Thermal Joint Resistance of Conforming Rough Surfaces with Grease-Filled Interstitial Gaps
Bibliographic record
Abstract
Thermal joint conductance and resistance models are presented for grease- lled joints formed by conforming rough surfaces under light contact pressures. One model includes the thermal effect of contacting asperities, whereas the second, simpler model is based on conduction across the gaps only. The models are compared against recently publishedgrease andphase-changematerial (PCM) dataobtained at onecontactpressure, copper surfaces having three levels of surface roughness, four values of grease thermal conductivity, and two values of PCM conductivity.The models and the data are found to be in agreement over a wide range of a joint parameter de ned as the ratio of the effective joint roughness and the thermal conductivity of the gap substance. The models can be used to predict an upper bound on the joint conductance and a lower bound on the speci c joint resistance for surfaces that are turned and milled. Nomenclature Aa, Ac, Ag = apparent, contact, and gap area, m2 c1 = Vickers correlation coef cient, MPa c2 = Vickers correlation coef cient dV = Vickers average diagonal,m HB = Brinell hardness,MPa H ¤B = dimensionlessBrinell hardness, HB=3178 Hc = contact microhardness,MPa HV = Vickers microhardness,MPa hc, hg, h j = contact, gap, and joint conductances,W/m2 ¢K kg = grease conductivity,W/m ¢K ks = harmonic mean thermal conductivity, 2k1k2=.k1C k2/, W/m ¢K k1, k2 = solid thermal conductivities,W/m ¢K m = effectivemean absolute asperity slope, p.m21Cm22) m p = mean plane in equivalent surface m1, m2 = mean absolute asperity slopes of surfaces m p1, m p2 = mean planes in surfaces 1 and 2 P = apparent contact pressure,MPa Q = joint heat transfer rate, W Rc, Rg, R j = contact, gap, and joint resistances,K/W r j = speci c joint resistance, 1=h j, m2K/W Y = separation of mean planes, m 1T j = joint temperature drop, K = effective joint surface roughness,p
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".