Fast-time simulation Traffic Flow Management (TFM) models can play a significant role in helping to identify best practices for weather impact scenarios, to apply these practices, and to review their efficacy. A model that can enforce Traffic Management Initiatives (TMIs) to provide sufficient real ism is highly desirable for these activities. The model must also be comprehensively weather-aware so as to faithfully reflect the effect of convective and non-convective weather on air traffic. We have leveraged the ability of the Dynamic Airspace Routing Tool (DART), a superfast-time simulation model, to objectively evaluate the potential benefits (or added impacts) of alternative TFM strategies in support of operationally relevant what-if testing possibilities. We describe a methodology enabled by these simulation capabilities to objectively, and with great detail, evaluate adverse-weather-related TMI utility and potential alternatives with the aim of building a library of ‘best practices’. This overall approach, developed for visual, operator-driven Strategic TFM analysis through comparison of baseline and alternative scenarios, can also be augmented by a parametric search, i.e. partial optimization, of feasible TMI solutions for still greater testing and automation support for impact management decision-making. We discuss how this simulation model has already been effectively applied to support alternative-response analyses for air traffic operations. The opportunities for Strategic TFM to evolve to a more consistent and efficient operation under this alternative support paradigm are discussed.
Ground delay programs (GDPs) are often initiated in the U.S. National Airspace System to balance demand with capacity at a capacity-constrained arrival airport. A GDP assigns departure delay at origin airports to modulate demand at the destination airport. Usually, one GDP affects hundreds of fligh ts. The substantial impact of GDPs on flight operations leads to our research interest in predicting GDP initiation. In this paper, we identify variables that play a significant role on GDP initiation decisions and quantify their impact using logistic regression. We consider lead times of from 1 to 4 hours and specify a logistic model for each lead time. This allows us to provide a GDP initiation prediction for flight operators for up to a 4-hour time horizon. Further, using cross-validation, we compare the predictions of these models, including a weighted accuracy, true positive rate, and precision. We find that the GDP initiation predictions over the longer time horizon are only slightly less reliable than that of one hour into the future. Whatever the time horizon, however, the model predictions are often incorrect, either predicting a GDP when one is not implemented or vice versa.
During its history, the air transportation system has evolved continuously through the integration of new technologies.During the last 20-30 years, revolutionary advances in cyber- (computing, communications, control) technologies have been pervasively translated to the airspace system, bringing about significant improvements in system performance as well as changes to operational paradigms.These new technologies are quite diverse in scope and function, ranging from Required Navigation Performance (RNP) hardware/software to decision aids for air traffic controllers, new weather radar systems, and connectivity for aircraft passengers and crew.Taken as a whole, the new technologies being deployed in the airspace system have the potential to enable system-level autonomy and coordination to a far greater extent than in the traditional system.Despite the extensive integration of cyber technologies in the airspace system, strategic traffic management (i.e., coordinated management of traffic flows at a system-wide scale and a multi-hour look-ahead time horizon) largely remains a manual process.For instance, in the United States National Airspace System (NAS), personnel from the Air Traffic Control System Command Center (ATCSCC), Air Route Traffic Control Centers (ARTCCs, or Centers), and the airlines collaborate on a strategic plan via an early-morning teleconference, and communicate revisions as needed throughout the day.While traffic-management personnel have some basic forecasting tools available to them for the strategic horizon, strategic-plan development is largely driven by historical knowledge, operator expertise, and the (sometimes competing) interests of the stakeholders involved in the teleconference.The relative lack of automation in strategic traffic management, as compared to other aspects of air transportation operations, is understandable for several reasons.First, traffic management has traditionally been primarily a regional exercise, with the need for strategic (system-wide, long-term) management only developing gradually as traffic densities increased.In addition, strategic management only indirectly impacts safety and instead is driven by longer-term operational and economic considerations, while automation in air transportation has naturally been focused on safety-and time-critical problems.Third, the strategic management problem is enormously complex, requiring coordination and control of multiple regional authorities and thousands of aircraft, suffering from large uncertainty (due to weather, departure-time variations, etc.), entailing design of specialized legacy management mechanisms, and involving multi-faceted and ill-defined performance metrics.
Motivated by challenges in strategic traffic flow management, a stochastic network model is introduced for the spatiotemporal evolution of weather impact at a strategic time horizon. Specifically, a model that represents weather-impact propagation using local probabilistic influences is shown to ca pture the rich dynamics and inherent variability in weather impact at the spatial and temporal resolution of interest. This model serves as a simulation tool to generate stochastic weather-impact trajectories for strategic air traffic management. To develop the simulator, first the underlying influence model concept is introduced. Next, an approach for parameterizing the model from probabilistic weather forecast data is developed, such that statistics of generated weather/weather-impact trajectories match the forecasts at snapshot times. An example weather-impact simulator for a particular bad-weather event, namely a long-duration convective weather event in Atlanta Center on 26 September 2010, is also developed. The framework shows promise for e.g. 1) managing weather contingencies for the airspace over a full day; 2) adjusting forecast resolution in data-limited areas for special management requirements; and 3) evaluating variability in airspace performance.
