A comparison study of two multifidelity methods for aerodynamic optimization

Kontogiannis, Spyridon G.; Demange, J; Kipouros, Timoleon; Savill, A. M.

doi:10.2514/6.2018-0415

Cited by 5 publications

(7 citation statements)

References 30 publications

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“…Mondal et al (2019) implemented a multifidelity Bayesian strategy for the optimization of a transonic compressor rotor, where a reduced number of costly high-fidelity CFD evaluations are used to enrich a lowfidelity aerodynamic surrogate. Kontogiannis et al (2020) Fig. 8 Design structure matrix of the multidisciplinary design optimization problem of a re-entry vehicle demonstrated that the multifidelity Bayesian optimization strategy provides better results than local multifidelity methods for the case of an high lift airfoil, where is required a wide-ranging exploration of the aerodynamic domain.…”

Section: Domain-aware Multifidelity Learningmentioning

confidence: 99%

Multifidelity domain-aware learning for the design of re-entry vehicles

Fiore

Maggiore

Mainini

2021

Struct Multidisc Optim

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The multidisciplinary design optimization (MDO) of re-entry vehicles presents many challenges associated with the plurality of the domains that characterize the design problem and the multi-physics interactions. Aerodynamic and thermodynamic phenomena are strongly coupled and relate to the heat loads that affect the vehicle along the re-entry trajectory, which drive the design of the thermal protection system (TPS). The preliminary design and optimization of re-entry vehicles would benefit from accurate high-fidelity aerothermodynamic analysis, which are usually expensive computational fluid dynamic simulations. We propose an original formulation for multifidelity active learning that considers both the information extracted from data and domain-specific knowledge. Our scheme is developed for the design of re-entry vehicles and is demonstrated for the case of an Orion-like capsule entering the Earth atmosphere. The design process aims to minimize the mass of propellant burned during the entry maneuver, the mass of the TPS, and the temperature experienced by the TPS along the re-entry. The results demonstrate that our multifidelity strategy allows to achieve a sensitive improvement of the design solution with respect to the baseline. In particular, the outcomes of our method are superior to the design obtained through a single-fidelity framework, as a result of the principled selection of a limited number of high-fidelity evaluations.

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Section: Domain-aware Multifidelity Learningmentioning

confidence: 99%

Multifidelity domain-aware learning for the design of re-entry vehicles

Fiore

Maggiore

Mainini

2021

Struct Multidisc Optim

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“…The measurements of the gap and overlap were provided in terms of a percentage of the chord length of the main element (gap = % C 1 , overlap = % C 1 ). The third parameter of the definition of the gap-overlap is the "deflection angle" (δ), which is defined as the angle between the main element and the flap chord; δ a positive value when the rotation flap is mobilized in a clockwise direction [29][30][31][32][33][34]42]. In addition to the mentioned variables, the flap chord length (C 2 ) was considered as a variable, which was given as a percentage of the main element chord (C 2 = % C 1 ), like the gap and the overlap.…”

Section: Description Of the Optimization Problemmentioning

confidence: 99%

“…Additionally, there are statistics or approximation mathematical methods [28]. Surrogate models can be used to predict the design objective(s) and optimize the operating conditions throughout the use of an optimizer to find the optimum design and, finally, to utilize the high fidelity approach to verify the proposed objective(s) [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Concerning the optimization schemes, they have been highlighted to range from Tabu search [29,31,32] to genetic algorithm (GA) [34,36], which are the most often used. In addition, Benini and coworkers [36] obtained satisfactory results when using Matlab ® R2019a software (R2019a, MathWorks Inc, Natick, MA, USA) [37], which offers a general vision of a multi-objective genetic algorithm (MOGA) that is available in the global optimization toolbox in Matlab (gamultiobj function of Genetic Algorithm and Direct Search ToolboxTM), which in turn uses a controlled, elitist genetic algorithm that is a variant of NSGA-II [38,39] to create a set of points on the Pareto front.…”

Section: Introductionmentioning

confidence: 99%

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Design and Optimization of a Multi-Element Hydrofoil for a Horizontal-Axis Hydrokinetic Turbine

et al. 2019

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Hydrokinetic turbines are devices that harness the power from moving water of rivers, canals, and artificial currents without the construction of a dam. The design optimization of the rotor is the most important stage to maximize the power production. The rotor is designed to convert the kinetic energy of the water current into mechanical rotation energy, which is subsequently converted into electrical energy by an electric generator. The rotor blades are critical components that have a large impact on the performance of the turbine. These elements are designed from traditional hydrodynamic profiles (hydrofoils), to directly interact with the water current. Operational effectiveness of the hydrokinetic turbines depends on their performance, which is measured by using the ratio between the lift coefficient (CL) and the drag coefficient (CD) of the selected hydrofoil. High lift forces at low flow rates are required in the design of the blades; therefore, the use of multi-element hydrofoils is commonly regarded as an adequate solution to achieve this goal. In this study, 2D CFD simulations and multi-objective optimization methodology based on surrogate modelling were conducted to design an appropriate multi-element hydrofoil to be used in a horizontal-axis hydrokinetic turbine. The Eppler 420 hydrofoil was utilized for the design of the multi-element hydrofoil composed of a main element and a flap. The multi-element design selected as the optimal one had a gap of 2.825% of the chord length (C1), an overlap of 8.52 %C1, a flap deflection angle (δ) of 19.765°, a flap chord length (C2) of 42.471 %C1, and an angle of attack (α) of –4°.

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“…Lu et al [34] used a Kriging surrogate model of the objective function along with genetic algorithm to reduce the adverse effects of aerodynamic interactions on UH-60 type fuselage of a helicopter. Kontogiannis et al [27] used Kriging and co-Kriging based multifidelity surrogates for aerodynamic optimization. Extensive review of surrogate models can be found in [15,19].…”

Section: Introductionmentioning

confidence: 99%

The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

Chatterjee

Essien

Ganguli

et al. 2021

Neural Comput & Applic

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This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty.

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A comparison study of two multifidelity methods for aerodynamic optimization

Cited by 5 publications

References 30 publications

Multifidelity domain-aware learning for the design of re-entry vehicles

Multifidelity domain-aware learning for the design of re-entry vehicles

Design and Optimization of a Multi-Element Hydrofoil for a Horizontal-Axis Hydrokinetic Turbine

The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

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