Images processing and flow measurement applied to the thermographic analysis of heat-losses in boilers' isolation
Bibliographic record
Abstract
Infrared thermography is a non-contact evaluation technique which allows not only the registration of the temperature distribution on a surface, but also the calculation of the amount of heat flowing through it. Boilers are important for industry and the quantification of the heat losses is beneficial to avoid fuel waste. The present work suggests a methodology to calculate the thermic flow through boiler's isolation surfaces, using thermic images. With this, it is possible to find the flow by using a thermogram taking into consideration: the thermogram's range, knowing the camera's FOV, surface's emmisivity and characteristic length, object-to-camera distance, environmental temperature, and the assigned grey-level calibration curve to determined temperature range. A software tool to upload and process the information was developed. This tool can calculate the surface's average convection coefficient hc by using empiric correlations developed for common geometries and heat transfer equations to calculate the thermic flow. To test the technique functioning, the information given by the software tool was compared to the data given by the heat flow measurement thermal sensor. This comparison showed a 3% error range of relative error. The final validation was made on a waterwall-boiler's home isolated walls and the highest error obtained was close to 15%. Regardless the calibration curve was found under laboratory conditions and the empiric correlations to calculate hc are for isometric surfaces, the methodology presented a good performance. This then is a first step to quantify the global heat losses on boiler's isolation surfaces.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".