With the advent of high-performance computers, Artificial Intelligence (AI) is gaining popularity in various domains. Initially, AI was developed to simulate human intelligence using exhaustive data using computers. AI programming is based on three cognitive processes – learning, reasoning, and self-correction. The learning process for AI programming focuses on gathering data and developing rules (algorithms) to transform it into useful information.

The best algorithm must be determined by which one delivers the desired result. This is a method of reasoning. The best algorithm is improved through self-correction. These algorithms can predict high accuracy results with large amounts of data and experiential learning. Speech-to-text, face recognition, chatbots, online payment methods, healthcare, and social media are a few examples of AI.

Machine learning (ML) is a sub-branch of AI. It relies on an enormous amount of data to develop an algorithm and to predict the behavior or characteristics accurately of any system. Overall, AI and ML are chunks of intelligent technology that add artificial intelligence to some processes to automate and enhance those that were previously handled manually. This speeds up their completion time and improves their accuracy and efficacy in general.

With the adoption of this technology, aerospace and defense industries may enhance their operations in several ways, increase organizational connectivity, and become more data-driven. Various applications of AI and ML are discussed here.

The airspace gets denser as air traffic volume increases continuously. Airspace management must overcome a significant obstacle to deal with the rise in traffic to maintain the level of safety. Better and more accurate flight planning will be needed for that aim. The importance of calculating trajectories for aircraft is increasing. With the volume of air traffic, even small changes to aircraft patterns can result in significant reductions in fuel consumption, air pollution, flight delays, and other factors like airspace optimization.

A good understanding of where aircraft will be at a given time is crucial for preventing potential air confrontations. Air traffic controllers are currently responsible for identifying and resolving potential conflicts in clearance-based operations. This won’t be sufficient in the future. We’ll need to devise fresh strategies for safely managing airspace. There are many ways to complete the upcoming assignments.

One is equipping air traffic controllers with new technologies that will help them spot potential issues earlier. As a result, aircraft are positioned closer to one another throughput optimizations and a gradual shift toward trajectory-based operations. All methods described requiring exact as feasible flight path calculation and prediction. Artificial Intelligence and Machine Learning can be employed in ATC to manage the aspects and ease the job of ATC personnel.

Also, lighter aircraft will reduce weight, consequently, reducing fuel consumption. The evolution of various stronger and lighter materials such as composites, and alloys enables designers and engineers to enhance designs that are more aerodynamic and lighter weight to help with fuel efficiency from an engineering aspect, AI is also employed in the design, selection of materials, construction of new aircraft parts.

AI is frequently used in training programs for pilots and engineers, simulating real-world situations and environments digitally. In the aerospace and defense industries, (AI) can be used in the repair, overhaul, and maintenance division, which can effectively identify the damaged components and subsequently orders new component or arranges repairs on a need basis.

This is possible with more thorough data tracking. To precisely forecast when a part needs to be changed and when to buy those parts, AI can track past data on part use, breakdowns, wear, and tear, and far more. Aerospace and defense firms may have various production challenges due to an inefficient supply chain and factory floor.

Delays, costs, lower output, and poor customer service arise from this. Without integrating automation, artificial intelligence, and machine learning into the processes, it will be difficult to plan the activities, acquire what is required at the right time, and recover control over the ordering. Given the enormous amount of pieces needed, it can be exceedingly difficult.

AI and ML can be used to get better and enhanced results in CFD. Some researchers show the application of AI and ML in the modeling and verification of heat and mass exchanges, the improvement of aerodynamic and turbulence models, and improved simulation and forecasting of complicated flow fields.

To sum up, as technology advances, AI and ML can be employed in many facets of the aerospace industry to do tasks more quickly and with more precision.

Authors:

Prof. Hariprasad Thimmegowda, Assistant Professor, Department of Aerospace Engineering, College of Engineering and Design, Alliance University

Prof. Gisa G S, Assistant Professor, Department of Aerospace Engineering, College of Engineering and Design, Alliance University