Modeling of Deposit Formation from “Waxy” Mixtures via Moving Boundary Formulation: Radial Heat Transfer under Static and Laminar Flow Conditions
Bibliographic record
Abstract
A mathematical formulation, based on heat transfer considerations, is presented for solids deposition from “waxy” or paraffinic mixtures. The proposed unsteady-state model uses the moving boundary problem approach for investigating the deposit-layer growth in a circular pipe from binary eutectic mixtures of n -C 16 H 34 and n -C 28 H 58 . The model equations were solved numerically to explore the deposition behavior and the growth of deposit layer with time in the radial direction under both static and laminar flow conditions. The deposit-layer growth was predicted to be dependent on the rate of heat transfer at the liquid−deposit interface as well as in the liquid and deposit regions. For cooling under static conditions, complete pipe gelling was predicted to be faster for relatively lower values of mixture temperature, pipe-wall temperature, concentration of C 28 in the mixture, and pipe diameter. For cooling under laminar flow conditions, higher values of mixture temperature, pipe-wall temperature and/or heat transfer coefficient yielded a thinner deposit layer with a faster approach to thermal steady state.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".