LUNDIsim: model meshes for flow simulation and scientific data compression benchmarks
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arXiv
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| Format: | Preprint |
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2025
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| author | Duval, Laurent Payan, Frédéric Preux, Christophe Bouard, Lauriane |
| author_facet | Duval, Laurent Payan, Frédéric Preux, Christophe Bouard, Lauriane |
| contents | The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies.
We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability.
Among remedies, lossless and lossy compression techniques are becoming popular to better manage dataset volumes. Performance assessment -- with comparative benchmarks -- require open datasets shared under FAIR principles (Findable, Accessible, Interoperable, Reusable), with MRE (Minimal Reproducible Example) ancillary data for reuse. We share LUNDIsim, an exemplary faulted geological mesh. It is inspired by SPE10 comparative Challenge. Enhanced by porosity/permeability datasets, this dataset proposes four distinct subsurface environments. They were primarily designed for flow simulation in porous media. Several consistent resolutions (with HexaShrink multiscale representations) are proposed for each model. We also provide a set of reservoir features for reproducing typical two-phase flow simulations on all LUNDIsim models in a reservoir engineering context. This dataset is chiefly meant for benchmarking and evaluating data size reduction (upscaling) or genuine composite mesh compression algorithms. It is also suitable for other advanced mesh processing workflows in geology and reservoir engineering, from visualization to machine learning.
LUNDIsim meshes are available at https://doi.org/10.5281/zenodo.14641958 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13636 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LUNDIsim: model meshes for flow simulation and scientific data compression benchmarks Duval, Laurent Payan, Frédéric Preux, Christophe Bouard, Lauriane Distributed, Parallel, and Cluster Computing 68P30, 94A08, 37M05 The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies. We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability. Among remedies, lossless and lossy compression techniques are becoming popular to better manage dataset volumes. Performance assessment -- with comparative benchmarks -- require open datasets shared under FAIR principles (Findable, Accessible, Interoperable, Reusable), with MRE (Minimal Reproducible Example) ancillary data for reuse. We share LUNDIsim, an exemplary faulted geological mesh. It is inspired by SPE10 comparative Challenge. Enhanced by porosity/permeability datasets, this dataset proposes four distinct subsurface environments. They were primarily designed for flow simulation in porous media. Several consistent resolutions (with HexaShrink multiscale representations) are proposed for each model. We also provide a set of reservoir features for reproducing typical two-phase flow simulations on all LUNDIsim models in a reservoir engineering context. This dataset is chiefly meant for benchmarking and evaluating data size reduction (upscaling) or genuine composite mesh compression algorithms. It is also suitable for other advanced mesh processing workflows in geology and reservoir engineering, from visualization to machine learning. LUNDIsim meshes are available at https://doi.org/10.5281/zenodo.14641958 |
| title | LUNDIsim: model meshes for flow simulation and scientific data compression benchmarks |
| topic | Distributed, Parallel, and Cluster Computing 68P30, 94A08, 37M05 |
| url | https://arxiv.org/abs/2508.13636 |