scholarly journals An Efficient Global Optimization Approach for Reliability Maximization of Friction-Tuned Mass Damper-Controlled Structures

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Fábio F. S. Nascentes ◽  
Rafael H. Lopez ◽  
Jose Eduardo S. Cursi ◽  
Rubens Sampaio ◽  
Leandro F. F. Miguel
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Structural Parameters ◽  
Computational Cost ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Efficient Global Optimization ◽  
Optimization Approach ◽  
Nonlinear Dynamical ◽  
Energy Dissipation Devices

The application of optimization techniques to design passive energy dissipation devices of structures subject to seismic excitation has rapidly increased in the past decades. It is now widely acknowledged that uncertainties inherent to the earthquake loading and structural parameters must be taken into account in the design process. In the case of friction-tuned mass dampers (FTMDs), this optimization under the uncertainty problem leads to the following issues: (a) the high computational cost of the objective function since we are dealing with time-dependent reliability analysis of nonlinear dynamical models and (b) the nonconvexity and multimodality of the resulting optimization objective function. In order to address these issues, we propose here the use of efficient global optimization (EGO) for the probability of failure minimization in FTMD design. EGO is a metamodel-(kriging-) based optimization scheme able to handle expenses to evaluate objective functions, and its capabilities have not been explored in the optimal FTMD design. In order to show the effectiveness of EGO, its results are compared to those of other algorithms from the literature. The results showed that EGO outperformed the competing algorithms, successfully providing the optimum solution of FTMD design under uncertainty within a reasonable computational effort.

Feasibility Robust Optimization via Scenario Generation and Reduction

2018 ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case ◽  
Robust Solution

Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains without any known probability distributions. The proposed approach integrates a new sampling-based scenario generation scheme with a new scenario reduction approach in order to solve feasibility robust optimization problems. An analysis of the computational cost of the proposed approach was performed to provide worst case bounds on its computational cost. The new proposed approach was applied to three test problems and compared against other scenario-based robust optimization approaches. A test was conducted on one of the test problems to demonstrate that the computational cost of the proposed approach does not significantly increase as additional uncertain parameters are introduced. The results show that the proposed approach converges to a robust solution faster than conventional robust optimization approaches that discretize the uncertain parameters.

A Comparison between Three Meta-Modeling Optimization Approaches to Design a Tube Hydroforming Process

2012 ◽  
Vol 504-506 ◽  
pp. 607-612 ◽  
Author(s):  
Giuseppe Ingarao ◽  
Laura Marretta ◽  
Rosa di Lorenzo
Keyword(s):  
Polynomial Regression ◽  
Tube Hydroforming ◽  
Local Approximation ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Modeling Techniques ◽  
Hydroforming Process ◽  
Meta Modeling ◽  
Research Challenge

Computer aided procedures to design and optimize forming processes have become crucial research topics as the industrial interest in cost and time reduction has been increasing. A standalone numerical simulation approach could make the design too time consuming while meta-modeling techniques enables faster approximation of the investigated phenomena, reducing the simulation time. Many researchers are, nowadays, facing such research challenge by using various approaches. Response surface method (RSM) is probably the most known one, since its effectiveness was demonstrated in the past years. The effectiveness of RSM depends both on the definition of the Design of Experiments (DoE) and the accuracy of the function approximation. The number of numerical simulations can be strongly reduced if a proper optimization approach is implemented: one of the main issues about optimization techniques is related to the design necessity of performing either global or local approximation. This paper aims to test the efficacy of some meta-modeling techniques in the optimization of a T-shaped hydroforming process. In this paper three optimization approaches based on different meta-modeling techniques are implemented. In particular, classical Polynomial Regression approach (PR), Moving Least Squares approximation (MLS) and Kriging method are applied. The results showed that, thanks to the peculiarities of MLS and Kriging methods, it is possible to strongly reduce the computational effort in sheet metal forming optimization, particularly in comparison with a classical PR approach. Differences were highlighted and quantified.

