Study on Low/High-Fidelity CFD and Its Integrated Surrogate Modeling Technique Toward Multi-Fidelity Aerodynamic Optimization of Supersonic Airfoil

2018 ◽  
Vol 2018.71 (0) ◽  
pp. B14
Author(s):  
Junichi SUGIMOTO ◽  
Koichi YONEMOTO ◽  
Takahiro FUJIKAWA ◽  
Hirotoshi TSUKAMOTO
Keyword(s):  
Surrogate Modeling ◽  
Modeling Technique ◽  
High Fidelity ◽  
Aerodynamic Optimization

Inverse surrogate modeling for low-cost geometry scaling of microwave and antenna structures

2016 ◽  
Vol 33 (4) ◽  
pp. 1095-1113 ◽  
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Surrogate Modeling ◽  
High Fidelity ◽  
Modeling Framework ◽  
Simulation Accuracy ◽  
Content Type ◽  
Scaling Process ◽  
Variable Fidelity ◽  
Em Simulation

Purpose – The purpose of this paper is to investigate strategies for expedited dimension scaling of electromagnetic (EM)-simulated microwave and antenna structures, exploiting the concept of variable-fidelity inverse surrogate modeling. Design/methodology/approach – A fast inverse surrogate modeling technique is described for dimension scaling of microwave and antenna structures. The model is established using reference designs obtained for cheap underlying low-fidelity model and corrected to allow structure scaling at high accuracy level. Numerical and experimental case studies are provided demonstrating feasibility of the proposed approach. Findings – It is possible, by appropriate combination of surrogate modeling techniques, to establish an inverse model for explicit determination of geometry dimensions of the structure at hand so as to re-design it for various operating frequencies. The scaling process can be concluded at a low computational cost corresponding to just a few evaluations of the high-fidelity computational model of the structure. Research limitations/implications – The present study is a step toward development of procedures for rapid dimension scaling of microwave and antenna structures at high-fidelity EM-simulation accuracy. Originality/value – The proposed modeling framework proved useful for fast geometry scaling of microwave and antenna structures, which is very laborious when using conventional methods. To the authors’ knowledge, this is one of the first attempts to surrogate-assisted dimension scaling of microwave components at the EM-simulation level.

scholarly journals Proper Orthogonal Decomposition as Surrogate Model for Aerodynamic Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Valentina Dolci ◽  
Renzo Arina
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Parameter Space ◽  
Surrogate Model ◽  
Orthogonal Decomposition ◽  
High Fidelity ◽  
Aerodynamic Optimization ◽  
Model Based ◽  
Flow Past ◽  
First Case ◽  
Proper Orthogonal

A surrogate model based on the proper orthogonal decomposition is developed in order to enable fast and reliable evaluations of aerodynamic fields. The proposed method is applied to subsonic turbulent flows and the proper orthogonal decomposition is based on an ensemble of high-fidelity computations. For the construction of the ensemble, fractional and full factorial planes together with central composite design-of-experiment strategies are applied. For the continuous representation of the projection coefficients in the parameter space, response surface methods are employed. Three case studies are presented. In the first case, the boundary shape of the problem is deformed and the flow past a backward facing step with variable step slope is studied. In the second case, a two-dimensional flow past a NACA 0012 airfoil is considered and the surrogate model is constructed in the (Mach, angle of attack) parameter space. In the last case, the aerodynamic optimization of an automotive shape is considered. The results demonstrate how a reduced-order model based on the proper orthogonal decomposition applied to a small number of high-fidelity solutions can be used to generate aerodynamic data with good accuracy at a low cost.

Design strategies for multi-objective optimization of aerodynamic surfaces

2017 ◽  
Vol 34 (5) ◽  
pp. 1724-1753 ◽  
Author(s):  
Anand Amrit ◽  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Pareto Front ◽  
Design Space ◽  
Optimization Problems ◽  
Computational Time ◽  
High Fidelity ◽  
Design Strategies ◽  
Aerodynamic Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Aerodynamic Surfaces

Purpose This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level. Design/methodology/approach Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate. Findings Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day. Originality/value The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

scholarly journals Reduced order surrogate modeling technique for linear dynamic systems

