Towards an Engineering Model of Material Characteristics for Input to Ham Transport Simulations - Part 1: An Approach
Bibliographic record
Abstract
Heat, Air and Moisture (HAM) modelling of building performance is a quite young research subject but the experimental determination of material properties is often based on classical methods. One should review the manner in which we define characteristic material parameters and there is a need to develop an approximation used to generate the required material functions for input to HAM-transport simulations. The paper presents such an approach, called an engineering model for hygrothermal material characterisation. The paper poses the question, how to arrive at input data that can be used for a model based on thermodynamically defined potentials (Only such a model allows introduction of new potential components (freezing depression, osmotic pressure, air pressure, overburden envelope pressure)) (e.g., Grunewald, J. (1997) and Grunewald, J. (1999)) and yet the respective functions used to describe changes in the material response as a function of the variables of state. Such functions should have a reasonable precision and goodness of fit while the number of measured points must be reduced to a minimum. Those measurements should be relatively easy to perform (i.e., they would not require determination of temporal and spatial profiles of moisture). This discussion paper highlights steps already taken (Part 1), and lists issues that need to be resolved before reaching this goal (Part 2).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".