AI Summary & Key Takeaways
AI InsightThrough the simulations, the impact of reducing the uncertainty in aircraft mass on climb time is illustrated.
📌 3 Key Takeaways
Abstract
Efficient trajectory prediction tools will be the crucial functions in future trajectory-based operations (TBO). In addition to win and controller actions, uncertainties in climbing flights are major components of prediction errors in a flight trajectory. Due to the operational concerns, aircraft take-off weight and climb speed intent, which are key performance parameters that define climb profiles, is not entirely available to round-based trajectory prediction infrastructure. In the scope of air traffic flow management, sector entry and exit times, including where the climb ends and descending starts, are the main inputs for demand- capacity balancing processes. In this work, we have focused on uncertainties over climb trajectory to quantify and analyze their impact on climb times to cruise altitudes. We have used model-driven data statistical approaches through aircraft flight record data sets (i.e. QAR). As result of this analyze, probabilistic definitions are generated for aircraft take-off weight and speed intent. The regression between these climb parameters and flight distance is acquired to reduce the uncertainty at strategical level. Moreover, reducing climb uncertainty through adaptive uncertainty reduction is also demonstrated at the tactical level of flight. Through the simulations, the impact of reducing the uncertainty in aircraft mass on climb time is illustrated.