ABSTRACT
Flight Safety Foundation's 75th annual International Air Safety Summit (IASS;https://flightsafety.org/) was held November 7-9, 2022, at the Omni Atlanta Hotel at CNN Center in Atlanta, GA, USA. The IASS 2022 agenda featured presentations and panel discussions on a range of safety-related topics with an emphasis on the industry's ongoing recovery from the COVID-19 pandemic and the recognition that the mental and emotional well-being of personnel and the development and maturation of robust organizational safety cultures are important elements of the safety landscape. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
In response to the construction process of Healthy China. it is rather important to create a safe, healthy and energy-efficient indoor environment for public buildings. The public building space is often densely populated, with a large flow of people and many types of air pollution, which presents non-uniform dynamic distribution characteristics. This brings great challenges to the control of indoor air safety, especially during the pandemic period of COVID-19. Excessive ventilation may not only cause large energy waste. but also lead to cross-contamination and even a cluster of infection. In this paper, an operation and maintenance (O&M) control system for indoor air safety is developed based on the core concepts and basic methods of human ergonomics. In this system, one of the important human environmental variables is focused for control, i.e.. indoor air pollution level. Especially after the outbreak of COVID-19. droplets and droplet nuclei from respiration are the most significant air pollution categories required for mitigation. Towards the efficient control of air pollution in large public buildings. it should further take into account the interaction of human, equipment and machines (i.e., ventilation_ air purification and disinfection and intelligent control system) and building environment. Firstly, on the basis of the online monitoring of indoor air pollution concentration and personnel flow, the non-uniform dynamic distribution of indoor pollutants and personnel can be obtained by using the non-uniform and low-dimensional rapid prediction models and computer vision processing. Then, the optimal setting results of ventilation parameters (e.g., ventilation modes, supply air rate. etc.) can be outputted by the environmental control decision system. Finally, based on a combination of monitoring sensors, controllers and actuator hardware equipment (at the location of fans or dampers), the intelligent regulation and control of ventilation system can be realized, aimed at minimizing energy consumption and reducing pollutant concentration and exposure level. Meanwhile, the air purification and disinfection system (especially for the disinfection of virus particles) are operated under the condition of the ventilated environment, which can serve as a powerful auxiliary to the maintenance of indoor air safety. The workflow and effect of the O&M control system are demonstrated by an engineering application case of the front hall in the International Convention and Exhibition Center. The results indicate that the non-uniform and low-dimensional rapid prediction model for pollutant concentration is effective for the ventilation control with the average prediction difference of 11.9%. The implementation of the intelligent ventilation system can reduce the risk of human infection to less than 4%. and its energy-saving ratio for the ventilation can be as high as about 45%. Through optimizing the layout strategies of disinfection devices based on the intelligent ventilation control, the space accessibility of negative oxygen ions can be well accepted, to further increase the removal efficiency of air pollution. The calculated value of space disinfection rate is more than 99%, which can further reduce the risk of infection by 1-2 orders of magnitude. This study can provide an important reference for the promotion and upgrading of O&M control system for indoor air safety.
ABSTRACT
In the commercial aviation industry, safety is always a primary concern, with a central focus on the well-being of passengers and crew members. Given the significance of safety, the COVID-19 pandemic has resulted in drastic changes to the way commercial airlines operate. The airline industry has had to adopt precautions to help ensure safety by minimizing the risk of transmission for both passengers and airline employees during air travel. We examined passengers' willingness to fly with various commercial airline COVID-19 safety precaution scenarios: control (none), face coverings (masks), negative PCR test, boarding and deplaning five rows at a time, and paired combinations of these precautions. The within-subject design assessed Willingness to Fly (Rice et al., 2015, 2020) for all scenarios in random order. Participants were recruited from the US Amazon Mechanical Turk (MTurk) user population. Willingness to fly was positive for all scenarios (N= 202). The control scenario, with no precautions listed, had the lowest willingness to fly (0.69);Scenario 3, negative PCR test required, had the highest willingness to fly (0.81). Flowever, contrary to expectations, there was no statistically significant difference in willingness to fly between the scenarios. These results suggest that participants are willing to fly regardless of the precautions, meaning that air travelers are still willing to fly under the commonly used pandemic precautions or without.
ABSTRACT
Due to events such as natural disasters and navigation equipment failures, enormous calamity may be caused by the interruption of the navigation network which is a guarantee for the flight safety of civil aviation aircraft. The navigation network consists of the navigation stations as nodes and the routes between them as edges. Different nodes have different effects on the vulnerability of the network due to their different abilities to maintain the stability of the network topology and the normal function of the network. To quantify this difference and identify key nodes that have a greater impact on the vulnerability of the navigation network, an indicator to assess the importance of a navigation station is proposed which combines the structural importance reflected by node topology centrality and functional importance reflected by node weight. The structural importance of a node corresponds to its topology features including local dominance of the node and its global influence, and the important contribution to both adjacent and nonadjacent nodes from this node, while the functional importance is indicated by the flight flow serviced by the node during a fixed period of time. Vulnerability evaluation shows that the navigation network is more vulnerable when subject to the intentional attack of nodes with higher comprehensive node importance than an intentional attack of nodes with a larger value of indicators used in previous literature. Finally, the vulnerability of the navigation network is improved through changing the topology of the most critical node and balancing the node importance of the whole network.
