Modeling of Biodiesel Fueled Micro Gas Turbine
Bibliographic record
Abstract
Biodiesel is an environmentally benign renewable alternative for conventional diesel fuel, and its utilization in macro gas turbines (MGT) is an interesting option for many applications. The objective of this work is to develop a steady-state model to evaluate the performance of a micro gas turbine fueled by the blends of biodiesel and petrodiesel. The concentration of inlet biodiesel to the model was 10%, 20%, and 30%. In order to validate the developed model, the results of modelling work were compared against the experimental data obtained from a micro gas turbine experimental unit. The engine was modified by mounting various sensors to monitor and record system performance parameters, such as pressure, temperatures, and flow rates at various locations as well as output power, and ambient conditions. The results indicate that most parameters are influenced, to some degree, by changes in the fuel composition. This indicates that although most MGTs can be potentially operated by a high concentration of biodiesel blends, before this fuel switching can be implemented, the system operational parameters should be evaluated by the system modeling to predict possible negative impacts of biodiesel in the inlet fuel on the engine.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".