BEARCAT: The SAFRAN Brand New Test Engine Heavily Instrumented for Accurate Comparison With CFD Calculations
Bibliographic record
Abstract
Abstract In this paper, we detail the development and goals of a brand new turboshaft engine called BEARCAT. “BEARCAT” is an acronyme for ‘’Banc d’Essai Avancé pour la Recherche en Combustion et Aérothermique des Turbomachines’’. BEARCAT is based on a MAKILA engine, a turboshaft developed by Safran Helicopter Engines (formerly Turboméca) and powering the H215 (2 Makila 1A1, 1820 SHP each) and the H225 (2 Makila 2A1, 2000 SHP each) of Airbus Helicopters. BEARCAT is developed by SAFRAN-Tech, the Research and Technology Center of the SAFRAN Group. This test engine is devoted to the fine characterization of aero-thermal phenomena occurring within the combustion chamber and the High Pressure Turbine as well as their interactions. Therefore, BEARCAT differs from a standard test engine by the implementation of metrologies inside the combustion chamber and the 2-stage High Pressure Turbine, in order to perform both steady and non-steady flow measurements which will be used to validate CFD codes and models. The engine instrumentation induces thorough modifications of several engine parts and also the development of original technical solutions to ensure metrologies integration in minimizing their impact on performances.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.006 | 0.002 |
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".