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Record W2900157453

Stick-slip behavior of ice interacting with concrete surfaces

2018· article· en· W2900157453 on OpenAlexaboutno aff
Nynke Nuus

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsStiffnessGeotechnical engineeringSea iceSlip (aerodynamics)Submarine pipelineGeologyArcticStructural engineeringEngineeringMaterials science
DOInot available

Abstract

fetched live from OpenAlex

When sea or lake ice interacts with concrete offshore structures in Arctic regions, the frictional forces between the ice and the structure cause abrasion of the concrete surface of the structure. This may endanger the structural integrity when the steel reinforcement gets exposed and experiences corrosion, and must therefore be taken into account in the design process. For the design of concrete offshore structures in Arctic conditions, an accurate description and prediction of ice-structure interaction is required. The interaction between moving ice and concrete surfaces is mainly governed by friction and the so-called stick-slip phenomenon. This phenomenon has been observed during laboratory and field testing and, although the physics of this phenomenon are believed to be well understood, the corresponding static and kinetic friction coefficients reported in literature have a widespread range and are inconclusive. This thesis aims at a more accurate identification of the ice-concrete friction coefficients.<br/><br/>For this graduation project, an experimental set-up was designed and stick-slip tests were carried out at Memorial University of Newfoundland, Canada. Additionally, a numerical model describing stick-slip behavior between ice and concrete was created. For the experimental set-up, a cylindrical fresh water columnar ice sample with a 50 mm radius and 50 mm height was attached to four springs with the same stiffness. The springs were attached to a support structure and throughout the test campaign, the stiffness of these springs was varied between 20 - 70 N/m per spring. To simulate one-dimensional ice-concrete interaction, the ice sample was placed near the edge of a rotating concrete slab. The normal load on the ice sample was varied from 0.7 to 2 kg by adding weight. In addition, the concrete velocity as experienced by the ice was varied between 0.15 and 0.82 m/s by increasing the rotational rate of the concrete slab. The static and kinetic friction coefficients were obtained from the experimental data and their dependence on normal load, velocity and spring stiffness was analyzed as well. The static friction coefficients found over the whole range of tests varied from 0.1 to 0.5. The analysis showed that the static friction coefficient decreases with increasing normal load and with increasing velocity. The kinetic friction coefficient was found to be in the range of 0.08 to 0.4 and may on average be obtained as 0.7 times the static friction coefficient. The kinetic friction coefficient, too, decreases with an increase in normal load and velocity. The influence of the spring stiffness was not clearly identified.<br/><br/>The friction coefficients that were calculated using the experimental data were provided as input to the numerical stick-slip model. An analysis was performed to verify that the model displays similar regression with the varied mass, velocity and spring stiffness, compared to what was observed in the experiment. The output was compared to the experimental data, and it was found that the model describes the stick-slip behavior as seen during the experiment with an accuracy between 84 and 99%. Although some further improvements to the model can be implemented, in general it is concluded that under the made assumptions, the model is valid for the prediction of stick-slip behavior as observed during the experiment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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