We use historical data to build two types of stochastic model of hourly Ground Delay Program (GDP) implementation for the three main airports near New York City: Newark Liberty International, LaGuardia, and John F. Kennedy International. The models predict the probability that a GDP will be initial ized or canceled in a given hour based on hundreds of features describing the situation at the airport, including features describing forecasted weather conditions and scheduled traffic. One is a regularized logistic regression model that ignores system dynamics and the other model is based on inverse reinforcement learning, so it considers system dynamics and the impact that GDP implementation actions have over time on some metrics. We evaluate the models based on two objectives: their ability to predict and simulate GDP implementation decisions in historical test data sets. As is expected based on the motivation for and objective of each type of model, regularized logistic regression models make superior predictions while simulations controlled by inverse reinforcement learning models produce average metric values that more closely match those produced by historical GDP implementation decisions. Finally, we draw insights about GDP implementation from the trained model parameters. For example, parameters of both models suggest while weather conditions and values of performance metrics such as airborne delay play a role in GDP decision making, parameters related to the predictability or continuity of existing GDP plans are also important.
Air Traffic Flow Management (ATFM) aims at structuring traffic in order to reduce congestion in airspace. Congestion being linked to aircraft located at the same position at the same time, ATFM organizes traffic in the spatial dimension (e.g. route network) and in the time dimension (e.g. sequencin g and merging of aircraft flows taking off or landing at airports). The objective of this paper is to develop a methodology that allows the traffic to self-organize in the time and space dimensions when demand is high. This structure disappears when the demand diminishes. In order to reach this goal, a multi-agent system has been developed, in which aircraft cooperate to structure traffic. Multi-agent systems have several advantages, including a good resilience when confronted with disruptive events. In this system, three algorithms have been implemented, aiming at reducing traffic complexity in three different ways. The first algorithm allows aircraft agents flying on a route network to regulate speed in order to reduce the number of conflicts, a conflict occurring when two aircraft do not respect separation norms. The second algorithm allows aircraft to solve conflicts when the traffic is not structured by a route network. The third algorithm creates temporary local route networks allowing to structure traffic. The three algorithms implemented in this multi-agent system allow to decrease overall traffic complexity, which becomes easier to manage by air traffic controllers. This algorithm was applied on realistic examples and was able to structure traffic in a resilient way.
The effect of fuel type (liquefied natural gas vs. kerosene) on the optimum cruise altitude for a single-aisle, medium-range transport aircraft is investigated. An automated aircraft synthesis program including a detailed mission analysis module was used to assess the impact of liquefied natural ga s (LNG) on fuel burn and equivalent CO2 emissions. Verification studies for four different reference aircraft showed that the mission analysis module was able to predict the fuel weight for various missions with an error between –1.5% and +5.9%. A baseline, kerosene-fuelled, mid-range, single-aisle aircraft was designed which was estimated to have an optimum cruise altitude for minimum fuel burn of 11,400m (37,500ft). It also showed the altitude for minimum equivalent CO2 emissions to be at 12,500m (41,000ft). Changing to LNG, the aircraft showed a maximum reduction in CO2-equivalent emissions of 21% at a cruise altitude of 10,400m (34,100ft). At altitudes above 9,000 meters, the higher H2O emissions rapidly increased the equivalent CO2 emissions. It was therefore concluded that, to a much larger degree than for kerosene-fuelled aircraft, cruise altitude is an important design parameter for LNG-fuelled aircraft if equivalent CO2 emissions are to be minimized.
The growing concern over environmental impacts of air transportation has broadened the focus of the aviation industry so that aircraft design now considers environmental impact along with economic and performance considerations. While technological advances can reduce the environmental impact of in dividual aircraft, the environmental impact of aviation is best measured at a fleet level, rather than at an individual aircraft level. Using a problem motivated by reported operations of the United States Air Force Air Mobility Command for illustration, this paper presents a quantitative approach that identifies the optimal design requirements and optimal aircraft sizing variables of new, yet-to-be-introduced aircraft. With this new aircraft serving alongside other existing aircraft, the fleet of aircraft satisfy the desired demand for cargo transportation, while maximizing fleet productivity and minimizing fuel consumption (which directly relates to CO2 emissions) via a multi-objective problem formulation. The approach accounts for uncertainty in demanded trips in the service network, and uses a descriptive sampling approach to reduce computational expense. Following the presentation of the results obtained, a summary discussion indicates how decision-makers might use these results to set requirements for new aircraft that meet operational needs while balancing the fuel consumption of the fleet with fleet-level performance.
