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Decadal Survey of Civil Aeronautics: Foundation for the Future D R&T Challenges for Dynamics, Navigation, and Control, and Avionics A total of 14 R&T Challenges were prioritized in the guidance, navigation, and control, and avionics Area. Table D-1 shows the results. The R&T Challenges are listed in order of NASA priority. National priority scores are also shown.1 This appendix contains a description of each R&T Challenge, including milestones and an item-by-item justification for each score that appears in Table D-1.2 D1 Advanced guidance systems Advanced guidance systems consist of subsystems and processes (hardware and software) assembled for the purpose of providing an aircraft, spacecraft, or other dynamic system with desired state trajectories. These trajectories can be defined using either discrete or continuous data and can include information such as current velocity, acceleration, time of arrival, and desired position. The determination of the desired trajectory usually takes into account mission-dependent constraints, which can include obstacles (such as terrain, wake vortices, or other aircraft), hazards (such as weather), coordination with other aircraft (such as cooperative and multiaircraft guidance, formation flight, or swarming), and regulatory constraints (such as airspace class restrictions) (Doebbler et al., 2005). State-of-the-art guidance systems enable aircraft to follow waypoints, perform automatic obstacle avoidance, and fly in formation with other aircraft (Schierman et al., 2004). Additional research is needed to develop guidance algorithms and mature them into flight-ready systems,3 to develop improved reconfigurable and adaptive guidance systems, and to develop advanced guidance systems for UAVs. One concern, for example, is the need to develop improved technologies to avoid controlled flight into terrain, particularly in the case of all-weather operation of advanced rotorcraft. Some important research is inhibited by the limited number of programs and facilities capable of implementing and flying these systems on real aircraft. Also, certification and regulatory issues must be resolved so that the air transportation system can take advantage of the full capabilities of current and future guidance systems for piloted aircraft and UAVs. Advanced guidance systems have the potential to greatly improve the capacity, safety, and efficiency of the air transportation system. In addition, they can enhance the performance of many existing and future military systems. Key milestones include Development of advanced algorithms and avionics for collision, terrain, and wake vortex avoidance; formation flight and cooperative and multiaircraft guidance; and ground operations guidance (taxi, takeoff, rollout, and turnoff). Expansion of facilities and programs capable of maturing the above technologies to flight-ready systems. Development and adoption of regulations for the certification and operation of autonomous UAVs in civil airspace. Relevance to Strategic Objectives Capacity (9): Advancing the state of the art in multiaircraft and cooperative guidance will allow more aircraft per unit time to move through the available airspace. Safety and Reliability (9): Advanced guidance systems will allow aircraft to operate more safely in closer quarters than is currently possible both in the air and on the ground. 1 The prioritization process is described in Chapter 2. 2 The technical descriptions for the first 10 Challenges listed below are essentially the same as the technical descriptions for these Challenges as they appear in Chapter 3. 3 R. Duren, associate professor, Baylor University, “Avionics research challenges,” Presentation to Panel D on November 15, 2005.
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Decadal Survey of Civil Aeronautics: Foundation for the Future TABLE D-1 Prioritization of R&T Challenges for Area D: Dynamics, Navigation, and Control, and Avionics Strategic Objective National Priority Why NASA? NASA Priority Score Capacity Safety and Reliability Efficiency and Performance Energy and the Environment Synergies with Security Support to Space Supporting Infrastructure Mission Alignment Lack of Alternative Sponsors Appropriate Level of Risk Why NASA Composite Score R&T Challenge Weight 5 3 1 1/4 each D1 Advanced guidance systems 9 9 9 3 3 3 132 9 9 3 9 7.5 990 D2 Distributed decision making, decision making under uncertainty, and flight-path planning and prediction 9 9 9 3 3 3 132 3 9 3 9 6.0 792 D3 Aerodynamics and vehicle dynamics via closed-loop flow control 1 9 9 3 3 3 92 9 9 3 9 7.5 690 D4 Intelligent and adaptive flight control techniques 3 9 9 3 3 9 108 3 9 3 9 6.0 648 D5 Fault-tolerant and integrated vehicle health management systems 3 9 3 1 3 9 84 9 9 3 9 7.5 630 D6 Improved onboard weather systems and tools 9 9 3 1 1 1 104 9 9 3 3 6.0 624 D7 Advanced communication, navigation, and surveillance technology 9 9 9 3 3 3 132 3 9 3 3 4.5 594 D8 Human-machine integration 3 9 9 1 3 3 96 3 9 3 9 6.0 576 D9 Synthetic and enhanced vision systems 3 9 3 1 1 3 76 9 9 3 3 6.0 456 D10 Safe operation of unmanned air vehicles in the national airspace 3 9 3 1 9 1 82 3 9 3 3 4.5 369 D11 Secure network-centric avionics architectures and systems to provide low- cost, efficient, fault-tolerant, onboard communications systems for data link and data transfer 9 9 9 1 9 3 132 3 3 1 3 2.5 330 D12 Smaller, lighter, and less expensive avionics 1 3 9 3 3 9 68 3 3 3 3 3.0 204 D13 More efficient certification processes for complex systems 3 9 9 1 1 3 94 3 1 1 3 2.0 188 D14 Design, development, and upgrade processes for complex, software-intensive systems, including tools for design, development, and validation and verification 3 9 3 1 1 3 76 1 3 1 1 1.5 114
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Decadal Survey of Civil Aeronautics: Foundation for the Future Efficiency and Performance (9): The ability to safely operate aircraft closer to each other will allow more efficient use of the airspace and airport real estate. Energy and the Environment (3): Advanced guidance systems can enable arrival and departure trajectories that reduce community noise. Also, multiaircraft guidance systems can increase fuel efficiency. Synergies with National and Homeland Security (3): Cooperative and autonomous capabilities are also applicable to military aircraft. Support to Space (3): Many multiaircraft guidance algorithms are applicable to satellite constellations and formations of mini- and microsatellites. Why NASA? Supporting Infrastructure (9): NASA has air transportation system simulation facilities and manned and unmanned aircraft simulation and flight test facilities. In addition, NASA has also been the primary facilitator of the unique and highly relevant Access 5 program. Mission Alignment (9): This Challenge will have a broad benefit for aeronautics in general, and NASA has often done related research. Lack of Alternative Sponsors (3): DoD is doing some military-specific work related to this Challenge. Relevant work by industry and academia is limited by certification and regulatory issues as well as the prohibitive cost of test facilities. Appropriate Level of Risk (9): This Challenge faces moderate to high risk. D2 Distributed decision making, decision making under uncertainty, and flight path planning and prediction Improving the decision-making process used by pilots and aircraft systems, when coupled with improvements in flight-path planning and prediction, has been theorized as an effective approach to improving air transportation system capacity and safety. This Challenge has the potential to significantly improve the timeliness of real-time decisions to alter flight paths in the dynamic environment of congested airspace (Ding et al., 2004; Helbing et al., forthcoming; Rong et al., 2002). Coordinated decision making, which includes the direct exchange of data among different aircraft and the deconfliction of flight paths without the need to rely on ground-based controllers, addresses the inherent limitations of centralized ATC systems in terms of