AI-Driven Optimization of The Actual Take- off Weight (ATOW)
Faculty Mentor Name
Rasheed AlMomani
Format Preference
Poster
Abstract
The aviation industry plays a critical role in global transportation, but its safety and environmental impact are increasingly under scrutiny. A significant concern is the frequency of aviation accidents linked to exceeding the allowable take-off weight, underscoring the need for more precise methods to monitor aircraft weight during this critical stage of a flight. Developing accurate systems for estimating takeoff weight is crucial to improving safety and optimizing aircraft performance, including fuel consumption, trajectory prediction, and key flight characteristics such as climb rate, range, and takeoff distance. Moreover, takeoff weight directly influences sustainability and operational efficiency. Currently, reliable and publicly available data on takeoff weight is scarce. In practice, takeoff weight is often approximated as a fixed percentage of the Maximum Takeoff Weight, typically between 80-90%, leading to significant inaccuracies that affect operational efficiency, safety, and environmental sustainability. This research seeks to address the gap by developing an Al-driven model capable of predicting takeoff weight with high accuracy. This model offers a robust alternative to traditional methods and promises several key benefits: 1) Enhanced Fuel Efficiency, 2) Improved Flight Safety, 3)Better Operational Planning. This research is pivotal for the future of automation in the aviation sector, as it offers the potential to revolutionize how aircraft operations are monitored and optimized. By leveraging Al to accurately predict take-off weight, this study could serve as a cornerstone in the development of fully automated systems that ensure more efficient and sustainable flight operations. With advancements in Al and machine learning, such models can be integrated into real-time flight management systems, contributing to smarter and more adaptive decision-making processes.
AI-Driven Optimization of The Actual Take- off Weight (ATOW)
The aviation industry plays a critical role in global transportation, but its safety and environmental impact are increasingly under scrutiny. A significant concern is the frequency of aviation accidents linked to exceeding the allowable take-off weight, underscoring the need for more precise methods to monitor aircraft weight during this critical stage of a flight. Developing accurate systems for estimating takeoff weight is crucial to improving safety and optimizing aircraft performance, including fuel consumption, trajectory prediction, and key flight characteristics such as climb rate, range, and takeoff distance. Moreover, takeoff weight directly influences sustainability and operational efficiency. Currently, reliable and publicly available data on takeoff weight is scarce. In practice, takeoff weight is often approximated as a fixed percentage of the Maximum Takeoff Weight, typically between 80-90%, leading to significant inaccuracies that affect operational efficiency, safety, and environmental sustainability. This research seeks to address the gap by developing an Al-driven model capable of predicting takeoff weight with high accuracy. This model offers a robust alternative to traditional methods and promises several key benefits: 1) Enhanced Fuel Efficiency, 2) Improved Flight Safety, 3)Better Operational Planning. This research is pivotal for the future of automation in the aviation sector, as it offers the potential to revolutionize how aircraft operations are monitored and optimized. By leveraging Al to accurately predict take-off weight, this study could serve as a cornerstone in the development of fully automated systems that ensure more efficient and sustainable flight operations. With advancements in Al and machine learning, such models can be integrated into real-time flight management systems, contributing to smarter and more adaptive decision-making processes.