Numerical stability analysis of solution methods for steady and harmonic balance equations
Bibliographic record
Abstract
This study investigates the stability and convergence properties of solution methods for the steady and unsteady Euler equations. A central scheme with artificial dissipation is used for spatial discretization. A Runge-Kutta scheme is used for the pseudo-time integration. The implicit residual smoothing for steady and harmonic balance solutions is achieved using the Lower-Upper Symmetric Gauss-Seidel(LU-SGS) method and the Lower-Upper Symmetric Gauss-Seidel/Block Jacobi(LU-SGS/BJ) method, respectively. Both the von Neumann and matrix methods are used to analyze the stability of the involved solution methods. The stability of these schemes obtained by the two stability analysis methods is identical for periodic boundary conditions as expected. However, only the matrix method can analyze the stability of solutions involving inlet, outlet, and slip wall boundary conditions. It is found that these boundary conditions enhance the stability of solution methods. Furthermore, the matrix method can also allow for the analysis of the impacts of non-uniformity in grids and flow fields on solution stability. For the harmonic balance equation system, the stability analysis demonstrates that the time spectral source term must be integrated implicitly to avoid instability for analysis with a large maximum grid-reduced frequency. The conclusions can be readily extended to the Reynolds averaged Navier-Stokes equations and verified using a Laval nozzle and the NASA rotor 37.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".