DEVELOPMENT OF TECHNOLOGIES FACILITATING THE TRANSITION TO RENEWABLE ENERGY SOURCES: OPPORTUNITIES FOR APPLICATION OF REACTIVE HYDRO-STEAM TURBINES FOR LOW-POTENTIAL HEAT RESOURCES
Bibliographic record
Abstract
The study discusses the opportunities for producing geothermal energy, which is a renewable resource providing a continuous supply of heat from the Earth's interior.Unlike fossil fuels, which are non-renewable and cause environmental degradation, geothermal energy is of great interest to researchers, particularly in the context of sustainable development principles.The authors demonstrate the advantages of a new hydro-steam turbine (HST) using the heat of water from geothermal springs and heating boilers for power generation.The HST is designed as a Segner wheel with a steam-water mixture flowing out of a Laval nozzle, the resulting reactive force driving the turbine rotor and the power generator.The features of the HST thermodynamic process are analyzed in the form of a case study describing the design of a 20 kW plant and providing the technical specifications of the installation for a heating boiler plant: hot water flow rate of 7 kg/s, temperature of 130°C, and inlet pressure of 0.6 MPa.The authors present the thermal schemes of the HST in two variants (at a boiler house and as a part of a geothermal power plant).The study concludes that the main advantages of the HST are the absence of elements subject to erosion under the action of steam and water flow and the simplicity of the design increasing the availability of geothermal energy for power generation.Among the limitations of the HST is its low economic efficiency narrowing down its application to conditions where geothermal fields are available for local power supply.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".