Multi-objective optimization on thermo-structural performance of honeycomb absorbers for concentrated solar power systems
Bibliographic record
Abstract
Honeycomb volumetric solar receivers have emerged as promising candidates for concentrating solar power applications because of their thermal and mechanical properties, enabling the efficient heating of fluids. Despite their potential, challenges remain in optimizing channel design and operating conditions to enhance thermodynamic performance. This study identifies design and operating configurations that maximize the thermodynamic performance and structural reliability of silicon carbide honeycomb volumetric solar receivers, focusing on thermal efficiency and factor of safety. We adopted a multi-objective optimization approach by integrating computational fluid dynamics, heat transfer, and thermal stress analysis. To streamline computational efforts, the Taguchi method was employed, reducing the number of required simulations while maintaining a relative error below 5 %. A critical mass flow to absorbed power ratio of 5 × 10 −6 (kg/s)/W was identified, beyond which thermal efficiency stabilizes, providing practical guidance for operational optimization. The optimal configuration achieved a thermal efficiency of 89.3 % and a factor of safety of 87.3 %, with a channel width of 3 mm, a thickness of 0.3 mm, an outlet static pressure of −70 Pa, and a radiation flux of 650 kW/m 2 . These findings establish a robust framework for optimizing honeycomb receivers, addressing thermal and structural performance while maintaining simplicity in manufacturing processes.
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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 | 0.000 | 0.000 |
| 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".