Surface temperature and emissivity assessment in an enclosed cavity at high temperature using IR thermography
Bibliographic record
Abstract
• A novel method for numerical evaluation of cavity emissivity. • Relationship between apparent and intrinsic emissivity regardless of the complexity of the cavity. • Multispectral imaging to link simulation and experimental results. • Enhanced thermal imaging and remote sensing applications in harsh, high temperature and oxidizing environments. Assessing emissivity and temperature using IR thermography is challenging, particularly at high temperatures. Moreover, in an enclosed cavity, the multiple reflections of the signal before reaching the camera can lead to a geometry-dependent apparent increase in emissivity. In this work, we describe a novel approach for solving this problem in the context of measurements within a model aircraft engine combustion chamber. By using thermocouples and a multi-spectral camera, we experimentally validate our radiometric model for the cavity. We first show how to evaluate the amplification factor of a cavity using numerical tools, and we then use these results to apply corrections on the camera signals for in-band radiance (IBR) measurements. As a non-invasive and non-destructive technique, this approach can be used to monitor in real time the evolution of the temperature and emissivity over a large temperature range. As a specific example, we present and compare values measured by the camera and thermocouples inside the combustion chamber. Following the calibration step, we determine the emissivity and temperature distribution of the entire scene. The calculations are compared across 3 different wavebands to ensure their validity, with a difference lower than 2 %. Finally, we showcase the importance of assessing the in situ emissivity of a surface, which can change drastically with a large temperature variation and in a harsh environment. Using a calibration point given by a carefully placed thermocouple, the 2D temperature mapping of the whole scene is evaluated and compared in two different wavebands, leading to temperatures within Δ T = 10 0 C across the wavebands when the combustion chamber is at 700℃.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".