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. Surrogatebased optimization of electrothermal wing anti. Surrogatebased analysis and optimization of interply shear stress induced in fiber reinforced thermoplastic composites during forming. Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. Therefore, it is advisable to adopt knowledge based surrogate modeling in engineering design optimization.
Surrogatebased analysis and optimization, progress in aerospace sciences, 41, 128. An introduction dimitri solomatine introduction this paper should be seen as an introduction and a brief tutorial in surrogate modelling. The striking difference of eicts from other surrogate based constrained optimization methodologies that it needs to construct only two surrogates, i. 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. Construct a surrogate model from a set of known data points. Surrogatebased analysis and optimization ntrs nasa. Numerical experiments are conducted in 3 to 15objective dtlz17 problems. A surrogatebased strategy for multiobjective tolerance. An evaluation of adaptive surrogate modeling based optimization. Simulations and numerical experiments of engineering problems are often expensive, which may restrict sensitivity analysis and design optimization. Statistical benchmarking of surrogatebased and other optimization methods constrained by fixed computational budget patrick h. 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.
It can be also used by students who would like to choose this area as a topic for their msc studies. 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. 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. Coello provided a comprehensive discussion on the fundamental issues arising from the use of surrogatebased analysis and optimization. In surrogate model based optimization, an initial surrogate is constructed using some of the available budgets of expensive experiments and or simulations. The paper proposes a global optimization algorithm employing surrogate. 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. Surrogatebased superstructure optimization framework. Technically, this might be best illustrated by the generic sequence of steps performed during surrogatebased optimization sbo. 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. Advances in surrogate based modeling, feasibility analysis, and optimization. An important distinction can be made between two different applications of surrogate models.
Search surrogate model the model can be searched extensively, e. In this chapter, the surrogate based optimization sbo paradigm is formulated. Cfdbased analysis and surrogatebased optimization of bio. A surrogatemodelbased method for constrained optimization. Surrogate based sensitivity analysis of process equipment. Existing tools are usually either accurate or efficient, bu. Cfdbased analysis and surrogatebased optimization of bioinspired surface riblets for. Determination of the extended druckerprager parameters using. 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. Adaptive surrogate modeling based optimization asmo is an effective and efficient method.
Surrogatebased pareto optimization of rapid annealing process for severely deformed steel. Surrogatebased multiobjective optimization of electrical. 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. In a practical simulationbased design process most of the required simulations are carried out by using commercial software producing simulation responses but no. 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. Surrogatebased analysis and optimization for the design of heat sinks with jet. 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. 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. Accurate, highfidelity models are typically time consuming and computationally expensive.
Mca free fulltext surrogatebased optimization using. Statistical benchmarking of surrogatebased and other. Surrogate modeling methodologies are currently being studied to construct approximation models of system responses based on a limited number of the expensive evaluations. Surrogate based optimization using kriging based approximation. Advances in surrogate based modeling, feasibility analysis. The great computational burden caused by complicated and unknown analysis restricts the use of simulation based optimization.
We discuss sbo on a generic level, including the optimization flow, fundamental properties of the sbo process, and. Radial basis functions rbf are a powerful tool for data interpolation and regression. The surrogatebased optimization method was validated by identifying some of the extended druckerprager constants directly from tension and compression tests. 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. The surrogate model is combined with a genetic algorithm. Cross validation is a model selection or validation tool used in. In order to mitigate this challenge, surrogate based global optimization methods have gained popularity for their capability in handling computationally expensive functions. 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.
Surrogatebased analysis and optimization sciencedirect. In order to mitigate this challenge, surrogatebased global optimization methods have gained popularity for their capability in handling computationally expensive functions. The cost is even more prohibitive for an optimization process, as a large number of simulations are needed. Handling constraints in surrogatebased optimization. Buckling surrogatebased optimization framework for. Surrogate model optimization toolbox file exchange. 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.
To address this, a number of derivativefree optimization dfo algorithms have been developed in the last couple of decades 12. Surrogate model optimization toolbox file exchange matlab. To reduce the number of optimization iterations and to get an initial screening of the design space, we used surrogate based optimization. Proceedings of the 12th aiaaissmo multidisciplinary analysis and optimization conference. Efficient design optimization assisted by sequential surrogate models. Learning surrogate models for simulationbased optimization. Surrogatebased evolutionary optimization for friction stir.
Surrogatebased evolutionary optimization for friction. Genesis structural analysis and optimization software. 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. Mca free fulltext surrogatebased optimization using an. Request pdf surrogatebased analysis and optimization a major challenge. These algorithms have shown a great potential to be applied for practical problems. Among them, the gradient based optimization with gradients calculated by using an adjoint approach has proved effective and got popularity for aerodynamic shape optimization. Another approach to conduct optimization is the socalled surrogatebased optimization sbo, which is the method used in this study. 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. A surrogatebased optimization method with rbf neural network enhanced by linear interpolation and hybrid infill strategy. A surrogate modeling and adaptive sampling toolbox for computer based design dirk. Knowledgebased surrogate modeling in engineering design. A surrogate model is an engineering method used when an outcome of interest cannot be. Surrogate model toolbox for unconstrained continuous constrained integer constrained mixedinteger global optimization problems that are computationally expensive.
