STRUCTURAL OPTIMIZATION USING AN ADAPTIVE RBF APPROACH

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Erica Jarosch ◽  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Optimization Algorithm ◽  
Optimization Method ◽  
Multidisciplinary Optimization ◽  
Engineering System ◽  
Optimal Point ◽  
Analysis And Design ◽  
Gradient Based ◽  
Sample Points ◽  
Optimization Loop ◽  
Nested Analysis

This paper presents an adaptive radial basis functions (RBFs) metamodeling method for design optimization of structures. Various numerical techniques have been developed and adopted in structural and multidisciplinary optimization. To evaluate responses of a structural or mechanical engineering system, finite element (FE) analyses are routinely used. An FE code shall be integrated with an optimization algorithm in a nested analysis and design of structures. Therefore, software input/output programming is required. A metamodeling method, on the contrary, expresses structural responses using an approximate function, so that the FE software is not directly coupled in the numerical optimization loop. Any optimization algorithm can be applied to find the optimal design, based on the explicit response functions. In this study, numerical examples were created and FE analyses were first performed at sample points. Subsequently, metamodels were constructed and a gradient-based optimization algorithm was applied. At the optimal point of one adaptive iteration, accuracy of the RBF metamodel was checked, and additional sample points were added to the sample pool to improve the model accuracy. The adaptive iterations continued, until the convergence of the objective function was achieved. The proposed optimization method worked well for a numerical example, and the optimal result was found within a few adaptive iterations.

AN ADAPTIVE RBF METHOD FOR DESIGN OPTIMIZATION OF BUILDING STRUCTURES

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Optimization ◽  
Optimization Algorithm ◽  
Design Method ◽  
Building Structures ◽  
Design Constraint ◽  
Gradient Based ◽  
Structural Responses ◽  
Sample Points ◽  
Objective Function Values ◽  
Adaptive Metamodeling

Design of building structures has long been based on a trial-and-error iterative approach. Structural optimization provides practicing engineers an effective and efficient approach to replace the traditional design method. A numerical optimization algorithm, such as a gradient-based method or genetic algorithm (GA), can be applied, in conjunction with a finite element (FE) analysis program. The FE program is used to compute the structural responses, such as forces and displacements, which represent the design constraint functions. In this method, reading and writing the input/output files of the FE program and interface programming are required. Another method to perform structural optimization is to create an approximate constraint function, which involves implicit structural responses. This is referred to as a surrogate or metamodeling method. The structural responses can be expressed as approximate functions, based on a number of preselected sample points. In this study, an adaptive metamodeling method was studied and applied to a building structure. The FE analyses were first performed at the sample points, and metamodels were constructed. A gradient-based optimization algorithm was applied. Additional samples were generated and additional FE analyses were conducted so that the model accuracy could be improved, close to the optimal design points. This adaptive scheme was continued, until the objective function values converged. The method worked well and optimal designs were found within a few iterations.

Gradient-based optimization method for interference suppression of linear arrays by the amplitude-only and phase-only control

2021 ◽  
pp. 1-7
Author(s):  
Renjing Gao ◽  
Yi Tang ◽  
Qi Wang ◽  
Shutian Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Interference Suppression ◽  
Computation Time ◽  
Optimization Method ◽  
Main Beam ◽  
Linear Arrays ◽  
Gradient Based

Abstract This paper presents a gradient-based optimization method for interference suppression of linear arrays by controlling the electrical parameters of each array element, including the amplitude-only and phase-only. Gradient-based optimization algorithm (GOA), as an efficient optimization algorithm, is applied to the optimization problem of the anti-interference arrays that is generally solved by the evolutionary algorithms. The goal of this method is to maximize the main beam gain while minimizing the peak sidelobe level (PSLL) together with the null constraint. To control the nulls precisely and synthesize the radiation pattern accurately, the full-wave method of moments is used to consider the mutual coupling among the array elements rigorously. The searching efficiency is improved greatly because the gradient (sensitivity) information is used in the algorithm for solving the optimization problem. The sensitivities of the design objective and the constraint function with respect to the design variables are analytically derived and the optimization problems are solved by using GOA. The results of the GOA can produce the desired null at the specific positions, minimize the PSLL, and greatly shorten the computation time compared with the often-used non-gradient method such as genetic algorithm and cuckoo search algorithm.

