Unsteady CFD Methods in a Commercial Solver for Turbomachinery Applications
Bibliographic record
Abstract
Modern CFD flow solvers can be readily used to obtain time-averaged results on industrial size turbomachinery flow problem at low computational cost and overall effort. On the other hand, time-accurate computations are still expensive and require substantial resources in CPU and computer memory. However, numerical techniques such as phase shift and time inclining method can be used to reduce overall computational cost and memory requirements. The unsteady effects of moving wakes, tip vortices and upstream propagation of shock waves in the front stages of multi-stage compressors are crucial to determine the stability and efficiency of gas turbines at part-load conditions. Accurate predictions of efficiency and aerodynamic stability of turbomachinery stages with strong blade row interaction based on transient CFD simulations are therefore of increasing importance today. The T106D turbine profile is under investigation as well as the transonic compressor test rig at Purdue. The main objective of this paper is to contribute to the understanding of unsteady flow phenomena that can lead to the next generation design of turbomachinery blading. Transient results obtained from simulations utilizing shape correction (phase shift) and time inclining methods in an implicit pressure-based solver, are compared with those of a full transient model in terms of computational cost and accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.036 | 0.015 |
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".