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Record W4415394246 · doi:10.1139/cgj-2025-0559

Hydraulic gradient based low-gravity simulation system for geomaterials

2025· article· en· W4415394246 on OpenAlexvenueno aff
Zhu Dongliang, Jian Chu, Xiaohui Cheng, Huafu Pei

Bibliographic record

VenueCanadian Geotechnical Journal · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Modeling and Analysis
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsDiscrete element methodHydrostatic equilibriumTriaxial shear testDirect shear testPenetration testHydraulic headWork (physics)Compatibility (geochemistry)Computer simulation

Abstract

fetched live from OpenAlex

Low-gravity experimental simulation is essential for advancing extraterrestrial geotechnical research, yet existing techniques face limitations in cost, duration, and scalability. This study presents a novel ground-based low-gravity system founded on the Hydraulic Gradient Similitude Method (HGSM), which applies the upward seepage force to counteract gravity. A first-generation small-scale apparatus was developed by simply modifying a conventional triaxial system, integrating a precision-controlled water supply system, a kaolin-boundary-modified triaxial chamber, and a cone penetration test (CPT) module. Key innovations include (1) stable low-gravity environments (>72 h) with a gravity ratio ( γ * ) adjustable from 1/6 (lunar gravity) to 1 (terrestrial gravity); (2) compatibility with conventional granular materials (e.g., quartz sands and lunar regolith simulants); and (3) high measurement precision validated through calibration tests. Experimental results confirmed the capability of the system to simulate low-gravity CPT responses, indicating notable changes in shear resistance mechanisms under reduced gravity. Comparative analysis with existing experimental data and discrete element simulations of CPT under low gravity further demonstrated the system’s reliability. This work provides a cost-effective, long-duration platform for simulating quasi-static geotechnical processes in low-gravity environments, offering a promising solution for pre-mission equipment testing and data interpretation in extraterrestrial exploration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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