Predicting the future use of airspace — not knowing how it will be used, what types of aircraft will be made, and how to manage it safely — is hair raising to most. Major companies like Airbus and Dassault Systèmes, among others, are simulating our unknown electric air mobility future. But what does the future really hold?
According to an International Air Transport Association (IATA) press release from October 2018, the number of airline passengers per year will double by 2037 to 8.2 billion. The largest growth will be in Asian markets. Commercial airliners worldwide will have to increase, from 29,093 in 2017 to an estimated 46,878 in 2037. That also means traffic, air traffic. It needs to be managed efficiently and safely.
Airbus Uses MDP for UTM
Urban Air Mobility (UAM) challenges the way our airspace is currently managed with future unknown operations. Unmanned Traffic Management (UTM) needs to not only be scalable but robust enough to account for uncertainty. The UTM program at Airbus looks into the uncertainty of that management by using, among other systems, the Markov Decision Process (MDP) in dense airspace. What exactly is the MDP process, might you ask?
The MDP is used to solve difficult challenges of the future of our UAM airspace. No one knows what and how this future looks. Decision making is an art and science. (Although, you wouldn’t know this watching politics these days.) When it comes to aviation matters, evaluating potential problems requires a precise methodology to predict the unknown. Statistical models can help make decisions using MDP. It is used to determine an optimal set of decisions efficiently even if the results of the actions are uncertain. MDP is a perfect fit for UTM and autonomous onboard systems, according to Airbus, since it considers the unknown.
MDP is mainly used to predict trajectory errors, unpredictable pilot and operator behavior, and imperfect sensor information that makes traffic management notoriously difficult, according to Airbus. They say that “MDP allows an explicit probabilistic formulation of these uncertainties,” for those statistics experts out there. Another way to imagine this is when an autonomous drone encounters an intruder that must guess exactly where the “intruder” could intercept its flight path. MDP helps represent the outcomes and compute an optimal route.
The UAM Airspace of the Future
Our future UAM autonomous airspace management needs close to flawless AI-driven collision avoidance systems and as close as possible to a bulletproof UTM strategy. Using MDP to predict complex and uncertain environments, Airbus UTM teamed up with the Stanford Intelligent Systems Laboratory for a white paper called Optimizing Collision Avoidance in Dense Airspace Using Deep Reinforcement Learning. It describes autonomous collision avoidance systems (CAS) able to scale conflicts and resolve them when facing a multitude of aircraft flight paths. CAS gets into the limitations of tactical deconfliction systems – how potential aviation problems get resolved. This CAS platform uses a neural network with a utility table to generate conflict resolution commands. What this means is the neural network corrects the base utility table for a more robust and efficient resolution to complex multi-intruder flight paths.
In laymen terms, a corrected CAS improves safety and efficiency at all levels of airspace density: from pairwise conflicts to multi-threat encounters. Airbus uses simulated flight trajectories (see below in the animated graphics) showing the differences between the uncorrected and the corrected CAS in identical cases.
Unsolved, these potential hazards can lead to secondary conflicts, raising the complexity of managing the density of the airspace over populated areas. An optimized CAS avoids high-density airspace potential disasters when enough aircraft reaches a certain threshold.
As unmanned aircraft systems (UAS) populate our future skies with drone deliveries, projected traffic densities will exceed what almost all airspaces can handle today. There needs to be a clear separation between UAS in this environment with more efficient CAS and a UTM that can accommodate even the most diverse operations. This means having a separate management function that allows for different systems to interact in harmony while ensuring safety at the highest levels of traffic density.
If you want to geek out this holiday week, I highly recommend you plow through the Airbus paper here. At the very least, it will show you those companies are putting serious resources into resolving these future problems.
Meanwhile, In the Simulation World
Dassault Systèmes (3DS) starts from the premise that 80 to 90% of the population will live in cities, according to the United Nations Department of Economic and Social Affairs. If left as is, the global population of 7.6 billion will balloon to 8.6 billion by the year 2030, 9.8 billion by 2050, and 11.2 billion by 2100. This means UAM must be a viable alternative, not just financially but practically. I’m glad I was raised on sci-fi movies and series!
Aviation environmental sustainability has to happen through electrification with low-noise, recyclable, and safe design. UAM is transitioning from a 2D world to 3D world, in both aerospace and automotive industries. Both are investing considerable resources and funds into developing personal and air-taxi eVTOL and fixed-wing aircraft to alleviate congestion from the streets.
While the focus is on the boldest eVTOL projects, keep your discerning eyes on the more practical electric fixed-wing aircraft that are easier to achieve before eVTOL aircraft can completely take off, pun intended. That and unmanned air vehicles (UAVs) — delivery drones especially — will start the electric UAM shift and curb down on road congestion by removing delivery trucks and bringing goods directly to consumers.
Specifically, DS focuses on multidisciplinary analyses to tackle the problems associated with UAM — aeroacoustics, aeromechanics, fluid-structure interaction (FSI), acoustic signature, and more. It is developing strict performance standards (optimized over thousands of regulation-dictated flight miles) that need to be met before electric UAM becomes a reality. Its 3DEXPERIENCE platform focuses on virtual prototyping. Simulations cut down on research and development costs, and improve efficiency and safety without relying on physical testing. This and much more saves companies money and time.
Everyone in the industry knows that electric vertical takeoff & landing (eVTOL) and (conventional takeoff & landing (eCTOL) aircraft are quieter than their thermal equivalents, a critical feature for advancing UAM. While aviation buffs point to rotor tips making the real noise, these simulations are working on lowering their acoustic signature as well. Jaunt Air Mobility is probably the best example of finding an elegant solution. By using a gyrocopter rotor concept it calls ROSA — short for Reduced-rotor Operating Speed Aircraft — it unloads the main rotor at cruise speed, resulting in less noise than an airplane and, of course, a helicopter. More to come on that topic as I compile my talks with the Jaunt team.
One way DS predicts the acoustic signature of a given aircraft is by using Lattice Boltzmann Methods (LBM). It simulates the unsteady aerodynamics required to accurately understand noise sources and propagation. The 3DEXPERIENCE Lab is working on how to minimize the impact of rotor blades creating vortices and propagating noise. This means understanding the trajectory of the vehicle, giving it optimized propulsion with excellent flight dynamics and aerodynamics coupled with aeroacoustics simulations to lowers noise. You can read more about DS’s labs here.
UAM is Serious Science & a Lot of Simulation
Finally, a holistic approach to green mobility involves autonomous vehicles (AV) on AV infrastructure — roads that accommodate AV technology. This must be developed in conjunction with smart UTM taking into consideration electric UAM not yet formed.
The third mode of transportation already thought of in the late 19th century by famed French author and visionary Jule Verne, who predicted “flights” to the moon and hollow tubes transporting “carriages-like vehicles” across continents, is hyperloop technology. I believe hyperloop technology is the third pillar of a seamless green mobility infrastructure arising worldwide. More to come on that topic in the new year.
It’s sad to often read in the comment sections of articles in both green and traditional mobility outlets readers dismissing most of these technologies without considering a global angle. Many companies are pouring billions of dollars and intense resources to predict and create how this future electric UAM world will look, and it won’t be as noisy as many think.
Automotive and aviation convergence will have to happen at some point, and hyperloop technology is fast maturing to become the third pillar of seamless future mobility experience. It’s challenges like these that define humanity’s uncanny ability to tackle seemingly impossible obstacles.
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