Adaptive Discontinuous-Galerkin Reduced-Basis Reduced-Quadrature Method for Many-Query CFD Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2716.vid We present a projection-based model reduction method for efficient solution of computational fluid dynamics problems in many-query scenarios, which require the evaluation of quantities of interest for many different flow-condition, geometry, or model parameters. Our goal is to construct reduced models that provide rapid and accurate output predictions and the associated a posteriori error estimates. To achieve this goal, our framework builds on the following key ingredients of adaptive high-order methods: the discontinous Galerkin method, which provides stability for conservation laws; the dual-weighted residual method, which provides effective output a posteriori error estimates. In addition, we incorporate two model reduction ingredients: reduced bases, which provide low-dimensional empirical approximation spaces tailored for the specific parametrized problem; reduced quadrature rules, which are the tailored quadrature rules for the reduced bases constructed using an empirical quadrature procedure. Both reduced bases and reduced quadrature rules are identified through an efficient and automatic offline training procedure that is informed by the behavior of a posteriori error estimates. We demonstrate the efficacy and versatility of the model reduction approach in four aerodynamics problems: Reynolds-averaged Navier-Stokes (RANS) flow over the ONERA M6 wing with the Mach number and the angle of attack as the parameters; laminar flow over shape-parametrized airfoils; uncertainty quantification of RANS flow with variabilities in the empirical parameters of the Spalart-Allmaras turbulence model; and unsteady flow past NACA0012 with the Reynolds number as the parameter. The reduced models achieve ~300-20000 speedup at less than 1% drag error level relative to an adaptive DG method and provide effective error estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it