This research presents a cost-effective microscale modeling analysis of high-efficiency particulate air (HEPA) filter media. We analyzed leak-free and leaking media with known leak sizes. A three-dimensional fibrous model with non-homogeneous fiber diameters (0.2-2 & micro;m) and a packing density of 7.5% was developed to replicate a HEPA filter sheet (H14) microstructure. The objective was to simulate and analyze the pressure drop and collection efficiency of nanofibrous media for aerosol particles with a diameter of dp <= 0.5 & micro;m. The results were compared to prior experimental and theoretical models, exhibiting a high level of agreement with pressure drop and efficiency predictions. We determined the pinhole size at which the collection efficiency of a leaky filter medium is influenced by collection through the fiber bed, and the size at which efficiency is controlled by collection through the pinhole. We also determined the smallest pinhole size at which a noticeable change in the filter medium's efficiency begins to occur. Ultimately, this microscale parametric CFD study provides a fundamental physical understanding of microleak transport, offering mechanistic guidance for future full-scale experimental work and the design of scanning equipment used in testing and certifying HEPA and ultra-low particulate air (ULPA) filters.
Many studies have developed data-driven Model Predictive Control algorithms and demonstrated their advantages in building control applications; however, their adoption in real buildings remains limited. Although most related studies have been conducted by researchers, deployment decisions depend primarily on industry stakeholders. Thus, it is crucial to survey industrial practitioners to understand the barriers and drivers. Existing efforts have been small-scale surveys and do not provide a comprehensive industry view. This study addresses that gap through a broad survey targeting control vendors, building practitioners and technicians, building owners and facility operators, utility representatives, researchers, and other professionals. The results identify several dominant barriers, including high first costs, fragmented hardware and software ecosystems, limited data accessibility and labeling, and the specialized expertise required to build and maintain models. The analysis also highlights key drivers, including decarbonization targets, renewable energy integration, and advances in artificial intelligence and analytics. Based on these findings, the study proposes actionable pathways for academia and industry. Recommendations include standardized datasets and benchmarks, transparent field demonstrations, interoperable plug-and-play architectures, and workforce training focused on practical deployment. These steps can help transition advanced building controls from bespoke pilot projects to scalable real-world practice.
