A Time Domain Full Order Parallel Method for Turbomachinery Unsteady Flows
Bibliographic record
Abstract
In this study, the parallel inverted dual time stepping (PIDTS) method has been investigated for analyzing turbomachinery unsteady flows. This is the first known effort in exploiting temporal parallelization to reduce wall-clock time for analyzing turbomachinery unsteady flows in all time scales. This method relaxes the sequential time dependency of solutions at different time instants in the dual time stepping method to achieve parallel solutions at the expense of an increased number of pseudo-time iterations. To demonstrate its parallel scalability and solution stability and accuracy, a one-dimensional De Laval nozzle with a time-periodic back pressure disturbance has been used as a test case. Study indicates that the higher number of pseudo-time iterations is inevitable with a larger number of time instants marching together. A hybrid explicit and implicit method for accelerating solution convergence significantly mitigates the need for an increased number of pseudo-time iterations. Further verification and application have been conducted using a case of rotor-stator interaction of a transonic compressor stage. The obtained parallel efficiency is about 96%, 90%, 83%, 77%, and 72% with 2, 4, 8, 12, and 16 time instants marching together, respectively. The recommended number of time instants for parallelization is 2-10, which is a result of balancing parallel benefits, time consumption of additional pseudo-time iterations, and additional memory consumption. The obtained wall-clock speedup compared with the dual time stepping method is 1.9 to 3.5.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".