Size-resolved particle-decay measurements in Rayleigh–Bénard turbulence within the Pi Convection-Cloud Chamber

This dataset underpins the paper “Observed deviation from Stokes’ Law in the dry deposition of heavy particles in Rayleigh–Bénard turbulence.” It contains every raw time-series measurement and all derived decay constants used to test whether small, heavy particles settle in buoyancy-driven turbulence according to Stokes' Law.Scientific contextDry deposition governs how long aerosols remain airborne, influencing air-quality, weather and climate. Theory predicts that, for very low-inertia particles (St ≪ 1), the settling and deposition velocity scales with the square of the particle diameter (Stokes settling), even in turbulent flows. We performed laboratory tests of this assumption in anisotropic, buoyancy-driven turbulence generated by Rayleigh–Bénard (RB) convection. Contrary to Stokesian expectations, we find an approximately linear diameter–dependence.Experimental apparatusFacility: 3.14 m³ Pi Convection-Cloud Chamber at Michigan Technological University.Flow: Dry RB turbulence driven by ΔT = 10 K and 20 K (Ra ≈ 1–2 × 10⁹). The large-scale circulation period (~60 s) is our characteristic mixing time τₘ.Particles:DEHS oil droplets, 1–10 µm, ρ = 912 kg m⁻³, generated continuously with a Palas MAG-3000.Solid, hollow, and ultra-hollow glass microspheres, 1–38 µm, ρ = 600–2500 kg m⁻³, injected in pulses with a compressed-air “air-cannon.”Resulting Kolmogorov-scale Stokes numbers 3 × 10⁻⁵ – 0.1.Optical particle counters (OPCs):Palas WELAS 2000 (up to 128 bins, 0.2–100 µm, 5 L min⁻¹)Alphasense OPC-N3 (24 bins, 0.35–40 µm, 0.28 L min⁻¹)Counters were mounted at top and bottom ports; most data sampled at 1 Hz, a subset at ~1 min cadence.Trials: 68 experiments (65 usable), July 2023 and May/June 2025, yielding 1,322 fitted decay constants across 83 size–density classes.Methodology & processing workflowInject particles.Record number concentration decay for each size bin for 30–120 min.Store raw data in a pandas MultiIndex DataFrame with levelsparticle-counter type [welas/opc] → location (top/bottom) → date → trial(see README for indexing examples).Apply centered rolling averages and censor start/end segments that deviate from log-linearity.Perform ordinary-least-squares fits to ln C(t) to obtain decay time-constant τ and regression diagnostics (r-value, points-used, etc.).Aggregate τ values by size-density class and compare against Stokes theory; all figure generation is scripted in plots.ipynb.Reproducibility notesReproducibility notesRunning plots.ipynb end-to-end in Google Colab reproduces all figures in ~5 minutes on a free GPU/CPU runtime. All graphical operations use publicly available libraries; no proprietary software is required.FileFormatContentsTypical loading commandparticle_decay_data.pklPickleRaw concentration time-series for every trial (MultiIndex DataFrame, units # cm⁻³)pd.read_pickle()metadata.pklPickleTrial-level, bin-by-bin metadata and all fitted parameters (1,322 rows)pd.read_pickle()plots.ipynbJupyter NotebookRecreates every figure from the paper (Colab-ready)Open in Colab or Jupyter; requires pandas ≥ 2.2, numpy ≥ 1.26, scipy ≥ 1.13, matplotlib ≥ 3.9README.pdfPDFDetailed file schema, example queries, software dependencies, and Colab setup instructions—