A systematic optimization design method for complex. It can be also used by students who would like to choose this area as a topic for their msc studies. This means, in particular, that surrogates must be accurate only in promising regions of the design space. Coello provided a comprehensive discussion on the fundamental issues arising from the use of surrogatebased analysis and optimization. Aerostructural design optimization of a 100passenger regional jet with surrogate based mission analysis rhea p.
Surrogate based sensitivity analysis of process equipment. Surrogate model optimization toolbox file exchange matlab. Surrogatebased analysis and optimization request pdf. The key idea of using surrogate based feasibility analysis is building a surrogate to represent feasibility function given a set of input parameters and blackbox simulation outputs for the feasibility function. For most challenging problems, given the sparseness. Advances in surrogate based modeling, feasibility analysis, and optimization. We also address how to conduct parametrical studies and design of experiments as part of design space exploration dse, and we show how to do exploratory data analysis and automatic knowledge extraction techniques. In this paper, a newly developed surrogate based optimization sbo method is.
Simulations and numerical experiments of engineering problems are often expensive, which may restrict sensitivity analysis and design optimization. Introduction in many engineering design problems, processes are so complex to the point to make experiments either time consuming or computationally expensive. The manyobjective optimization performance of the kriging surrogate based evolutionary algorithm ea, which maximizes expected hypervolume improvement ehvi for updating the kriging model, is investigated and compared with those using expected improvement ei and estimation est updating criteria in this paper. Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. Cfd computations were performed with the cfd software package phoenics cham, london, uk using the. Feb 24, 2010 the dakota design analysis kit for optimization and terascale applications toolkit provides a flexible and extensible interface between simulation codes computational models and iterative analysis methods. Determination of the extended druckerprager parameters using. An overview of surrogatebased analysis and optimization was presented in this journal by queipo et al. Dakota is a software tool developed at sandia national laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. To improve the computational efficiency and global optimizing ability of the surrogate based optimization of hierarchical stiffened composite shells, an enhanced variance reduction method based on latinized partially stratified sampling and multifidelity analysis methods is proposed in this paper and then integrated into the surrogate based.
In surrogate model based optimization, an initial surrogate is constructed using some of the available budgets of expensive experiments and or simulations. Search surrogate model the model can be searched extensively, e. In optimization context, the goal is to find optimal solution and not to predict responses away from optimality. Surrogatebased superstructure optimization framework. These algorithms have shown a great potential to be applied for practical problems. Surrogatebased analysis and optimization sbao has been shown to be an effective approach for the design of computationally expensive models such as those found in aerospace systems, involving aerodynamics, structures, and propulsion, among other disciplines. A surrogatebased strategy for multiobjective tolerance.
Another approach to conduct optimization is the socalled surrogate based optimization sbo, which is the method used in this study. Surrogatebased pareto optimization of rapid annealing process for severely deformed steel. To aid the discussions of the issues involved, we summarize recent e. The dakota project delivers both stateoftheart research and robust, usable software for optimization and uq. Multiscale modeling, surrogatebased analysis, and optimization of lithiumion batteries for vehicle applications du, wenbo. Airfoils are of great importance in aerodynamic design, and various tools have been developed to evaluate and optimize their performance. Aerostructural design optimization of a 100passenger regional jet with surrogatebased mission analysis rhea p. In a practical simulationbased design process most of the required simulations are carried out by using commercial software producing simulation responses but no. Finite element analysis is based on the finite element method for static, normal modes, direct and modal frequency analysis, random response analysis, heat transfer and system buckling calculations. A major challenge to the successful fullscale development of modern aerospace systems is to address competing objectives such as improved performance, reduced costs, and enhanced safety. Adaptive surrogate modeling based optimization asmo is an effective and efficient method. Aerostructural design optimization of a 100passenger. A study on manyobjective optimization using the kriging.
