Inhaled Mass and Particle Removal Dynamics in Commercial Buildings And Aircraft Cabins

Computational fluid dynamics (CFD) models of an aircraft cabin and an indoor commercial space (ICS) were used to characterize the spread of aerosols generated by a coughing or breathing person suffering from a respiratory illness. Occupant exposure to these aerosols was then compared between the ICS and the aircraftcabin. The lifetime of the aerosols, system designs and airflow patterns that reduce their concentration over time were also examined. Differences between steady state and well-mixed conditions were identified and comparisons made between the model environments. The CFD analysis results were also compared to empirical data from a U.S. Transportation Command study that tracked particles introduced by simulated infectious individuals in an airplane cabin environment.

Impact of Seat Inclination and Misalignment on Airborne Pollutant Transport in a Single-Aisle Aircraft Cabin

Airborne pollutant transport in an aircraft cabin is greatly affected by the created airflow. The seat layout can impact the flow and thus the pollutant transport. Most studies have adopted symmetric upright seats for simplicity. The influence of seat inclination and seat misalignment on airflow and pollutant transport is still unclear. This investigation adopted a validated computational fluid dynamics (CFD) method to study the airflow and airborne pollutant distribution in a single-aisle cabin with seven rows of seats. The pollutant was assumed to be released from a passenger seated in the middle of three adjacent seats. A total of five different seat layouts were considered, including all of the upright seats, the inclination of three adjacent seats, the inclination of all of the seats in half a cabin, the inclination of all of the seats in a whole cabin, and the misalignment seat rows across the aisle. The flows in both the cross and longitudinal sections were compared. The pollutant concentrations in the respiratory zone of the passengers in different seats were adopted to evaluate the cross-contamination. The results revealed that the symmetric seat layout aids to circumscribe the released pollutant in a small region and reduces the cross-contamination either by maintaining the upright seats or inclining all of the seats. Contrarily, any inclination of seats or a misalignment of seat rows should be avoided during the pandemic since an asymmetric seat layout would generate asymmetric flow and strengthen the spreading of pollutants.

Personalized Ventilation as a Possible Strategy for Reducing Airborne Infectious Disease Transmission on Commercial Aircraft

Featured ApplicationPersonalized ventilation systems for improving air quality around passengers in confined vehicles, such as airplanes.In the last decade, there has been an increase in ease and affordability of air travel in terms of mobility for people all around the world. Airplane passengers may experience different risks of contracting airborne infectious diseases onboard aircraft, such as influenza or severe acute respiratory syndrome (SARS-CoV-1 and SARS-CoV-2), due to nonuniform airflow patterns inside the airplane cabin or proximity to an infected person. In this paper, a novel approach for reducing the risk of contracting airborne infectious diseases is presented that uses a low-momentum personalized ventilation system with a protective role against airborne pathogens. Numerical simulations, supported by nonintrusive experimental measurements for validation purposes, were used to demonstrate the effectiveness of the proposed system. Simulation and experimental results of the low-momentum personalized ventilation system showed the formation of a microclimate around each passenger with cleaner and fresher air than produced by the general mixing ventilation systems.

A Deep Learning Approach to Analyze Airline Customer Propensities: The Case of South Korea

In the airline industry, customer satisfaction occurs when passengers’ expectations are met through the airline experience. Considering that airline service quality is the main factor in obtaining new and retaining existing customers, airline companies are applying various approaches to improve the quality of the physical and social servicescapes. It is common to use data analysis techniques for analyzing customer propensity in marketing. However, their application to the airline industry has traditionally focused solely on surveys;hence, there is a lack of attention paid to deep learning techniques based on survey results. This study has two purposes. The first purpose is to find the relationship between various factors influencing customer churn risk and satisfaction by analyzing the airline customer data. For this, we applied deep learning techniques to the survey data collected from the users who have used mostly Korean airplanes. To the best of our knowledge, this is the one of the few attempts at applying deep learning to analyze airline customer propensities. The second purpose is to analyze the influence of the social servicescape, including the viewpoints of the cabin crew and passengers using aircraft, on airline customer propensities. The experimental results demonstrated that the proposed method of considering human services increased the accuracy of predictive models by up to 10% and 9% in predicting customer churn risk and satisfaction, respectively.