Physical coefficients play a pivotal role in the engineering and sciences for the quantified expression of the physical state. Accordingly, determining the physical coefficients with high accuracy and reliability is an important topic in Fluid mechanics. Hence, we attempt to predict various coefficients, such as the friction factor, based on artificial intelligence (AI) technology. AI technology shows an outstanding performance than traditional relations. Also, our generalized AI model contains the unknown relationships of fluid physics, which can present novel insight in future research by extracting the relationships.

Coronavirus disease-19 (COVID‐19) outbreak has attracted significant attention to the effect of airflow patterns on the dispersion of airborne hazardous materials within indoor spaces. To effectively reduce the airborne particles in space, it is important to apply an appropriate airflow pattern by controlling a ventilation system. AI-based dynamic airflow pattern control can be a simple strategy for optimal ventilation, that is possible to remove rapidly airborne particles and keep them at low concentrations. We present a ventilation control strategy using AI technology based on various distributions of PM in an isolation room. Control strategies were constructed by changing the airflow patterns through the selective switching on/off of ventilation inlets to rapidly remove airborne particles. The proposed AI-based dynamic airflow pattern control can effectively prevent the spreading of the airborne particles to reduce the risk of indoor infection transmission.