uncertainty and fault tolerance. A coordinated, distributed approach to decision making increases air transportation system reliability and safety by distributing control and mission management capabilities among multiple agents. It also allows for rapid response to changing dynamics and minimizes vulnerability to system failures. Automated systems can help improve decision making and flight path planning. Levels of automation ranging from “pilot aid” (that is, systems that advise pilots to take specific action) to “fully autonomous” are achievable but have not yet been developed to the point where they can support high levels of automation for civil aircraft. Until now, coordinated distributed algorithms for constraint reasoning (for example, to optimize flight paths) have not been applied to the air transportation system because implementation with such a complex system would require aircraft to exchange a large number of messages, which raises substantive communications, bandwidth, and man–machine interface issues. This Challenge should address the needs of a wide variety of conventional and unconventional aircraft types, including those with no distributed decision-making capability. Aircraft types of interest include commercial airliners, general aviation aircraft, civil helicopters, military aircraft, and UAVs. This Challenge also has the potential to be of great benefit when applied to complex, nonaviation systems that operate in dynamically changing environments and require high-quality, real-time decision making. Key milestones include Develop fundamental system requirements, architectures, and system logic that are compatible with current and future regulatory requirements and ATM systems. This Challenge should include studies to determine the levels of automation appropriate to a wide range of decision-making applications. Develop simulation capabilities for evaluation and demonstration of certain high-performing strategies in the execution of realistic system architectures and applications. Develop a requirements flowdown to all affected aircraft systems, such as advanced communications, navigation, and surveillance (CNS) systems. Develop improved, automated logic and processes for contingency management. Develop a methodology to support verification and validation of future systems technologies developed by this Challenge. Relevance to Strategic Objectives Capacity (9): The capability to accomplish dynamic real-time flight path planning and replanning in the dynamic airspace environment will allow closer separations and increase capacity. Safety and Reliability (9): The capability to accomplish dynamic high-quality flight path planning and replanning in the dynamic airspace environment will allow closer aircraft separations with increased safety. Efficiency and Performance (9): The capability to accomplish automated and autonomous high-quality, real-time
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Decadal Survey of Civil Aeronautics: Foundation for the Future flight path planning and replanning in the dynamic airspace environment will reduce aircrew and controller workloads as well as the demands on the entire ATM system. Training requirements will also be reduced at all levels within the system. Energy and the Environment (3): Dynamic flight path replanning can be used to improve mission efficiency and reduce fuel burn. Synergy with National and Homeland Security (3): The capability to accomplish dynamic, high-quality, real-time decision making (e.g., planning and replanning) will have many applications in the national and homeland security environment, including the future integration of UAVs into the air transportation system. Support to Space (3): Coordinated distributed decision making is very beneficial for spacecraft guidance, navigation, and control (GNC) tasks such as flight planning, rendezvous and docking, and reentry. Why NASA? Supporting Infrastructure (3): NASA has significant facilities and some experience relevant to this Challenge. Mission Alignment (9): This Challenge will directly support multiple R&T Challenges and benefit military and civil aviation, as well as future applications in space. Lack of Alternative Sponsors (3): Industry is developing technology relevant to this Challenge, but it is more geared to military systems with a focus on UAVs. Appropriate Level of Risk (9): Mid- and long-term research can address issues related to this Challenge, and the results can be transferred to future civil and military applications. D3 Aerodynamics and vehicle dynamics via closed-loop flow control Closed-loop flow control appears to offer tremendous promise in improving aerodynamic performance. For example, active flow control approaches should allow the airfoil lift:drag (L/D) to remain high over large changes in angle of attack.4 Flow control R&T could also be used to develop a spoiler-aileron to replace complex and heavy control surfaces and to reduce or eliminate turbulent flow over aircraft surfaces to reduce skin-friction drag. These applications could lead to new aircraft configurations (Chavez and Schmidt, 1994). The mechanization of flow control systems may require a large number of distributed sensors measuring pressure or shear stress over the wing and changes in the boundary layer. Actuation might be accomplished by morphing the wing or introducing devices that induce sucking or blowing along the wing. These distributed sensors and actuators are coordinated so that control is obtained over large flight regimes, angles of attack, and attitudes. Distributed sensing and actuation would also permit structures to be self-aware for health monitoring, thereby increasing system reliability. Airframe and engine structures could be monitored for changes in behavior. Some of the techniques developed by this Challenge may also advance modeling and design capabilities applicable to morphing aircraft (Tandale et al., 2005; Valasek et al., 2005). Heretofore, aircraft have generally been fixed-frame structures. Morphing aircraft would be designed with distributed actuation and controls and with mechanization as an inherent property. They would lead to new capabilities and concepts in aircraft design. Examples include (1) biomorphic aircraft, such as ornithopters, that could maneuver robustly in complex environments and (2) hunter-killer aircraft that change shape to optimize performance for different tasks (e.g., surveillance, reconnaissance, and ground attack). Morphing technology might also enable aircraft capable of perching. Key milestones include Develop simpler representations of the aircraft system dynamics for control design. Develop distributed control algorithms and architectures. Demonstrate the ability to numerically solve distributed control algorithms at the Reynolds numbers associated with manned aircraft flight to demonstrate control performance. Implement integrated, distributed closed-loop flow control systems. Design and develop lightweight, mechanized, shape-changing structures. Experimentally verify the performance of shape-changing aerodynamic structures before flight testing. Relevance to Strategic Objectives Capacity (1): This Challenge has minimal application to this Objective, although networks of sensors and actuators could be utilized to monitor and maintain fleet readiness. Safety and Reliability (9): Large arrays of sensors and actuators vastly improve system redundancy. Efficiency and Performance (9): Adaptable flight characteristics will improve mission performance over a variety of conditions. Energy and the Environment (3): Extremely high L/D reduces fuel usage, and adaptable engines could significantly reduce noise and emissions. Synergies with National and Homeland Security (3): This Challenge will facilitate the development of UAVs with long endurance for surveillance. 4 The flow over the specially shaped GLAS II airfoil remains naturally separated at the rear of its upper surface over a wide range of angles of incidence; in the absence of active control, its L/D does not exceed 25. At an incidence angle of 10 degrees, its L/D is nearly 500 (Glauert, 1945, 1948).
