Bibliographic record
Abstract
In this thesis, a numerical model for heat and mass transfer in a membrane is developed to identify the onset of saturation (frosting) in the membrane and verified. The numerical model is a porous media model based on the theory of local volume averaging and local thermal equilibrium and determines the temperature and relative humidity profiles inside the membrane in order to show the location and time of saturation. Warm and humid air flows above the membrane and cold liquid desiccant flows below the membrane. The goal of this research is to determine how to avoid saturation conditions through the membrane because saturation is essential for frosting. The numerical model is validated with experimental data and shows that frost formation can be prevented or delayed by controlling the moisture transfer rate through the membrane which is a new idea and thus a contribution to the research literature. \nThe results of the numerical model show that the temperature and humidity profiles inside the membrane are linear at steady-state conditions. Therefore, an analytical model based on thermal and mass resistances is used to accurately predict the temperature and relative humidity at the top and bottom surfaces of the membrane under steady-state conditions. The analytical model is verified with experimental and numerical data at steady-state conditions. With the analytical model, the conditions that result in saturation conditions can be determined by directly solving two algebraic equations.\nThe numerical and analytical models are also used to determine the sensitivity of several parameters on the time and location of saturation, including: the vapor diffusion coefficient, the heat and mass transfer coefficients, the thickness of the membrane, the liquid desiccant concentration, and the thermal conductivity of the membrane.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".