scholarly journals Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 507
Author(s):  
Lima ◽  
Relvas ◽  
Barbosa-Póvoa ◽  
Morales
Keyword(s):  
Robust Optimization ◽  
Oil Industry ◽  
Computational Effort ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Solution Quality ◽  
Inaccurate Information ◽  
Computational Performance ◽  
Adjustable Robust Optimization

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.

scholarly journals Non-Convex Feasibility Robust Optimization Via Scenario Generation and Local Refinement

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Eliot Rudnick-Cohen ◽  
Jeffrey W. Herrmann ◽  
Shapour Azarm
Keyword(s):  
Robust Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Scenario Generation ◽  
Test Problems ◽  
Optimization Approach ◽  
Uncertain Parameters ◽  
Worst Case

Abstract Feasibility robust optimization techniques solve optimization problems with uncertain parameters that appear only in their constraint functions. Solving such problems requires finding an optimal solution that is feasible for all realizations of the uncertain parameters. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. The proposed approach is based on an integration of two techniques: (i) a sampling-based scenario generation scheme and (ii) a local robust optimization approach. An analysis of the computational cost of this integrated approach is performed to provide worst-case bounds on its computational cost. The proposed approach is applied to several non-convex engineering test problems and compared against two existing robust optimization approaches. The results show that the proposed approach can efficiently find a robust optimal solution across the test problems, even when existing methods for non-convex robust optimization are unable to find a robust optimal solution. A scalable test problem is solved by the approach, demonstrating that its computational cost scales with problem size as predicted by an analysis of the worst-case computational cost bounds.

Optimum Structure/Control Design Using Multiobjective Optimization Techniques

1995 ◽  
Author(s):  
M. Sunar ◽  
S. S. Rao
Keyword(s):  
Multiobjective Optimization ◽  
Objective Function ◽  
Flexible Structures ◽  
Computational Cost ◽  
Control Design ◽  
Optimization Techniques ◽  
Control Technique ◽  
Optimum Structure ◽  
Structure Control ◽  
Single Objective

Abstract A novel multiobjective optimization formulation is presented for the combined structural and control design of flexible structures using several multiobjective optimization techniques. The quadratic performance index, control energy and structural weight are integrated into one single objective function for minimization and various stability and performance constraints along with the constraints on the closed-loop eigenvalues are imposed on the structure. Weighting method, goal programming technique and modified game theory are used as multiobjective optimization techniques to collapse the three objective functions into one single objective function. A substructural control technique is employed for the structural control design in order to reduce the computational cost of the optimization procedure. The proposed multiobjective control optimization scheme produces optimum structure/control design and because of its numerical efficiency, it can be applied to large flexible structures.

Development of an automated non-axisymmetric endwall contour design system for the rotor of a 1-stage research turbine – part 1: System design

2019 ◽  
Vol 234 (5) ◽  
pp. 565-581
Author(s):  
Jonathan Bergh ◽  
Glen Snedden ◽  
Daya Reddy
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Global Optimization ◽  
Secondary Flow ◽  
Optimization Procedure ◽  
Secondary Flows ◽  
Design System ◽  
Efficient Global Optimization ◽  
Optimization Approach ◽  
Blade Row

Secondary flows are a well-known source of loss in turbomachinery flows, contributing up to 30% of the total aerodynamic blade row loss. With the increase in pressure on aero-engine manufacturers to produce lighter, more powerful and increasingly more efficient engines, the mitigation of the losses associated with secondary flow has become significantly more important than in the past. This is because the production of secondary flow is closely related to the amount of loading and hence the work output of a blade row, which then allows part counts and overall engine weight to be reduced. Similarly, higher efficiency engines demand larger engine pressure ratios which in turn lead to reduced blade passage heights in which secondary flows then dominate. This article discusses the design and application of an automated turbine non-axisymmetric endwall contour optimization procedure for the rotor of a low speed, 1-stage research turbine, which was used as part of a research program to determine the most effective objective functions for reducing turbine secondary flows. In order to produce as effective as possible designs, the optimization procedure was coupled to a computational fluid dynamics routine with as high a degree of fidelity as possible and an efficient global optimization scheme based on the so-called efficient global optimization algorithm. In order to compliment the requirements of the efficient global optimization approach, as well as offset some of the computational requirements of the computational fluid dynamics, the DACE metamodel was used as an underlying surrogate model.