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Decadal Survey of Civil Aeronautics: Foundation for the Future Support to Space (3): This Challenge could (1) improve the performance of aerodynamic first-stage launch vehicles, which would increase payload capacity, and (2) enhance the endurance of aircraft design to fly in the martian atmosphere. Shape control also allows for safer transatmospheric flight. Why NASA? Supporting Infrastructure (9): NASA has been a leader in computational fluid dynamics, producing the first direct numerical computation of the Navier-Stokes equations. Furthermore, Langley Research Center and Ames Research Center have conducted flow control R&T. Finally, existing NASA wind tunnels could be important for experimental verification of flow control algorithms. Mission Alignment (9): This Challenge is very relevant to NASA’s mission. Lack of Alternative Sponsors (3): DoD is doing a lot of research relevant to this Challenge and has coupled it with funding to many universities. Appropriate Level of Risk (9): This Challenge faces moderate to high risk. D4 Intelligent and adaptive flight control techniques The missions and capabilities of future aircraft, both manned and unmanned, will be more multifunctional than those of the current generation of specialized aircraft. Achieving aggressive performance targets in range, payload, reliability, safety, noise, and emissions will require a total system that is integrated to a far higher level than existing aircraft. R&D for military aircraft has been able to push the technological envelope associated with intelligent and adaptive flight control techniques farther than R&D for civil aircraft because of different safety limits. In the far term, as it advances, application of military technology to civil aircraft may be possible. The vehicle management systems (VMS) paradigm offers the most promising path to realizing goals related to this Challenge. VMS takes a top-down systems approach to specifying, designing, and validating the aircraft as a single system with highly integrated inner and outer loops. It thereby unifies the traditionally separate fields of propulsion control, flight control, structural control, noise control, emissions control, and health monitoring. The current state of the art in VMS uses traditional feedback control, consisting of measurements of vehicle states such as airspeed, altitude, angle of attack, and linear and angular acceleration (Jaw and Garg, 2005). By incorporating an online learning capability to cope with new and unforeseen events and situations and nonlinear adaptive control, in which the controller self-tunes to maintain stability and tracking in the presence of disturbances and changing vehicle parameters, an intelligent and adaptive VMS can be developed with the promise of significant advances in capability, safety, and supportability (Tandale and Valasek, 2003). Significant advances in the state of the art are required to develop an intelligent and adaptive VMS. Current nonlinear adaptive control approaches assume that (1) sensor information is reliable and (2) known nonlinearities can be modeled as slowly varying parameters that affect the system linearly. However, advanced actuators for flow control and structural control will have characteristics that are much more nonlinear than those of conventional control actuators. Control laws and control actuator allocation are currently treated as separate problems, such that optimization of the integrated control law is difficult or impossible. Finally, the problem of multiple correlated, simultaneous failures remains unsolved. Approaches that use analytic redundancy to finding failed sensors generally assume that aircraft dynamics have not changed, while adaptive or reconfigurable control approaches assume that sensor information is reliable. On an affordable aircraft with limited or no sensor redundancy, it is difficult or impossible to tell the difference between a degraded sensor and damage to the aircraft that changes the way it flies. Key milestones include Develop an adaptive, intelligent, fully integrated VMS that can operate safely without reliable sensor information. Demonstrate a mature methodology for designing and analyzing flight control laws for aircraft with large numbers of highly distributed control actuators and sensors— for example, shape memory alloys and piezoelectrics. Demonstrate a mature methodology for using information of different degrees of reliability without compromising flight safety (e.g., using data from what would traditionally be considered non-flight-critical systems within an inner control loop). Demonstrate long-term learning so that adaptation would only need to be used in novel situations. For example, following damage, the system adapts the first time it enters a particular part of the flight envelope but does not need to readapt if it leaves that part of the envelope and returns. Validate complex nonlinear systems to seek out worst-case scenarios that may not be identified with exhaustive testing. Relevance to Strategic Objectives Capacity (3): Advancing the state of the art in propulsion and flight control will allow manned and unmanned aircraft to operate more safely, thereby permitting UAV flight operations over highly populated areas and improving the ability of all aircraft to operate in poor weather conditions. Safety and Reliability (9): Advanced propulsion and flight control will improve the ability of aircraft to continue operating in spite of control upsets, atmospheric disturbances
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Decadal Survey of Civil Aeronautics: Foundation for the Future such as gusts and turbulence, and damage (either natural or terrorist induced). Efficiency and Performance (9): Advanced propulsion control increases engine efficiency, and flight control techniques such as relaxed static stability or de facto stability can improve range. Energy and the Environment (3): Advanced propulsion control can reduce engine emissions. Synergies with National and Homeland Security (3): Advanced propulsion and flight control can improve mission capability, safety, and supportability, which are important to military aircraft. Support to Space (9): The intelligent and adaptive VMS for advanced propulsion and flight control directly applies to launch vehicles, spacecraft, and planetary landers and reentry vehicles. Why NASA? Supporting Infrastructure (3): NASA has a significant investment in high-quality vehicle system integration laboratories, flight simulators, and flight test facilities that are equal to any in DoD or industry and superior to any in academia. Mission Alignment (9): This Challenge directly aligns with and enhances legacy NASA research in the stability and control of aircraft. Lack of Alternative Sponsors (3): R&T related to advanced propulsion and flight control is being done by DoD, industry, and academia. However, these efforts are specialized to military aircraft and are not unified in objectives and scope. Appropriate Level of Risk (9): This Challenge faces moderate to high risk. D5 Fault-tolerant and integrated vehicle health management systems Development of integrated vehicle health management (IVHM) system technologies is key to the acceptance of the automation needed in the transformation of the air transportation system. The technology provides an increased capability to accurately discover and assess system faults and reconfigure or recover from them. Although highly integrated, health management aspects consist of related components: fault detection and isolation, recovery and reconfiguration, and condition-based maintenance (CBM). In addition, modeling plays an important role in the development of these functions (Garg, 2005; Litt et al., 2005; Tandale and Valasek, 2006). Fault detection, isolation, recovery, and reconfiguration Fault detection, isolation, recovery, and reconfiguration involve processes and approaches that enable robust detection of faults from measured or estimated error residuals and isolation of faults with minimal latency in the presence of noise and environmental effects during aircraft operation. Fault detection, isolation, recovery, and reconfiguration are platform specific and should cover all flight regimes and mission types. Recovery and reconfiguration systems are developed with regard to the possibilities of faults, the nature of the latency of the fault detection and isolation system, and the controls available for recovery and reconfiguration. Redundancy management strategies for avionics and the airframe directly influence options for recovery and reconfiguration. Condition-based maintenance CBM involves maintenance processes and capabilities derived from real-time assessment of aircraft system conditions obtained by software from embedded and redundant sensors. The combination of software and sensors can create important communications and bandwidth challenges. More robust diagnostics and prognostics are needed to achieve the goal of CBM, which is to perform maintenance only on evidence of need, to prevent a failure that would reduce aircraft availability. In addition, CBM includes processes that couple real-time assessment of system and component performance with ground- and air-based logistics to improve aircraft system readiness and maintenance practices. CBM is a form of proactive equipment maintenance that forecasts incipient failures. CBM also aims to ensure safety, equipment reliability, and reduction of total ownership cost. Fault tolerance is achieved when CBM is married to decision strategies for safe and reliable operation of manned and unmanned aircraft. Modeling Physics-based models of sensors, actuators, avionics, components, and vehicle flight dynamics contribute to the development of methods for forecasting aircraft system performance, thereby helping to uncover faults. In addition, these models can be used for examining architectures and control strategies to reconfigure systems and ensure safety and reliability. An aircraft is a very complex system. While individual fault-tolerant functions can be set up for each subsystem, the value of fault-tolerant designs is maximized when the system is modeled as a whole, since the behavior of each subsystem can influence that of other subsystems. The advantage of working with a total system model lies in the ability to discover a fault through its effects on other parts of the system before it is discovered in the individual subsystem itself. One primary thrust of fault-tolerant technology development is to identify system models that characterize the behavior of systems properly without developing an overly detailed and unnecessary representation. In other words, an optimum system is not a collection of optimized subsystems.
