TY - BOOK
T1 - Report on the Digital Twin of a cemented wase package, geochemical evolution and mechanical integrity modelling
T2 - PREDIS Deliverable 7.5
AU - Miron, George-Dan
AU - Seetharam, Suresh
AU - Phung, Quoc Tri
AU - Laloy, Eric
AU - Dekkers, Gert
AU - Abbas, Jafari
AU - Unger, Jörg F.
AU - Idiart, Andrés
AU - Meeussen, Johannes C. L.
AU - Hu, Guang
AU - Pfingsten, Wilfried
AU - Dähn, Rainer
N1 - Score=1
RN - PREDIS-D7.5
PY - 2024/2/27
Y1 - 2024/2/27
N2 - The concept of Digital Twins (DT) has gained prominence over the past two decades, revolutionizing decision-making processes across diverse industries. The development is driven by data models, physics-based simulations, or hybrid approaches, and enhance decision-making processes. Building a digital twin involves challenges like long-term process monitoring, scaling models from lab to waste package, parameterizing models, quantifying uncertainties, and integrating feedback between complex processes. This report presents the efforts of WP 7.4 consortium towards developing a proof of concept of certain aspects of digital twin technology for the predisposal management of radioactive waste, especially for low and intermediate level waste packages.
AB - The concept of Digital Twins (DT) has gained prominence over the past two decades, revolutionizing decision-making processes across diverse industries. The development is driven by data models, physics-based simulations, or hybrid approaches, and enhance decision-making processes. Building a digital twin involves challenges like long-term process monitoring, scaling models from lab to waste package, parameterizing models, quantifying uncertainties, and integrating feedback between complex processes. This report presents the efforts of WP 7.4 consortium towards developing a proof of concept of certain aspects of digital twin technology for the predisposal management of radioactive waste, especially for low and intermediate level waste packages.
KW - Digital twin
KW - Digital toolkit
KW - Waste package degradation
KW - Chemical evolution
KW - Surrogate models
KW - Machine learning
KW - Alkali silica reaction
KW - Bayesian method
UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/86305461
M3 - Third partyreport
T3 - PREDIS Reports
BT - Report on the Digital Twin of a cemented wase package, geochemical evolution and mechanical integrity modelling
PB - Predis
ER -