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\"Air traffic management (ATM) comprises a highly complex socio-technical system that keeps air traffic flowing safely and efficiently, worldwide, every minute of the year. Over the last few decades, several ambitious ATM performance improvement programmes have been undertaken. Such programmes have mostly delivered local technological solutions, whilst corresponding ATM performance improvements have fallen short of stakeholder expectations. In hindsight, this can be substantially explained from a complexity science perspective: ATM is simply too complex to address through classical approaches such as system engineering and human factors. In order to change this, complexity science has to be embraced as ATM's 'best friend'. The applicability of complexity science paradigms to the analysis and modelling of future operations is driven by the need to accommodate long-term air traffic growth within an already-saturated ATM infrastructure\"--Provided by publisher.

An automated air quality forecasting system (AI‐Air) was developed to optimize and improve air quality forecasting for different typical cities, combined with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Model (CUACE), and used in a typical inland city of Zhengzhou and a coastal city of Haikou in China. The performance evaluation results show that for the PM2.5 forecasts, the correlation coefficient (R) is increased by 0.07–0.13, and the mean error (ME) and root mean square error (RMSE) is decreased by 3.2–3.5 and 3.8–4.7 μg/m³. Similarly, for the O3 forecasts, the R value is improved by 0.09–0.44, and ME and RMSE values are reduced by 7.1–22.8 and 9.0–25.9 μg/m³, respectively. Case analyses of operational forecasting also indicate that the AI‐Air system can significantly improve the forecasting performance of pollutant concentrations and effectively correct underestimation, or overestimation phenomena compared to the CUACE model. Additionally, explanatory analyses were performed to assess the key meteorological factors affecting air quality in cities with different topographic and climatic conditions. The AI‐Air system highlights the potential of AI techniques to improve forecast accuracy and efficiency, and with promising applications in the field of air quality forecasting. Plain Language Summary Currently, artificial intelligence (AI) technology provides an innovative technological way to solve air quality problems with its excellent capability. This work develops an advanced automated air quality forecasting system (AI‐Air), based on the China Meteorological Administration Unified Atmospheric Chemical Environmental Model (CUACE). By comparing the forecasting results with the existing numerical models, the AI‐Air system shows its excellent performance in both overall performance evaluation and case‐specific forecasting. The AI‐Air system not only surpasses the conventional methods in forecasting accuracy but also demonstrates its fine forecasting ability in detail. In addition, this study provides an in‐depth discussion of the key factors affecting air quality in different types of cities and conducts a feature importance analysis. This analysis deepens the understanding of the intrinsic mechanisms of air quality changes in different urban environments and provides a scientific basis for formulating more precise air quality management strategies. Overall, the development and application of the AI‐Air system not only improves the science and accuracy of air quality prediction, but also provides strong technical support for urban environmental management and policy formulation. Key Points An automated ML system (AI‐Air) is developed for urban air quality forecasting Operational analyses show effective correction of under‐/overestimation phenomena Explanatory analyses explore key influencing factors in inland and coastal cities

Air conditioning system is used for various application, in passenger car it gives comfort to the passenger. Now a days huge advancement have been included in the air conditioning system, especially automatic air conditioning system plays a vital role in passenger car. These air conditioning systems are performing well and have the capability of maintaining the temperature for long time with energy consumption. However, in some vehicle the performance of these air conditioning system is not achieved, while some vehicle achieved better performance. In later study it is found that, the structure of vehicle body also influence the performance of air conditioning system. In some structure the air conditioning air-flow a long distance in short time and have the capability to enhance the air conditioning performance. It is also found that the air conditioning performance can be improved by the structure of vehicle body. In this paper, we considered an Indian small budget car. The structure of the car is slightly modified and replaced the position of the air conditioning outlet. Then the residual temperature inside the car is analyzed with and without air conditioning. Here the CFD is used to analysis the temperature inside car at various position.

In both centralized and decentralized air-conditioning systems, the performance, sustainability, and efficiency of the systems in delivering thermal comfort within a specific area are assessed as part of the air thermal management platform evaluation process. The evaluation of air thermal management platforms entails a thorough examination of numerous elements, customized to the unique features of these systems, such as system components, energy efficiency, control systems, maintenance procedures, and environmental concerns. The study considers mathematical modeling of energy-efficient techniques based on meteorological data of cooperative centralized and decentralized air-conditioning systems for external air recirculation treatment. Three systems were considered: an independently functioning central air conditioner, a central system functioning together with a local air conditioner, and a central system operating together with an adiabatic humidifier. Technological aspects of cycle performance are shown to be dependent on the acceptable design capacity of the air cooler and the adiabatic humidifier air wet-bulb temperature limit. Increasing the setting capacities of the air cooler to 0.00786 kg m −2  s −1 and the adiabatic humidifier to 0.03864 kWh, the air flow rate decreases from 0.0072 to 0.004 kg m −2  s −1 , and when the setting capacities of the air cooler are 0.01011 kg m −2  s −1 and the adiabatic humidifier is 0.04831 kWh, the air flow rate decreases to a minimum limit of 0.002 kg m −2  s −1 . Comparing the annual heating, cooling, and humidification load consumption without and with utilization of the second air recirculation, for the heating load 39.48 and 5.01 kWh, the costs increased by a factor of 7.9; for the cooling load 1850 and 1320 kWh, the costs increased 1.4 times; and for the moisture load 331.5 and 1245 kg m −2  s −1 , the costs decreased 3.8 times. The research conducted has led to the development of a methodology that combines the justification of energy-saving modes with formulated climatic tables and a probabilistic-statistical model. This methodology facilitates the selection of subsystem equipment’s AC setting capacities, the calculation of heating, cooling, and moisture load consumption at various times, and the technological scheme for heating and humidity air treatment. The refined AC can operate at peak efficiency and reduce energy loss thanks to this iterative approach. Moreover, this method's progressive design enables it to gradually increase in efficiency over time.