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Decadal Survey of Civil Aeronautics: Foundation for the Future To advance the state of the art in fault-tolerant aircraft systems, fundamental R&T is required in the three topics above to develop a more robust image of the state, or health, of an aircraft in the presence of uncertainty. With a better model of itself, the aircraft can trace back system anomalies through the multitude of discrete state and mode changes to isolate aberrant behavior. Fault-tolerant systems combine simple rule-based reasoning, state charts, model-free monitoring of cross-correlations among state variables, and model-based representations of aircraft subsystems. Together, these models form a hybrid system model. Advances in computing resource technology have allowed hybrid system models to run in real time. Fault-tolerant aircraft systems, coupled with CBM, may improve aircraft safety and reduce aircraft life-cycle maintenance and ownership costs. Critical research tasks include developing (1) robust and reliable hardware and software tools for monitoring components, detecting faults, and identifying anomalies; (2) prognosis analysis tools for predicting the remaining life of key components; (3) approaches for recovering from detected faults, including reconfiguration of the flight control system for in-flight failures of manned and unmanned aircraft; and (4) low-cost, lightweight, wireless, self-powered sensors with greater memory and processing capability. Key milestones include Specify nominal models and model behavior, interface, and test requirements for component and integrated system capability affected by degraded or failed operation of a representative subset of avionics and flight system components. Define suitable thresholds for levels of degraded and failed operation for component-level and system-level operations. Work with aircraft subsystem and flight system vendors to specify parameters that are candidates for maintenance logging. Develop models and compact representations that can incorporate measurements of these parameters in near real-time and develop thresholds that can be used for on-demand maintenance activities. Evaluate component capability in a simulated environment (ground test and hardware in the loop). That is, take a particular subsystem, such as a real landing gear system that has been represented by an appropriate behavioral model as specified and insert simulated faults to test for proper operation of the health monitoring system. Perform these tests for all representative subsystems that were specified above. Evaluate integrated system capability in a simulated environment. Take the subsystem health models previously specified and insert faults, preferably ones that were not detected as quickly as necessary by the individual component models that were evaluated in the above set of tests, and use the system models in order to evaluate the efficacy of their integrated operation. Test component and integrated system capability in a flight environment. Relevance to Strategic Objectives Capacity (3): Knowing the health of key components of an aircraft system reduces aircraft downtime and thus improves capacity. Fault-tolerant systems increase airport capacity through improved on-time dispatch of a flight. Safety and Reliability (9): This Challenge addresses a primary component of safe and reliable operation. Monitoring the performance of key aircraft components significantly improves overall system safety and reliability and reduces uncontrolled flight into terrain for manned and unmanned aircraft. Efficiency and Performance (3): With improved fault tolerance, overall flight performance is enhanced because faults can be isolated, thus ensuring robust operation of the aircraft. More efficient use of maintenance resources reduces aircraft downtime. Energy and the Environment (1): Improved fault tolerance reduces life-cycle maintenance and operation costs and makes more efficient use of parts and supplies. This reduces environment effects, but only to a small degree. Synergies with National and Homeland Security (3): Fault tolerance is important to military aircraft. Support to Space (9): Fault-tolerant architectures for aircraft will be of great use to spacecraft systems, and fault tolerance has a major impact on space travel. Why NASA? Supporting Infrastructure (9): NASA has done R&T related to this Challenge and has a unique capability in applying fault tolerance to space applications. NASA has unique propulsion test facilities that would be critical for characterizing drive trains and engines for aircraft and rotorcraft. In addition, NASA has unique modeling and simulation capabilities that support fault-tolerant aircraft system modeling. Mission Alignment (9): This research will benefit aeronautics in general, and NASA has often done similar research. Lack of Alternative Sponsors (3): While industry and the military are looking at technology relevant to this Challenge, NASA has assembled a core competency that is unmatched for civil aircraft applications, especially for rotorcraft. NASA support is essential to address civil aeronautics applications. Appropriate Level of Risk (9): More data are needed to determine how fault-tolerant systems impact the life-cycle costs of civil aircraft. Some information is available on how such systems can improve aircraft safety, especially for rotorcraft. D6 Improved onboard weather systems and tools Pilots—and the avionics software that provides in-flight, four-dimensional trajectory replanning and commands to the pilot or autopilot—require additional weather information to
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Decadal Survey of Civil Aeronautics: Foundation for the Future minimize the impact of weather on the control of flight in heavy traffic. Basic research is needed to determine the most cost-effective way of integrating real-time weather information into four-dimensional, integrated control of flight. This information might include information from data links with ground sites and other aircraft and weather video from ground stations and satellites (Bokadia and Valasek, 2001; Lampton and Valasek, 2005, 2006). Other aircraft could provide information about geospatial position, wind, icing conditions, turbulence, lightning, and precipitation, as well as imagery from radars and other sensors. Data links with the ground could provide actual and forecast information on winds at different flight levels, pressure, icing potential, precipitation, ground-level temperatures, weather fronts, severe weather, airport surface conditions, and other information from significant meteorological information reports (SIGMETs), pilot reports (PIREPs), meteorological aviation reports (METARs), terminal area forecasts (TAFs), imagery from satellites and radars, and so on. Key milestones include Develop robust and reliable data links for collecting information from onboard sensors. Develop processes and tools for integrating weather information from onboard sensors and data links to the ground and other aircraft. Demonstrate effectiveness in practical decision-support applications relating to weather, with varying levels of information quality and uncertainty. Relevance to Strategic Objectives Capacity (9): Improving the quality and use of weather information will enable aircraft to avoid or fly through weather more effectively, which will reduce delays due to weather. Safety and Reliability (9): Improving the quality and use of weather information, including information on runway conditions, will reduce the number of aircraft accidents caused by weather. Pilots will be less likely to fly into weather that they or their aircraft cannot handle. Efficiency and Performance (3): Fuel consumption can be reduced by flight plans that incorporate real-time information on winds aloft to maintain the desired four-dimensional flight trajectory. Energy and the Environment (1): This Challenge has little impact on this Objective. There might be some indirect benefit through the reduction of emissions or ground noise by optimizing flight plans, allowed by improved onboard weather systems and tools. Synergy with National and Homeland Security (1): This Challenge would also benefit DoD and DHS flight operations involving civil airspace. Support to Space (1): This Challenge has little or no application to this Objective. Why NASA? Supporting Infrastructure (9): NASA has an outstanding research facility for icing tests and evaluation and the infrastructure to develop and test weather-related tools. Mission Alignment (9): This Challenge is central to NASA’s safety and capacity mission. Lack of Alternative Sponsors (3): Some airlines have invested in developing capabilities relevant to this Challenge, and the FAA and Air Force are also interested. However, these efforts are limited in comparison to what NASA could do. Appropriate Level of Risk (3): Weather-related tools exist, but integration into other systems, especially onboard systems, is needed. This Challenge faces low risk, and transfer to industry is likely, because industry is developing some tools related to the integration of weather information. D7 Advanced communication, navigation, and surveillance (CNS) technology The capacity of the air transportation system is dependent on minimum spacing requirements for safe operation. Minimum spacing depends on many factors, including the capability of each aircraft to precisely fly a predetermined, geospatially time-referenced flight path. Advanced, integrated, accurate, secure, and reliable CNS capabilities are required for network-centric operations, which can increase capacity in very high density airspace. Each aircraft may be considered a node in a network-centric, distributed, fault-tolerant ATM system. Communications between nodes (aircraft to aircraft, aircraft to ground, and aircraft to satellite to ground) must be highly reliable. (For example, the probability of a missed or incorrect message should be less than 10–7 per flight hour, depending on the consequence of the fault). Safe, secure, accurate, and certifiable CNS technologies that provide required capabilities are needed. More precision aircraft navigation, coupled with the precise six-dimensional5 guidance algorithms used in advanced flight management systems, will enable reduced spacing between aircraft operating en route and in the terminal airspace. CNS system functions must be tightly coupled in terms of information integrity, and they should allow pilots to operate cooperatively with ground systems without controllers continuously in the control loop. The CNS should transmit navigation, guidance, and other sensor data to other aircraft and ground operation centers via multichannel data links while, at essentially the same time, they receive similar information about other aircraft, the weather, airport conditions, etc. This information can prevent accidents by revealing the current and future status of other aircraft, weather 5 The six dimensions refer to three position coordinates and three velocity vectors to define aircraft location, speed, and direction of motion.
