Verification and validation of impinging round jet simulations using an adaptive FEM
Bibliographic record
Abstract
Abstract This paper illustrates the use of an adaptive finite element method as a means of achieving verification of codes and simulations of impinging round jets, that is obtaining numerical predictions with controlled accuracy. Validation of these grid‐independent solution is then performed by comparing predictions to measurements. We adopt the standard and accepted definitions of verification and validation (Technical Report AIAA‐G‐077‐1998, American Institute of Aeronautics and Astronautics, 1998; Verification and Validation in Computational Science and Engineering. Hermosa Publishers: Albuquerque, NM, 1998). Mesh adaptation is used to perform the systematic and rigorous grid refinement studies required for both verification and validation in CFD. This ensures that discrepancies observed between predictions and measurements are due to deficiencies in the mathematical model of the flow. Issues in verification and validation are discussed. The paper presents an example of code verification by the method of manufactured solution. Examples of successful and unsuccessful validation for laminar and turbulent impinging jets show that agreement with experiments is achieved only with a good mathematical model of the flow physics combined with accurate numerical solution of the differential equations. The paper emphasizes good CFD practice to systematically achieve verification so that validation studies are always performed on solid grounds. Copyright © 2004 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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".