Sparse flow reconstruction methods to reduce the costs of analyzing large unsteady datasets
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Bibliographic record
Abstract
The cost of writing, transferring , and storing large amounts of data from unsteady simulations limits the accessibility of the entire solution, often leaving the majority of the flow under-sampled or not analyzed. For example, modeling the transient behavior of rare, but important, dynamic events requires three-dimensional snapshots written at high sampling rates , over a long duration. As such, the simulation time needed and large quantity of data produced, makes this a challenging problem for practical computational fluid dynamic (CFD) workflows, where memory resources are often limited and the writing penalty for modern GPU computing is much costlier. In this work, multiple sparse flow reconstruction (SFR) methods are developed to approximate a full unsteady solution by writing far fewer sparse measurements from the CFD solver, thus diminishing writing costs, data storage , and enabling greater sampling rates . SFR is motivated by a large-eddy simulation (LES) example pursuing rare inlet distortion events, demonstrating that a down-sampling in full snapshots, supplemented by high-frequency sparse measurements, can substantially reduce writing time for a GPU solver and nearly eliminate the writing cost for a CPU solver. In its simplest form, the “snapshot” SFR method is a single equation and can be further compressed with Proper Orthogonal Decomposition (POD-SFR) or its smaller and faster double POD-SFR variant. A streaming SFR modification reconstructs snapshots more efficiently when local memory cannot store the entire solution. A sensitivity study evaluates the SFR scaling trade-off between sparse sampling rates and reconstruction accuracy, outlining best practices. To offset error of using random sparse measurements, the SFR approach exactly preserves dynamics in designated flow regions by additionally specifying sparse measurement locations, used here to capture the inlet distortion events. Distortion events are evaluated using the conditional space-time proper orthogonal decomposition (CST-POD) to pursue physical insights that characterize the upstream causality at full resolution. A validation study of CST-POD modes confirms SFR effectiveness at retaining the event dynamics with substantial computational and memory savings.
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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) | 0.000 | 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