Robust optimization of a marine current turbine using a novel robustness criterion
The present paper aims to establish a systematic robust optimization framework for the
hydrodynamic performance of marine current turbines against uncertain conditions. To this …
hydrodynamic performance of marine current turbines against uncertain conditions. To this …
An efficient Bayesian inversion method for seepage parameters using a data-driven error model and an ensemble of surrogates considering the interactions between …
H Yu, X Wang, B Ren, T Zeng, M Lv, C Wang - Journal of Hydrology, 2022 - Elsevier
The Bayesian method has been increasingly applied to the inversion of seepage
parameters owing to its superiority of considering the uncertainty in the inversion process …
parameters owing to its superiority of considering the uncertainty in the inversion process …
Investigation on uncertainty quantification of transonic airfoil using compressive sensing greedy reconstruction algorithms
H Handuo, S Yanping, Y Jianyang, L Yao… - Aerospace Science and …, 2024 - Elsevier
Uncertainty quantification constructs the stochastic responses of system output under
uncertainties. Traditional uncertainty quantification methods such as Monte Carlo and Full …
uncertainties. Traditional uncertainty quantification methods such as Monte Carlo and Full …
Probabilistic CFD analysis on the flow field and performance of the FDA centrifugal blood pump
The present study is set out to systematically investigate the combined impact of operational,
geometrical, and model uncertainties on the hemodynamics and performance …
geometrical, and model uncertainties on the hemodynamics and performance …
Research, Application and Future Prospect of Mode Decomposition in Fluid Mechanics
In fluid mechanics, modal decomposition, deeply intertwined with the concept of symmetry,
is an essential data analysis method. It facilitates the segmentation of parameters such as …
is an essential data analysis method. It facilitates the segmentation of parameters such as …
An adaptive dimension-reduction method-based sparse polynomial chaos expansion via sparse Bayesian learning and Bayesian model averaging
W He, G Zhao, G Li, Y Liu - Structural Safety, 2022 - Elsevier
Polynomial chaos expansion (PCE) is a popular surrogate method for stochastic uncertainty
quantification (UQ). Nevertheless, when dealing with high-dimensional problems, the well …
quantification (UQ). Nevertheless, when dealing with high-dimensional problems, the well …
Stochastic simulation of the FDA centrifugal blood pump benchmark
In the present study, the effect of physical and operational uncertainties on the
hydrodynamic and hemocompatibility characteristics of a centrifugal blood pump designed …
hydrodynamic and hemocompatibility characteristics of a centrifugal blood pump designed …
[HTML][HTML] Proper orthogonal decomposition and physical field reconstruction with artificial neural networks (ANN) for supercritical flow problems
The development of mathematical models, and the associated numerical simulations, are
challenging in higher-dimensional systems featuring flows of supercritical fluids in various …
challenging in higher-dimensional systems featuring flows of supercritical fluids in various …
Fast simulation of high resolution urban wind fields at city scale
High resolution urban wind field simulations are limited in small simulation domain, short
simulation period, or stable boundary conditions due to the high computational …
simulation period, or stable boundary conditions due to the high computational …
Model order reduction for film-cooled applications under probabilistic conditions: sparse reconstruction of POD in combination with Kriging
A Mohammadi-Ahmar, A Mohammadi… - Structural and …, 2022 - Springer
In this paper, to reduce the computational cost of Proper Orthogonal Decomposition (POD)
method in film-cooling problems, a new method based on the combination of POD method …
method in film-cooling problems, a new method based on the combination of POD method …