Effect of evaporator length on the performance of a self-oscillatingfluidic heat engine (SOFHE)
Bibliographic record
Abstract
This paper reports the effect of evaporator length on the performance of a self-oscillating fluidic heat engine (SOFHE). The SOFHE is a thermal energy harvester, when coupled with an electro-mechanical transducer that was proposed to power wireless sensors widely used in the Internet of Things (IoT). The mechanical power of the SOFHE is in the order of fraction of milliwatts, which makes it a promising power supply for a range of wireless sensors with the power requirements of 10s µW. The SOFHE consists of a vapor bubble trapped by an oscillating liquid plug acting as a piston. The working principle of the SOFHE is similar to a singlebranch pulsating heat pipe. The engine is a small tube (inner diameter of 2 mm) filled with deionized water heated from a closed end and cooled from the opposite open end. By perturbing the equilibrium of the vapor bubble-liquid plug, oscillation start and are sustained by cyclic evaporationcondensation from a thin film in the vapor bubble. To characterize SOFHE’s mechanical power as a function of the evaporator length, measurements of pressure, oscillation amplitude, and frequency are conducted. As the evaporator length decreases (from 7 cm to 1 cm), the oscillation amplitude decreases (from 5.9 mm to 1.5 mm) while the frequency increases (from 27 Hz to 52 Hz). In theory, the power of SOFHE is proportional to the square of frequency and amplitude, so the trend in power is not obvious given the opposing effects. The results show a decrease in the mechanical power from 380 µW to 180 µW, which implies that the negative effect of the amplitude decrease dominates over the increase in frequency. A fourfold decrease was also observed in the net evaporation rate (from 1027 to 242 µg/s), which explains why the amplitude decreases with the evaporator length. The research findings contribute to the design of both SOFHEs and pulsating heat pipes by suggesting that a longer heated zone improves the performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".