shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models
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ID: 199199
2018
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Abstract
The approaches taken to describe and develop spatial discretisations of the
domains required for geophysical simulation models are commonly ad hoc,
model- or application-specific, and under-documented. This is particularly
acute for simulation models that are flexible in their use of multi-scale,
anisotropic, fully unstructured meshes where a relatively large number of
heterogeneous parameters are required to constrain their full description. As
a consequence, it can be difficult to reproduce simulations, to ensure a
provenance in model data handling and initialisation, and a challenge to
conduct model intercomparisons rigorously.
This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.
Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/Shingle. Further details of the project presented at http://shingleproject.org.
This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions that enable full accounts of provenance, sharing, and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed that is automated, robust and repeatable, quick-to-draft, rigorously verified, and consistent with the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.
Library code, verification tests, and examples available in the repository at https://github.com/shingleproject/Shingle. Further details of the project presented at http://shingleproject.org.
| Reference Key |
candy2018geoscientificshingle
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|---|---|
| Authors | ;A. S. Candy;J. D. Pietrzak |
| Journal | international journal of quantum chemistry |
| Year | 2018 |
| DOI |
10.5194/gmd-11-213-2018
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| URL | |
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