Surrogate-Based Aerodynamic Shape Optimization of a Wing-Body Transport Aircraft Configuration

Han, Zhong-Hua; Abu-Zurayk, Mohammad; Görtz, Stefan; Ilić, Časlav

doi:10.1007/978-3-319-72020-3_16

Cited by 15 publications

(11 citation statements)

References 47 publications

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“…They reported that the AGENN model can successfully determine the required design parameters and operating conditions to generate the desired total heat transfer rate of the heat exchanger. Han et al [33] used surrogate-based shape optimization of the wing-body of aircraft. They used Latin Hypercube sampling; however, to choose new sample points they used infill-criterion, so as to generate new designs based on the known designs.…”

Section: Machine Learning-based Surrogate Model Optimization Techniquesmentioning

confidence: 99%

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Qidwai¹,

Badruddin

Khan

et al. 2021

Mathematics

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This study aimed to present the design methodology of microjet heat sinks with unequal jet spacing, using a machine learning technique which alleviates hot spots in heat sinks with non-uniform heat flux conditions. Latin hypercube sampling was used to obtain 30 design sample points on which three-dimensional Computational Fluid Dynamics (CFD) solutions were calculated, which were used to train the machine learning model. Radial Basis Neural Network (RBNN) was used as a surrogate model coupled with Particle Swarm Optimization (PSO) to obtain the optimized location of jets. The RBNN provides continuous space for searching the optimum values. At the predicted optimum values from the coupled model, the CFD solution was calculated for comparison. The percentage error for the target function was 0.56%, whereas for the accompanied function it was 1.3%. The coupled algorithm has variable inputs at user discretion, including gaussian spread, number of search particles, and number of iterations. The sensitivity of each variable was obtained. Analysis of Variance (ANOVA) was performed to investigate the effect of the input variable on thermal resistance. ANOVA results revealed that gaussian spread is the dominant variable affecting the thermal resistance.

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Section: Machine Learning-based Surrogate Model Optimization Techniquesmentioning

confidence: 99%

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Qidwai¹,

Badruddin

Khan

et al. 2021

Mathematics

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show abstract

“…All the optimization problems are started with DOE consisting in 5d = 45 profiles, the design space dimension being d = 9. The influence of the number of initial sample designs (in the context of bayesian deterministic optimization) has been studied by Han et al [47], along with the effect of randomness of the initial sampling (considering five LHS samples of the same size generated with different seeds). For 40 design variables, as a rule of thumb, they suggested to use between 0.5d and 2d as the size of the initial DOE, and found very similar convergence histories for the different LHS seeds.…”

Section: Optimization Processmentioning

confidence: 99%

“…For 40 design variables, as a rule of thumb, they suggested to use between 0.5d and 2d as the size of the initial DOE, and found very similar convergence histories for the different LHS seeds. In the present study, we decided to exceed significantly with the DOE size (5d) with respect to the recommendations reported in [47], with the aim of enhancing the reliability of the surrogate optimization.…”

Section: Optimization Processmentioning

confidence: 99%

Quantile-based robust optimization of a supersonic nozzle for organic rankine cycle turbines

Razaaly

Persico

Gori

et al. 2020

Applied Mathematical Modelling

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Organic Rankine Cycle (ORC) turbines usually operate in thermodynamic regions characterized by high-pressure ratios and strong non-ideal gas effects, complicating the aerodynamic design significantly. Systematic optimization methods accounting for multiple uncertainties due to variable operating conditions, referred to as Robust Optimization may benefit to ORC turbines aerodynamic design. This study presents an original and fast robust shape optimization approach to overcome the limitation of a deterministic optimization that neglects operating conditions variability, applied to a well-known supersonic turbine nozzle for ORC applications. The flow around the blade is assumed inviscid and adiabatic and it is reconstructed using the opensource SU2 code. The non-ideal gasdynamics is modeled through the Peng-Robinson-Stryjek-Vera equation of state. We propose here a mono-objective formulation which consists in minimizing the α-quantile of the targeted Quantity of Interest (QoI) under a probabilistic constraint, at a low computational cost. This problem is solved by using an efficient robust optimization approach, coupling a state-of-the-art quantile estimation and a classical Bayesian optimization method. First, the advantages of a quantile-based formulation are illustrated with respect to a conventional mean-based robust optimization. Secondly, we demonstrate the effectiveness of applying this robust optimization framework with a low-fidelity inviscid solver by comparing the resulting optimal design with the ones obtained with a deterministic optimization using a fully turbulent solver.

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“…Once the initial sampling is evaluated, the surrogate model is constructed. The approximation of the objective function by Kriging, using Bayesian statistics, is among the most used surrogate models in optimization [22,29]. A detailed explanation of the construction of Kriging surrogates is given by Forrester in [30].…”

Section: Surrogate Constructionmentioning

confidence: 99%

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

Sabater

Goertz

2019

AIAA Scitech 2019 Forum

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Surrogate-Based Aerodynamic Shape Optimization of a Wing-Body Transport Aircraft Configuration

Cited by 15 publications

References 47 publications

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Optimization of Microjet Location Using Surrogate Model Coupled with Particle Swarm Optimization Algorithm

Quantile-based robust optimization of a supersonic nozzle for organic rankine cycle turbines

An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty

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