TY - JOUR
T1 - New techniques for modelling glass dissolution
AU - Aertsens, Marc
AU - Dominique, Ghaleb
PY - 2001
Y1 - 2001
N2 - Due to the large increase in computation power, new methods for modelling glass disoolution are becoming available. We give a short description of two classes of such methods. For each method, we first explain where it is based on, then we list existing aplication sin glass dissolution and finally, we discuss what it could provide to glass dissolution modelling.
The first class of models consists of molecular modelling. These are methods with a solid mathematical basis, like ab initio calculations, molecular dynamics or Monte Carlo simulations. These methods are complementary, not only to one another but also to existing analytical or geochemical models, which will not become superfluous. Instead, one method can provide input for another method, either by calculating values or by confirming its basic assumptions.
The second class of models consists of soft computing techniques like neural networks, fuzzy systems or genetic algorithms. These methods, which can be viewed as complementary to traditional methods, are more empirically based and can be useful for modelling systems that are ill defined or not completely understood yet.
AB - Due to the large increase in computation power, new methods for modelling glass disoolution are becoming available. We give a short description of two classes of such methods. For each method, we first explain where it is based on, then we list existing aplication sin glass dissolution and finally, we discuss what it could provide to glass dissolution modelling.
The first class of models consists of molecular modelling. These are methods with a solid mathematical basis, like ab initio calculations, molecular dynamics or Monte Carlo simulations. These methods are complementary, not only to one another but also to existing analytical or geochemical models, which will not become superfluous. Instead, one method can provide input for another method, either by calculating values or by confirming its basic assumptions.
The second class of models consists of soft computing techniques like neural networks, fuzzy systems or genetic algorithms. These methods, which can be viewed as complementary to traditional methods, are more empirically based and can be useful for modelling systems that are ill defined or not completely understood yet.
KW - molecular modelling
KW - ab initio
KW - molecular dynamics
KW - monte carlo
KW - neural work
KW - fuzzy logic
KW - glass dis
UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/axs_1131141
U2 - 10.1016/S0022-3115(01)00575-X
DO - 10.1016/S0022-3115(01)00575-X
M3 - Article
SN - 0022-3115
VL - 298
SP - 37
EP - 46
JO - Journal of Nuclear Materials
JF - Journal of Nuclear Materials
IS - 1-2
T2 - Glass in it's disposal environment, Brugge, 11-14
Y2 - 1 January 2001
ER -