Experimental Validation of a Design Method for Small Scale Pillow Plate Heat Exchangers
Bibliographic record
Abstract
Heat exchangers play a crucial role in various industries, with increasing demand for innovative and efficient designs.Pillow Plate Heat Exchangers (PPHEs) offer promising advantages, yet their potential remains underexplored, particularly in small-scale applications.This study addresses the gap in understanding the thermo-hydraulic behaviour of compact PPHEs by investigating a novel Small-Scale Pillow Plate Heat Exchanger (SSPPHE) designed using the effectiveness-NTU (ε-NTU) method.We developed and experimentally validated a design approach for SSPPHEs with the smallest achievable geometric parameters.Our methodology combines theoretical modelling with rigorous experimental testing, utilizing water as the working fluid in both channels.Key findings reveal that the ε-NTU model predictions align well with experimental data, with thermal power and efficiency deviations within ±15%.However, we observed an underestimation of 15-30% in the Darcy friction factor, highlighting the significance of border effects in compact designs.This research not only validates the applicability of existing correlations to SSPPHEs but also provides critical insights into their performance characteristics.Our findings pave the way for more efficient and compact heat exchanger designs, with potential applications across various industries, from microelectronics cooling to energy 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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".