Optimizing the Performance of Liquid-Based Medium-Temperature Volumetric Solar Thermal Receivers Using Genetic Algorithms
Bibliographic record
Abstract
Abstract Liquid-based volumetric solar thermal receivers are a promising alternative to the traditional tubular receivers. By absorbing solar energy directly in a semi-transparent medium, volumetric receivers achieve higher capture efficiency and more uniform temperatures, overcoming the low capture efficiency issues faced by tubular receivers at high temperatures. However, accurately predicting the thermofluid behavior of volumetric receivers under various operating conditions is challenging due to the complex interactions between radiation, convection, conduction, and volumetric heating. A mechanistic model validated under a 6.5 kW solar simulator was developed, and demonstrated short-term (up to 7min) accuracy of predicting such behavior, yet the optimization of key parameters which can increase system efficiency and avoid the development of hot spots inside the receiver remains unexplored. This study addresses this parameter exploration and optimization using an evolutionary algorithm, specifically a genetic algorithm (GA), focusing on five critical parameters: attenuation coefficient, solar flux, receiver depth, top surface emissivity, and heating time. By using MATLAB’s multi-objective genetic algorithm solver, we identified multiple solutions representing trade-offs between efficiency and temperature uniformity inside the receiver. This evolutionary computation method constructs the Pareto front—a set of solutions that represent the best trade-offs between competing objectives—by iteratively selecting, recombining, and mutating candidate solutions while maintaining population diversity. The ability to produce multiple optimal solutions is highly valuable, as it provides a range of possible outcomes that can be adjusted to different operational conditions, ultimately improving the performance of volumetric receiver systems.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".