Construct a surrogate model from a set of known data points. A surrogatebased optimization method with rbf neural network. The cost is even more prohibitive for an optimization process, as a large number of simulations are needed. Pdf surrogatebased analysis and optimization for the design of. Surrogatebased analysis and optimization uf mae university of. We discuss sbo on a generic level, including the optimization flow, fundamental properties of the sbo process, and typical ways of constructing the surrogate. The goal of this work is to demonstrate the use of uncertainty quantification methods in dakota with nek5000. This paper surveys the fundamental issues that arise in surrogatebased global optimization.
Therefore, it is advisable to adopt knowledge based surrogate modeling in engineering design optimization. In order to mitigate this challenge, surrogatebased global optimization methods have gained popularity for their capability in handling computationally expensive functions. Surrogatebased evolutionary optimization for friction stir welding 2016 cem c tutum, shaayaan sayed and risto miikkulainen friction stir welding fsw is an innovative manu facturing process, which is used to join two pieces of metal with frictional heating and plastic deformation due to stirring action. The sbo method consists of constructing a mathematical model also known as a surrogate, response surface, metamodel, emulator from a limited number of observations cfd simulations, in our case. Accurate, highfidelity models are typically time consuming and computationally expensive. The user can choose beween different options for the surrogate model the sampling strategy the initial experimental design. Surrogatebased multiobjective optimization of electrical. A surrogate model is an engineering method used when an outcome of interest cannot be.
Genesis structural analysis and optimization software is a fully integrated analysis and design optimization software package, written by leading experts in structural optimization. However, the curse of dimensionality is still an obstacle for large and complex engineering design problems. The surrogatebased optimization method was validated by identifying some of the extended druckerprager constants directly from tension and compression tests. In this chapter, the surrogate based optimization sbo paradigm is formulated.
Mader y, edmund lee university of toronto institute for aerospace studies, toronto, on, canada. Broadly, the dakota software s advanced parametric analyses enable design exploration, model calibration, risk analysis, and quantification of margins and uncertainty with computational models. Surrogatebased evolutionary optimization for friction stir. Surrogatebased optimization of electrothermal wing anti. Surrogatebased analysis and optimization for the design of heat sinks with jet. Multiscale modeling, surrogatebased analysis, and optimization of lithiumion batteries for vehicle applications by wenbo du a dissertation submitted in partial fulfillment of the requirements for the degree of doctor of philosophy aerospace engineering in the university of michigan 20 doctoral committee. An introduction dimitri solomatine introduction this paper should be seen as an introduction and a brief tutorial in surrogate modelling. Miller1 1national energy technology laboratory, pittsburgh, pa 15236 2department of chemical engineering, carnegie mellon university, pittsburgh, pa 152 october 5, 20 abstract we address a central problem in modeling, namely that of learning.
The surrogates are constructed using data drawn from highfidelity models, and provide fast approximations of the objectives and constraints at new design points, thereby making sensitivity and optimization studies feasible. By employing objectoriented design to implement abstractions of the key components. Surrogate modeling methodologies are currently being studied to construct approximation models of system responses based on a limited number of the expensive evaluations. Learning surrogate models for simulationbased optimization alison cozad1,2, nikolaos v. Surrogate models are used extensively in the surrogatebased optimization and least squares methods, in which the goals are to reduce expense by minimizing the number of truth function evaluations and to smooth out noisy data with a global data fit. The term refers to models of a system that is fast and simple enough that you can tune their inputs to optimize the output. The great computational burden caused by complicated and unknown analysis restricts the use of simulationbased optimization. A surrogatemodelbased method for constrained optimization. Among them, the gradient based optimization with gradients calculated by using an adjoint approach has proved effective and got popularity for aerodynamic shape optimization. Since 2012, pseven software platform for simulation automation, data analysis and optimization is developed and marketed by datadvance, incorporating pseven core. They covered some of the most popular methods in design space sampling, surrogate model construction, model selection and validation, sensitivity analysis, and surrogatebased optimization. Initial sample selection the experiments andor simulations to be run. Flowchart of the surrogatebased op timization with a bilevel framework. Surrogatemodel based method and software for practical.
