Implementation of Numerical Optimization Techniques in Constitutive Model Calibration

Abstract This paper presents an approach, based on numerical optimization techniques, to identify an ideal (5 star) crash pulse and generate a band of acceptable crash pulses surrounding that ideal pulse. This band can be used by engineers to quickly determine whether a design will satisfy government and corporate safety requirements, and whether the design will satisfy the requirements for a 5 star crash rating. A piecewise linear representation of the crash pulse with two plateaus is employed for its conceptual simplicity and because such a pulse has been shown to be sufficient for reproducing occupant injury behavior when used as input into MADYMO models. The piecewise linear crash pulse is parameterized with 7 design variables (5 for time domain and 2 for acceleration domain) in the optimization process. A series of sample runs are conducted to validate that pulses falling within the acceptable crash pulse band do in fact satisfy 5 star requirements.

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Evolutionary Algorithms for Constrained Parameter Optimization Problems

Evolutionary Computation ◽

10.1162/evco.1996.4.1.1 ◽

1996 ◽

Vol 4 (1) ◽

pp. 1-32 ◽

Cited By ~ 1027

Author(s):

Zbigniew Michalewicz ◽

Marc Schoenauer

Keyword(s):

Nonlinear Programming ◽

Evolutionary Algorithms ◽

Evolutionary Computation ◽

Numerical Optimization ◽

Optimization Problems ◽

Optimization Techniques ◽

Test Cases ◽

Nonlinear Constraints ◽

Nonlinear Programming Problem ◽

General Nonlinear Programming

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.

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Calibration of a Nonlinear DC Motor under Uncertainty Using Nonlinear Optimization Techniques

Periodica Polytechnica Electrical Engineering and Computer Science ◽

10.3311/ppee.16165 ◽

2021 ◽

Vol 65 (1) ◽

pp. 42-52

Author(s):

Hamed Keshmiri Neghab ◽

Hamid Keshmiri Neghab

Keyword(s):

Nonlinear Optimization ◽

Model Calibration ◽

Optimization Methods ◽

Dc Motor ◽

Optimization Techniques ◽

Unknown Parameters ◽

Labview Software ◽

Dc Motors ◽

Industrial Software ◽

Nonlinear Optimization Methods

The use of DC motors is increasingly high and it has more parameters which should be normalized. Now the calibration of each parameters is important for each motor, because it affects in its performance and accuracy. A lot of researches are investigated in this area. In this paper demonstrated how to estimate the parameters of a Nonlinear DC Motor using different Nonlinear Optimization techniques of fitting parameters to model, that called model calibration. First, three methods for calibration of a DC motor are defined, then unknown parameters of the mathematical model with the nonlinear optimization techniques for the fitting routines and model calibration process, are identified. In addition, three optimization techniques such as Levenberg-Marquardt, Constrained Nonlinear Optimization and Gauss-Newton, are compared. The goal of this paper is to estimate nonlinear parameters of a DC motor under uncertainty with nonlinear optimization methods by using LabVIEW software as an industrial software and compare the nonlinear optimization methods based on position, velocity and current. Finally, results are illustrated and comparison between these methods based on the results are made.

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Combining Analytical Models and Mesh Morphing Based Optimization Techniques for the Design of Flying Multihulls Appendages

Journal of Sailing Technology ◽

10.5957/jst/2021.6.1.151 ◽

2021 ◽

Vol 6 (01) ◽

pp. 151-172

Author(s):

Ubaldo Cella ◽

Corrado Groth ◽

Stefano Porziani ◽

Alberto Clarich ◽

Francesco Franchini ◽

...

Keyword(s):

Numerical Optimization ◽

Fluid Dynamic ◽

Optimization Procedure ◽

Optimization Techniques ◽

Analytical Models ◽

High Fidelity ◽

Mesh Morphing ◽

Wide Range ◽

Global Equilibrium ◽

Range Of Values

Abstract The fluid dynamic design of hydrofoils involves most of the typical difficulties of aeronautical wings design with additional complexities related to the design of a device operating in a multiphase environment. For this reason, “high fidelity” analysis solvers should be, in general, adopted also in the preliminary design phase. In the case of modern fast foiling sailing yachts, the appendages accomplish both the task of lifting up the boat and to make possible upwind sailing by contributing balance to the sail side force and the heeling moment. Furthermore, their operative design conditions derive from the global equilibrium of forces and moments acting on the system which might vary in a very wide range of values. The result is a design problem defined by a large number of variables operating in a wide design space. In this scenario, the device performing in all conditions has to be identified as a trade-off among several conflicting requirements. One of the most efficient approaches to such a design challenge is to combine multi-objective optimization strategies with experienced aerodynamic design. This paper presents a numerical optimization procedure suitable for foiling multihulls. As a proof of concept, it reports, as an application, the foils design of an A-Class catamaran. The key point of the method is the combination of opportunely developed analytical models of the hull forces with high fidelity multiphase analyses in both upwind and downwind sailing conditions. The analytical formulations were tuned against a database of multiphase analyses of a reference demihull at several attitudes and displacements. An aspect that significantly contributes to both efficiency and robustness of the method is the approach adopted to the geometric parametrization of the foils which was implemented by a mesh morphing technique based on Radial Basis Functions.

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Efficient Structural Design Using a Numerical Optimization Technique

IABSE Congress, New York, New York 2019: The Evolving Metropolis ◽

10.2749/newyork.2019.0499 ◽

2019 ◽

Author(s):

Qian Wang ◽

Lucas Schmotzer ◽

Yongwook Kim

Keyword(s):

Structural Design ◽

Numerical Optimization ◽

Optimization Technique ◽

Optimization Method ◽

Optimization Techniques ◽

Convergence Criteria ◽

Efficient Design ◽

Concrete Building ◽

Gradient Based ◽

Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

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