--- license: cc-by-4.0 language: - en tags: - piv - particle-image-velocimetry - fluid-dynamics - turbulence - channel-flow - validation - benchmark - synthetic-data - hot-wire-anemometry pretty_name: PIVtools Turbulent Channel Validation Dataset size_categories: - 10K_instantaneous.yaml as config.yaml pivtools-cli instantaneous pivtools-cli apply-calibration --all-paths --type-name instantaneous pivtools-cli statistics # directory 2: _ensemble.yaml as config.yaml pivtools-cli ensemble pivtools-cli apply-calibration --all-paths --type-name ensemble ``` **Stereo cases** (instantaneous only — the paper uses no stereo ensemble): ```bash pivtools-cli instantaneous pivtools-cli apply-stereo --all-paths pivtools-cli statistics --source-endpoint stereo ``` Outputs land under the `base_paths` folder you set: calibrated vectors in `calibrated_piv//...`, statistics in `statistics//.../mean_stats/mean_stats.mat` (stereo: `statistics//stereo/Cam1_Cam2/...`). Point the Tier-1 figure commands at your own outputs instead of `reference_results/` (the `--ens-*-dir` arguments want the folder holding `ensemble_result.mat` + `coordinates.mat`; the `--*-stats` arguments want a `mean_stats.mat`). **GUI instead of CLI:** load the same `config.yaml` in the PIVtools GUI and run the same stages from their tabs (Process → Calibration → Statistics) — identical engine, identical outputs. The calibration tab picks up the shipped model through the same `calibration_sources` path in the config. ## Performance benchmark (optional) The paper's strong-scaling benchmark is packaged with PIVtools as the `pivtools-bench` console command — no scheduler scripts required. From the same path-edited `planar_clean` working directory as Tier 2: ```bash pivtools-bench scaling --config config.yaml \ --dataset \ --n-images 3840 --sweep workers # add --dry-run to preview the matrix ``` This runs the full production pipeline (Dask cluster, load, correlate, save) at a geometric sweep of worker counts sized to your machine's cores, two OpenMP threads per worker, at fixed total workload — a strong-scaling measurement. Add `--iterations 3` for error bars. Three outputs land in `bench_results/` under the working directory: | Output | Contents | |---|---| | `scaling_.csv` | one row per run: timings, throughput, full provenance (git SHA, FFT backend, host, CPU) | | `scaling__summary.csv` | per-config median/IQR statistics plus speedup and parallel efficiency | | `scaling__paper.png` | throughput vs the ideal linear bound + parallel efficiency — the same two-panel layout as the paper's scaling figure | (Two additional diagnostic figures are also written; the `matrix`/`oversub` sweep modes explore worker×thread trade-offs beyond the paper's sweep — see `pivtools-bench scaling --help`.) Throughput numbers are hardware-dependent: the paper's peak of 117 pairs/s was measured on a 192-core dual-EPYC node, and your core count sets your ceiling. The scaling *shape* — near-linear until the memory bandwidth of the machine saturates — is the reproducible claim. On a cluster, wrap this same command in your scheduler of choice; nothing about it is scheduler-specific. ## Tier 3 — re-derive the calibration models The shipped models under `calibration/*/calibration/` reproduce the paper as-is. To rebuild them from the board images instead: ```bash # planar (single camera; ChArUco 10 x 7 squares, 12 mm, DICT_4X4_1000) pivtools-cli detect-charuco --source ./tc/calibration/planar \ --camera 1 --image-format "calib%05d.tif" --n-views 20 --square-size 0.012 # stereo pair (boards under Cam1/, Cam2/) pivtools-cli detect-stereo --source ./tc/calibration/stereo --camera-pair 1,2 \ --image-format "calib%05d.tif" --n-views 20 --square-size 0.012 ``` The synthetic boards are rendered through the same camera models that rendered the particle images, so the recovered calibration corresponds exactly to the imaging system (`calibration/stereo/ground_truth.npz` holds the true cameras; expected recovery: stereo angle ≈ 89.9° vs the true 90.0°, RMS < 0.1 px). Detection writes proof figures beside the model. The experimental case uses a uniform scale-factor model rather than a fitted camera. The shipped model (`calibration/experimental/calibration/Cam1/scale_factor_planar/model/`) carries everything: 16.72 px/mm, the wall origin (origin_px [254.41, 1626.65] → origin_mm [0, 11.17]) and the axis directions (col_sign −1, row_sign −1). Rebuilding it requires clicking the origin on the board image — use the GUI Scale Factor tab, or `pivtools-cli scale-factor --help` with the constants above. ## Key constants | Quantity | Synthetic | Experimental | |---|---|---| | dt | 0.0057553 s | 4.96e-05 s | | Scale factor | 13.6533 px/mm | 16.72 