[HTML][HTML] AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology

K Kobayashi, D Kumar, SB Alam - Progress in Nuclear Energy, 2024 - Elsevier
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 …

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

S Abraham, M Raisee, G Ghorbaniasl, F Contino… - Journal of …, 2017 - Elsevier
Polynomial Chaos (PC) expansions are widely used in various engineering fields for
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

P German, M Tano, JC Ragusa, C Fiorina - Progress in Nuclear Energy, 2020 - Elsevier
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 …

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 …

Multi-criteria decision making under uncertainties in composite materials selection and design

D Kumar, M Marchi, SB Alam, C Kavka, Y Koutsawa… - Composite …, 2022 - Elsevier
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 …

Non-intrusive Sparse Subspace Learning for Parametrized Problems

D Borzacchiello, JV Aguado, F Chinesta - Archives of Computational …, 2019 - Springer
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 …

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 …

Methodological improvements in the risk analysis of an urban hydrogen fueling station

J Shi, Y Chang, F Khan, Y Zhu, G Chen - Journal of Cleaner Production, 2020 - Elsevier
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 …

Quantitative risk assessment of a high power density small modular reactor (SMR) core using uncertainty and sensitivity analyses

D Kumar, SB Alam, T Ridwan, CS Goodwin - Energy, 2021 - Elsevier
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 …

An efficient multifidelity ℓ1-minimization method for sparse polynomial chaos

S Salehi, M Raisee, MJ Cervantes… - Computer Methods in …, 2018 - Elsevier
Abstract The Polynomial Chaos Expansion (PCE) methodology is widely used for
uncertainty quantification of stochastic problems. The computational cost of PCE increases …