Validation of a Gas Turbine Thermodynamic Model Without Accurate Component Maps
Bibliographic record
Abstract
The authors have developed an engine performance model for use within a physics-based analysis tool to predict gas turbine engine life. The model employs a multivariate optimization method to solve the gas turbine thermodynamic equations, and incorporates a calibration phase to capture the behavior of individual engines without requiring accurate component maps. To validate this approach, a database of test cell data for a turboprop engine has been used. The data consists of approximately 80 engine tests; each one with five operating points. Using a cross-validation method, each engine was uniquely calibrated using four of the operating points, and then validated using parameters from the fifth operating point. To benchmark the calibration process, these analyses were repeated without the calibration stage. The un-calibrated outputs showed a lack of both precision and accuracy, due to imprecision in the component maps, and variation from engine to engine. In contrast, the calibrated outputs of compressor discharge temperature (CDT), compressor discharge pressure (CDP), and turbine inlet temperature (TIT) were predicted within 1% error for more than 95% of all cases. Although most of the key thermodynamic parameters were predicted accurately, we have found that the shaft power calculation demonstrates some significant deviations from the test cell data. This has been attributed to the formulation of the turboprop thermodynamic model, and ongoing work is attempting to mitigate this issue. This understanding of the characteristic engine algorithms will provide valuable guidance in selecting suitable engine parameters as inputs and references.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".