TY - BOOK
T1 - Report describing the benchmarks to be carried out during the WP DONUT
T2 - EURAD-DONUT Deliverable 4.4
AU - Prasianakis, Nikolaos I.
AU - Masin, David
AU - Samper, Javier
AU - Cernochova, Katerina
AU - Gens, Antonio
AU - Jacques, Diederik
AU - Kulik, Dmitrii A.
AU - Mon, A.
AU - Montenegro, Luis
AU - Svoboda, Jiri
AU - Tournassat, Christophe
AU - Villar, M. V.
AU - Claret, Francis
N1 - Score=1
RN - EURAD-DONUT D4.4
PY - 2023/6/12
Y1 - 2023/6/12
N2 - A specific outcome of DONUT work package is the definition of benchmarks that will be use both inside DONUT and outside to foster interactions. While international benchmarks initiative are existing (Bildstein et al., 2021; Birkholzer et al., 2019; Steefel et al., 2015), the goal here is to define benchmarks of methods and tools to quantify efficiency and added-value in terms of :
• increase of knowledge (e.g. better physical representation, integration of couple processes, exchange between viewpoints of different disciplines)
• accuracy, robustness, computational cost,
• robustness of scale-transition approaches
• ability to manage uncertainty and sensitivity analyses
To tackled this issue, three benchmark exercise are running within DONUT. The first one is relevant to machine learning and geochemistry, the second one aims at modelling the Thermo Hydro Mechanical behaviour of bentonite and the third one deals the reactive transport modelling of two-phase flow coupled Thermo Hydro Chemical processes. Last but not least the first one provides a clear link with the EURAD WP ACED and FUTURE, the second one with HITEC while the last one is linked to ACED and GAS.
AB - A specific outcome of DONUT work package is the definition of benchmarks that will be use both inside DONUT and outside to foster interactions. While international benchmarks initiative are existing (Bildstein et al., 2021; Birkholzer et al., 2019; Steefel et al., 2015), the goal here is to define benchmarks of methods and tools to quantify efficiency and added-value in terms of :
• increase of knowledge (e.g. better physical representation, integration of couple processes, exchange between viewpoints of different disciplines)
• accuracy, robustness, computational cost,
• robustness of scale-transition approaches
• ability to manage uncertainty and sensitivity analyses
To tackled this issue, three benchmark exercise are running within DONUT. The first one is relevant to machine learning and geochemistry, the second one aims at modelling the Thermo Hydro Mechanical behaviour of bentonite and the third one deals the reactive transport modelling of two-phase flow coupled Thermo Hydro Chemical processes. Last but not least the first one provides a clear link with the EURAD WP ACED and FUTURE, the second one with HITEC while the last one is linked to ACED and GAS.
KW - EURAD
KW - DONUT
UR - https://ecm.sckcen.be/OTCS/llisapi.dll/open/85480142
M3 - Third partyreport
T3 - EURAD Reports
BT - Report describing the benchmarks to be carried out during the WP DONUT
PB - EURAD - European Joint Programme on Radioactive Waste Management
ER -