Effect of Dynamic Camber Morphing on Dynamic Stall Characteristics of the Bombardier CRJ-700
Bibliographic record
Abstract
This paper aims to present a new methodology to model the aerodynamic coefficients and predict the flow structure and the behavior of dynamic stall vortices surrounding a pitching CRJ-700 airfoil. This new methodology, called the Combined Morphing Leading Edge and Trailing Edge (CoMpLETE), aimed to manage dynamic stall control using the effects of leading and trailing edge morphing mechanisms. A framework for unsteady parametrization was created to simulate the transient leading edge and trailing edge motions. The parabolic airfoil parametrization approach was used to obtain its morphing motion, and it was coupled with Laplace Diffusion dynamic mesh techniques. Precise and reliable simulations validated the geometry deflection and mesh deformation schemes because the mesh quality criteria were respected throughout the deformation process. The 〖γ-Re〗_(θ ) turbulence model adequately captured the flow structures of dynamic airfoils associated with leading-edge vortex formations for a wide range of Reynolds numbers. The numerical results have shown that the new radius of curvature of the CRJ-700 morphing airfoil can minimize the streamwise adverse pressure gradient and further prevent significant flow separation by delaying the occurrence of Dynamic Stall Vortex (DSV). Results with the pitching-oscillation motion of the CRJ-700 airfoil and its parameters, such as the droop nose amplitude and the time at which the leading-edge morphing starts, revealed better aerodynamic performance. The CoMpLETE airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (c_(l,max)).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".