Innovations in Body Force Modeling of Transonic Compressor Blade Rows
Bibliographic record
Abstract
Aeroengine fans and compressors increasingly operate subject to inlet distortion in the transonic flow regime. In this paper, innovations to low-order numerical modeling of fans and compressors via volumetric source terms (body forces) are presented. The approach builds upon past work to accommodate any axial fan/compressor geometry and ensures accurate work input and efficiency prediction across a range of flow coefficients. In particular, the efficiency drop-off near choke is captured. The model for a particular blade row is calibrated using data from single-passage bladed computations. Compared to full-wheel unsteady computations which include the fan/compressor blades, the source term model approach can reduce computational cost by at least two orders of magnitude through a combination of reducing grid resolution and, critically, eliminating the need for a time-resolved approach. The approach is applied to NASA stage 67. For uniform flow, at 90% corrected speed and peak-efficiency, the body force model is able to predict the total-to-total pressure rise coefficient of the stage to within 1.43% and the isentropic efficiency to within 0.03%. With a 120∘ sector of reduced inlet total pressure, distortion transfer through the machine is well-captured and the associated efficiency penalty predicted with less than 2.7% error.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".