non-parametrically, gives rise to certain covariate imbalance metrics and optimal designs. This recovers many popular imbalance metrics and designs previously developed ad hoc, including randomized block designs, pairwise-matched allocation and rerandomization. We develop a new design method called kernel allocation based on the optimal design when structure is expressed by using kernels, which can be parametric or non-parametric. Relying on modern optimization methods, kernel allocation, which ensures nearly perfect covariate balance without biasing estimates under model misspecification, offers sizable advantages in precision and power as demonstrated in a range of real and synthetic examples. We provide strong theoretical guarantees on variance, consistency and rates of convergence and develop special algorithms for design and hypothesis testing.

Efficient targeted energy transfer of bistable nonlinear energy sink: application to optimal design

Abstract: This paper is dedicated to the optimal design of a bistable nonlinear energy sink (NES) for the vibration control of a periodically excited linear oscillator. This system with negative linear and cubic nonlinear coupling is analytically studied with the method of multiple scales. As a result, a slow invariant manifold is obtained and is applied to predict four typical response regimes at different energy levels. Moreover, asymptotic analysis and Melnikov analysis are, respectively, used to obtain the thresholds of these typical responses. Through their efficiency comparison, it is observed that the bistable NES can be efficient and robust in a broad range of excitation amplitude. With the Hilbert transform and wavelet transform, targeted energy transfer with transient or permanent 1:1 resonance is found to be responsible for the effectiveness of such responses as strongly modulated response and 1:1 resonance. Finally, an optimal design criterion and a corresponding parameter configuration are proposed to guide the application of this type of NES.

Reliability-based optimal design of nonlinear viscous dampers for the seismic protection of structural systems

Abstract: Viscous dampers are widely employed for enhancing the seismic performance of structural systems, and their design is often carried out using simplified approaches to account for the uncertainty in the seismic input. This paper introduces a novel and rigorous approach that allows to explicitly consider the variability of the intensity and characteristics of the seismic input in designing the optimal viscous constant and velocity exponent of the dampers based on performance-based criteria. The optimal solution permits controlling the probability of structural failure, while minimizing the damper cost, related to the sum of the damper forces. The solution to the optimization problem is efficiently sought via the constrained optimization by linear approximation (COBYLA) method, while Subset simulation together with auxiliary response method are employed for the performance assessment at each iteration of the optimization process. A 3-storey steel moment-resisting building frame is considered to illustrate the application of the proposed design methodology and to evaluate and compare the performances that can be achieved with different damper nonlinearity levels. Comparisons are also made with the results obtained by applying simplifying approaches, often employed in design practice, as those aiming to minimize the sum of the viscous damping constant and/or considering a single hazard level for the performance assessment.

A Fuzzy Approach for Optimal Robust Control Design of an Automotive Electronic Throttle System

Abstract: In this paper, we propose a fuzzy approach for optimal robust control design of an automotive electronic throttle (ET) system. Compared with the conventional ET control systems, we establish the fuzzy dynamical model of the ET system with parameter uncertainties, nonlinearities, and external disturbances, which may be nonlinear, (possibly fast) time varying. These uncertainties are assumed to be bounded, and the knowledge of the bound only lies within a prescribed fuzzy set. A robust control that is deterministic and is not the usual if–then rules-based control is presented to guarantee the controlled system to achieve the deterministic performance: uniform boundedness and uniform ultimate boundedness. Furthermore, a fuzzy-based system performance index including average fuzzy system performance and control cost is proposed based on the fuzzy information. The optimal design problem associated with the control can then be solved by minimizing the fuzzy-based performance index. With this optimal robust control, the performance of the fuzzy ET system is both deterministically guaranteed and fuzzily optimized.

Simultaneous Optimization of Geometry and Firing Angles for In-Wheel Switched Reluctance Motor Drive

Abstract: Interests in using rare-earth free motors such as switched reluctance motors (SRMs) for electric and hybrid electric vehicles continue to gain popularity, owing to their low cost and robustness. Optimal design of an SRM, to meet specific characteristics for an application, should involve simultaneous optimization of the motor geometry and control in order to achieve the highest performance with the lowest cost. This paper presents a constrained multiobjective optimization framework for design and control of an SRM based on a nondominated sorting genetic algorithm II. The proposed methodology optimizes SRM operation for high volume traction applications by considering multiple criteria including efficiency, average torque, and torque ripple. Several constraints are defined by the considered application, such as the motor stack length, outer diameter, minimum operating power, minimum desired efficiency, rated speed, rated current, and supply voltage. The outcome of this optimization includes an optimal geometry, outlining variables such as air gap length, rotor inner diameter, stator pole arc angle, rotor pole arc angle, rotor back iron, stator pole height, and stator inner diameter as well as optimal turn-on and turn-off firing angles. Then the machine is manufactured according to the obtained optimal specifications. Comprehensive finite-element analysis and experimental results are provided to validate the theoretical findings.

