A Novel Reduced-Order Mathematical Model for Radial Turbines
Bibliographic record
Abstract
Abstract Radial turbines are commonly used to extract otherwise unused energy from engine exhausts, and are therefore an important component of many emission reducing technologies. There is a desire to quickly and reliably predict the performance of radial turbines over a variety of different operating conditions. Reduced-order models meet the computational speed requirements but often do not obtain sufficient accuracy without the tuning of multiple unknown parameters. In order for a model to be predictive, we need a way to reduce the number of unknowns and find ways to calibrate these parameters to known geometric values. In this paper, we develop a new reduced-order model from first principles by averaging the fundamental Navier-Stokes equations of fluid motion. All losses are derived from the deviatoric stress tensor to ensure consistency. Only four unknown parameters are required, each with a physical interpretation and showing evidence of universality. The model simulates gas properties throughout the turbine, shows the loss distribution over the operating range, and predicts turbine performance curves. Validation is provided by a remarkable fit to experimental data. Therefore, this model has the potential to become a valuable tool for turbine manufacturers during early design stages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".