px/mm | | u_τ | 8.4206 mm/s (DNS) | 0.700 m/s (PIV, freestream-matched); 0.68138 m/s (hot wire, OFI) | | ν | — | 1.4943e-05 m²/s | | Re_τ | 998 | — | | δ_ν | 0.15030 mm | — | | Hot-wire l+ | — | 45.6 | ## Image specifications (synthetic) | Parameter | Value | |-----------|-------| | Image size | planar 2048 × 2048 px; stereo 1440 × 2080 px — 8-bit TIFF | | Scale factor (planar) | 13.6533 px/mm (2048 px over the 150 mm half-height) | | Particle diameter | ≈ 3 px | | Laser sheet thickness | 1.2 mm (≈ 16 px at the planar scale) | | Number of pairs | 4000 per case and geometry | | Case A particle count | 85 000 per image (≈ 5.2 ppw at 16 × 16 windows), no noise | | Case B particle count | 22 000 per image (≈ 1.3 ppw) | | Case B noise | Gaussian, mean = 80, std = 16, SNR ≈ 8 — independent per frame | | Stereo geometry | ± 45° side-scatter, Scheimpflug ± 2.39°, full-frame domain (no masks) | ## Ground truth One file per case, each in its own subdirectory so the figure script can point at them via `--gt-clean-dir` / `--gt-noisy-dir`: - `ground_truth/clean/direct_stats.mat` — Case A reference (85 000 particle trajectories) - `ground_truth/noisy/direct_stats.mat` — Case B reference (22 000 trajectories) Both are computed directly from the JHTDB particle position snapshots used to render the corresponding images (the truth derives from the particle files, not the rendered images). The stereo cases share the same files. Schema: | Key | Shape | Description | |-----|-------|-------------| | `y_plus` | (N,) | wall-normal coordinate, wall units | | `U_plus` | (N, 3) | mean velocity [U, V, W] in wall units | | `stress_plus` | (N, 3, 3) | Reynolds stress tensor in wall units | | `stress_ci_lo`, `stress_ci_hi` | (N, 3, 3) | 95% confidence interval | | `umean_ci_lo`, `umean_ci_hi` | (N, 3) | 95% CI for mean velocity | | `u_tau` | scalar | friction velocity (mm/s) | | `delta_nu` | scalar | viscous length scale (mm) | | `Re_tau` | scalar | friction Reynolds number | ## Hot-wire data `hotwire/hwa.mat` is an HDF5 file with a `caseData` group holding exactly what the experimental figure needs: | Field | Description | |---|---| | `y_corrected_poly` | wall-normal positions (m), wall-origin corrected | | `meanvel_poly_corrected` | mean velocity (m/s) | | `variance_poly_corrected` | streamwise variance (m²/s²) — wall-origin corrected, **not** spatial-resolution corrected | | `utau_ofi` | friction velocity from oil-film interferometry (0.68138 m/s) | | `lplus` | wire length in wall units (45.6) | | `nu_air` | kinematic viscosity (m²/s) | The figure plots the uncorrected variance deliberately: at l+ ≈ 46 the wire attenuates the near-wall peak, and the comparison discusses that rather than hiding it behind a correction. ## Image orientation There are no masks: the stereo renders fill the full frame and the planar images are full-field. Both synthetic geometries read physically — the channel wall is at the bottom row, fast flow at the top. The stereo renders have a single deterministic vertical flip composed into both the images and the calibration boards, so calibration and images are consistent. Do not flip anything downstream. ## Provenance The synthetic images were rendered from JHTDB turbulent-channel particle trajectories with the EUROPIV Synthetic Image Generator; noise was added post-render with independent per-frame seeds. The full generation chain (JHTDB download, particle conversion, 85k → 22k downsampling, SIG configs, render recipes, noise and ground-truth scripts) was removed from the current tree to keep the capsule focused, but remains available in this repository's git history. ## Citation If you use this dataset, please cite both the PIVtools paper and the underlying DNS source. ```bibtex @article{taylor_pivtools, title={PIVtools: an open-source PIV framework with integrated planar, stereoscopic, and ensemble pipelines}, author={Taylor, M.T. and Lawson, J.M. and Ganapathisubramani, B.}, journal={SoftwareX}, note={submitted} } @article{lee2015direct, title={Direct numerical simulation of turbulent channel flow up to Re_tau = 5200}, author={Lee, M. and Moser, R.D.}, journal={J. Fluid Mech.}, year={2015} } @article{li2008public, title={A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence}, author={Li, Y. and others}, journal={J. Turbulence}, year={2008} } ``` DNS reference data is from the **Johns Hopkins Turbulence Database** (JHTDB). ## License CC-BY-4.0 — free to use, modify, and redistribute with attribution to the PIVtools paper. The DNS ground truth is derived from publicly accessible JHTDB data and is redistributed here under the same permissive terms; consult the JHTDB usage policy (http://turbulence.pha.jhu.edu) for their citation requirements.