It provides a seamless integration with third party cad and cae software tools, powerful multiobjective and robust optimization algorithms. Statistical benchmarking of surrogatebased and other. A surrogate modeling and adaptive sampling toolbox for. There are two approaches that are provably globally. Technically, this might be best illustrated by the generic sequence of steps performed during surrogatebased optimization sbo. Genesis structural analysis and optimization software. Two key factors for this class of problems are the choice of surrogate model and sampling strategy. Knowledgebased surrogate modeling in engineering design. Efficient design optimization assisted by sequential surrogate models. The required number of highfidelity evaluations becomes tremendously large in a highdimensional space. This paper provides a comprehensive discussion of the fundamental issues that arise in surrogate based analysis and optimization sbao, highlighting concepts, methods, techniques, as well as. Surrogate model toolbox for unconstrained continuous constrained integer constrained mixedinteger global optimization problems that are computationally expensive. Statistical benchmarking of surrogatebased and other optimization methods constrained by fixed computational budget patrick h. J recent developments in algorithms and software for trust region methods.
Pdf surrogatebased analysis and optimization tushar. Center for computing research sandia national laboratories. The surrogate model is combined with a genetic algorithm. Another approach to conduct optimization is the socalled surrogatebased optimization sbo, which is the method used in this study. An evaluation of adaptive surrogate modeling based optimization. In summary, the surrogate model technology is useful for the optimization of complex electromechanical systems, and it has a successful application in the optimization of some products. Determination of the extended druckerprager parameters. Mar 09, 2015 this is optimization based on a surrogate model. In order to mitigate this challenge, surrogate based global optimization methods have gained popularity for their capability in handling computationally expensive functions. The mathematical model for optimization of process parameters is accurately. To improve the computational efficiency and global optimizing ability of the surrogatebased optimization of hierarchical stiffened composite shells, an enhanced variance reduction method based on latinized partially stratified sampling and multifidelity analysis methods is proposed in this paper and then integrated into the surrogatebased.
An important distinction can be made between two different applications of surrogate models. To reduce the number of optimization iterations and to get an initial screening of the design space, we used surrogate based optimization. Advances in surrogate based modeling, feasibility analysis. Surrogatebased modeling and optimization applications in. Surrogatebased analysis and optimization ntrs nasa. It provides a seamless integration with third party cad and cae software tools, powerful multiobjective and robust optimization algorithms, data analysis and uncertainty quantification tools. Conference and 14th aiaaissmo multidisciplinary analysis and optimization. Proprietary numerical simulation tools allow very detailed analysis, but their use often requires. Run and update experimentsimulation at a new location s found by search and add to sample. Mca free fulltext surrogatebased optimization using. The optimization algorithms and the code coupling interface is provided through the dakota library 28 version 6. As these simulations are computationally expensive or have no available closed form, this problem is often nontrivial.
Cross validation is a model selection or validation tool used in. Cfdbased analysis and surrogatebased optimization of bio. Surrogatebased analysis and optimization for the design. In this chapter, the surrogatebased optimization sbo paradigm is formulated. Buckling surrogatebased optimization framework for. Radial basis functions rbf are a powerful tool for data interpolation and regression. Surrogatebased evolutionary optimization for friction. Surrogatebased optimization multifidelity optimization surrogate models. Surrogatebased analysis and optimization for the design of heat sinks with jet impingement xueguan song, jie zhang, member, ieee, sanghoon kang, mingyao ma, bing ji, member, ieee, wenping cao, senior member, ieee, and volker pickert, member, ieee abstractheat sinks are widely used for cooling electronic devices and systems. Miller1 1national energy technology laboratory, pittsburgh, pa 15236. Surrogatebased analysis and optimization sciencedirect.
