Multicanonical Monte Carlo evaluation of drag probability distribution functions

Tong, Ming Sze; Yevick, David

doi:10.1063/1.5020174

Physics of Fluids

2018

DOI: 10.1063/1.5020174

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Multicanonical Monte Carlo evaluation of drag probability distribution functions

Ming Sze Tong

David Yevick

Abstract: The probability distribution of the drag generated by a two-dimensional square/rectangular obstacle is calculated both for quasi-random input flow patterns and for random surface roughness by employing the multicanonical Monte Carlo procedure in conjunction with the lattice Boltzmann method. The results demonstrate that the multicanonical method can estimate the probability distribution function in low-probability regions with far less computational effort than standard techniques.

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Cited by 2 publications

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Multicanonical analyses of bluff airfoil drags in subcritical flows

Tong

Yevick

2021

Physics of Fluids

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We apply the multicanonical procedure in conjunction with an improved multi-resolution lattice Boltzmann model to evaluate the probability distribution function of the drag resulting from small but rarely occurring statistical deformations of a bluff airfoil NACA (National Advisory Committee for Aeronautics) 0050 and on a more streamlined NACA0030 airfoil with a larger aspect ratio. This analysis enables the effect of surface roughness on drags in a subcritical fluid flow to be examined in detail and demonstrates that the magnitude of the pressure drag, which is the primary component of the bluff airfoil drag, correlates with the width of the Fourier spectrum of the perturbation on the airfoil, while surface smoothness correlates with the frictional drag reduction in the subcritical fluid flow. Moreover, the multicanonical analysis differentiates between bluff and streamlined bodies without separately evaluating frictional and pressure drags.

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Multicanonical analyses of bluff airfoil drags in subcritical flows

Tong

Yevick

2021

Physics of Fluids

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A data-driven robust design optimization method and its application in compressor blade

2023

Physics of Fluids

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The probability-based robust optimization methods require a large amount of sample data to build probability distribution models of uncertain parameters. However, it is a common situation that only scarce sampled data are available in practice due to expensive tests. This study proposes a data-driven robust optimization framework by embedding a novel uncertainty quantification (UQ) method, which can quantify the uncertainty based on the statistical moments of scarce input data. The computational robustness and accuracy of the developed UQ methods are validated. Then, the data-driven multi-objective optimization framework is applied to improve the mean performance and aerodynamic robustness of a two-dimensional compressor blade with real stagger angle errors. Uncertainty analysis shows that there is a probability of 47.55% to deviate from the nominal total pressure loss coefficient by more than 1% for the actual performance values at high positive incidence i=7° condition. In the optimization process, the total pressure loss coefficient is selected as the objective function, while the static pressure ratio is used as a constraint. The Gaussian process regression model is trained to improve the robust optimization efficiency. The robust optimization is conducted under the most sensitive conditions. Optimized results indicate that compared with the nominal blade, the mean performance of the selected robust blades is increased by 10.9%, 8.56%, and 0.83%; the performance dispersion is decreased by 19.0%, 24.8%, and 35.3%, respectively. The optimized results can provide useful references for the robust design of compressor blades.

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Multicanonical Monte Carlo evaluation of drag probability distribution functions

Cited by 2 publications

References 14 publications

Multicanonical analyses of bluff airfoil drags in subcritical flows

Multicanonical analyses of bluff airfoil drags in subcritical flows

A data-driven robust design optimization method and its application in compressor blade

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