Modeling complex multi-component reactive-transport systems: Towards a simulation environment based on the concept of a knowledge base.

*(English)*Zbl 1015.92050Summary: A modelling framework within which transport processes in the hydrosphere can be described and interfaced with relevant biogeochemical reactions is presented. Three key elements of this simulation environment are discussed:

(1) a numerical engine for solving sets of coupled nonlinear process equations; (2) an automated procedure for model code generation (‘Automatic Code Generator’); (3) a Web-distributed Knowledge Base (KB) of processes.

The Automatic Code Generator translates the information selected in the KB into computer algorithms using the principles defined in the numerical engine. The code CONTRASTE is a first attempt at developing such a modelling framework. It allows one to easily select, adapt and combine a specific set of biogeochemical processes relevant to a user-defined application. The workings of CONTRASTE are described by means of examples which demonstrate how the various components of the simulation environment are coupled and automated. Prospects for future developments towards a fully automated model generation procedure are discussed.

(1) a numerical engine for solving sets of coupled nonlinear process equations; (2) an automated procedure for model code generation (‘Automatic Code Generator’); (3) a Web-distributed Knowledge Base (KB) of processes.

The Automatic Code Generator translates the information selected in the KB into computer algorithms using the principles defined in the numerical engine. The code CONTRASTE is a first attempt at developing such a modelling framework. It allows one to easily select, adapt and combine a specific set of biogeochemical processes relevant to a user-defined application. The workings of CONTRASTE are described by means of examples which demonstrate how the various components of the simulation environment are coupled and automated. Prospects for future developments towards a fully automated model generation procedure are discussed.

##### MSC:

92D40 | Ecology |

65C20 | Probabilistic models, generic numerical methods in probability and statistics |

92E20 | Classical flows, reactions, etc. in chemistry |

92-08 | Computational methods for problems pertaining to biology |

86A05 | Hydrology, hydrography, oceanography |

68U20 | Simulation (MSC2010) |

##### Keywords:

biogeochemistry; mass transport; numerical models; knowledge base; automated differentiation
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\textit{P. Regnier} et al., Appl. Math. Modelling 26, No. 9, 913--927 (2002; Zbl 1015.92050)

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