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Enregistrement W7009737095

Facilitating and enabling large-scale, hyper-resolution, groundwater modeling with distributed-memory parallel computing

2024· dissertation· en· W7009737095 sur OpenAlexaboutno aff

Notice bibliographique

RevueUtrecht University Repository (Utrecht University) · 2024
Typedissertation
Langueen
DomaineEngineering
ThématiqueUAV Applications and Optimization
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGroundwaterGroundwater flowGridGroundwater modelMODFLOWUnstructured gridWater resourcesScale (ratio)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Water managers and policymakers worldwide face the major challenge of securing the availability of fresh groundwater under excessive groundwater extraction and climate change. To this end, they need future projections of groundwater resources computed with numerical groundwater models. These models must have a sufficiently high spatial resolution (i.e., sufficiently small grid cell size) to capture the relevant physical processes. This has raised the call for models with grid cells that are "hyper resolution", i.e., with sizes that are less than or equal to 1 km. However, applying hyper-resolution numerical groundwater models at larger scales typically results in long runtimes and large memory requirements. To solve this problem, this research investigated the potential of distributed-memory parallel computing. MODFLOW, the world’s most widely used groundwater simulation code, was fully parallelized including the linear Krylov solvers applying the additive Schwarz preconditioner. Experiments were conducted on the Dutch national computer cluster, up to a (relatively small) maximum of 1024 processor cores. Two real-world existing groundwater models were facilitated, that still use structured grids, as well as two new applications were enabled that use more flexible (quad-based) unstructured grids. For facilitating existing models, the (quantitative) integrated National Hydrological Model of the Netherlands (NHM) was considered and the Sand Engine model, a (qualitative) 3D variable-density groundwater flow and salt transport model. Orthogonal recursive bisection partitioning was applied, and strong parallel scaling was evaluated. Large, obtained speedups with a relatively low number of cores (speedup of 22 and 86 with 64 and 256 cores, respectively), show that the applied parallelization could significantly increase the practical applicability of these existing groundwater models. As a first new enabled application, GLOBGM was developed, the world's first time-dependent global groundwater model with a resolution of 30 arc seconds (~ 1 km at the Equator). The METIS graph partitioner was applied in both a straightforward and (hydrological) area-based manner. Three continental-scale groundwater models and one for the remaining islands were derived, for a total of 278 million grid cells. Necessarily, parallel pre-processing of input data was applied. With a relatively low number of cores (382 cores in total), 58 years could be computed in parallel in one night (corresponding to a speedup of 138 with 224 cores for the largest Afro-Eurasia model). This demonstrated that GLOBGM could also be used by modelers who lack access to very large computer clusters. As a second new enabled application, a multi-resolution groundwater model was explored for the Netherlands to incorporate regional-scale models in the NHM. Again, area-based METIS partitioning was applied, but now for a smaller set with a larger variation in size. For evaluating weak parallel scaling, nationwide grid refinements up to a regional-scale resolution of 12.5 m were considered, resulting in a model with more than one billion grid cells. With a relatively low number of cores (up to 469 with a speedup of 326), 8 years could be computed in parallel in 2 days. This shows that these very large groundwater models are already within reach with today's computers.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,361
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,005
Tête enseignante GPT0,165
Écart entre enseignants0,160 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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