Dynamically Controlled Vertiport Operations for Different UAM Aircraft Combining Live Sensor Feedback and Predictive Downwash/Outwash Scenarios

This paper introduces a novel concept for an AI-powered system designed to manage vertiport takeoffs and landings by proactively addressing the safety-critical issues of downwash and outwash. The proposed system utilizes a stream of live feedback from on-site sensors, combined with a robust predictive modeling engine, to generate optimal, aircraft-specific approach and landing trajectories in real-time. By leveraging a comprehensive database of pre-computed downwash/outwash scenarios for a multitude of UAM aircraft configurations, the AI can accurately predict the unique outwash operational footprint for each individual landing operation, based on the approaching aircraft and under the prevailing conditions. This powerful predictive capability allows the system to calculate and assign an optimal approach vector that actively minimizes risk by directing hazardous airflows away from personnel, active walkways, and other sensitive ground assets. This represents a paradigm shift from static, reactive safety measures to a proactive, intelligent, and performance-based operational model; thereby paving the way for the safe, efficient, and large-scale implementation of UAM aircraft and air taxi operations.