Enhancing the performance of a self-oscillating fluidic heat engine (SOFHE) through thermodynamic cycle and phase change characterization
Bibliographic record
Abstract
The aim of this thesis is to better understand the working principles of a recently discovered self-oscillating fluidic heat engine (SOFHE) by characterizing the thermodynamic cycle (P-V diagram) and phase change (evaporation-condensation). The SOFHE is proposed for thermal energy harvesting, coupled with an electromechanical transducer, for powering wireless sensors used in the Internet of Things (IoT). The SOFHE is a vapor bubble trapped by a liquid plug (acting as a piston) in a small diameter tube. This vapor bubble-liquid plug is set in oscillations by a cyclic evaporation-condensation of a thin liquid film formed by a wicking fiber. The first experimental demonstration of the SOFHE showed a low electrical power of 1 μW. However, it is still unclear how the unknown thermodynamic cycle of the SOFHE behaves under a load and how much mechanical power density the SOFHE can generate. To address this question, the thermodynamic cycle and power density of the SOFHE are experimentally characterized for the first time under a varying mechanical load. The main contribution of this characterization is to provide a baseline for impedance matching that is crucial for designing a compatible load for the SOFHE. It is also shown that the mechanical power density of the SOFHE is in the range of milliwatts/cm3 (maximum 0.5 mW/cm3) which makes it a promising solution to power a range of wireless sensors with a power requirement of tens of microwatt. We also studied the effect of the operating heat source temperature and two design parameters, including the length of the wicking fiber and the length of the liquid plug on the power of SOFHE. The significant increase of the power by increasing the fiber length was the driving force behind the second phase of our study in which we characterized the complex and unknown phase change profile (evaporation-condensation) of the SOFHE. A new setup was designed to visualize the variation of the thin film around the fiber as we played with its length inside the vapor zone. The observations proved our hypothesis of forming capillary corners between the fiber and the inner wall of the tube that pumps liquid from the liquid plug toward the vapor zone. This leads to the formation of a thin film with a very small thermal resistance that feeds evaporation. The rate of change of mass of vapor, the so-called phase change rate, is also measured. It is shown that to maximize the amplitude of the oscillation and consequently the power of the SOFHE, the amplitude of the phase change rate should increase and be completely out of phase with the position. A dimensionless number is also proposed to evaluate the effectiveness of the phase change rate profile. Finally, to better control the phase change, a new design of the SOFHE is proposed in which we can integrate tailored wicking structures to mimic the effect of the inserted fiber. The device is a square cross-section microchannel with sharp corners as well as an etched capillary path on the bottom wall that is fabricated by a standard microfabrication process. It is shown that the amplitude and consequently the power of SOFHE increase (a fivefold increase from 30 to 150 μw/ cm3) as we add a capillary path. This opens a new path towards engineering the phase change of the SOFHE by designing different wicking structures to improve its 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 | 0.000 |
| 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 | 0.000 | 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".