Thermal-mechanical coupled stress prediction of printed circuit heat exchanger in the supercritical CO2 Brayton cycle
Bibliographic record
Abstract
Printed circuit heat exchanger (PCHE) is widely recognized as the most promising heat exchanger for supercritical CO 2 (SCO 2 ) Brayton cycle. Stress assessment is critical to ensuring the safety and longevity of PCHE. This study addresses a critical gap in the thermal-mechanical stress assessment of PCHE for SCO 2 Brayton cycles by developing novel quantitative models to predict equivalent stresses at semicircular channel tips. Unlike conventional ASME codes, which overlook thermal stress, the pseudo-2D ANSYS Workbench model integrating both thermal and mechanical stresses, was used to offer a comprehensive evaluation. Key structural parameters (channel diameter, plate thickness, ridge thickness) and operational parameters (pressure, temperature difference) were analyzed. The results reveal that mechanical stress is most sensitive to cold-side pressure, while thermal stress correlates linearly with temperature gradients. Dimensional analysis yielded predictive formulas for thermal stress (±13.3% error) and mechanical stress (±14.3% error), validated against finite element method results. A backpropagation neural network further improved prediction accuracy (errors <10%). The proposed models streamline PCHE design verification and dynamic control optimization, ensuring safer and more efficient SCO 2 cycle operation. This research advances sustainable energy systems by providing reliable tools for PCHE stress assessment, with potential applications in solar, nuclear, and waste heat recovery 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".