CFD characterization of monolithic reactors for kinetic studies
Bibliographic record
Abstract
A laboratory reactor for kinetic studies has been simulated using computational fluid dynamics (CFD). Analysis of temperature distribution within the system shows that adding an inert monolith upstream the catalyst enhances heat conduction and therefore significantly reduces radial temperature gradients. Adjustment of the heating coil plays an important role as well by allowing the gas phase to smooth out the radial temperature profile. Radiative heat transfer and its effects on both the heat losses from the catalyst and on the measurements with an unprotected thermocouple have been particularly investigated. In order to prevent both the falsifying effect of radiation and the influences from the ongoing reaction, thermocouples should be placed and shielded inside a clogged channel. An inert monolith that is placed downstream the catalyst effectively serves as a radiation shield and drastically reduces both axial and radial gradients. Studies of the dispersion in the system reveal that the FTIR's gas cell is the most important source of the overall broadening in the concentration signal, with the reactor tube being the next major source of the distortion. An algorithm based on the Tikhonov regularization method has been developed for calculating the deconvolution of the concentration data, which has refined the time‐resolution in transient experiments from 20 to 2 s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".