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Record W4220940798 · doi:10.5194/egusphere-egu22-8636

Finite-Volume Flux Reconstruction and Semi-Analytical Particle Tracking for Finite-Element-Type Models of Variably Saturated Flow in Porous Media

2022· preprint· en· W4220940798 on OpenAlexaff
Philipp Selzer, Jonas Allgeier, René Therrien, Olaf A. Cirpka

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFinite element methodFlow (mathematics)Conservation of massFinite volume methodTracking (education)Type (biology)MechanicsMathematicsMathematical analysisPhysicsGeologyThermodynamics

Abstract

fetched live from OpenAlex

<p align="justify"><span>Particle tracking is the most direct and a computationally efficient method to determine travel times and trajectories in subsurface flow modeling. Accurate and consistent particle tracking requires element-wise mass conservation and conforming velocity fields, which ensure continuity of the normal-flow component on element boundaries. These conditions are not met by standard finite-element-type methods. D</span><span>e</span><span>spite this shortcoming, finite-element-type methods are often used in subsurface flow modeling because they continuously approximate the potential-head field and can easily handle unstructured grids and full material tensors. Acknowledging these advantages and the wide-spread use of finite-element-type models in subsurface flow simulations, we present a novel postprocessing technique that reconstructs a cell-centered finite-volume approximation from a finite-element-type primal solution of the variably-saturated subsurface flow equation to obtain conforming, mass-conservative fluxes. Using the resulting velocity fields, we derive a semi-analytical, parallelized particle tracking scheme applicable to triangular prisms, which leads to consistent and mass-conservative trajectories and associated travel times. Compared to other postprocessing schemes, our flux reconstruction is stable, robust, and fast as it only solves a linear elliptic problem on the order of the number of elements, whereas the original flow problem was transient and non-linear. The methods are implemented as postprocessing codes and linked to the </span><span>finite-element-type</span> <span>code</span><span> HydroGeoSphere, but could also be linked to any other software yielding a solution of variably saturated flow in porous media on triangular prisms. The postprocessing codes can handle catchment-scale models including heterogeneous materials, geometries, and boundary conditions, and facilitate to track a million particles through a catchment in just a few minutes on a Standard-PC in Matlab. The approach is described by Selzer et al. (2021).</span></p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.273
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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