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Use of AI in Aerospace Engineering: Enhancing Aviation Safety through Data Sync

Introduction

Aerospace is a sophisticated branch of engineering that is becoming more and more challenging every day. Space and aerospace operations are having a greater impact on our modern world than ever before. With the increase of Artificial intelligence in every field, including the aviation and aeronautics industry, it is becoming easier for pilots, astronauts, and other flight attendants to navigate the geo map and the condition of the aircraft. This Research Topic presents a broad discussion on Data Sync. and Voice Control AI, in addition to giving a full review of the current state of knowledge on the application of AI in the field of Aerospace Engineering. Intelligent systems serve two significant roles in aerospace engineering: (a) as intelligent assistants that enhance the expertise of humans and (b) as substitutes for human skill in pursuits that save resources, time, and lives (1).

From previous data, many air crashes in the previous several decades have resulted from mechanical failures, such as in engines or turbines or unexpected map errors. In a recent accident involving an air ambulance in Nevada, the authorities stated that at 9:45 p.m., the Guardian Flight plane, which had been flying at 19,000 feet, suddenly began to descend. According to sources, evidence suggests the air ambulance "broke up in flight" at around 11,000 feet (2). As sensor defects account for approximately 80% of all problems in the aero-engine control system, enhancing the dependability of aero-engine control systems relies heavily on fault detection and isolation (FDI) of aero-engine sensors (3). Alberto Panero, a passenger aboard Houston River Flight 1549 in 2009, told CNN, "The plane shook a bit and immediately, you could smell smoke or fire and immediately, the plane basically just started turning in another direction” (4). Aircraft Accident Investigation Commission in Nepal reports in Yeti flight 691, the pilots continued to write a lack of engine power as they dropped throughout the journey. At 10:57:26, the stick shook twice, and the plane "banked towards the left abruptly." At 10:57 local time (5:12 UTC), the FDR and CVR ceased recording, matching the final accessible ADS-B data (5). Last but not least, in the 2022 flight accident of the Florida plane crash, the crash of the experimental plane led investigators to suspect a fuel leak. An eyewitness said the experimental aircraft had trouble staying above before it crashed in 2200 block of Jamaica Drive (6).

Overall, it can be observed that turbulence failure, fuel leaks, and unexpected engine malfunctions are major causes of air accidents, putting many people's lives in danger. Improving the system robustness as well as the fault tolerance ability should always be a crucial design requirement (7). However, the danger of these data mishaps can be reduced with the help of Artificial Intelligence and its suitable implementations within the next decade. The use of AI approaches allows us to train the system to be ready for the probable circumstances that may be seen and may be different for pieces of the expected heterogeneous infrastructure (8). Indeed, machines are given the power to learn, adapt, make decisions, and demonstrate novel behaviors through the integration of a wide range of cutting-edge technologies that make up "Machine Intelligence" (9). Even with the increase in traffic, safety and security measures remain a top concern, with an 80% reduction in accidents expected by 2050 compared to 2000 (10). According to Russell et al. (11), there are now two main strands in the development of AI, which are referred to as "Humanistic AI" and "Rationalistic AI," respectively. The field of humanistic artificial intelligence (AI) studies how machines can think and behave like people. In contrast, the area of rationalistic (AI) analyzes how devices can be constructed using an understanding of intelligent human behavior. Rational AI will be superior at spotting aircraft problems with pinpoint accuracy when comparing the two types of AI. This is because rationalistic AI strongly emphasizes data analysis and logical decision-making, both of which are essential for protecting the integrity of intricate systems like those found in aerospace. Defined by Luger et al. (12), rationalistic AI means, “The branch of computer science that is concerned with the automation of intelligent behavior”. The ''laws of thought'' approach to artificial intelligence placed a significant amount of focus on making reasonable conclusions. One system to act rationally is to reason logically to the conclusion that a specific action would achieve one's goals and then work on that conclusion; therefore, it is crucial to draw the proper decisions (11). Since AIs will be able to model and develop themselves, we may expect them to be more logical than humans. They will be driven to correct the irrationalities presently being used and any irrationalities that may be exploited in the future (13).

