Heat flux measurement using 3D-printed continuous wire polymer composite sensors
Bibliographic record
Abstract
Fused filament fabrication (FFF) 3D printing was used to fabricate continuous wire polymer composite (CWPC) heat flux sensors; the integrated wires acted as resistive sensing elements. Sensor configurations consisting of polylactic acid (PLA) with copper wire (Cu), PLA with nickel (Ni) wire, and thermoplastic polyurethane (TPU) with Cu wire were investigated. For each composition, samples with different numbers of layers were 3D printed to investigate the effect of sensor thickness on performance. Element spacing, polymer conductivity, and wire temperature coefficient of resistances were quantified. Performance testing of the 3D-printed CWPC as a heat flux sensor showed promising results for all compositions and demonstrated their ability to be used as heat flux sensors for low temperature and low heat flux applications. Measurement errors were less than 17% for all configurations and less than 10% for 2-layer samples at the higher heat fluxes. A case study demonstrated the use of a 3D-printed flexible CWPC heat flux sensor to estimate heat loss from an insulated system with good accuracy. This sensor fabrication approach can potentially be employed in a wide range of applications because it allows for custom geometries and can use different types of polymers and sensing elements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".