Optical performance enhancement of solar flux distribution uniformity of solar parabolic dish cavity system
Bibliographic record
Abstract
The design of the cavity receiver plays a crucial role in the conversion from solar to thermal energy that is absorbed in the cavity receiver to the heat transfer fluid. Nonuniform heat flux at the surface of cavities generates structural failure due to thermal unbalance. This research focuses on the optimization of cavity design based on position, aperture diameter, and eccentricities of elliptical-shaped cavity to enhance the uniformity of solar flux distribution on the cavity walls. The optical analysis is performed using Monte Carlo ray tracing using COMSOL Multiphysics on conical, cylindrical, rectangular, spherical, and elliptical shapes to determine the solar heat flux distribution. The validation of the computational model is performed through published experimental and analytical data. The flux distribution is more dispersed in spherical and elliptical-shaped cavities when positioned above the focal plane. The flux distribution uniformity increases when the cavities are positioned at a higher distance from the focal plane whereas the larger aperture opening eases the absorption of radiation flux on the surface. Partial obstruction of incoming rays starts to occur when positioning the cavity at a higher distance as well as in smaller aperture openings. The optimum configuration of the elliptical cavity is with 30% eccentricity, 100 mm above the focal plane, and 250 mm opening, providing a solar flux distribution uniformity factor of 0.585.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".