Physical, mathematical, and numerical modeling of a gas flow in pipeline networks with low Mach number expansion
ORAL
Abstract
To address the low Mach averaged model, we conceive a numerical method based on the characteristics method and the projection technique. In the initial stage, we present a numerical simulation for the "thermosiphon." This setup consists of two horizontal adiabatic pipes and two vertical pipes with prescribed wall temperatures, resulting in a temperature-driven flow. We incorporate in our algorithm the treatment of Dirac distributions as derivatives of the discontinuous gravity term at the corners and of periodic conditions.
We construct a quasi-exact solution serving as a benchmark for the validation of our numerical results.
Moving forward, we propose laws governing the junctions between multiple pipes and develop an algorithm capable of ensuring proper transmission conditions.
This analysis allows us to present numerical results for more complex pipeline configurations, providing quasi-exact solutions whenever feasible.
Overall, this study investigates further low Mach number gas flows through pipeline networks, employing advanced numerical techniques and validating our findings against established benchmarks.
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Publication: 1. Modeling of a gas flow under low Mach regime for pipeline networks, Mechanics & industry, in peer reviewing process<br>2. Modeling gas flow in a thermosyphon with a 1 D low Mach number expansion, Journal of Computational Physics, in peer reviewing process<br>3. Modeling of a 1 D gas flow and establishment of the junction conditions in a pipeline network with Low Mach Number Expansion, planned
Presenters
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Giuseppe Parasiliti Rantone
Institute Jean le Rond D'Alembert, CNRS, Sorbonne University
Authors
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Giuseppe Parasiliti Rantone
Institute Jean le Rond D'Alembert, CNRS, Sorbonne University
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Pierre-Yves Lagrée
Institute Jean le Rond D'Alembert, CNRS, Sorbonne University
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Nora Aïssiouene
Summit, Sorbonne University
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Yohan Penel
Lab. Jacques-Louis Lions (LJLL),Sorbonne University, CNRS, Université de Paris; INRIA, team ANGE