Electrostatic precipitators (ESPs) are widely used for particulate matter control in buildings due to their cost-effectiveness and low maintenance requirements. However, existing ESPs exhibit low removal efficiency for submicron particles and inefficient dust cleaning after accumulation. To address these limitations, a collection module in ESP, featuring electrodes coated with a PVDF/H-SiO₂ superhydrophobic coating, is proposed. The applied voltage, electrode gap, and coating material are evaluated in this study. Results showed that the water contact angle on the coated surface was 152.4°, attributed to the combination of low-surface-energy groups and rough structures. When superhydrophobic electrodes with a 2-mm gap were used and air velocity of 1.5 m/s, the removal efficiency for 0.3–0.5 μm particles was 92.5%. Additionally, the removal efficiency of the collection module with superhydrophobic coating could be restored to 99.7% of its initial efficiency, compared to 83.1% for the collection module without the coating. Moreover, the removal efficiency of the ESP remained consistently high over 21 days with an average of 91.3%. The proposed novel ESP demonstrates significant potential for air removal in ventilation systems for clean and sustainable building environments.

Due to the challenges of adjustment after installation and the elevated failure rate associated with the use of electric valves, which contributes to increased weight among other issues, single-hole restrictors (SHRs) are commonly employed within the ventilation system of the manned space station. These restrictors are pre-configured with parameters to ensure that the airflow at the termination of the ventilation system meets the designated requirements. However, given their inability to be modified post-installation, a preliminary optimization design process becomes imperative. Past solutions relying solely on experience are insufficient in precise and optimal outcomes. While computational fluid dynamics (CFD) simulations offer accurate results, they encounter difficulties in modeling larger systems and are time-intensive due to multiple iterative simulations. Hence, an optimizing design method through quicker Flowmaster simulations is proposed in this study. Utilizing the Functional Mock-up Interface (FMI) protocol, the ventilation system model established in Flowmaster could be exported as FMU files and fine-tuned within Python using a genetic algorithm (GA), swiftly achieving a well-balanced ventilation system design by adjusting the parameters of the SHRs. The findings from CFD calculations can corroborate the simulations conducted in Flowmaster. The vents’ unbalanced factor and the pressure drops of the system are used as optimization objectives. After optimization, the system unbalanced factor and total pressure drop were 5.51% and 5.99 Pa, respectively, both of which are better than the results obtained using CFD through empirical and trial-and-error methods, and the computation time was reduced by 99.16%.

The window purifying ventilator is a relatively new idea that involves replacing one part of the window frame with a ventilator, for which the core component is a cross-flow fan (CFF). When a CFF is applied to the window purifying ventilator, a filter should be added, and its casing should be modified. In this paper, first, time-accurate solutions with a sliding mesh technique were developed for modelling the two-dimensional viscous and incompressible model of CFF. Three prevalent two-equation turbulence models (Standard, RNG and Realizable k-ε models) for CFF simulation were compared and validated in the experiments, and the most suitable model for this type CFF was found. Based on the time-accurate simulation, the formation of flow construction inside the CFF was also observed and explained. Next, the CFF was optimized for window purifying ventilator by redesigning and modifying the shape and location of vortex wall and rear wall, both numerically and experimentally. From the research results, the vortex wall’s position has a great influence on the ventilator’s performance by determining the position and intensity of eccentric vortex, and the shape of the rear wall also affects the ventilator’s performance by determining the flow pattern and internal flow resistance. Through appropriate designing the vortex and rear wall, finally, the CFF could apply to the window purifying ventilator with acceptable flow rates.