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Record W4283580944 · doi:10.11159/ffhmt22.140

Experimental Investigation of Contact Heat Transfer at High Pressures

2022· article· en· W4283580944 on OpenAlexvenueno aff
Tim Göttlich, Thorsten Helmig, Nicklas Gerhard, Thomas Bergs, Reinhold Kneer

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsHeat transferMaterials scienceMechanicsPhysics

Abstract

fetched live from OpenAlex

Besides the mechanical description of technical systems, a thermal modelling is frequently required. In this context, contact heat transfer coefficients are essential boundary conditions for the thermal simulation of technical systems consisting of several individual components. This parameter is mainly influenced by the surface structure and roughness of the contact partners as well as the applied contact pressure. However, although the influencing parameters are well known, an analytical determination is quite difficult. Therefore, an experimental quantification is mandatory. So far, experiments in literature have primarily focused on the investigation of contact heat transfers at moderate loads up to 100 MPa. Nevertheless, there are some applications where solids are in contact at very high pressure and resulting heat transfer between them plays an essential role, such as the interface between the tool and the workpiece during machining. The aim of this work is to present a novel experimental methodology to determine contact heat transfers at high loads. In this approach, infrared thermography is used to measure the temperature data, which is consequently used to solve an inverse problem using the conjugate gradient method, which provides the corresponding contact heat transfer coefficients. Furthermore, first experimental results for a load-dependent heat transfer for loads between 200 and 1200 MPa are presented and occurring effects are discussed.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicAdhesion, Friction, and Surface InteractionsFrench-language works237,207