This study aims to utilize neural networks (NNs) for the performance prediction and design optimization of solid rocket motors (SRMs). The optimization of SRM design parameters is crucial for achieving desired performance. Herein, an NN-based method is proposed to achieve optimized design that improves efficiency. To train the NN models, datasets were generated by considering various design parameter values and erosive burning effects on grain combustion. Multiple NN architectures were trained, and their training accuracies were compared. The trained NNs were then employed to predict target values and determine the optimal coefficients and design parameters for an SRM. Furthermore, a sensitivity analysis was performed to assess the influence on the trained NNs. Subsequently, the obtained results were compared with target performance characteristics through simulations using the predicted values acquired from the trained NNs. By leveraging NNs, this study aims to improve the performance prediction and design optimization of SRMs, thereby providing insights into the influence of design parameters and erosive burning on the SRM performance.

The COVID-19 pandemic has highlighted the significant infection risks posed by aerosol-generating procedures (AGPs), such as intubation and cardiopulmonary resuscitation (CPR). Despite existing protective measures, high-risk environments like these require more effective safety solutions. In response, our research team has focused on developing a novel respiratory barrier enclosure designed to enhance the safety of healthcare workers and patients during AGPs. We developed a hood that covers the patient’s respiratory area, incorporating a negative pressure system to contain aerosols. Using computational fluid dynamics (CFD) analysis, we optimized the hood’s design and adjusted the negative pressure levels based on simulations of droplet dispersion. To test the design, Polyalphaolefin (PAO) particles were generated inside the hood, and leakage was measured every 10 s for 90 s. The open side of the hood was divided into nine sections for consistent leakage measurements, and a standardized structure was implemented to ensure accuracy. Our target was to maintain a leakage rate of less than 0.3%, in line with established filter-testing criteria. Through iterative improvements based on leakage rates and intubation efficiency, we achieved significant results. Despite reducing the hood’s size, the redesigned enclosure showed a 36.2% reduction in leakage rates and an approximately 3204.6% increase in aerosol extraction efficiency in simulations. The modified hood, even in an open configuration, maintained a droplet leakage rate of less than 0.3%. These findings demonstrate the potential of a CFD-guided design in developing respiratory barriers that effectively reduce aerosol transmission risks during high-risk medical procedures. This approach not only improves the safety of both patients and healthcare providers but also provides a scalable solution for safer execution of AGPs in various healthcare settings.

The COVID-19 pandemic has highlighted the significant infection risks posed by aerosol generating procedures (AGPs). We developed a hood that covers the patient’s respiratory area, incorporating a negative pressure system to contain aerosols. This study analyzed the movement and containment of aerosols within a developed negative pressure isolation chamber. Using particle image velocimetry (PIV) technology, in the optimized design, the characteristics of aerosols were analyzed under both negative and non-negative pressure conditions. The results demonstrated that in the absence of negative pressure, droplets dispersed widely, with diffusion angles ranging from 26.9° to 34.2°, significantly increasing the risk of external leakage. When negative pressure was applied, the diffusion angles narrowed to 20.0–35.1° and inward airflow effectively directed droplets away from the chamber boundary, preventing external dispersion. Additionally, sensor data measuring particle concentrations confirmed that droplets smaller than 10 µm were fully contained under negative pressure, strongly supporting the chamber’s effectiveness. The strong agreement between PIV flow patterns and sensor measurements underscores the reliability of the experimental methodology. These findings highlight the chamber’s ability to suppress external leakage while offering superior flexibility and portability compared to conventional isolation systems, making it ideal for emergency responses, mobile healthcare units, and large-scale infectious disease outbreaks.

Healthcare providers performing aerosol-generating procedures (AGPs) face significant infection risks, emphasizing the critical need for effective aerosol containment systems. In this study, we developed and validated a negative pressure chamber enhanced with an innovative aerodynamic cap structure designed to optimize aerosol containment. Initially, computational fluid dynamics (CFD) simulations were performed to evaluate multiple structural improvement ideas, including air curtains, bidirectional suction, and aerodynamic cap structures. Among these, the aerodynamic cap was selected due to its superior predicted containment performance, practical feasibility, and cost-effectiveness. The CFD analyses employed realistic transient boundary conditions, precise turbulence modeling using the shear stress transport (SST) k–ω model, and detailed droplet evaporation dynamics under realistic humidity conditions. A full-scale prototype incorporating the selected aerodynamic cap was fabricated and evaluated using physical polyalphaolefin (PAO) particle leakage tests and biological aerosol validation with aerosolized Bacillus subtilis. For the physical leakage tests, the chamber opening was divided into nine sections, and the aerosol dispersion was tested in three distinct directions: ceiling-directed, toward the suction hole, and opposite the suction hole. These tests demonstrated significantly stabilized airflow and substantial reductions in aerosol leakage, consistently maintaining containment levels below the critical threshold of 0.3%, especially under transient coughing conditions. The biological aerosol experiments, conducted in a simulated emergency department environment, involved aerosolizing bacteria continuously for one hour. The results confirmed the effectiveness of the aerodynamic cap structure in achieving at least a one millionth (10⁻⁶) reduction in the aerosolized bacterial leakage compared to the control conditions. These findings highlight the importance and effectiveness of advanced CFD modeling methodologies in accurately predicting aerosol dispersion and improving containment strategies. Although further studies assessing the structural durability, long-term operational ease, and effectiveness against pathogenic microorganisms are required, the aerodynamic cap structure presents a promising, clinically practical infection control solution for widespread implementation during aerosol-generating medical procedures.