Thermal Performance and Pressure Drop Optimization in Particle-Based Solar Receivers for Next-Generation CSP Plants
Bibliographic record
Abstract
Solar particle receivers offer significant potential for enhancing next-generation concentrated solar power (CSP) plant efficiency through ultra-high operating temperatures (>700℃).This study addresses the critical challenge of balancing thermal performance against hydraulic losses by developing an integrated optimization methodology combining high-fidelity multiphase computational fluid dynamics (CFD), response surface methodology (RSM), and multi-objective evolutionary algorithms (NSGA-II).Parametric analysis evaluated receiver geometry (inclination angle: 30° -75° , hydraulic diameter: 0.05-0.20 m), particle flow dynamics (mass flow rate: 0.5-2.0kg/s), and incident radiation (≤800 kW/m² ).Results quantified a fundamental trade-off: thermal efficiency (ηth) declined by 24% as mass flow rate increased from 0.5 to 2.0 kg/s, while pressure drop (ΔP) rose by 320%.Pareto-optimal solutions revealed high-efficiency designs achieving ηth > 82.3% at ΔP > 5.8 kPa and low-resistance configurations maintaining ΔP < 2.1 kPa with ηth = 71.6%.Crucially, the balanced solution (ηth = 78.1%,ΔP = 3.4 kPa) reduced pumping power requirements by 32% compared to maximum-efficiency designs.Optimal operational windows were identified at inclination angles of 55° -65° and hydraulic diameters of 0.12-0.17m, with a quantified trade-off of 2.9% ηth reduction per 1 kPa ΔP decrease near the Pareto knee.This work establishes actionable design protocols for achieving >78% thermal efficiency with minimized hydraulic penalties, advancing economically viable high-temperature CSP 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 | 0.000 | 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 | 0.001 | 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".