2018 ◽  
Vol 111 ◽  
pp. 172-193 ◽  
Author(s):  
Vahid Yaghoubi ◽  
Sadegh Rahrovani ◽  
Hassan Nahvi ◽  
Stefano Marelli
Keyword(s):  
Dynamic Systems ◽  
Surrogate Modeling ◽  
Modeling Technique ◽  
Reduced Order ◽  
Linear Dynamic Systems ◽  
Linear Dynamic

A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies

2017 ◽  
Author(s):  
Yong Hoon Lee ◽  
R. E. Corman ◽  
Randy H. Ewoldt ◽  
James T. Allison
Keyword(s):  
Surrogate Model ◽  
Design Space ◽  
Surrogate Modeling ◽  
Pareto Set ◽  
Test Problems ◽  
High Fidelity ◽  
Sampling Strategies ◽  
Nsga Ii ◽  
Sample Points ◽  
Fidelity Model

A novel multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) framework is proposed to utilize a minimal number of training samples efficiently for sequential model updates. All the sample points are enforced to be feasible, and to provide coverage of sparsely explored sparse design regions using a new optimization subproblem. The MO-ASMO method only evaluates high-fidelity functions at feasible sample points. During an exploitation sample phase, samples are selected to enhance solution accuracy rather than the global exploration. Sampling tasks are especially challenging for multiobjective optimization; for an n-dimensional design space, a strategy is required for generating model update sample points near an (n − 1)-dimensional hypersurface corresponding to the Pareto set in the design space. This is addressed here using a force-directed layout algorithm, adapted from graph visualization strategies, to distribute feasible sample points evenly near the estimated Pareto set. Model validation samples are chosen uniformly on the Pareto set hypersurface, and surrogate model estimates at these points are compared to high-fidelity model responses. All high-fidelity model evaluations are stored for later use to train an updated surrogate model. The MO-ASMO algorithm, along with the set of new sampling strategies, are tested using two mathematical and one realistic engineering problems. The second mathematical test problems is specifically designed to test the limits of this algorithm to cope with very narrow, non-convex feasible domains. It involves oscillatory objective functions, giving rise to a discontinuous set of Pareto-optimal solutions. Also, the third test problem demonstrates that the MO-ASMO algorithm can handle a practical engineering problem with more than 10 design variables and black-box simulations. The efficiency of the MO-ASMO algorithm is demonstrated by comparing the result of two mathematical problems to the results of the NSGA-II algorithm in terms of the number of high fidelity function evaluations, and is shown to reduce total function evaluations by several orders of magnitude when converging to the same Pareto sets.

scholarly journals Multi-Objective Optimization of a Permanent Magnet Actuator for High Voltage Vacuum Circuit Breaker Based on Adaptive Surrogate Modeling Technique

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4695
Author(s):  
Jiaming Jiang ◽  
Heyun Lin ◽  
Shuhua Fang
Keyword(s):  
Permanent Magnet ◽  
High Voltage ◽  
Circuit Breaker ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Modeling Technique ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Vacuum Circuit Breaker ◽  
Permanent Magnet Actuator

A novel mono-stable permanent magnet actuator (PMA) for high voltage vacuum circuit breaker (VCB) and its optimal design method are proposed in this paper. The proposed PMA is featured with a structure of separated magnetic circuits, which makes the holding part and closing driving part work independently without interference. The application of an auxiliary breaking coil decreases the response time in the initial phase of opening operation, and an external disc spring is adopted to accelerate the opening movement, which makes the PMA meet the fast-breaking requirement of high voltage VCB. As calculating the characteristics of the PMA accurately through numerical simulation is a time-consuming process, a multi-objective optimization (MOO) algorithm based on surrogate modeling technique and adaptive samples adding strategy are proposed to reduce the workload of numerical simulations during optimization. Firstly, initial surrogate models are constructed and evaluated, and then iteratively updated to improve their global approximating abilities. Secondly, according to the approximate MOO results obtained by the global surrogate models, additional samples are added to constantly update the surrogate models to gradually improve the models’ local accuracies in optimal solution regions and finally guide the algorithm to the true Pareto front. The efficiency and accuracy of the proposed algorithm are verified by test functions. By applying the optimization strategy to the design of the proposed PMA, a set of satisfying Pareto optimal solutions, which improve the overall performance of the PMA obviously, can be derived at a reasonable computation cost.

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