Results 81 to 90 of about 3,846 (183)

A Non‐Intrusive, Online Reduced Order Method for Non‐Linear Micro‐Heterogeneous Materials

International Journal for Numerical Methods in Engineering, Volume 126, Issue 5, 15 March 2025.
ABSTRACT In this contribution we present an adaptive model order reduction technique for non‐linear finite element computations of micro‐heterogeneous materials. The presented projection‐based method performs updates of the reduced basis during the iterative process and at the end of each load step.
Yasemin von Hoegen, Rainer Niekamp, Jörg Schröder   +2 more
wiley   +1 more source

Airfoil Design Under Uncertainty Using Non-Intrusive Polynomial Chaos Theory and Utility Functions

Procedia Computer Science, 2017
Abstract Fast and accurate airfoil design under uncertainty using non-intrusive polynomial chaos (NIPC) expansions and utility functions is proposed. The NIPC expansions provide a means to efficiently and accurately compute statistical information for a given set of input variables with associated probability distribution. Utility functions provide a
Xiaosong Du, Leifur Leifsson, Slawomir Koziel, Adrian Bekasiewicz   +3 more
openaire   +1 more source

Uncertainty Quantification for Aerothermal Characteristics of HP Turbine Vanes Under Combined Hot-Streak and Turbulence Intensity Effects

Aerospace Research Communications
This study presents a systematic framework for quantifying aerothermal uncertainties in high-pressure turbine nozzle guide vanes (NGV) under combustor-turbine interaction, focusing on the combined impacts of hot streak spatial variations and turbulence ...
Ruocheng Li, Liangliang Wang, Zhiduo Wang, Bei Zhang, Bei Zhang, Xiaoben Du, Jinze Li, Xiangyu Wang, Zhenping Feng   +8 more
doaj   +1 more source

Parameter Estimation with Data-Driven Nonparametric Likelihood Functions

Entropy, 2019
In this paper, we consider a surrogate modeling approach using a data-driven nonparametric likelihood function constructed on a manifold on which the data lie (or to which they are close).
Shixiao W. Jiang, John Harlim
doaj   +1 more source

Robust optimization of geometrical properties of flow diverter stents for treating cerebral aneurysm: A proof-of-concept study

Computer Methods and Programs in Biomedicine Update
This study presents a novel approach to optimize the design of flow diverter (FD) stents for cerebral aneurysm (CA) treatment. By addressing sources of uncertainty in cardiovascular simulations, including geometrical and physical properties and boundary ...
Zahra Darbandi, Mahkame Sharbatdar, Mehrdad Raisee   +2 more
doaj   +1 more source

Combined effects of double flat aerodisks and rear opposing jets on hypersonic spiked blunt body drag and heat reduction: An uncertainty and sensitivity analysis

Case Studies in Thermal Engineering
This study numerically investigates the combined effects of double flat aerodisks and rear opposing jets on the drag and heat reduction performance of a hypersonic spiked blunt body. Uncertainty quantification and sensitivity analysis are performed using
Ni He, Yu Pan, Zhenkang Zhang, Jian Chen
doaj   +1 more source

Uncertainty quantification of the standard k-ε turbulence model closure coefficients in predicting aerodynamics of high-speed train

Engineering Applications of Computational Fluid Mechanics
Turbulence modelling is crucial in predicting train aerodynamics while its performance is closely related to the model closure coefficients. To reveal the influence, uncertainty quantification and sensitivity analysis are conducted on the closure ...
Hongkang Liu, Zhiqiang Kong, Guoshun Li, Can Chen, Yatian Zhao, Shishang Zhang   +5 more
doaj   +1 more source

Uncertainty Quantification of Fiber Orientation and Epicardial Activation. [PDF]

Comput Cardiol (2010), 2023
Rupp LC, Busatto A, Bergquist JA, Gillette K, Narayan A, Plank G, MacLeod RS.   +6 more
europepmc   +1 more source

Constrained non-intrusive polynomial chaos expansion for physics-informed machine learning regression

, 2023
PUBLISHED This work presents a constrained polynomial chaos expansion (PCE) as a physics-informed machine learning (ML) technique to supplement data with physical constraints in the regression framework. PCE is a popular metamodeling technique for uncertainty quantification of expensive computational models.
Novak, Lukas, Shields, Michael, Sharma, Himanshu, ICASP14   +3 more
openaire   +1 more source

Data-driven reduced-order modeling of nonlinear multivalued FRFs: Applications to beam and SDOF gear systems

Results in Engineering
We present a novel approach to developing non-intrusive Reduced Order Models (ROMs) for predicting nonlinear, multivalued Frequency Response Functions (FRFs).
Hady Mohamed, Nils Brödling, Fabian Duddeck   +2 more
doaj   +1 more source