Large Aircraft operators conducting regular passenger transport must satisfy regulatory requirements such as considering engine failure at takeoff at the worst point of the takeoff roll. Such constraints can severely restrict commercial payload, for example in airports surrounded by high terrain. H owever, current methods for the analysis of takeoff paths are largely manual and require significant time to yield an allowable payload. In contrast research in robotics, particularly on unmanned aerial vehicles, has created a wealth of automatic path planning techniques that enable high speed online guidance and navigation. Building on robotic path planning techniques, in this paper we address this complex problem in order to provide the aircraft performance engineer with automated methods of generating high quality escape paths that combine complex aircraft kinematics, terrain models and regulatory constraints in a unified mathematical framework. Specifically, we develop a general approach to the problem, formalize our method and provide results from extensive empirical evaluation of its implementation on a sample of real-world airports.
Innovative solutions from aircraft manufacturers and radical changes from operations are required to maintain airlines' economic viability while simultaneously mitigating current pressures on the air transportation system due to increasing passenger demand, cost of fuel, and greater emphasis on env ironmental efficiency. For long–range routes in particular, a possible solution to mitigate these concerns is to design aircraft for shorter ranges and operate them using intermediate stops or stages. This paper explores the tradeoffs in design and operations that arise from a coupled multi-objective optimization approach to aircraft design and staging allocation as applied to a real-world route network. The tighter integration between the design of the aircraft and its assignment results in smaller and lighter optimal aircraft configurations which shift route operations to higher levels of efficiency by aggregating the effect of changes in design range, route staging and cruise Mach numbers. Fuel burn and operating cost savings up to 38% and 22% respectively are possible, as compared to a reference aircraft of similar technology-level. Significant decrease in fuel burn comes at a penalty of increased flight time up to 12% of total time. However, some of the optimal configurations provide significant reduction in fuel burn and cost with negligible time penalties. Possible adverse environmental effects arising from staging could be mitigated by the configuration changes found for the optimum staging aircraft and their significant reductions in fuel burn.
Aviation operations are projected to increase, potentially resulting in increased environmental impacts with respect to fuel burn, NOx emissions, and community noise. A number of programs are involved in identifying technological advances required to mitigate these environmental impacts. These tech nologies must be analyzed at the vehicle-level, but also at the fleet-level to predict the expected impact in the face of increasing operations. Airport community noise is particularly difficult to model due to the spatial and temporal nature of noise, resulting in a reduced understanding of noise exposure contributions by certain aircraft types. The objective of this research is to analyze the contribution to the total noise exposure at several airport types. By using a generic framework to intelligently reduce aircraft and airport diversity, contributions of aircraft types at different airport types can be reported. Results include spatial analysis of noise contributions, demonstrating that the largest contributors affect the lateral regions of a noise contour, while a greater number of vehicle classes impact the noise near the ground track. Results demonstrate that there is some variation in the greatest contributors by airport type between the Regional Jet, Small Single-Aisle, Large Single-Aisle, and Small Twin-Aisle classes. Conversely, the Large Twin-Aisle and the Very Large Aircraft generally contribute little to total airport noise exposure.
Local effects of noise mitigation are preferably researched with high-fidelity tools rather than standardized noise models. The added advantage is that it is even possible to actually listen to the audible results from these tools. Such an audible result can be generated with the help of noise synt hesis techniques. Aircraft noise synthesis, based on separate modeling of airframe and engine noise components, is described in this study for the use in a virtual reality noise simulator. The present study also shows the effect of atmospheric wind to a synthesized take-off procedure. This was studied in order to quantify the role of the wind in a previous comparison between measurements and synthesized results. The present analysis shows that there is an impact of the wind on the audible result. Hence, the modeling of wind effects can be important to bring measurements and simulation more inline. However, given the current mild wind case, the impact is relatively small. Therefore it is concluded that the observations from the previous research, the comparison without wind effects, will still hold.
