A New Approach to Solve Inverse Boundary Design of a Radiative Enclosure With Specular–Diffuse Surfaces
Bibliographic record
Abstract
Abstract The maintenance of uniform temperature distribution affects the efficiency in most industrial applications. In this study, a novel strategy has been developed for inverse radiative boundary design problems in radiant enclosures with participating medium. This study presents the Backward Monte Carlo method to investigate the inverse boundary design of an enclosure composed of specular and diffuse surfaces. A new optimized Monte Carlo method is proposed to determine the temperature distribution of heaters to achieve desirable prescribed uniform heat flux on the design surfaces. The proposed approach is highly efficient and simple to implement with appropriate results. The evaluated heat fluxes on design surfaces and temperature distribution of heaters are compared with the case where the reradiating walls are assumed to be perfectly diffuse. In the proposed approach, for a specific range of specularity, the absorptivity of the reradiating surfaces does not affect the temperature distribution of heaters. Compared to the diffuse walls, the specular walls have a more uniform temperature distribution and heat flux of heaters. This finding will provide insight into solar furnaces design to enhance temperature uniformity, making specular surfaces suitable in many industrial applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".