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Decadal Survey of Civil Aeronautics: Foundation for the Future phenomena, terrain, buildings, and vehicles on the ground at airports. This Challenge should also increase the affordability of onboard avionics to encourage aircraft owners and operators to procure more capable avionics. This Challenge encompasses the following CNS issues: Communications issues. Fault-tolerant network connectivity and security. Dynamic network control and reconfiguration. Quality of service. Spectrum allocation and usage. Adequate communication bandwidth. Required communications capability as a function of geospatial location and phase of flight. Navigation issues. High-precision, six-dimensional estimate of aircraft state as a function of time. Integration of satellite navigation with other navigation modes. Navigation system capability, including reliability and quality, of input signals. Functional integration of navigation system with guidance and flight control systems to ensure high-integrity, integrated control of flight during automatic and manual modes. Surveillance issues. Capability of data links to provide accurate time-referenced data from navigation systems, guidance systems, and other sensors when interrogated by external systems or periodic broadcast. Handling of multiple, simultaneous interrogations using multiple channels to provide high-integrity, secure data. Processing and reacting to incoming data about other aircraft, hazardous weather, etc. Continuous improvement in situational awareness through advanced sensors, communication links, and human–system interfaces. Key milestones include Simulate avionics on an individual aircraft to determine the capability of each avionics function (communication, navigation, guidance, control, and surveillance). Demonstrate (1) fault-tolerant degradation of CNS capability (in terms of accuracy and availability of modes) and (2) processes needed to ensure that the individual aircraft can still transmit the needed aircraft state information and receive information and air traffic control commands with an extremely low probability of communication error. Evaluate different tracking and control algorithms with various faults that could occur in either the ATM system or airborne aircraft to determine whether the algorithms are able to detect the faults, identify them, and recover from them by reconfiguring the system in which the fault occurred as well as other systems to provide a satisfactory level of service. Document the feasibility of using space-based communications and surveillance as both a primary and backup means of ATM. Demonstrate modeling and real-time simulation using distributed control centers and different traffic levels, ranging from the current peak hourly load of about 6,000 airborne aircraft in the continental U.S. airspace to a predicted hourly load of 18,000 airborne aircraft, using current demand patterns. This effort is required to verify that the network of communication links, processing nodes in the network, and control algorithms provides the desired capacity while satisfying safety criteria. Demonstrate a means to provide seamless information flow between an aircraft’s multiband antenna and the fiber-optic local area network that manages the information flow between aircraft systems and the radio channels. Demonstrate a robust IVHM system that detects permanent and transient onboard system faults and communicates system status to pilots and ground systems. For aircraft equipped with autothrottles, develop performance algorithms linked to aircraft dynamics to maintain the approved flight trajectory while minimizing fuel consumption. For aircraft that are not equipped with autothrottles, document the information required by the flight management system to generate speed commands to be displayed to pilots while minimizing pilot workload. Develop an air-ground communication protocol that (1) optimally allocates functions among pilots, avionics, air traffic controllers, and automated ground systems and (2) includes a means to alert ground systems and controllers that the data link or an onboard system has failed. This will require control algorithms that can handle multiple failures in terms of controlling the aircraft with the failures as well as adjacent traffic to minimize the impact on airspace capacity and efficiency. Relevance to Strategic Objectives Capacity (9): Tripling the number of aircraft in the airspace requires reducing the uncertainty of six-dimensional aircraft state (position and velocity) to less than one-third of the current required navigation precision of 0.1 nautical mile. Time-tagged state information must be broadcast so that adjacent aircraft as well as ground systems know the relative position and velocity of aircraft and each aircraft’s deviation from its planned flight trajectory. Safety and Reliability (9): This Challenge will provide fault-tolerant aircraft and ground systems that will permit safely reducing separation standards.
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Decadal Survey of Civil Aeronautics: Foundation for the Future Efficiency and Performance (9): This Challenge will enable aircraft to fly flight trajectories that minimize fuel consumption. Energy and the Environment (3): This Challenge will enable aircraft to fly flight trajectories that reduce noise and emissions. Synergy with National and Homeland Security (3): This Challenge will benefit military aircraft that operate aircraft in civil airspace. Support to Space (3): The systems developed for operation in Earth’s airspace and atmosphere should have application to operations in a martian atmosphere. Why NASA? Supporting Infrastructure (3): NASA research centers have the engineering skills, computing and simulation facilities, and test aircraft to develop the technologies required to make advanced CNS a reality. Mission Alignment (9): This Challenge is very relevant to NASA’s mission. Lack of Alternative Sponsors (3): Industry and federal government agencies are developing some CNS technologies, but they have lagged behind European development of some new technologies required to operate in the global airspace. Appropriate Level of Risk (3): The individual technologies are low risk. The best methods of integrating these technologies remains to be developed and demonstrated. D8 Human–machine integration The ever-increasing demand for air transportation, combined with the rapid pace of technological change, poses significant challenges for effective integration of humans and automation. For the foreseeable future, humans will continue to play a central role in key decision-making tasks that directly influence the efficiency and safety of civil aviation. As technology evolves, it may be anticipated that the role of humans and the nature of their task will change accordingly. In order to maintain or improve upon existing standards of performance and safety, it is critical that the allocation of functions between humans and automation and the design of the human–machine interface be optimized based on a solid foundation of scientific principles that reflect our best understanding of human sensory, perceptual, and cognitive processes. Human–machine integration should remain an important element of NASA research directed toward civil aeronautics applications.6 However, the emphasis should be shifted from development and testing of specific input and output devices toward more fundamental research involving modern instruments that measure brain physiology. Research should also include voice command and recognition technology, coupled with increased machine contextual understanding, to reduce workload. This will help define the future role of humans in complex, highly automated systems. Key milestones related to human–machine integration methods and tools include Develop improved system engineering processes and tools for determining optimum roles of humans and automation in complex systems and demonstrate the benefits of this improved methodology in a trial application. This milestone should include provisions for dynamic human–machine task allocation and monitoring of human performance by machines (e.g., automated terrain avoidance). Conduct fundamental research on the causes of human error and on human contributions to safety and document design guidelines that will (1) help minimize the potential for design-induced error and (2) facilitate positive human intervention in the event of system failures. Transfer these guidelines to government program offices and industry. Develop constructive models of human performance and decision making and validate model predictions against objective performance data acquired in high-fidelity human-in-the-loop flight simulation experiments. Develop and demonstrate rapid prototyping tools that enable comparative evaluations of alternative automation schemes early in system development. Develop and validate a technique for integrating human reliability estimates into system safety and reliability analyses. Key milestones related to human–machine integration technologies for vehicle applications include Develop and test enabling technologies for pilot workload management and reduced crew operations (e.g., improved human–machine integration for a flight management system) while keeping pilot awareness at the proper level. Develop display concepts for maintaining operator situational awareness while monitoring highly automated processes. Demonstrate the ability of operators to rapidly and accurately intervene in the event of system failures. Develop technologies and/or display concepts enabling effective fusion of information from multiple sources, including real-world and synthetic imagery (i.e., augmented reality). Demonstrate the effectiveness of these concepts in practical decision support applications with varying levels of information quality and uncertainty (in terms of accuracy, timeliness, etc.). Develop and demonstrate technologies for machine vision (image-based object detection). 6 J. Vagners, professor emeritus, aeronautics and astronautics, University of Washington, Presentation to Panel D on November 15, 2005.
