Experimental Investigation of Contact Heat Transfer at High Pressures
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".