Authors - Trupti Shripad Tagare, K.L.Sudha, Nagendra Kumble, Sanketh T S, Belliappa M Abstract - The current developments in the design of aircraft have remarkably improved their overall performance. The parameter Rate of Climb (RoC) plays a very vital role in planning the trajectory of the flight, optimum fuel utilization and flight safety and is of significance for both technicians and pilots. The factors affecting RoC are weight of the aircraft, its design, and the atmospheric state. In this study, the estimation of real time RoC using predictive AI and deep learning is presented. The model is trained on real time flight data collected from Radome Technologies, Bengaluru. The parameters like drag, thrust, weight, climb angle and airspeed are provided as inputs to the model after preprocessing. The results show that the system achieves an enhanced predication accuracy with R2 of 0.9396, Root Mean Squared Error (RMSE) of 861.69 feet per minute and Mean Absolute Error (MAE) of 659 feet per minute. The efficiency and capability of several aircrafts can be measured and analysed using the rate of climb. The work greatly finds its important role in ground-based flight planning tools and in onboard decision-support systems. The fuel requirements for the aircraft can be reduced significantly by setting an optimum ROC. This will result in reduced costs and sustainable solutions. This work contributes to overall performance and safety, as the aircraft will maintain the optimal ascent using AI driven climb profile optimization.
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