Mesh and load distribution requirements for actuator line CFD simulations
Bibliographic record
Abstract
ABSTRACT Fundamental numerical testing has been carried out to determine mesh density and force distribution guidelines for an actuator line‐based computational fluid dynamics method for simulating kinetic turbines. The method computes forces from lifting surfaces (i.e. wings or blades) by using the evolving flowfield and tabulated airfoil data. The forces are applied to the flow as momentum source terms distributed with a Gaussian smoothing function about the physical locations of the blade/wing quarter‐chord line. The chosen length scale of the Gaussian distribution affects the magnitude and distribution of the resulting induction and necessitates a minimum grid resolution for accurate results. Tests have been conducted to determine appropriate distribution length scales and mesh spacing by using an infinite span wing and finite span wings with constant and elliptical spanwise circulation distributions. These test cases were chosen because they have simple analytical solutions derived from lifting line theory. The eventual goal is to simulate turbine rotors; however, these fundamental test cases provide a means to evaluate the required mesh spacing and the appropriate distribution length scale without the complexity of modeling a turbine rotor wake. It was found that the source distribution length scale ϵ should be proportional to the local airfoil chord length c with a ratio ϵ / c of approximately 1/4 and that the mesh spacing at the actuator line should satisfy ϵ / Δgrid ≥ 4. This limit is likely somewhat code specific and should be evaluated for all solvers used for actuator line simulations. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".