In order to better prepare for the future, modern aviation may use AI to collect and analyze historical accident data. For aeronautical equipment to take in data accurately and effectively, machine learning is crucial, and this requires ongoing vigilance. Artificial intelligence (AI) and machine learning (ML) are intertwined with the concept of Big Data, which refers to the massive amount of information produced by CFD models or experimental measurements of aircraft aerodynamic performance (14,15). The ability of machine-learning algorithms to serve as "universal approximators" is one of its many attractive properties (16). When provided with a large enough training dataset, they may pick up on patterns in a system's behavior and mimic them (16). During evaluations, it was found that the AI-powered engine took the proper steps in response to the data it was given. These algorithms may learn from experience and improve in the most common scenarios (8). However, the management of data storage is a complex problem. The data that is being saved must be placed on resources that are able to cater to the requirements of the users (8). Data interpretation is only one of the many uses of artificial intelligence (AI) and machine learning (ML) that have been brought to the aerospace sector as part of a more significant effort to lessen the adverse effects of aviation on the environment (17). When machine learning (ML) methods are applied to Big Data, creating low- dimensional systems like reduced order models (ROMs) becomes feasible (18,19), capable of providing reliable forecasts of how flow dynamics will develop (20,21), aerodynamic forces and moments acting on the aircraft as a function of various factors (i.e., Reynolds number, Mach number, geometrical form, etc.) may be predicted using either full-scale model (which take into account the whole boundary layer transition) or surrogate models (10).

Thus, it is feasible to coordinate data using rationalistic AI to get the best possible outcome in reducing aircraft accidents. However, ’Automotive Voice Control' or the 'Intelligent voice control for Aerospace’ is also included and is a crucial part of the system. Although the idea is still relatively novel, the desire for it has reached its pinnacle. Autopilot systems have been integrated into every aspect of the aviation industry for several decades. As autopilot systems rely on a combination of sensors, algorithms, and actuators to fly the aircraft, autopilot can become problematic due to sensor inaccuracy, power supply, or weather situations. The process of converting spoken words into machine-readable text is known as Voice Recognition (22). Voice commands enable hands-free management of auxiliary equipment, which can assist crew members in flight in accomplishing their duties. This makes it possible for voice commands to support crew members (23). Engineers and researchers at Honeywell Aerospace are working on new cockpit technologies enabling pilots to manage their aircraft using voice commands. These new systems are currently in the development stage (24). For a perfectly synchronized voice activation AI in aerospace, a good speaker is needed which can be able to detect and interact on long speeches. VCD (Voice Control Detection) is the technology that will work optimally and be able to read what the user is directing to do in the most accurate manner for vehicles such as cars, airplanes, rockets, and the like. According to Fezari et al. (25), the Voice Command and Direction (VCD) system is a voice-guided robot arm. Approximately 63 unique voice commands with more than two words are suggested to be decoded in an online manner with a low false alarm rate using a VCD system, which offers advantages (23).

As we go further into the 21st century, every aerospace industry sector is working towards producing aircraft and rockets at the cutting edge of technological innovation and efficiency. Monitoring the plane’s performance, locating any possible problems, and making the necessary modifications to maintain a safe and effective operation are now much simpler tasks. In addition to assisting in safer decision-making, these devices can help reduce the likelihood of catastrophes. For example, the French company Safety Line has developed a machine-learning tool that may help pilots improve climb profiles before every trip (26). To provide another illustration, the average commercial flight consumes the fuel equivalent of 4 liters (0.9 gallons) each second, 240 liters (63.4 gallons) per minute, and 14,400 liters (about half the volume of a big U-Haul truck) per hour (27). By utilizing AI technology, we can cut down on gasoline use by 5–7% (26). The use of AI in the aircraft industry may also help produce a variety of applications that can conserve fuel and monitor its consumption, improve operational performance, and assist in regulating air traffic (26). More AI applications, such as dynamic ticket pricing, delay predictions, flight optimization, and AI thermal cameras, can be developed to improve aviation safety and fuel demand in the coming years. In 2020, the worldwide artificial market for the aerospace sector was valued at $373.6 million, and by 2028, it is expected to reach $5,826.1 million, at a CAGR of 43.4% (28). Again, in another statistics, the global market for AI in aviation is projected to grow from its 2021 valuation of US$653.74 mln to a value of US$9.985.86 mln by 2030, representing a compound annual growth rate (CAGR) of 35.38% between 2022 and 2030 (29).

The empirical findings in the articles collected here shed fresh light on the intersection of the aviation industry and artificial intelligence (AI). Future aviation research and development should center on streamlining accreditation and upgrade processes, as well as enhancing general aviation, aircraft, airlines, airports, air traffic management, and maintenance, repair, and overhaul as components of the larger air travel system (10). There has been a surge of hope that recent developments in artificial intelligence (AI), especially the significant increases in prediction produced by (multi-layer) neural networks (NN), could hasten scientific discovery (30–32). Therefore, the perfect synchronization of artificial intelligence will undoubtedly be a game changer in the aerospace industry. In order for humanity to reach Mars and far beyond within the next half-century, it will be essential to outfit rockets and space shuttles with flawless machine-learning operators.



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