Shape optimization for fluid flow with parametric level set method and deep neural networks
Bibliographic record
Abstract
This study presents a novel application of the parametric level-set (PLS) method to develop a shape optimization process by directly modifying flow dynamics. The present method employs linear superimposition of polynomial perturbations to traditional PLS as shape optimization parameters. This enables smooth shape changes without any change in topology and limits the design variables to only the number of polynomials required for arbitrary hydrofoil morphing. During optimization, deep convolutional neural networks are integrated with the point clouds of the uniform level set to provide a surrogate model for flow dynamics. The present shape optimization method is employed here to delay stall via mitigation of flow separation on the suction surface of the NACA66 hydrofoil at high angles of attack. Shape optimization mitigates the forward movement of trailing edge flow reversal via changes in hydrofoil thickness and camber forward of the maximum hydrofoil thickness point. The optimized design shows more than two order reductions in mean flow reversal compared to NACA66 under the design condition angle of attack of 11 . 5 ∘ . At 14°, NACA66 shows complete flow separation while the optimized design exhibits almost three orders lower mean reversal magnitude of top surface flow than that of NACA66, indicating significantly delayed flow separation characteristics. The surrogate-based optimization is performed at four orders of magnitude lower computation time than full-order flow solvers. The results demonstrate the potential of the proposed PLS and deep neural network methodology to perform fast data-driven (non-intrusive) shape optimization of fluid flow. • Novel parametric level set framework with smooth shape morphing capabilities. • Scalable fluid flow optimization via integration with convolutional neural networks. • Shape optimization enables robust mitigation of hydrofoil flow separation and stall. • Increasing leading-edge camber is an important hydrofoil stall mitigation mechanism. • Maximum thickness in optimized hydrofoils converges to 32% of chord length.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".