Existing tools are usually either accurate or efficient, bu. The great computational burden caused by complicated and unknown analysis restricts the use of simulation based optimization. To make it practical, this work presents a surrogatebased optimization approach using proper orthogonal decomposition, in conjunction with kriging. Surrogatebased analysis and optimization for the design of heat sinks with jet impingement. During this training, we deal with gradientbased methods, derivativefree methods and surrogatebased optimization sbo. In this study, a hybrid optimization framework of injection molding is developed systematically on the basis of fe analysis software moldflow, ffd method, sego, and nondominated sorting. In addition, it was found that the nonassociative plastic flow assumption can describe plastic deformation of polypropylene composites more properly.
The paper proposes a global optimization algorithm employing surrogate. Proceedings of the 12th aiaaissmo multidisciplinary analysis and optimization conference. Surrogatebased multiobjective optimization of electrical machine designs facilitating tolerance analysis abstract. These capabilities may be used on their own or as components within advanced strategies such as hybrid optimization, surrogatebased optimization, mixed integer nonlinear programming, or optimization. Dakota design analysis kit for optimization and terascale. A surrogate modeling and adaptive sampling toolbox for computer based design dirk. To aid the discussions of the issues involved, we will summarize recent efforts in investigating cryogenic cavitating flows, active flow control based on dielectric barrier discharge concepts, and lithiumion batteries. Surrogatebased multiobjective optimization of multipoint forming process for dimpling and wrinkling reduction. Surrogate model optimization toolbox file exchange. A common attribute of electricpowered aerospace vehicles and systems such as unmanned aerial vehicles, hybrid and fullyelectric aircraft, and satellites is that their performance is usually limited by the.
Using two industrial optimization scenarios, we show that the surrogatebased approach can offer very valuable insights regarding the local and global sensitivities of the considered objectives at a fraction of the. Handling constraints in surrogatebased optimization. We discuss sbo on a generic level, including the optimization flow, fundamental properties of the sbo process, and. Numerical experiments are conducted in 3 to 15objective dtlz17 problems. Using two industrial optimization scenarios, we show that the surrogate based approach can offer very valuable insights regarding the local and global sensitivities of the considered objectives at a fraction of the computational cost required by a fe based strategy. Surrogatebased analysis and optimization, progress in aerospace sciences, 41, 128. Surrogatebased optimization using an opensource framework. To address this, a number of derivativefree optimization dfo algorithms have been developed in the last couple of decades 12. Mca free fulltext surrogatebased optimization using an.
Multiobjective optimization algorithms are becoming ever more popular in the field of electrical machine design as they provide engineers with an automated way of efficiently exploring huge design spaces when searching for. It begins by presenting the basic concepts and formulations of the surrogate based modeling and optimization paradigm and then discusses relevant modeling techniques, optimization algorithms and design procedures, as well as stateoftheart developments. In surrogate model based optimization, an initial surrogate is constructed using. With the above descriptions and assumptions, our objective here is to build a surrogate model for predicting the output of the computer code for any untried site x that is, to.
Learning surrogate models for simulationbased optimization. Surrogatebased analysis and optimization of interply shear stress induced in fiber reinforced thermoplastic composites during forming. Among them, the gradientbased optimization with gradients calculated by using an adjoint approach has proved effective and got popularity for aerodynamic shape optimization. Surrogate based analysis and optimization sbao has been shown to be an effective approach for the design of computationally expensive models such as those found in aerospace systems, involving aerodynamics, structures, and propulsion, among other disciplines. Request pdf surrogatebased analysis and optimization a major challenge. Multiscale modeling, surrogate based analysis, and optimization of lithiumion batteries for vehicle applications by wenbo du a dissertation submitted in partial fulfillment of the requirements for the degree of doctor of philosophy aerospace engineering in the university of michigan 20 doctoral committee. In this context, the socalled surrogatebased approach for analysis and optimization can play a very valuable role. In many cases, optimization of such objectives in a straightforward way, i.
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