A Simulation-Optimization Framework for Developing Wind-Resilient AAM Networks

TL;DR

This work addresses how environmental wind variability impacts the planning and operation of advanced air mobility (AAM) networks. It introduces a simulation-optimization framework that couples a wind-aware, agent-based vertiport simulator (VertiSim) with a nonlinear charging and fleet-sizing optimization, enabling dynamic scheduling and charging across a multi-vertiport network. Key contributions include the wind-enabled network simulation environment, a time-discretized optimization model that minimizes fleet size while respecting energy and demand constraints, and a methodology to integrate offline optimization with discrete-event simulation. Results demonstrate that wind can markedly increase energy consumption and charging needs, especially for longer flights, and can drive substantial fleet-size adjustments; the framework offers a practical pathway to designing wind-resilient AAM networks for safer and more efficient urban mobility.

Abstract

Environmental factors pose a significant challenge to the operational efficiency and safety of advanced air mobility (AAM) networks. This paper presents a simulation-optimization framework that dynamically integrates wind variability into AAM operations. We employ a nonlinear charging model within a multi-vertiport environment to optimize fleet size and scheduling. Our framework assesses the impact of wind on operational parameters, providing strategies to enhance the resilience of AAM ecosystems. The results demonstrate that wind conditions exert significant influence on fleet size even for short-distance flights, their impact on fleet size and energy requirements becomes more pronounced over longer distances. Efficient management of fleet size and charging policies, particularly for long-distance networks, is needed to accommodate the variability of wind conditions effectively.

Figures (9)

• Figure 1: Simulation - Optimization framework
• Figure 2: VertiSim software architecture
• Figure 3: A visual representation that illustrates the interaction between the aircraft's true air velocity ($\Vec{V}_T$), ground velocity ($\Vec{V}_{GS}$), wind velocity ($\Vec{V}_{W}$), angle of $\Vec{V}_W$ ($\phi$) and the strategic adjustment of the aircraft's heading ($\psi$) to maintain course towards the destination vertiport in the presence of wind.
• Figure 4: Simulation - Optimization integration illustration example
• Figure 5: Distribution of flight demand for the analysis