A novel framework for cost-effectively reconstructing the global flow field by super-resolution
Bibliographic record
Abstract
Fluid data are of great significance for analyzing the fluid structure and understanding the law of fluid movement. Apart from the experimental test, the computational fluid dynamics (CFD) method has been widely applied in the field of fluid dynamics over the past few decades. However, due to the high computational costs of CFD method and the limitation of computational resources, it is still challenging to accurately calculate and obtain the high-resolution (HR) flow fields. To this end, a novel framework based on the super-resolution (SR) algorithm, namely, new enhanced down-sampled skip-connection and multi-scale (E-DSC/MS), is reported to achieve the HR global flow reconstruction from low-resolution data. Through the new SR flow reconstruction method, the HR flow fields of two benchmark 2D cases (i.e., cylinder and hydrofoil) are precisely and efficiently predicted using a universal SR model. The effectiveness of the new E-DSC/MS algorithm is tested by comparing it with the traditional super-resolution convolution neural network and U-net in terms of the velocity field prediction of the self-region (training region) and other-region (untrained region). The result shows that the universal SR flow reconstruction framework is able to increase the spatial resolution of velocity field by 16 times, and flow fields reconstructed by E-DSC/MS are in good agreement with the ground-truth data. In addition, the E-DSC/MS model could reconstruct the global flow field with a correlation coefficient of more than 99% regardless of the selection of the arbitrary region/window for SR training. The present method overcomes the limitation of the existing techniques in efficiently reconstructing HR flow field, which helps to reduce the requirement for expensive experimental equipment and to accelerate the CFD simulation process.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".