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Design and Optimization under Uncertainties: A Simulation and Surrogate Model Based Approach
Linköping University, Department of Management and Engineering, Machine Design. Linköping University, The Institute of Technology.
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with development of complex products via modeling and simulation, and especially the use of surrogate models to decrease the computational efforts when probabilistic optimizations are performed. Many methods that can be used to perform probabilistic optimizations exist and this thesis strives to present and demonstrate the capabilities of a few of them. Hopefully, this information can be helpful for someone who wants to choose a method.

Knowledge about several different topics is required to perform a probabilistic optimization. First, it is necessary to incorporate the probabilistic behavior into the analysis by estimating how the uncertainties and variations in the model and its parameters are affecting the performance of the system. The focus in this thesis is on sampling based methods to estimate these probabilities. Secondly, an optimization algorithm should be chosen so that the computer can search for and present an optimal solution automatically.

The probabilistic optimization process can be computationally demanding since numerous simulations of the model are performed each time the value of the objective function is estimated. It is therefore desirable to speed up the process by incorporating computationally effective surrogate models. This is especially important if the simulated model is computationally demanding on its own, e.g. a finite element model with many nodes.

Each of these topics is presented in its own chapter of this thesis. A few  methods are presented and their performances demonstrated for each topic.

Surrogate models can also be used to improve the performances of optimization algorithms when the desire is to optimize computationally expensive objective functions. With this in mind, efforts have been made to improve the Complex-RF optimization algorithm. A modified algorithm is presented in this thesis and the main difference is that it creates and utilizes surrogate models iteratively during the optimization process. The modified algorithm is compared with Complex-RF and is demonstrated to be superior for computationally expensive models.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. , 81 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1556
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-84850ISBN: 978-91-7519-753-1 (print)OAI: oai:DiVA.org:liu-84850DiVA: diva2:562480
Presentation
2012-11-02, Mass, A-huset, Campus Valla, Linköpings universitet, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Funder
EU, FP7, Seventh Framework Programme, 234344
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2012-10-24Bibliographically approved
List of papers
1. Comparison of Sampling Methods for a Dynamic Pressure Regulator
Open this publication in new window or tab >>Comparison of Sampling Methods for a Dynamic Pressure Regulator
2011 (English)In: 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, AIAA American Institute of Aeronautics and Astronautics , 2011Conference paper, Published paper (Refereed)
Abstract [en]

Concepts for complex products are often developed using computer models, introducinguncertainties both in design and model accuracy. There exist several methods forapproximating these uncertainties and this paper presents and compares some of them. Thefocus is on sampling based methods including or excluding response surfaces, and they arecompared by accuracy and computation time, using a Monte Carlo sampling as reference.The application is a simplified system model of a dynamic pressure regulator that controlsthe air supply in the environmental control system of an aircraft.

Place, publisher, year, edition, pages
AIAA American Institute of Aeronautics and Astronautics, 2011
Series
AIAA, AIAA-2011-1205
Keyword
Sampling Robust Design Monte Carlo
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-67960 (URN)10.2514/6.2011-1205 (DOI)978-1-60086-950-1 (ISBN)
Conference
49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, Jan. 4-7, 2011.
Projects
CRESCENDO
Available from: 2011-05-04 Created: 2011-05-04 Last updated: 2012-10-24Bibliographically approved
2. Multidisciplinary design optimization of modular Industrial Robots
Open this publication in new window or tab >>Multidisciplinary design optimization of modular Industrial Robots
2011 (English)In: Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2011, August 28- 31, 2011, Washington, DC, USA, The American Society of Mechanical Engineers (ASME) , 2011, Vol. 5, 867-876 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a multidisciplinary design optimization framework for modular industrial robots. An automated design framework, containing physics based high fidelity models for dynamic simulation and structural strength analyses are utilized and seamlessly integrated with a geometry model.

The proposed frameworkutilizes well-established methods such as metamodeling and multi-level optimization inorder to speed up the design optimization process. The contributionof the paper is to show that by applying amerger of well-established methods, the computational cost can be cutsignificantly, enabling search for truly novel concepts.

Place, publisher, year, edition, pages
The American Society of Mechanical Engineers (ASME), 2011
Keyword
MDO, CAD, Optimization
National Category
Other Mechanical Engineering
Identifiers
urn:nbn:se:liu:diva-71765 (URN)10.1115/DETC2011-48196 (DOI)000324076700080 ()978-0-7918-5482-2 (ISBN)
Conference
The 37th Design Automation Conference (DAC), ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington DC, USA, August 28-31
Available from: 2011-11-10 Created: 2011-11-04 Last updated: 2016-05-13Bibliographically approved
3. Comparison of Different Uses of Metamodels for Robust Design Optimization
Open this publication in new window or tab >>Comparison of Different Uses of Metamodels for Robust Design Optimization
2013 (English)Conference paper, Published paper (Other academic)
Abstract [en]

This paper compares different approaches for using kriging metamodels for robust design optimization, with the aim of improving the knowledge of the performance of the approaches. A popular approach is to first fit a metamodel to the original model and then perform the robust design optimization on the metamodel. However, it is also possible to create metamodels during the optimization. Additionally, the metamodel need not necessarily reanimate the original model; it may also model the mean value, variance or the actual objective function. The comparisons are made with two analytical functions and a dynamic simulation model of an aircraft system as an engineering application. In the comparisons, it is seen that creating a global metamodel before the optimization begins slightly outperforms the other approaches that involve metamodels.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84848 (URN)
Conference
51st AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition 7 - 10 January 2013, Texas, USA
Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2015-03-24Bibliographically approved
4. Optimization of the Complex-RFM Optimization Algorithm
Open this publication in new window or tab >>Optimization of the Complex-RFM Optimization Algorithm
2015 (English)In: Optimization and Engineering, ISSN 1389-4420, E-ISSN 1573-2924, Vol. 16, no 1, 27-48 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents and compares different modifications made to the Complex-RF optimization algorithm with the aim of improving its performance for computationally expensive models. The modifications reduces the required number of objective function evaluations by creating and using surrogate models of the objective function iteratively during the optimization process. The chosen surrogate model type is a second order response surface. The performance of the modified algorithm is compared with a number of existing algorithms and demonstrated for a few analytical and engineering problems.

Place, publisher, year, edition, pages
Springer-Verlag New York, 2015
Keyword
Optimization, Surrogate models, Meta-optimization
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-84849 (URN)10.1007/s11081-014-9247-9 (DOI)000351842300002 ()
Note

This article status has been changed from Manuscript to Article in Journal.

Available from: 2012-10-24 Created: 2012-10-24 Last updated: 2017-12-07Bibliographically approved

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Persson, Johan

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