TY - JOUR
T1 - New techniques for modelling glass dissolution
AU - Aertsens, Marc
AU - Ghaleb, Dominique
PY - 2001/9
Y1 - 2001/9
N2 - Due to the large increase in computation power, new methods for modelling glass dissolution 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 applications in 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 me thods, 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 dissolution 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 applications in 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 me thods, are more empirically based and can be useful for modelling systems that are ill defined or not completely understood yet.
UR - http://www.scopus.com/inward/record.url?scp=0035448492&partnerID=8YFLogxK
U2 - 10.1016/S0022-3115(01)00575-X
DO - 10.1016/S0022-3115(01)00575-X
M3 - Article
AN - SCOPUS:0035448492
SN - 0022-3115
VL - 298
SP - 37
EP - 46
JO - Journal of Nuclear Materials
JF - Journal of Nuclear Materials
IS - 1-2
ER -