Energy Harvesting by a Novel Substitution for Expansion Valves: Special Focus on City Gate Stations of High-Pressure Natural Gas Pipelines
Bibliographic record
Abstract
A countless amount of energy has been wasted in all kinds of expansion valves (EV) in industries. In fact, EVs, including regulators, throttling valves, capillary tubes, etc., have been used to intentionally reduce the potential of carrier fluid. City gate stations (CGS) have been recognized as one of the important points with high potential for energy harvesting due to its function for regulating natural gas (NG) pressure by EV. In this study, Tesla turbine (TT) is introduced as a new candidate for substitution of EV, particularly those that have been employed in CGS on high-pressure NG pipelines, as well as those applications in which high-potential fluid must be reduced to a low-potential state to form a complete thermodynamic cycle or to be used at end-user equipment. Although harvesting energy is one of the hottest fields of science and engineering, there are few traces of research on using a TT as an alternative for EVs, even for the industries possessing high-pressure lines. This numerical experiment intends to show the capability of TT as a robust candidate for substituting regulation valves through investigating thermohydrodynamic characteristics of the turbulent high-pressure compressible NG flow through a TT under different operation conditions. This study, with the objective of managing the exploitation of resources, can be considered as one step forward toward reinforcing economic and environmental pillars of sustainable development. It is also found that the generated power by TT can support the 285 7W LED simultaneously, or it is equivalent to 84.4 m2 area of the solar panel (150 W, 15.42% efficiency) for the climate condition of Toronto, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".