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Decadal Survey of Civil Aeronautics: Foundation for the Future Develop tools and metrics to compare effectiveness of machine and human operators in see-and-avoid tasks to improve machine performance. Relevance to Strategic Objectives Capacity (3): This Challenge will address human performance limits that constrain overall performance of the air transportation system, such as aircraft separation, wake vortex avoidance, operations in reduced visibility, high-speed turnoffs, baggage and cargo handling, aircraft maintenance and servicing, etc. Safety and Reliability (9): Human factors are the predominant cause of accidents and incidents in civil and military aircraft operations. Operational safety statistics show that 65 to 75 percent of mishaps are attributable to human error. This Challenge will reduce human error. Efficiency and Performance (9): Reducing the workload of pilots and controllers would make more efficient use of human and automation resources. Energy and the Environment (1): This Challenge has little or no impact on this Objective. Synergies with National and Homeland Security (3): This Challenge will address issues related to command and control of complex, highly automated systems, situation awareness, and safety of flight operations, all of which are of interest to DoD. Elements of this Challenge that address information management and decision support systems (e.g., data mining, decision making under uncertainty, modeling and prediction of human behavior, etc.) are also relevant to DHS. Support to Space (3): Elements of this Challenge directed toward ATM improvements may have applicability to ground-based control of space systems. Some research related to advanced human–machine integration devices (e.g., synthetic vision) may also be beneficial to ongoing manned spaceflight programs. Why NASA? Supporting Infrastructure (3): NASA has substantial capability (personnel and facilities) to do world-class research in human–machine integration. The facilities at NASA Ames and Langley Research Centers provide a unique environment for integrated, human-in-the-loop simulation of both flight deck and ATM technologies. These facilities have been specifically designed and instrumented for evaluation of advanced concepts for human–machine integration in a high-fidelity operational environment. Mission Alignment (9): Advanced human–machine integration research is a mainstream activity for NASA Ames and Langley Research Centers and is very relevant to NASA’s mission. Lack of Alternative Sponsors (3): Some human–machine integration issues might be addressed by DoD labs, industry, or academia, if not addressed by NASA. However, research on fundamental human–machine integration principles and research directed specifically at civil aeronautics would probably be neglected without NASA leadership. The FAA historically looks to NASA to perform human–machine integration research, particularly as it relates to ATM. Appropriate Level of Risk (9): Human–machine integration represents a broad spectrum of moderate- to high-risk technical challenges with both near- and far-term implications. D9 Synthetic and enhanced vision systems Synthetic and enhanced vision systems provide an out-the-window view of terrain, obstacles, and traffic. These systems can also be used as flight crew interfaces for flight trajectory and planning operations (Kelly et al., 2005). The synthetic vision systems that use databases to generate terrain and obstacles require high-fidelity, high-integrity information and a self-healing capability. Enhanced vision systems use forward-looking sensors such as infrared, radar, and laser ranging to allow the flight crew to visualize the real world when visibility is hindered. Currently, vision systems are limited by weather, human factors issues, and other issues. New sensors and improved sensor fusion are needed. A combined synthetic and enhanced vision system has future potential as a navigation, approach, and landing sensor. The ability to “see” the airport in poor weather has the potential to reduce the likelihood of a go-around. Information fusion that exploits the capabilities of sensors and compensates for their deficiencies is needed, and the immature state of this art represents the most difficult obstacle to achieving these benefits. Synthetic and enhanced vision systems are also intended to aid airport surface operations in poor weather, reducing runway occupancy and taxiing errors and reducing gate-to-gate travel time. Research topics of interest are as follows: Database integrity and quality Information fusion Object detection and avoidance Human–machine interface issues Verification of accuracy, fault tolerance, and reliability Key milestones include Prepare an accurate and complete terrain and obstacles database and demonstrate real-time database monitoring and error correction. Develop procedures and rules for fusing image information from multiple imaging sensors as well as stored terrain data and traffic; identify common viewing parameters; and determine what role enhanced vision systems and synthetic vision systems should play in an integrated system.
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Decadal Survey of Civil Aeronautics: Foundation for the Future Demonstrate increased situational awareness and alerting to avoid air traffic, airport surface traffic, wires, and cables. Demonstrate displays that (1) eliminate image fusion artifacts that lead to misleading information and (2) present conformal information to pilots in a way that facilitates its transition to the outside world. Demonstrate tools for verifying database accuracy, fault tolerance, reliability, and overall system accuracy. Relevance to Strategic Objectives Capacity (3): This Challenge increases capacity due to better area navigation performance in the terminal area and improved surface operations, which increase capacity by compensating for the effects of bad weather and night vision constraints. Safety and Reliability (9): Accurate synthetic and enhanced vision systems can increase safety dramatically during approach, landing, and ground operations. The intuitive information provided on advanced display is more easily understood than needles and gauges used in nonglass cockpits, especially with inexperienced pilots. Efficiency and Performance (3): Terrain and obstacle information, combined with flight trajectory information, can result in more efficient flight paths. Energy and the Environment (1): This Challenge has no impact on this Objective. Synergy with National and Homeland Security (1): This Challenge does not apply to this Objective. Support to Space (3): The technology developed for this Challenge can also be used by vehicles landing on other planets. Why NASA? Supporting Infrastructure (9): NASA has supported R&T relevant to this Challenge and has relevant expertise, aircraft platforms, and other test and evaluation facilities. Mission Alignment (9): This Challenge is consistent with NASA’s commitment to aviation safety. Lack of Alternative Sponsors (3): Industry and DoD support R&T relevant to this Challenge. NASA’s involvement is needed to provide overall leadership and to continue to push the envelope. Appropriate Level of Risk (3): This Challenge faces low risk. Some commercial development has already occurred. D10 Safe operation of unmanned aerial vehicles in the national airspace The use of UAVs for a variety of civil applications (e.g., farming, communications relays, border monitoring, power line and pipeline monitoring, and firefighting) will continue to increase. Flight operations of military UAVs in civil airspace is also expected to increase. To facilitate these operations, UAVs should be integrated into the air transportation system. This requires them to be at least as safe as manned aircraft. Most UAV technologies, capabilities, and processes are shared with manned aircraft and require research in several key topics, including the following four: Aircraft. Automation, system upgrade issues, and communications systems, all of which are distinct from those for manned aircraft. Human–machine interaction. Function allocation, human interface design, situational awareness, training, and required level of proficiency in the remote operation of the aircraft. Maintenance and support. In matters where UAVs differ distinctly from traditional aircraft. Flight operations. Sense- or see-and-avoid issues, person-to-person interfaces between operators and controllers, assurance of positive control of the aircraft (especially with highly automated UAVs that are not directly controlled by ground-based operators in real time), and automated contingency management. Key milestones include Develop and demonstrate secure, reliable communications as well as procedures for interaction between UAVs and air traffic controllers. Design, develop, and demonstrate human interfaces for remote UAV operators under conditions extant in the air transportation system. Develop and test training programs for remote UAV operators. Develop and demonstrate sense-and-avoid technologies for UAVs. Demonstrate technologies for maintaining positive control of UAVs under adverse conditions. Develop and demonstrate automated contingency management for control of UAVs. Relevance to Strategic Objectives Capacity (3): This Challenge will enable more UAV flight operations in civil airspace. There is little impact on the movement of people but might be a significant impact on freight movement in the future. Safety and Reliability (9): This Challenge is essential for safe and reliable operation of UAVs in civil airspace, both controlled and uncontrolled. Efficiency and Performance (3): It takes months to obtain a waiver to allow UAV operations in civil airspace. This Challenge should help alleviate this situation and facilitate efficient commercial UAV operations.