The main purpose of this study is to evaluate the applicability of a liquid desiccant and evaporative cooling-assisted 100% outdoor air system (LD-IDECOAS) in six typical cities in China. The six cities are located in different climatic zones in China and are selected because they are comparable owing to the outdoor air conditions. Many studies have shown that the annual operating energy consumption of LD-IDECOAS is nearly half compared with the conventional variable air volume (VAV) system. Because the climate characteristics of the six selected cities were different, the appropriate mode of operation of the LD-IDECAOS was applied to each studied city, and energy simulations were performed. Based on the design conditions of each region, the required cooling and heating loads were calculated for office buildings using transient system simulations (TRNSYS) 17, and the performance of the LD-IDECOAS and its energy consumption were simulated with a commercial engineering equation solver (EES) program. Depending on the climate characteristics of each city, adequate modifications were evaluated with simulations in terms of energy consumption. The proposed system was compared with the VAV system and the evaporative cooling assisted 100% outdoor air-conditioning system (IDECOAS) for detailed simulation results in the effort to evaluate the energy-saving potential. Finally, the results show that the proposed system saves considerable energy over conventional VAV systems and, in summer, the applications save even more energy than IDECOAS. However, there is a slight difference between the different geographical regions in terms of the annual operating energy consumption. In summary, the proposed system can yield significant energy-saving benefits in hot and humid regions whereas, in dry regions, the proposed system is more applicable in the summer. Consequently, LD-IDECOAS can be adopted for different climatic zones as a heating, ventilating, and air-conditioning (HVAC) system by introducing 100% outdoor air.

The safe integration of a large number of unmanned aircraft systems (UASs) into the National Airspace System (NAS) is essential for advanced air mobility. This requires reliable air-to-air transmission systems and robust collision avoidance algorithms. Automatic Dependent Surveillance-Broadcast (ADS-B) is a potential solution for a dependable air-to-air messaging system, but its reliability when stressed with hundreds to thousands of vehicles operating simultaneously is in question. This paper presents an ADS-B model and analyzes the capabilities of the Universal Access Transceiver (UAT), which operates at a frequency of 978 MHz. We use a probabilistic collision avoidance algorithm to examine the impact of varying parameters, including the number of vehicles and the transmission power of the UAT, on the overall safety of the vehicles. Additionally, we investigate the root causes of co-channel interference, proposing enhancements for safe operations in environments with a high density of UAS. Simulation results show message success and collision rates. With our proposed enhancements, UAT ADS-B can provide a decentralized air traffic system that operates safely in high-density situations.

Decades of continuous efforts to address known hazards in the national airspace system (NAS) and to respond to issues illuminated by analysis of incidents and accidents have made commercial airlines the safest mode of transportation. The task of maintaining a high level of safety for commercial airlines is complicated by the dynamic nature of the NAS. The number of flights by commercial transports is increasing; air traffic control systems and procedures are being modernized to increase the capacity and efficiency of the NAS; increasingly autonomous systems are being developed for aircraft and ground systems, and small aircraft-most notably unmanned aircraft systems-are becoming much more prevalent. As the NAS evolves to accommodate these changes, aviation safety programs will also need to evolve to ensure that changes to the NAS do not inadvertently introduce new risks. Real-time system-wide safety assurance (RSSA) is one of six focus areas for the National Aeronautics and Space Administration (NASA) aeronautics program. NASA envisions that an RSSA system would provide a continuum of information, analysis, and assessment that supports awareness and action to mitigate risks to safety. Maintaining the safety of the NAS as it evolves will require a wide range of safety systems and practices, some of which are already in place and many of which need to be developed. This report identifies challenges to establishing an RSSA system and the high-priority research that should be implemented by NASA and other interested parties in government, industry, and academia to expedite development of such a system.

A complete reference text to airdrop recovery systems with self-inflating airbags, focusing on analysis, test data, and engineering practicalities Comprehensively covers the fundamental theories, design, matching, and analysis of airdrop recovery systems that include a parachute and self-inflating airbag system Gives step-by-step guidance to aid.

The stability of small unmanned air systems can be challenged by turbulence during low-altitude flight in cluttered urban environments. This paper explores the benefits of a tandem wing aircraft configuration with the implementation of a pressure-based phase-advanced turbulence sensory system on a small unmanned air system for gust mitigation. The objective was to utilise passive and active methods to minimise gust-induced perturbations. Experimentation in repeatable turbulence within a wind tunnel’s test section was conducted. The experiments focus on the roll axis, which is isolated through a specially designed roll-axis rig. The results show improvement over conventional aircraft. This work is part of a larger research project aimed at enabling safe, stable and steady small unmanned air systems flight in urban environments.

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management.