Inlet Air Supercharging of a 70 kW Microturbine
Bibliographic record
Abstract
The significant reduction in power output of small gas turbines at high ambient temperatures places the technology at a significant disadvantage compared with reciprocating engines. On site power applications in many jurisdictions are experiencing high power costs during summer peak times. A variable speed industrial fan combined with an evaporative cooler has been constructed and operated in the CETC laboratory in Ottawa, Canada to supply supercharged inlet air to a microturbine rated at 70 kW at ISO conditions. The supercharging system can raise the inlet air pressure by 10.5 kPa (42” wc). A mapping of the turbine performance has been done as a function of boost pressure, relative humidity and ambient air temperature. A net power increase has been observed from 57 kW to 70 kW at an ambient air temperature of 33°C (91°F) and RH of 60%, a 23% increase. Supercharging at lower temperatures yields lower net power increases since the microturbine generator rating is the limiting factor; for example an 11% increase in net power was observed at an inlet air temperature of 11°C (52°F) and RH of 60%. Supercharging was shown to decrease net fuel-to-electricity efficiency of this recuperated turbine by about 3%, at an air temperature of 33°C (91°F). An economic analysis using published power prices and weather data from Toronto explores the business case of using supercharging, with the best economies likely for multiple units or larger microturbines, such as 250 kW units. The objective of the project was to demonstrate the concept leading to a field trial in Toronto or in Calgary where the altitude offers a further benefit to the inlet air supercharging concept. Work is underway to design a control system suitable for field deployment for the concept.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".