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Decadal Survey of Civil Aeronautics: Foundation for the Future Energy and the Environment (1): This Challenge has little or no impact on this Objective. Synergies with National and Homeland Security (9): The ability to routinely operate UAVs in civil airspace will enhance national defense and homeland security operations. Support to Space (1): This Challenge has little application to this Objective, although some synergies may exist with regard to automation and communications for spacecraft vehicle control. Why NASA? Supporting Infrastructure (3): NASA has more experience than any other entity in the test and evaluation of UAV systems. Mission Alignment (9): This Challenge is very relevant to NASA’s mission. Lack of Alternative Sponsors (3): Industry is addressing DoD-related issues regarding airframes, operators, and maintenance. The commercial and government applications arena is hindered by operational issues that NASA can effectively address. Appropriate Level of Risk (3): This Challenge faces low risk. D11 Secure network-centric avionics architectures and systems to provide low-cost, efficient, fault-tolerant, onboard communications systems for data link and data transfer As NASA moves into the network-centric vision of the future, data link assurance will become increasingly important. Threats to the integrity of information can be grouped into two categories: natural threats and malicious threats. Natural threats are associated with unintended system failures and include hardware and software flaws, lightning strikes, cosmic rays, and human error. Malicious threats are intelligent directed attacks. Historically, the former have posed the greater threat. However, as future aircraft become more network-centric, a new class of malicious threats could become increasingly destructive. Data must be considered a valuable and critically important asset. Data loss and corrupt data can cause significant problems, especially in cooperative networked systems. In addition, data separation is required to protect International Traffic in Arms Regulations (ITAR) data from unauthorized disclosure. Considerations such as ITAR are increasing the need for avionics architectures that allow different nodes to communicate with each other over encrypted data links at different levels of security. Traditional approaches for assuring the security of networked information, such as the Transmission Control Protocol (TCP), have problems in high-latency environments such as deep space and parts of the air transportation system. DARPA has been developing a trusted key distribution mechanism, but it is unknown if this mechanism has similar problems with operating in such an environment. It would allow for NASA to address the needs of both legacy and new systems to function in a network-centric manner. Key milestones include Develop fundamental system requirements, architecture, and system logic that are compatible with current and future network-centric system requirements. Complete fundamental research necessary to develop and incorporate encryption techniques, threat detection, and counterthreat strategies. Develop simulation capabilities for evaluation and demonstration of certain high-performing strategies in the execution of realistic system architectures and applications. Develop requirements flowdown to all affected aircraft systems, such as advanced CNS. Develop requirements and methodology to support verification, validation, and certification of future systems incorporating this technology. Relevance to Strategic Objectives Capacity (9): This Challenge will increase capacity by developing key technologies and capabilities related to secure network communications, cooperative distributed networking systems, networked weather systems, and the like. Safety and Reliability (9): Unreliable and insecure data links increase the likelihood of catastrophic failure. Secure data links prevent malicious and accidental corruption of data. Efficiency and Performance (9): Improved communications security allows full exploitation of available communication infrastructure. Energy and the Environment (1): This Challenge has little or no impact on this Objective. Synergy with National and Homeland Security (9): Reliable and secure data links are required for national and homeland security to prevent both malicious and/or accidental corruption of data, especially with missions involving UAVs. Support to Space (3): Reliable and secure data links are required for space missions to prevent both malicious and/or accidental corruption of data. Why NASA? Supporting Infrastructure (3): NASA has strong credentials in avionics architectures, network architectures, and encryption technology. NASA also has domain expertise to evaluate the applicability of these technologies. Mission Alignment (3): This research will directly support multiple R&T Challenges and benefit military and civil aviation, as well as future applications in space. Lack of Alternative Sponsors (1): Industry and DoD labs are developing technology related to this Challenge, but it is
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Decadal Survey of Civil Aeronautics: Foundation for the Future predominately focused on military systems. The commercial information technology business sector is very active in commercial encryption and networking technologies. Appropriate Level of Risk (3): Near-term research can address issues related to this Challenge, and the results can be transferred to future civil and military applications. D12 Smaller, lighter, and less expensive avionics Today’s commercial and military aircraft have benefited from a modest size, weight, and cost savings as a result of integrated avionics systems and fly-by-wire flight control systems, smaller antennas, smaller sensors, and digital data buses. The expansion of smaller and lighter aircraft (in particular UAVs) resulted in development of significantly smaller and lighter avionics. While not common, entire systems weighing less than 1 pound have demonstrated that basic avionic functionality (navigation, communication, and autoflight) can be fit into a package (or set of packages) that are much smaller than and very different from conventional commercial and military avionics. To maximize the efficiency and performance of future commercial and military aircraft, new technology is needed that significantly reduces the cost, size, and weight of current avionics as well as their supporting installation infrastructure (by minimizing or eliminating equipment mounting hardware and aircraft wiring). Key milestones include Demonstrate technologies that provide wireless onboard communications. Demonstrate methods to reduce processing requirements and power requirements. Demonstrate methods to improve avionics system capability, integrity, and reliability using low-cost components/sensors, including greater use of commercial off-the-shelf (COTS) components. Demonstrate greater use of microcontrollers as main processors and in distributed processing. Document common data standards that have broad application and usage. Relevance to Strategic Objectives Capacity (1): This Challenge could prompt some aircraft owners (especially in general aviation) to install more avionics, which would increase aircraft capability and indirectly increase capacity, although this effect would likely be small. Safety and Reliability (3): Low-cost avionics could prompt some aircraft owners (especially in general aviation) to install more avionics, which would increase safety. However, the incorporation of COTS technologies into modern avionics systems has met greater scrutiny from regulatory agencies than ever before. The industry needs an affordable means to improve the performance, quality, and safety of increasingly complex avionics systems. These advanced avionics should come with or enable inherently fewer failure modes. Efficiency and Performance (9): One pound of dead weight on a commercial transport increases fuel costs on the order of $100 per aircraft per year. Transport aircraft typically carry 200 pounds of avionics, not counting associated wiring. Smaller aircraft have a larger proportion of their weight in avionics, so the savings in fuel costs should be a larger proportion of total fuel costs. In addition, ultralight aircraft can have longer mission times if they can carry less weight. Common standards that permit cross-product strategies, such as wireless data protocols and transfer media reduce costs. ARINC standards are good examples of common protocols, but generally they focus on airline applications. Energy and the Environment (3): Avionics weight savings reduce fuel consumption and will have a positive effect on the environment. Synergies with National and Homeland Security (3): Technologies that enable weight and size savings, specifically wireless technologies that interconnect onboard systems and components, will need to be sufficiently secure and reliable to maintain aircraft safety. New systems that meet these needs may result in by-products that can be used in ground-based vehicles and fixed-base stations. Support to Space (9): Smaller and lighter avionics are enablers for future space flight, especially for missions that would benefit from significantly reduced avionics power requirements. Why NASA? Supporting Infrastructure (3): The Jet Propulsion Laboratory (JPL) conducts relevant research, but its focus is space-based. This Challenge would use technologies developed for space to support aeronautics. Mission Alignment (3): This Challenge will benefit the complete civil aviation community and have application in future space travel. Lack of Alternative Sponsors (3): Industry is developing smaller and less expensive avionics, but they are geared toward unique applications like UAVs. In general, manufacturers of commercial and military aircraft are slow to make revolutionary changes in technology without a clearly understood business case or a clear assessment of risk; regulatory constraints are a major factor. Appropriate Level of Risk (3): This Challenge faces low risk. D13 More efficient certification processes for complex systems Certification of aircraft and aircraft systems has focused on airworthiness using process-based standards. Products for