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

An Improved Shape Annealing Method for Truss Topology Generation

1994 ◽  
Author(s):  
Giridhar Reddy ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Truss Structures ◽  
Model Design ◽  
Topology Generation ◽  
Truss Topology ◽  
Gradient Based ◽  
Annealing Algorithm ◽  
Annealing Method

Abstract A method for the design of truss structures which encourages lateral exploration, pushes away from violated spaces, models design intentions, and produces solutions with a wide variety of characteristics is introduced. An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, implements the method. The algorithm features a shape grammar to model design intentions, an ability to incorporate geometric constraints to avoid obstacles, and a shape optimization method using only simulated annealing with more consistent convergence characteristics; no traditional gradient-based techniques are employed. The improved algorithm is illustrated on various structural examples generating a variety of solutions based on a simple grammar.

scholarly journals An Arbitrary Hybrid Turbulence Modeling Approach for Efficient and Accurate Automotive Aerodynamic Analysis and Design Optimization

Fluids ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 407
Author(s):  
Saule Maulenkul ◽  
Kaiyrbek Yerzhanov ◽  
Azamat Kabidollayev ◽  
Bagdaulet Kamalov ◽  
Sagidolla Batay ◽  
...  
Keyword(s):  
Flow Field ◽  
Design Optimization ◽  
Automotive Industry ◽  
Turbulence Modeling ◽  
Turbulence Models ◽  
Analysis And Design ◽  
Eddy Simulation ◽  
Gradient Based ◽  
Automotive Aerodynamics ◽  
Single Flow

The demand in solving complex turbulent fluid flows has been growing rapidly in the automotive industry for the last decade as engineers strive to design better vehicles to improve drag coefficients, noise levels and drivability. This paper presents the implementation of an arbitrary hybrid turbulence modeling (AHTM) approach in OpenFOAM for the efficient simulation of common automotive aerodynamics with unsteady turbulent separated flows such as the Kelvin–Helmholtz effect, which can also be used as an efficient part of aerodynamic design optimization (ADO) tools. This AHTM approach is based on the concept of Very Large Eddy Simulation (VLES), which can arbitrarily combine RANS, URANS, LES and DNS turbulence models in a single flow field depending on the local mesh refinement. As a result, the design engineer can take advantage of this unique and highly flexible approach to tailor his grid according to his design and resolution requirements in different areas of the flow field over the car body without sacrificing accuracy and efficiency at the same time. This paper presents the details of the implementation and careful validation of the AHTM method using the standard benchmark case of the Ahmed body, in comparison with some other existing models, such as RANS, URANS, DES and LES, which shows VLES to be the most accurate among the five examined. Furthermore, the results of this study demonstrate that the AHTM approach has the flexibility, efficiency and accuracy to be integrated with ADO tools for engineering design in the automotive industry. The approach can also be used for the detailed study of highly complex turbulent phenomena such as the Kelvin–Helmholtz instability commonly found in automotive aerodynamics. Currently, the AHTM implementation is being integrated with the DAFoam for gradient-based multi-point ADO using an efficient adjoint solver based on a Sparse Nonlinear optimizer (SNOPT).

An Optimization Method for Optimal Engineering Structure Design Combining Intelligent Optimization Algorithm and CAE Code

2011 ◽  
Vol 399-401 ◽  
pp. 2296-2300
Author(s):  
Wen Jie Peng ◽  
Rui Ge ◽  
Ming Kai Gu
Keyword(s):  
Analytical Solution ◽  
Normal Stress ◽  
Optimization Algorithm ◽  
Optimization Method ◽  
Structure Design ◽  
Stacking Sequence ◽  
Intelligent Optimization ◽  
Engineering Structure ◽  
Intelligent Optimization Algorithm ◽  
Computer Aided

This paper presents an optimization method for optimal engineering structure design. An interface procedure is essentially developed to combine the intelligent optimization algorithm and computer aided engineering (CAE) code. An optimization example is carried out to minimize the interlaminar normal stress of a laminate which affect the delamination failure of a laminate via arranging the stacking sequence. The analytical solution is calculated to validate the accuracy of optimization results.

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