The current push in automation, communication, and electrical energy storage technologies has the potential to lift urban mobility into the sky. As several urban air mobility (UAM) concepts are conceivable, all relevant physical effects as well as mutual interrelations of the UAM system have to be addressed and evaluated at a sufficient level of fidelity before implementation. Therefore, a collaborative system of systems modeling approach for UAM is presented. To quickly identify physical effects and cross-disciplinary influences of UAM, a pool of low-fidelity physical analysis components is developed and integrated into the Remote Component Environment (RCE) workflow engine. This includes, i. a., the disciplines of demand forecast, trajectory, vertiport, and cost modeling as well as air traffic flow and capacity management. The definition and clarification of technical interfaces require intensive cooperation between specialists with different areas of expertise. To reduce this communication effort, the Common Parametric Aircraft Configuration Schema (CPACS) is adapted and used as central data exchange format. The UAM system module is initially applied for a 24-hour simulation of three generic networks in Hamburg City. After understanding the basic system-level behavior, higher level analysis components and feedback loops must be integrated in the UAM system module for evaluation and optimization of explicit operating concepts.
The aerodynamic formation flight, which is also known as aircraft wake-surfing for efficiency (AWSE), enables aircraft to harvest the energy inherent in another aircraft’s wake vortex. As the thrust of the trailing aircraft can be reduced during cruise flight, the resulting benefit can be traded for longer flight time, larger range, less fuel consumption, or cost savings accordingly. Furthermore, as the amount and location of the emissions caused by the formation are subject to change and saturation effects in the cumulated wake of the formation can occur, AWSE can favorably affect the climate impact of the corresponding flights. In order to quantify these effects, we present an interdisciplinary approach combining the fields of aerodynamics, aircraft operations and atmospheric physics. The approach comprises an integrated model chain to assess the climate impact for a given air traffic scenario based on flight plan data, aerodynamic interactions between the formation members, detailed trajectory calculations as well as on an adapted climate model accounting for the saturation effects resulting from the proximity of the emissions of the formation members. Based on this approach, we derived representative AWSE scenarios for the world’s major airports by analyzing and assessing flight plans. The resulting formations were recalculated by a trajectory calculation tool and emission inventories for the scenarios were created. Based on these inventories, we quantitatively estimated the climate impact using the average temperature response (ATR) as climate metric, calculated as an average global near surface temperature change over a time horizon of 50 years. It is shown, that AWSE as a new operational procedure has a significant mitigation potential on climate impact. For a global formation flight scenario, we estimated the average relative change of climate response to range between 22% and 24% while the relative fuel saving effects sum up to 5–6%.
Efficiency, safety, feasibility, sustainability and affordability are among the key characteristics of future urban mobility. The project "HorizonUAM -Urban Air Mobility Research at the German Aerospace Center (DLR)" provides first answers to this vision by pooling existing competencies of individual institutes within DLR. HorizonUAM combines research about urban air mobility (UAM) vehicles, the corresponding infrastructure, the operation of UAM services, as well as public acceptance and market development of future urban air transportation. Competencies and current research topics including propulsion technologies, flight system technologies, communication and navigation go along in conjunction with the findings of modern flight guidance and airport technology techniques. The project analyses possible UAM market scenarios up to the year 2050 and assesses economic aspects such as the degree of vehicle utilization or cost-benefit potential via an overall system model. Furthermore, the system design for future air taxis is carried out on the basis of vehicle family concepts, onboard systems, aspects of safety and security as well as the certification of autonomy functions. The analysis of flight guidance concepts and the sequencing of air taxis at vertidromes is another central part of the project. Selected concepts for flight guidance, communication and navigation technology will also be demonstrated with drones in a scaled urban scenario. This paper gives an overview of the topics covered in the HorizonUAM project, running from mid-2020 to mid-2023, as well as an early progress report.
While different vehicle configurations enter the AAM market, airlines declare different ticket fares for their operations. This research investigates the operating cost of an airline and the economic viability with the announced fare per km rates. For this purpose, three use cases in the metropolitan area of Hamburg showcase representative applications of an AAM system, whereby a flight trajectory model calculates a flight time in each case. The direct operating cost are investigated for each use case individually and are sub-classified in five categories: fee, crew, maintenance, fuel and capital costs. Here, each use case has its own cost characteristics, in which different cost elements dominate. Additionally, a sensitivity analysis shows the effect of a variation of the flight cycles and load factor, that influences the costs as well as the airline business itself. Based on the occurring cost, a profit margin per available seat kilometer lead to a necessary fare per km, that an airline has to charge.
Due to airspace restrictions and limited availability of navigational aids, ideal direct Air Traffic Services (ATS) routes are usually difficult to achieve in real operations. Moreover, aviation is a fast growing industrial sector causing congestion of the airspace. Hence, in order to improve the flight efficiency and approximate the optimal performance, the redesign and optimization of the ATS route network is required. Route optimization on a tactical level taking into account wind, as one of the most influencing factors, has been broadly discussed in the literature. In contrast, here we present a methodology, which aims at redesigning ATS routes on a strategic level, considering wind situations of a whole year. To achieve this, the average air distance, e.g. the distance corrected by wind effects, along the route has to be minimized. The methodology is then applied to a segment of the Chinese ATS route A461. Since there are several different aircraft types operating on this route, the fleet mix is analyzed to determine typical cruise flight conditions on that route. For the optimization, graph theory based techniques are applied in which a high resolution graph is modeled as search space for the optimization. Assuming mean altitude and mean cruise speed, the air distances between all nodes of the graph are computed for every day of the year 2014 based on wind data from the ECMWF. The air distances of each connections are then averaged and used as cost function within a Dijkstra Algorithm, which is applied in order to obtain annually and quarterly optimized routes. Subsequently, the efficiency increase of the resulting tracks is compared to the theoretical average potential of a tactical daily-based route optimization scenario. Furthermore it is examined, whether the obtained tracks optimizing the year 2014 would have offered efficiency increases for the years 2012 and 2013 as well, in order to estimate a possible long-term feasibility. Finally, an analysis of the tracks in the terms of flight performance is carried out to quantify possible fuel savings.
Although conventional multi-objective evolutionary optimization algorithms (MOEAs) are proven to be effective in general, they are less superior when applied to solve a large-scale combinational real-world optimization problem with tightly coupled decision variables. For the purpose to enhance the capability of MOEAs in such scenarios, one may consider the importance of interaction topology in information exchange among individuals of MOEAs. From this standpoint, this article proposes a non-dominated sorting genetic algorithm II with dynamic topology (DTNSGAII), which applies a dynamic individual interaction network topology to improve the crossover operation. The dynamic topology and inter-individual interaction are determined by the solution spread criterion in the objective space as well as the solution relationships and similarities in the decision space. The combination of two aspects contributes to the balance of the exploitation and exploration capability of the algorithm. Finally, as an example to real-world applications, the DTNSGAII is used to solve a network-wide flight trajectory planning problem, which demonstrates that the application of dynamic topology can improve the performance of the NSGA-II.
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