ARC4CFD: Learning how to leverage High-Performance Computing with Computational Fluid Dynamics
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
Computational Fluid Dynamics (CFD) is a field of computational physics that relies heavily on modern Advanced Research Computing (ARC) resources (Cant, 2002).The spatial and temporal resolutions required to solve modern CFD problems means that they can take advantage of the full benefits of large-scale distributed-memory parallelization that is available on high-performance computing (HPC) systems on ARC infrastructure.The CFD user base is broad, diverse and interdisciplinary.As CFD tools have progressed over the past decades, the improved robustness, predictive capabilities, and user-friendliness led them to be adopted by nontraditional HPC users such as new graduate students, experimentalists, theoreticians, and student design teams.Advanced Research Computing for Computational Fluid Dynamics, or ARC4CFD, is an open source, asynchronous online course (https://arc4cfd.github.io) that was developed to give users a basic understanding of fluid dynamics and CFD to bridge the knowledge gap toward an effective use of CFD on modern ARC resources.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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