Recomputing Causality Assignments on Lumped Process Models When Adding New Simplification Assumptions

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Author
Belmonte, Antonio
Garrido, J.
Jiménez-Hornero, Jorge E.
Vázquez Serrano, Francisco J.
Publisher
MDPIDate
2018Subject
Structural analysisBipartite graph
Bipartite matching
Model simplification assumptions
Over-constrained systems
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This paper presents a new algorithm for the resolution of over-constrained lumped process
systems, where partial differential equations of a continuous time and space model of the system
are reduced into ordinary differential equations with a finite number of parameters and where the
model equations outnumber the unknown model variables. Our proposal is aimed at the study and
improvement of the algorithm proposed by Hangos-Szerkenyi-Tuza. This new algorithm improves
the computational cost and solves some of the internal problems of the aforementioned algorithm
in its original formulation. The proposed algorithm is based on parameter relaxation that can be
modified easily. It retains the necessary information of the lumped process system to reduce the time
cost after introducing changes during the system formulation. It also allows adjustment of the system
formulations that change its differential index between simulations.