[HTML][HTML] AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology
within next-generation nuclear systems, advancements in modeling methods and simulation …
within next-generation nuclear systems, advancements in modeling methods and simulation …
A robust and efficient stepwise regression method for building sparse polynomial chaos expansions
Polynomial Chaos (PC) expansions are widely used in various engineering fields for
quantifying uncertainties arising from uncertain parameters. The computational cost of …
quantifying uncertainties arising from uncertain parameters. The computational cost of …
Comparison of Reduced-Basis techniques for the model order reduction of parametric incompressible fluid flows
The applicability of two Reduced-Basis techniques to parametric laminar and turbulent
incompressible fluid-flow problems in nuclear engineering is studied in this work. The …
incompressible fluid-flow problems in nuclear engineering is studied in this work. The …
Surrogate model based uncertainty quantification of CFD simulations of the viscous flow around a ship advancing in shallow water
L Xia, ZJ Zou, ZH Wang, L Zou, H Gao - Ocean Engineering, 2021 - Elsevier
Abstract Usually, Computational Fluid Dynamics (CFD) simulations are carried out under
deterministic conditions. However, there are actually many uncertain factors such as fluid …
deterministic conditions. However, there are actually many uncertain factors such as fluid …
Multi-criteria decision making under uncertainties in composite materials selection and design
During a composite application's initial design stages, the main objective is to have the
optimal performance of the final structure. There is a vast demand for lightweight structures …
optimal performance of the final structure. There is a vast demand for lightweight structures …
Non-intrusive Sparse Subspace Learning for Parametrized Problems
We discuss the use of hierarchical collocation to approximate the numerical solution of
parametric models. With respect to traditional projection-based reduced order modeling, the …
parametric models. With respect to traditional projection-based reduced order modeling, the …
Sparse polynomial chaos expansion based on D-MORPH regression
K Cheng, Z Lu - Applied Mathematics and Computation, 2018 - Elsevier
Polynomial chaos expansion (PCE) is widely used by engineers and modelers in various
engineering fields for uncertainty analysis. The computational cost of full PCE is …
engineering fields for uncertainty analysis. The computational cost of full PCE is …
Methodological improvements in the risk analysis of an urban hydrogen fueling station
Abstract The Fuel Cell Vehicles are going to be introduced in domestic cities of China, which
will require urban Hydrogen Refueling Stations (HRS). Such urban refueling center would …
will require urban Hydrogen Refueling Stations (HRS). Such urban refueling center would …
Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses
The use of uncertainty quantification and machine learning platforms in ensuring the
robustness of small modular reactor (or popularly known as SMR) core design is rare. Most …
robustness of small modular reactor (or popularly known as SMR) core design is rare. Most …
An efficient multifidelity ℓ1-minimization method for sparse polynomial chaos
Abstract The Polynomial Chaos Expansion (PCE) methodology is widely used for
uncertainty quantification of stochastic problems. The computational cost of PCE increases …
uncertainty quantification of stochastic problems. The computational cost of PCE increases …