Optimal Design for Realizing a Grounded Fractional Order Inductor Using GIC

Abstract: This paper addresses the issue of the optimal design of a grounded fractional order inductor using a generalized impedance converter. The nonidealities of the op amps have been taken into account while formulating the approximate frequency characteristics of the fractional order inductor for both Type-I and Type-II realizations. Based on these formulations a set of design guidelines is proposed to achieve minimum phase and magnitude errors for the realized fractional inductor. The minimization conditions have been validated by several simulation and experimental results.

Signal Multiobjective Optimization for Urban Traffic Network

Abstract: This paper proposes a multiobjective optimization method for signal control design at intersections in urban traffic network. The cell transmission model is employed for macroscopic simulation of the traffic. Additional rules are introduced to model different route choices from origins to destinations. Vehicle turning, merging, and diverging behaviors at intersections are considered. A multiobjective optimization problem (MOP) is formulated considering four measures in network traffic performance, i.e., maximizing system throughputs, minimizing traveling delays, enhancing traffic safety, and avoiding spillovers. The design parameters for an intersection include turning signal type, cycle time, signal offset, and green time in each phase. The resulting high-dimensional MOP is solved with the genetic algorithm (GA). An algorithm is proposed to assist the user to select and implement the optimal designs from the Pareto optimal solution set. A case study in a grid network of nine intersections is carried out to test the optimization algorithm. It is observed that the proposed method is able to achieve the optimal network performance with different traffic demands. The convergence and coefficient selection of GA are discussed. The guidelines for network signal design and operation from the current studies are presented.

Optimization of filament winding parameters for the design of a composite pipe

Abstract: In the oil&gas field, common steel pipelines experience well-known problems of corrosion and maintenance. The design with composite materials could avoid these problems and provide lightness to the overall structures. Standards and regulations guide the designer through the qualification steps, but they are constantly under review based on the increasing knowledge of the long term mechanical behaviour of these materials. The aim of this work is to provide the designer with an analytical tool for the optimal design of a composite plain pipe, i.e. minimizing the wall thickness. The paper presents considerations useful in the design stage for the selection of the optimal fiber, matrix, volume fraction V f , and winding angle θ. The study simulates tests with inner pressure and axial loads, in accordance with the main applicable standards. Based on the analytical estimations, we found a locus of optimal technological parameters with volume fraction 40% V f

Optimal design of tuned liquid column dampers for seismic response control of base-isolated structures

Abstract: In this paper, the use of a tuned liquid column damper (TLCD) as a cost-effective means to control the seismic response of a base-isolated structure is studied. A straightforward direct approach for the optimal design of such a device is proposed, considering a white noise model of the base excitation. On this base, a direct optimization procedure of the TLCD design parameters is performed and optimal design charts are presented as a ready-to-use practical design tool. Comparison with the optimal parameters obtained considering a classical iterative statistical linearization technique proves the reliability of the proposed approach. The performance of the base-isolated TLCD-controlled structure is examined and compared with that of the simple base-isolated one (without TLCD), using a set of 44 recorded ground motions as base excitation. Theoretical and numerical results show that the TLCD is rather effective in reducing the response of base-isolated structures under strong earthquakes. Therefore, considering its advantageous characteristics and its overall beneficial effects, TLCDs can be considered as practical and appealing means to control the seismic response of base-isolated structures.

Omnidirectional Aerial Vehicles with Unidirectional Thrusters: Theory, Optimal Design, and Control

Abstract: This paper presents a theoretical study on omni-directional aerial vehicles with body-frame fixed unidirectional thrusters. Omniplus multi-rotor designs are defined as the ones that allow to exert a total wrench in any direction using positive-only lift force and drag moment (i.e., positive rotational speed) for each rotor blade. Algebraic conditions for a design to be omniplus are derived, a simple necessary condition being the fact that at least seven propellers have to be used. An energy optimal design strategy is then defined as the one minimizing the maximum norm of the input set needed to span a certain wrench ellipsoid for the adopted input allocation strategy. Two corresponding major design criteria are then introduced: firstly, a minimum allocation-matrix condition number aims at an equal sharing of the effort needed to generate wrenches in any direction; secondly, imposing a balanced design guarantees an equal sharing of the extra effort needed to keep the input in the non-negative orthant. We propose a numerical algorithm to solve such optimal design problem and a control algorithm to control any omnidirectional platform. The work is concluded with informative simulation results in non-ideal conditions.

Sensitivity Analysis and Optimal Design of a Dual Mechanical Port Bidirectional Flux-Modulated Machine

Abstract: This paper presents an optimal design methodology of a dual-mechanical-port bidirectional flux-modulated machine for electric continuously variable transmission in hybrid electrical vehicles. The machine utilizes bidirectional flux modulation effect to combine two rotors and one stator together, aiming to realize electrical and mechanical power flexible split and combination. Due to the complexity of the machine structure, conventional optimization methods using analytical model are inapplicable. Therefore, an effective and practical method that combines the genetic algorithm and finite-element method (GA-FEM) is proposed to optimize the design of the machine in this paper. Since the computational cost increases exponentially with the increasing of number of design parameters, to reduce the computational cost in the optimization process, the design parameters are divided into two levels basing on a sensitivity analysis. And, then, the sensitive parameters are optimized using the GA-FEM coupled method. Finally, a prototype is fabricated to verify the effectiveness of the optimal design.