Companion code and data for "A predictive design framework for flat-panel recirculating air cleaners: from the fan–filter operating point to an exact optimum"

Companion code, inputs, and figure-reproduction scripts for the manuscript "A predictive design framework for flat-panel recirculating air cleaners: from the fan-filter operating point to an exact optimum" (R. A. Satnoianu). The deposit reproduces the analytical results and figures of the paper: the coupled fan-filter operating point, the geometric-programming (GP) optimization with its unique global optimum, the CADR-noise Pareto frontier, the sound-power/CADR law, the multi-zone monotonicity result, and the two-device validation. The primary artifact, verify_core_results.py, reproduces from first principles (NumPy/SciPy only) the operating-point posynomial identity, the two-parameter noise-law calibration, the GP global optimum in convex log-space, and the closed-form Pareto frontier, printing each checked quantity against the values reported in the manuscript. A low-velocity bench protocol (filter_resistance_protocol.py) documents measurement of the single empirical input, the filter frontal (Darcy) resistance. All numerical inputs are public (manufacturer datasheets and published third-party test data); no proprietary device data are included. Requires Python 3.9+ with numpy, scipy, and matplotlib. Code is released under the MIT License; documentation and data files under CC BY 4.0.