Among them, the gradientbased optimization with gradients calculated by using an adjoint approach has proved effective and got popularity for aerodynamic shape optimization. We discuss sbo on a generic level, including the optimization flow, fundamental properties of the sbo process, and typical ways of constructing the surrogate. 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. Surrogatebased analysis and optimization uf mae university of. Surrogatemodel based method and software for practical. Aerostructural design optimization of a 100passenger regional jet with surrogate based mission analysis rhea p. In optimization context, the goal is to find optimal solution and not to predict responses away from optimality. Since 2012, pseven software platform for simulation automation, data analysis and optimization is developed and marketed by datadvance, incorporating pseven core. For most challenging problems, given the sparseness.
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 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. However, the curse of dimensionality is still an obstacle for large and complex engineering design problems. Multiscale modeling, surrogatebased analysis, and optimization of lithiumion batteries for vehicle applications du, wenbo. Surrogatebased analysis and optimization request pdf.
As these simulations are computationally expensive or have no available closed form, this problem is often nontrivial. A study on manyobjective optimization using the kriging. A surrogatebased optimization method with rbf neural network. Cfd computations were performed with the cfd software package phoenics cham, london, uk using the. This means, in particular, that surrogates must be accurate only in promising regions of the design space. Dakota design analysis kit for optimization and terascale. Dakota is a software tool developed at sandia national laboratories containing optimization, sensitivity analysis, and uncertainty quantification algorithms. The required number of highfidelity evaluations becomes tremendously large in a highdimensional space.
Initial sample selection the experiments andor simulations to be run. Proprietary numerical simulation tools allow very detailed analysis, but their use often requires. Surrogatebased analysis and optimization for the design. In many cases, optimization of such objectives in a straightforward way, i. By employing objectoriented design to implement abstractions of the key components.
Surrogatebased optimization using an opensource framework. This paper surveys the fundamental issues that arise in surrogatebased global optimization. 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. Run and update experimentsimulation at a new location s found by search and add to sample. Broadly, the dakota software s advanced parametric analyses enable design exploration, model calibration, risk analysis, and quantification of margins and uncertainty with computational models. A surrogate modeling and adaptive sampling toolbox for. 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. The mathematical model for optimization of process parameters is accurately. J recent developments in algorithms and software for trust region methods. They covered some of the most popular methods in design space sampling, surrogate model construction, model selection and validation, sensitivity analysis, and surrogatebased optimization.
In this context, the socalled surrogatebased approach for analysis and optimization can play a very valuable role. Pdf surrogatebased analysis and optimization for the design of. Flowchart of the surrogatebased op timization with a bilevel framework. During this training, we deal with gradientbased methods, derivativefree methods and surrogatebased optimization sbo. The user can choose beween different options for the surrogate model the sampling strategy the initial experimental design. To aid the discussions of the issues involved, we summarize recent e. 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. Surrogatebased multiobjective optimization of multipoint forming process for dimpling and wrinkling reduction. Center for computing research sandia national laboratories. Another approach to conduct optimization is the socalled surrogate based optimization sbo, which is the method used in this study. Mar 09, 2015 this is optimization based on a surrogate model. There are two approaches that are provably globally.
A systematic optimization design method for complex. The goal of this work is to demonstrate the use of uncertainty quantification methods in dakota with nek5000. 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. 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. Miller1 1national energy technology laboratory, pittsburgh, pa 15236.
Genesis structural analysis and optimization software is a fully integrated analysis and design optimization software package, written by leading experts in structural optimization. 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. An overview of surrogatebased analysis and optimization was presented in this journal by queipo et al. 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. To make it practical, this work presents a surrogatebased optimization approach using proper orthogonal decomposition, in conjunction with kriging. Introduction in many engineering design problems, processes are so complex to the point to make experiments either time consuming or computationally expensive. The dakota project delivers both stateoftheart research and robust, usable software for optimization and uq. In addition, it was found that the nonassociative plastic flow assumption can describe plastic deformation of polypropylene composites more properly. The term refers to models of a system that is fast and simple enough that you can tune their inputs to optimize the output. 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.
Mader y, edmund lee university of toronto institute for aerospace studies, toronto, on, canada. Determination of the extended druckerprager parameters. Learning surrogate models for simulationbased optimization alison cozad1,2, nikolaos v. Airfoils are of great importance in aerodynamic design, and various tools have been developed to evaluate and optimize their performance. Surrogatebased multiobjective optimization of electrical machine designs facilitating tolerance analysis abstract. In this paper, a newly developed surrogate based optimization sbo method is. Two key factors for this class of problems are the choice of surrogate model and sampling strategy. The optimization algorithms and the code coupling interface is provided through the dakota library 28 version 6. Aerostructural design optimization of a 100passenger regional jet with surrogatebased mission analysis rhea p. Pdf surrogatebased analysis and optimization tushar. Surrogatebased optimization multifidelity optimization surrogate models.
It provides a seamless integration with third party cad and cae software tools, powerful multiobjective and robust optimization algorithms. 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. Surrogatebased modeling and optimization applications in. The great computational burden caused by complicated and unknown analysis restricts the use of simulationbased optimization. Conference and 14th aiaaissmo multidisciplinary analysis and optimization. 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.
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