Urban air mobility (UAM) is a growing research technology that focuses on what it takes to move people and goods around a dense city - in the air. These are rough equivalents to flying cars, which would take people on short trips with high safety, speed and convenience. Once this technology matures, it would be joining a crowded urban airspace that is also likely occupied by drones.

However, before a large part of the world's transportation infrastructure ends up in the air, engineers need to work on the best way to combine all the new technologies in a harmonized system - and the best way to do that is by simulating it.

Simulations of complex systems, like an entire infrastructure of moving aircraft, can get complicated. Most of this aircraft will be powered with an electrical vertical takeoff and landing (eVTOL) system. eVTOLs can range in size from typical, consumer-level drones to scaled-up versions that can hold significant payloads.

However, simulations of complex systems, like an entire infrastructure of moving aircraft, can get complicated. Weather conditions, loads and power requirements complicate finding optimized paths, trajectories and landing locations for eVTOL technologies. Each aircraft must be simulated accurately and in consideration of dozens of variables. And on the city scale, this modeling gets intensive.

A new paper from North Carolina A&T University (NCA&T) researchers, supported by grants from the U.S. Defense Department and NASA, breaks the problem down into two more addressable parts.

Solving for traffic management

Unmanned traffic management (UTM) is itself a hot research topic. UTM systems would essentially administer the flights, communication and operations of vehicles outside the U.S. Federal Aviation Administration's (FAA's) air traffic management responsibilities. It would consist of users, vehicles, base stations and management organizations feeding data and needs into a system, that would offer instructions for things like flight paths, landing spots and hazards. It would need to consider collisions, prioritization for things like first responders, and plenty of other variables, which is why simulation is so important.

Stakeholders in a UTM framework. Source: EVO VI/CC BY 4.0A UTM system would enable beyond visual line of sight (BVLOS) operations. This is what the FAA has cracked down on, leading to a significant stall in developing things like Amazon's drone package delivery services. Basically, operators need to stay within visual contact of their craft at all times. Operating an eVTOL beyond visible sight range requires reliable communication networks, weather monitoring and perhaps most importantly, accurate collision avoidance systems.

However, each of those requirements also needs integration with other systems. Developing standards like data exchange protocols and architectures for handling data in both systems will take time, especially when considering the complicated nature of finding a single communication medium for various government agencies, such as the FAA, law enforcement and infrastructure repair teams.

Cybersecurity is another primary concern. If a malicious actor can intentionally spoof data to an eVTOL, they could deliberately cause a fatal crash. Or they might accidentally do so by making the system blind to potential hazards. Either way, ensuring that eVTOL communications are robust and not prone to disruption or delusion is a key aspect of any future UTM system.

Simulating all of these potential variables is difficult, to say the least. Some preliminary open-source frameworks try to account for at least some of them. But even if they capture all of the nuances of the UTM requirements, they're still missing a huge part of any effective real-world simulation — the eVTOL itself.

A solution emerges

According to the NCA&T paper, over 130 novel eVTOL concepts have been developed since 2019. Each has its own requirements regarding size, shape, aerodynamic, and other considerations. Evaluating how each would operate in a real-world environment, mainly when bounded by the requirements of a manageable UTM system, has proven difficult, which is why many of the UTM frameworks ignore the physical manifestation of the eVTOL itself entirely.

Doing so ignores one of the system's most important parts—the machines that will actually use it. That is why the researchers decided to try to integrate a system to model eVTOL performance with a UTM simulation.

The eVTOL performance simulator they used is the Stanford University Aerospace Vehicle Environment (SUAVE). It's an open-source, Python-based solution that allows users to model different aspects of an eVTOL before assembly to test the impact a design choice would have on the performance of a real system.

SUAVE takes lots of variables as inputs or controls. It uses things like a flight profile and the physical characteristics of the system. It then outputs performance metrics, including range and fuel consumption, of an eVTOL being studied. SUAVE is already widely used, and not just in the study of eVTOLs. However, it requires a lot of computing power for its realistic physics engine to truly shine. That also means it takes a lot of time to perform the calculations. To deal with that delay, the paper's authors turned to an even more rapid research area — machine learning.

Machine learning is built on data analysis, and to make use of it the researchers constructed a simulated database of both eVTOL performance characteristics and algorithms that define a viable UTM path. After doing so, they developed a high-fidelity eVTOL model that used sparse sampling and SUAVE to get realistic output parameters. But, in parallel, they also created a low-fidelity eVTOL performance model that could feed into the UTM system in real time.

Running both of those systems in parallel allowed the integration of eVTOL flight performance parameters into a UTM simulator in real time. This opens up the possibility of exploring what types of UTM systems work well with certain configurations of eVTOL and vice versa.

There is still additional work to be done, though. Further research includes adding configurations to the eVTOL model database and adding things like buildings and trees to the UTM database. Those might seem like simple things, but every addition to a simulation increases its complexity and integrating all of those additional features could prove a challenge.

Summary

Work like this is necessary if the reality of flying cars is to become a reality. They have been a dream a long time coming and pose significant risks to existing ways of doing things. But with significant resources being devoted to making them safe, reliable, and eventually superior to existing transportation methods, it's only a matter of time before eVTOLs truly take flight. Simulating their journey is only one of the first steps down that path.