Bibliographic record
Abstract
A hydraulic air compressor (HAC) is an isothermal gas compressor that uses hydropower to \ncompress air, originally developed by Charles Taylor in the 1890s to supply industry with \ncompressed air. In the modern revival of this technology, the hydropower will be provided by \npumps rather than natural sources. As such, energy efficiency is an important driver of \ncomponent design; all of the hydropower is consumed either to overcome irreversibility or to \ncompress air. The compressor relies on the increasing pressure of water flowing downward in a \ndowncomer to compress air in the form of bubbles being dragged along with the flow. The air \nentrainment process at the top of the downcomer is facilitated by a mixing head. At the bottom of \nthe downcomer, the bubbles are separated from the flow in a separator vessel. The objective of \nthis thesis is to develop the design methodology for the air entrainment and air-water separation \ncomponents on either end of the downcomer process. \nSeveral mixing heads were tested on a small (4.5 m height) prototype HAC. The test without a \nmixing head successfully entrained air, confirming that air entrainment is a system effect. Two \nheads with dissimilar geometry were associated with the lowest irreversibility, leading to the \nconclusion that the best design at that scale is a mixing head incorporating some form of vortex \nbreaker. Air entrainment is driven by a system energy balance and not exclusively by a local \nVenturi geometry. \nThe fraction of the air successfully captured in the plenum of the separator is called the separator \neffectiveness. Mechanistic models have been created to characterize both the irreversibility and \nseparator effectiveness of two types of gravity separator (horizontal and vertical orientation) for \niv \nthe design of separators for future commercial-scale compressors. The separator effectiveness \nmodels require as input the flow field information from computational fluid dynamics analysis \nand the bubble size distribution at inlet. The bubble size distribution was measured on the small \nprototype and used to select a bubble size prediction model for testing on a much larger scale (29 \nm height) demonstrator HAC. The displacement model for horizontal separators matched the \nactual performance at the prototype scale well, particularly at high flow rate. The vertical \nvelocity model produced a good match for the separator on the demonstrator HAC, but not for \nthe same bubble size model identified on the small prototype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".