The work presented culminates in the development of a value driven conceptual design assessment framework for a small Unmanned Air System (UAS) to be utilized in a defence application. In the field of Multi-Disciplinary Design Optimisation, most recent systematic search has been devoted to fixed to pology parametric geometries, pertaining to a single concept, with very little stress put on the optimization of variable topologies describing alternative design concepts. The search is conducted in a highly novel manner, generating a broad range of combinations of UAS configurations and geometries by systematically searching alternative concepts and design configurations through the parameterization of the aircraft geometric topologies. Moreover, the “value” of proposed solutions is assessed in an objective way both from performance and economic perspectives, while the optimal solution is identified based on the user’s needs after relaxing all of the design constraints. During the multi-criteria decision analysis, the quantification/conversion of the linguistic preferences of the user between the various attributes to numerical values has disclosed some deficiencies introduced by the unjustifiable numerical scales used in the Analytic Hierarchy Process (AHP) and a novel value model for consistent value assessment is introduced, synthesizing the AHP assessment methodologies with multi-attribute value-focused analysis.
Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditi ons airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate other such models with different data sets is encouraged.
Research into future air vehicles incorporating novel technologies is characterized by a high number of interacting disciplines which need to be considered. Despite advances in numeric interfacing techniques for participative Multidisciplinary Design and Optimisation (pMDO), it is not well understood how to build a team of specialists who jointly operate shared tools and gain system level insight. This contribution shifts focus to the human MDO participants and their working environment. Three aspects of collaboration are considered: (a) design of cognitive experiments to measure engineering performance in different settings; (b) integration of prior experience through a Lessons Learned process; and (c) the application of the above into the enhancement of Integrated Design Laboratory (IDL). The pronunciation of competence and working environment, rather than software tools or data, opens opportunities for attractive use cases.}
Developed in the late 80ies, the Concurrent Engineering (CE) approach is based on the idea that different phases of a product life cycle should be conducted concurrently and initiated as early as possible within the product creation process (PCP). The primary goal of CE is to increase the efficiency of the PCP and to reduce errors and, subsequently, unnecessary changes in the late phases of the PCP. While starting with a design-manufacturing alignment, gradually the CE way of thinking has been extended to incorporate more lifecycle functions together with a stronger focus on and involvement of both customers and suppliers. In the past two decades CE has become the substantive basic methodology in many industries (automotive, aerospace, machinery, shipbuilding, consumer goods, process industry, environmental engineering, service industry) and has been also adopted in the development of new services [1]. CE was also included in the engineering education. In the meantime the initial, basic CE concepts have grown up and have become the foundations of many new ideas, initiatives, approaches and tools. Generally, the present CE concentrates on enterprise collaboration and its many different elements, from integrating people and processes to very specific complete multi/inter/trans-disciplinary solutions. Current research on CE is driven again by many factors like increased customer demands, globalization, (international) collaboration and environmental strategies. The successful application of CE in the past opens also the perspective for applications like overcoming of natural catastrophes and sustainable mobility concepts with electrical vehicles. CE was also a powerful driver for development on new IT concepts and tools. With the increasing size and complexity of development projects at large companies and organizations in the aviation industry, CE and integrated aircraft design has become of crucial importance in the design process of new products. In order to remain a competitive position and achieve a customer driven approach, aspects of the product’s life cycle should be adopted at an early stage in the design process. These aspects include, among others: the overall cost performance, the ability of new system integration, challenges
Concurrent Design Facility (CDF) is an effective IT environment to apply Concurrent Engineering principles. In aerospace engineering education, CDF can be invaluable by enabling student teams to gain cross-discipline skills and at the same time stay at the cutting edge of technology. This paper giv es an overview of CDF configurations in use at different industries, research organisations and universities around the world and concludes with a proposal for a relatively a low cost CDF framework based on cloud computing which is particularly suitable for aerospace engineering education. An important aspect of CDF is collaboration between multidisciplinary specialists or virtual specialists within one environment, which requires dedicated hardware or software to exchange file, manage knowledge, collaborative work on writing report, and even remote communicate with other work teams. Emergence and development of cloud computing has made these requirements relatively easy to be fulfilled. Some public cloud computing servers, such as Google Drive, SkyDrive, Dropbox, Mendeley, can be used in CDF to save cost on hardware and software related to data, file, and information exchange. Google Talk and Skype can be used for communication with remote work teams. This CDF framework has many potential benefits, such as reduced cost of hardware, software and support, reduced preparation time, and easy to deploy.
No decision maker willingly sets themselves up for failure. However, when introducing new operations or making changes to existing operations, there is little chance of success if targets are set too high without considering the company's capability. An executable model using a capability score integrated with the performance and anticipated value has been proposed to assess the likelihood of success and failure of meeting performance gain targets from improvements for new aviation operations development. The method indicates to companies when and where their capability needs to be adjusted. This alleviates the problem aviation services providers have when they embark on operations improvement initiatives with an organisational setup that limits their chance of success. By matching the performance targets for operations improvement projects to organisational capability score, a higher hit-rate of success to change can be achieved.
