Tackling compressible turbulent multi-component flows with dynamic hp-adaptation
Bibliographic record
Abstract
In this paper, we present an hp-adaptive hybrid Discontinuous Galerkin/Finite Volume method for simulating compressible, turbulent multi-component flows. Building on a previously established hp-adaptive strategy for hyperbolic gas- and droplet-dynamics problems, this study extends the hybrid DG/FV approach to viscous flows with multiple species and incorporates non-conforming interfaces, enabling enhanced flexibility in grid generation. A central contribution of this work lies in the computation of both convective and dissipative fluxes across non-conforming element interfaces of mixed discretizations. To achieve accurate shock localization and scale-resolving representation of turbulent structures, the operator dynamically switches between an h-refined FV sub-cell scheme and a p-adaptive DG method, based on an a priori modal solution analysis. The method is implemented in the high-order open-source framework FLEXI and validated against benchmark problems, including the supersonic Taylor-Green vortex and a triplepoint shock interaction, demonstrating its robustness and accuracy for under-resolved shock-turbulence interactions and compressible multi-species scenarios. Finally, the method’s capabilities are showcased through an implicit large eddy simulation of an under-expanded hydrogen jet mixing with air, highlighting its potential for tackling challenging compressible multi-species flows in engineering.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".