A Simple Empirical Method for Estimating Extreme Air Velocities from CFD Mean-Flow Statistics: Validation Using ISS Columbus and CASA Ventilation Data

Abstract

Computational Fluid Dynamics (CFD) simulations based on Reynolds-Averaged Navier–Stokes (RANS) models are widely used for spacecraft cabin ventilation design; however, they primarily provide mean-flow quantities and often underestimate velocity extremes relevant to crew comfort, ventilation effectiveness, and stagnation-zone assessment. This study presents a simple empirical post-processing framework for estimating extreme air velocities from CFD mean-flow statistics. The proposed approach expresses the upper velocity envelope as a function of the local mean velocity and standard deviation through a single empirical coefficient, Kc. Throughout this study, the upper velocity envelope is defined consistently as the 99th percentile of the velocity magnitude distribution, denoted p99, and the empirical peak factor is accordingly defined as Kc,p99 = (p99 − μ)/σ. This definition is applied uniformly to the calibration dataset and to all external validation datasets, so that the same target variable is used at every stage of the analysis. The method is calibrated using published velocity-field data from International Space Station (ISS) Crew Quarters studies and subsequently examined against independent ISS ventilation datasets, including the Columbus module and Crew Alternative Sleeping Area (CASA) configurations. Analysis of digitized experimental and CFD-derived velocity distributions yields a robust median coefficient of approximately Kc = 4.5, while external datasets indicate p99-equivalent values generally within the range Kc ≈ 4–6. Independent validation evidence from Columbus ventilation studies demonstrates that accounting for velocity fluctuations substantially improves agreement with experimental observations, reducing distribution mismatch by approximately 83% compared with uncorrected mean-velocity predictions. The external comparison should be understood as a retrospective consistency check rather than a prospective prediction: the peak factors reported for the Columbus and CASA datasets were derived from the same distributions against which they are compared, and they therefore describe the data rather than forecast it. Accordingly, the proposed framework is presented as an exploratory upper-envelope indicator whose transferability is suggested, but not yet demonstrated, by the observed clustering of independently derived peak factors. The results suggest that a simple statistical correction applied to standard CFD outputs can provide practical estimates of extreme ventilation velocities without the computational cost of Large Eddy Simulation (LES). While the proposed framework should be regarded as an exploratory upper-envelope indicator rather than a universal turbulence closure, and while it does not by itself provide a conservative bound or identify stagnation risk, the consistency observed across multiple ISS ventilation datasets indicates that it offers a useful preliminary screening tool for spacecraft cabin ventilation assessment and design.