Adaptive Refinement Strategies for the Simulation of Gas Flow in Networks using a Model Hierarchy
For engineers simulating gas networks, this work improves computational efficiency of adaptive refinement, though it is an incremental improvement over existing methods.
The paper addresses adaptive refinement for gas flow network simulation using a model hierarchy, combining spatial, temporal, and model adaptivity. Two novel greedy-like strategies significantly reduce computational cost compared to the current strategy, as shown in theoretical and realistic experiments.
A model hierarchy that is based on the one-dimensional isothermal Euler equations of fluid dynamics is used for the simulation and optimisation of gas flow through a pipeline network. Adaptive refinement strategies have the aim of bringing the simulation error below a prescribed tolerance while keeping the computational costs low. While spatial and temporal stepsize adaptivity is well studied in the literature, model adaptivity is a new field of research. The problem of finding an optimal refinement strategy that combines these three types of adaptivity is a generalisation of the unbounded knapsack problem. A refinement strategy that is currently used in gas flow simulation software is compared to two novel greedy-like strategies. Both a theoretical experiment and a realistic gas flow simulation show that the novel strategies significantly outperform the current refinement strategy with respect to the computational cost incurred.