Set Up and Operational Experience With a Micro-Turbine Engine for Research and Education
Bibliographic record
Abstract
Set up and operation of a mid to large size gas turbine requires a significant investment in the engine and the test cell. The continuing cost of operation and maintenance is also substantial. Both the capital and operating costs are well outside the budget of many educational institutions, and small research centres. Micro-turbines, in particular engines used for model aircraft, are a viable alternative. Their capital costs are low and existing facilities can often be modified to support them. Micro-turbines are complete turbojets often utilizing a centrifugal compressor from a turbo-charger and an axial turbine. This work utilized a micro-jet engine commonly used to propel remote control aircraft. The engine has been set up for research into component degradation and as a laboratory for an upper year engineering course in turbomachinery. It is rated by the manufacturer to produce 150 N of thrust at 132,000 RPM. This paper examines some of the problems encountered in installing small total pressure and temperature probes in a micro engine. The entire test rig, including measurement of thrust and mass flow is presented. As well, the addition of a PC based control system is discussed. Operating data is presented and compared to larger engines to demonstrate the viability of this engine as a test bed. Some problems encountered in using an engine such as this beyond its normal operating envelope, along with some solutions, are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".