Metamodel-based design optimization employing a novel sequential sampling strategy

2017 ◽  
Vol 34 (8) ◽  
pp. 2547-2564 ◽  
Author(s):  
Leshi Shu ◽  
Ping Jiang ◽  
Li Wan ◽  
Qi Zhou ◽  
Xinyu Shao ◽  
...  
Keyword(s):  
Global Optimization ◽  
Design Optimization ◽  
Sequential Sampling ◽  
Computational Cost ◽  
Sampling Strategy ◽  
Local Optimum ◽  
Optimization Approach ◽  
Great Ability ◽  
Content Type ◽  
Sample Points

Purpose Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization. Design/methodology/approach A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed. Findings The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum. Originality/value The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

AN ASSESSMENT OF THE PERFORMANCE OF GLOBAL OPTIMIZATION METHODS FOR GEO-ACOUSTIC INVERSION

2008 ◽  
Vol 16 (02) ◽  
pp. 199-223 ◽  
Author(s):  
MIRJAM SNELLEN ◽  
DICK G. SIMONS
Keyword(s):  
Global Optimization ◽  
Optimization Methods ◽  
Simplex Algorithm ◽  
Optimization Techniques ◽  
Acoustic Properties ◽  
Superior Performance ◽  
Efficient Global Optimization ◽  
Acoustic Inversion ◽  
Global Optimization Methods ◽  
Downhill Simplex

Having available efficient global optimization methods is of high importance when going to a practical application of geo-acoustic inversion, where fast processing of the data is an essential requirement. A series of global optimization techniques are available and have been described in literature. In this paper three optimization techniques are considered, being a genetic algorithm (GA), differential evolution (DE), and the downhill simplex algorithm (DHS). The performance of these three methods is assessed using a test function, demonstrating superior performance of DE. Additionally, the DE optimal setting is determined. As a next step DE is applied for determining the geo-acoustic properties of the upper seabed sediments from simulated seabed reflection loss, indicating good DE performance also for real geo-acoustic inversion problems.

scholarly journals The Effect of Multi-Additional Sampling for Multi-Fidelity Efficient Global Optimization

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1499
Author(s):  
Atthaphon Ariyarit ◽  
Tharathep Phiboon ◽  
Masahiro Kanazaki ◽  
Sujin Bureerat
Keyword(s):  
Global Optimization ◽  
Surrogate Model ◽  
Computer Aided Design ◽  
Computational Cost ◽  
Sampling Technique ◽  
Efficient Global Optimization ◽  
Kriging Surrogate Model ◽  
Engineering Product ◽  
Sampling Points ◽  
Aided Design

Powerful computer-aided design tools are presently vital for engineering product development. Efficient global optimization (EGO) is one of the most popular methods for design of a high computational cost problem. The original EGO is proposed for only one additional sample point. In this work, parallel computing is applied to the original EGO process via a multi-additional sampling technique. The weak point of the multi-additional sampling is it has slower convergence rate when compared with the original EGO. This paper applies the multi-fidelity technique to the multi-additional EGO process to see the effect of the number of multi-additional sampling points and the converge rate. A co-kriging method and a hybrid RBF/Kriging surrogate model are selected for the surrogate model in the EGO process to show the advantage of the multi-additional EGO process compared with the single-fidelity Kriging surrogate model. In the experiment, single-additional sampling points and two to four number of multi-additional sampling per iteration are tested with symmetry and asymmetry mathematical test functions. The results show the hybrid RBF/Kriging surrogate model can obtain the similar optimal points when using the multi-additional sampling EGO.

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