ABSTRACT
The recognition of aircraft wake vortex can provide an indicator of early warning for civil aviation transportation safety. In this paper, several wake vortex recognition models based on deep learning and traditional machine learning were presented. Nonetheless, these models are not completely suitable owing to their dependence on the visualization of LiDAR data that yields the information loss of in reconstructing the behavior patterns of wake vortex. To tackle this problem, we proposed a lightweight deep learning framework to recognize aircraft wake vortex in the wind field of Shenzhen Baoan Airport’s arrival and departure routes. The nature of the introduced model is geared towards three aspects. First, the dilation patch embedding module is used as the input representation of the framework, attaining additional rich semantics information over long distances while maintaining parameters. Second, we combined a separable convolution module with a hybrid attention mechanism, increasing the model’s attention to the space position of wake vortex core. Third, environmental factors that affect the vortex behavior of the aircraft’s wake were encoded into the model. Experiments were conducted on a Doppler LiDAR acquisition dataset to validate the effectiveness of the proposed model. The results show that the proposed network has an accuracy of 0.9963 and a recognition speed at 100 frames per second was achieved on an experimental device with 0.51 M parameters.
ABSTRACT
In response to the construction process of Healthy China. it is rather important to create a safe, healthy and energy-efficient indoor environment for public buildings. The public building space is often densely populated, with a large flow of people and many types of air pollution, which presents non-uniform dynamic distribution characteristics. This brings great challenges to the control of indoor air safety, especially during the pandemic period of COVID-19. Excessive ventilation may not only cause large energy waste. but also lead to cross-contamination and even a cluster of infection. In this paper, an operation and maintenance (O&M) control system for indoor air safety is developed based on the core concepts and basic methods of human ergonomics. In this system, one of the important human environmental variables is focused for control, i.e.. indoor air pollution level. Especially after the outbreak of COVID-19. droplets and droplet nuclei from respiration are the most significant air pollution categories required for mitigation. Towards the efficient control of air pollution in large public buildings. it should further take into account the interaction of human, equipment and machines (i.e., ventilation_ air purification and disinfection and intelligent control system) and building environment. Firstly, on the basis of the online monitoring of indoor air pollution concentration and personnel flow, the non-uniform dynamic distribution of indoor pollutants and personnel can be obtained by using the non-uniform and low-dimensional rapid prediction models and computer vision processing. Then, the optimal setting results of ventilation parameters (e.g., ventilation modes, supply air rate. etc.) can be outputted by the environmental control decision system. Finally, based on a combination of monitoring sensors, controllers and actuator hardware equipment (at the location of fans or dampers), the intelligent regulation and control of ventilation system can be realized, aimed at minimizing energy consumption and reducing pollutant concentration and exposure level. Meanwhile, the air purification and disinfection system (especially for the disinfection of virus particles) are operated under the condition of the ventilated environment, which can serve as a powerful auxiliary to the maintenance of indoor air safety. The workflow and effect of the O&M control system are demonstrated by an engineering application case of the front hall in the International Convention and Exhibition Center. The results indicate that the non-uniform and low-dimensional rapid prediction model for pollutant concentration is effective for the ventilation control with the average prediction difference of 11.9%. The implementation of the intelligent ventilation system can reduce the risk of human infection to less than 4%. and its energy-saving ratio for the ventilation can be as high as about 45%. Through optimizing the layout strategies of disinfection devices based on the intelligent ventilation control, the space accessibility of negative oxygen ions can be well accepted, to further increase the removal efficiency of air pollution. The calculated value of space disinfection rate is more than 99%, which can further reduce the risk of infection by 1-2 orders of magnitude. This study can provide an important reference for the promotion and upgrading of O&M control system for indoor air safety.
ABSTRACT
The Unmanned Aerial Vehicle (UAV) has been used for the delivery of medical supplies in urban logistical distribution, due to its ability to reduce human contact during the global fight against COVID-19. However, due to the reliability of the UAV system and the complex and changeable operation scene and population distribution in the urban environment, a few ground-impact accidents have occurred and generated enormous risks to ground personnel. In order to reduce the risk of UAV ground-impact accidents in the urban logistical scene, failure causal factors, and failure modes were classified and summarized in the process of UAV operation based on the accumulated operation data of more than 20,000 flight hours. The risk assessment model based on the Bayesian network was built. According to the established network and the probability of failure causal factors, the probabilities of ground impact accidents and intermediate events under different working conditions were calculated, respectively. The posterior probability was carried out based on the network topology to deduce the main failure inducement of the accidents. Mitigation measures were established to achieve the equivalent safety level of manned aviation, aiming at the main causes of accidents. The results show that the safety risk of the UAV was reduced to 3.84 × 10−8 under the action of risk-mitigation measures.