Thermophysical Properties Measurement of Interior Car Materials vs. Temperature and Mechanical Compression
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Thermophysical properties of materials used in the design of automotive interiors are needed for computer simulation of climate conditions inside the vehicle. These properties are required for assessment of the vehicle occupants' thermal sensation as they come in contact with the vehicle interior components, such as steering wheels, arm rests, instruments panel and seats.</div><div class="htmlview paragraph">This paper presents the results of an investigation into the thermophysical properties of materials which are required for solving the non-linear Fourier equations with any boundary conditions and taking into account materials' specific heat, volume density, thermal conductivity, and thermal optical properties (spectral and total emissivity and absorptivity). The model and results of the computer simulation will be published in a separate paper.</div><div class="htmlview paragraph">The tested materials included foam, leather/foam laminated materials, and a few plastic laminated materials, which were used in the construction of various automotive interior parts. One original aspect of this work was the testing of plastic foam samples both at a wide temperature range from −20°C to +60°C and at varying compression levels up to 60%. Such data is needed for calculation of heat conduction within occupied automotive seats which are undergoing different static and dynamic loads.</div><div class="htmlview paragraph">Thermal conductivity was determined as the product of thermal diffusivity and volume specific heat. The thermal diffusivity was measured in accordance to ASTM STP 1320, and the specific heat was measured according to ASTM E1269, both in the temperature range from −20°C to +60°C. The emissivity and absorptivity were measured according to ASTM E408 and ASTM E903, respectively at room temperature.</div><div class="htmlview paragraph">The new experimental data obtained has scientific and practical interests for thermal science and engineering applications. The thermal conductivity results of the foam materials in the wide range of temperatures and compression/deformation are of interest for the development of heat transfer mechanisms in porous media, for materials science and technology. All data together enable the development of advanced mathematical models for the design, assessment and optimization of climate conditions inside cars and other vehicles.</div></div>
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 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".