Optimizing sensor placement in naturally ventilated livestock buildings (NVBs) is essential for capturing airflow dynamics and enabling reliable assessment of ventilation performance, yet conventional measurement strategies remain largely heuristic, sensor-intensive, and inflexible. This study presents a CFD-informed Sensor Placement Optimization (CISPO) framework that integrates validated CFD simulations, entropy-based informativeness analysis, and surrogate modelling to support efficient and objective environmental sensing strategies in livestock buildings. For a NVB model, twelve wind-direction cases were simulated and experimentally validated using a boundary-layer wind tunnel, establishing the CFD outputs as ground-truth references. Seven cross-sectional planes were discretized into uniform grids, and Shannon entropy was applied to characterize the spatial variability of air velocity and turbulence kinetic energy. Regions near openings and recirculation zones were identified as direction-diagnostic, while end-plane regions remained largely direction-agnostic. Correlation analysis between grid-level velocity and volume-averaged local mean age of air (LMA) was then used to identify 35 sentinel grids with high predictive relevance. A log-log surrogate model constructed from these grids achieved strong accuracy (R2 = 0.88, NRMSE ≈ 2.8%), with mean velocity emerging as the dominant predictor. Monte Carlo analyses demonstrated that a reduced pool of 15–20 sentinel grids reproduced approximately 95% of the accuracy of the full set, highlighting the efficiency of the approach. The CISPO framework provides a reproducible and scalable pathway for translating high-dimensional CFD datasets into practical sensor placement strategies. As an enabling technology, CISPO supports the development of adaptive environmental monitoring and data-driven ventilation assessment in smart livestock housing systems.
