WITHDRAWN: Adjoint-Based Shape Optimization Framework for High-Lift Design Using Three-Dimensional Mixed-Element Unstructured Grids
Bibliographic record
Abstract
An adjoint-based aerodynamic optimization framework is proposed for three-dimensional high-lift design. This framework contains multiple components that provide an accurate and efficient evaluation of adjoint linear systems, solutions, and surface gradients. The flow Jacobian is constructed using a novel process for a finite-volume computational aerodynamics flow solver based on unstructured heterogeneous meshes of up to four different element shapes. Due to the heterogeneous grid structure, applying automatic differentiation directly to the flow residual would incur a high computational cost, as each row of the residual can potentially have a unique configuration. Automatic differentiation by operator overloading is applied to primitive calculations, such as flux and boundary condition calculations, to build the Jacobian. Since these primitive calculations possess only a few variations, only a few sets of instructions to differentiate each term need to be stored. These calculations are then combined using the chain rule to form the Jacobian matrix. This approach enables us to take advantage of the code maintainability provided by automatic differentiation at a manageable computational cost. The constructed ill-conditioned adjoint linear system is solved by a preconditioned iterative method. An incremental linear elasticity mesh mover and mesh adjoint approach is employed to enhance the robustness and efficiency of mesh movement and surface gradient evaluation. In this work, the convergence of the preconditioned iterative method, applied to solve the adjoint linear system of a three-dimensional high-lift common research model (CRM-HL), is investigated. The surface gradient computed by the mesh adjoint is verified. The rigid-body design framework is applied to improve the aerodynamic performance of a CRM-HL airfoil.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".