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Decadal Survey of Civil Aeronautics: Foundation for the Future which the processes have been followed are assumed to have acceptable quality. Many of the required processes in the standards, however, are expensive and time-consuming. In addition, following a good process does not necessarily imply the product is safe. An alternative approach to process-based certification is to certify the product itself. Product-based certification and more efficient ways to do process-based certification could greatly reduce the time and cost of creating aircraft systems while potentially increasing safety. How to do such certification, particularly for software-intensive systems, is unknown, and research that could provide a valid and demonstrated basis for product-based certification could increase quality assurance while decreasing costs and could significantly influence industry standards and FAA advisory circulars. Recently, a third, performance-based approach to specifying certification requirements has been applied to some systems. In this approach, a required performance level is specified rather than the process required to produce it or ways to evaluate the product. An example is required navigation performance (RNP), in which the performance level of certain navigational systems is specified. R&T is still needed, however, to demonstrate that the system will satisfy the performance requirement. A final problem occurs when completely new technologies or procedures, such as reduced aircraft separation, are introduced in the air transportation system. Changes foreseen as necessary to transform the system and to solve critical problems in capacity involve significant new technology and operational procedures. Assurance of safety in past systems has relied on making few major changes and relying on historical data and experience, which will not be available when major changes are implemented over a short period of time. New, revolutionary approaches will be required to provide the necessary level of confidence in these new systems. Key milestones include Validate that following specific processes will produce required assurance levels. Demonstrate processes for certifying products such as software, where testing is unable to provide required levels of confidence. Demonstrate approaches to assuring that required performance levels will be achieved in complex systems, despite failures or environmental disturbances. Demonstrate approaches to ensuring the safety of proposed changes to the national airspace system for which historical data and prior experience are not available. Relevance to Strategic Objectives Capacity (3): New enhancements to the air transportation system that increase capacity will need to be certified and shown to be safe. Safety and Reliability (9): Because the goal of certification is to ensure safety, more efficient and effective certification approaches could have a major impact on safety. Efficiency and Performance (9): More efficient certification processes should reduce the number of resources required to develop and certify new aircraft capabilities, systems, and products. Energy and the Environment (1): This Challenge has no impact on this Objective. Synergies with National and Homeland Security (1): This Challenge has no impact on this Objective. Support to Space (3): Some of the safety and reliability technologies developed by this Challenge will apply to space systems. Why NASA? Supporting Infrastructure (3): Some NASA infrastructure could be used in the evaluation of new certification approaches. Mission Alignment (1): Certification is the responsibility of the FAA and the aircraft manufacturers, not NASA. NASA has capabilities, however, that could effectively help the FAA and industry address certification issues. Lack of Alternative Sponsors (1): The FAA and other certification agencies have the responsibility for certification and thus interest in improvement. Aircraft and aircraft system manufacturers have great interest in this topic. Appropriate Level of Risk (3): New certification approaches (including capability-based approaches) are feasible and are being recommended and used (e.g., RNP), so this Challenge faces low risk, but more research is needed into the effectiveness of the new and proposed approaches and how to implement them. D14 Design, development, and upgrade processes for complex, software-intensive systems, including tools for design, development, and validation and verification The introduction of software and digital components has allowed the development of increasingly complex systems but at the same time has required changes and additions to basic engineering approaches and methodologies. Basic research in new tools and techniques is needed for designing and testing software-intensive systems and for maintaining and upgrading them over time. For example, most software cannot be exhaustively tested, and the verification difficulties become even greater when nondeterministic artificial intelligence techniques are employed. There is a need for new ways to provide assurance, particularly for critical systems. One technology relevant to this Challenge is model-based development, whereby models that can be executed and analyzed are constructed prior to system implementation and construction. Model-based development potentially can aug-
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Decadal Survey of Civil Aeronautics: Foundation for the Future ment the detection of conceptual design errors early in development, when they are much less expensive to correct; decrease development time and risk; and allow for greater reuse of system engineering effort. Research should strive to improve analysis and specification of design rationale, visualization and simulation tools, and so forth. Key milestones include Develop easily used and reviewed modeling languages. Develop automated design and code generation. Relevance to Strategic Objectives Capacity (3): The development of new aircraft capabilities is increasingly being driven by the requirement to develop and upgrade software. Software development is a component of most approaches to increasing capacity, but it is not the most critical component. Safety and Reliability (9): Because the behavior of complex systems is increasingly controlled by software, software can have a significant impact on safety and reliability. Efficiency and Performance (3): Software costs are a driving factor in design and development. Improving design, development, and upgrade processes would increase efficiency and performance. Energy and the Environment (1): This Challenge has relatively little effect on energy use and the environment, and the effect is indirect. Synergies with National and Homeland Security (1): Software-intensive systems might be used to implement some new technologies, but the benefit to DoD and DHS would be indirect. Support to Space (3): Some of the R&T relevant to this Challenge would apply to space control systems, but design requirements are very different. Why NASA? Supporting Infrastructure (1): NASA has some capable researchers in fields related to this Challenge, but NASA has outsourced most R&T relevant to this Challenge. Mission Alignment (3): This Challenge is not well aligned with NASA’s mission. Relevant tools and methodologies are applicable to any complex system, not just aerospace. Lack of Alternative Sponsors (1): Industry, DoD, and academia are developing many tools relevant to this Challenge. Appropriate Level of Risk (1): This Challenge faces very low risk. Many relevant tools and techniques exist and much of the basic research has been done. REFERENCES Bokadia, S., and J. Valasek. 2001. Severe Weather Avoidance Using Informed Heuristic Search. AIAA-2001-4232, AIAA Guidance, Navigation, and Control Conference, Montreal, Canada, August 6-9. Chavez, F.R., and D.K. Schmidt. 1994. Analytical aeropropulsive-aeroelastic hypersonic-vehicle model with dynamic analysis. Journal of Guidance, Control, and Dynamics 17(6): 1308-1319. Ding, Y., J. Rong, and J. Valasek. 2004. Feasibility Analysis of Aircraft Landing Scheduling for Non-Controlled Airports. AIAA-2004-5241, Proceedings of the AIAA Guidance, Navigation, and Control Conference, Providence, R.I., August 16-19. Doebbler, J., P. Gesting, and J. Valasek. 2005. Real-Time Path Planning and Terrain Obstacle Avoidance for Aircraft. AIAA-2005-5825, Proceedings of the AIAA Guidance, Navigation, and Control Conference, San Francisco, Calif., August 15-18. Garg, S. 2005. NASA Glenn Research in Controls and Diagnostics for Intelligent Aerospace Propulsion Systems. NASA/TM—2005-214036, Glenn Research Center, Cleveland, Ohio, December. Glauert, M.B. 1945. The design of suction aerofoils with very large CL-range. Aeronautics Research Council Reports and Memoranda 2111, National Research Establishment, November. Glauert, M.B., W.S. Walker, W.G. Raymer, and N. Gregory. 1948. Windtunnel tests on a thick suction aerofoil with a single slot. Aeronautics Research Council Reports and Memoranda 2646, National Research Establishment, October. Helbing, K., T. Spaeth, and J. Valasek. Forthcoming. Improving aircraft sequencing and separation at a small aircraft transportation system airport. Journal of Aircraft. Jaw, L.C., and S. Garg. 2005. Propulsion Control Technology Development in the United States: A Historical Perspective. NASA/TM-2005-213978, October. Kelly, W., J. Valasek, D.Wilt, J. Deaton, K. Alter, and R. Davis. 2005. The Design and Evaluation of a Traffic Situation Display for a SATS Self Controlled Area. Proceedings of the 24th Digital Avionics Systems Conference, Washington, D.C., October 30-November 3. Lampton, A., and J. Valasek. 2005. Prediction of Icing Effects on the Stability and Control of Light Airplanes. AIAA-2005-6219, Proceedings of the AIAA Atmospheric Flight Mechanics Conference, San Francisco, Calif., August 15-18. Lampton, A., and J. Valasek. 2006. Prediction of Icing Effects on the Lateral/Directional Stability and Control of Light Airplanes. AIAA-2006-6487, Proceedings of the AIAA Atmospheric Flight Mechanics Conference, Keystone, Colo., August 21-24. Litt, J.S., D.L. Simon, S. Garg, T. Guo, C. Mercer, R. Millar, A. Behbahani, A. Bajwa, and D.T. Jensen. 2005. A Survey of Intelligent Control and Health Management Technologies for Aircraft Propulsion Systems. NASA/TM-2005-213622, ARL-TR-3413, May. Rong, J., S. Geng, J. Valasek, and T. Ioerger. 2002. Air Traffic Conflict Negotiation and Resolution Using An Onboard Multi-Agent System. DASC-345, 21st Digital Avionics Systems Conference (DASC) on Air Traffic Management for Commercial and Military Systems, Irvine, Calif., October 22. Schierman, J.D., D.G. Ward, J.R. Hull, N. Gandhi, M.W. Oppenheimer, and D.B. Doman. 2004. An approach to integrated adaptive guidance and control with flight test results. Journal of Guidance, Control and Dynamics 27(6). Tandale, M.D., and J. Valasek. 2003. Structured Adaptive Model Inversion Control to Simultaneously Handle Actuator Failure and Actuator Saturation. AIAA-2003-5325, AIAA Guidance, Navigation, and Control Conference, Austin, Tex., August 11-14. Tandale, M.D., and J. Valasek. 2006. Fault tolerant structured model inversion control. Journal of Guidance, Control, and Dynamics 29(3). Tandale, M.D., J. Valasek, J. Doebbler, and A.J. Meade. 2005. Improved Adaptive-Reinforcement Learning Control for Morphing Unmanned Air Vehicles. AIAA-2005-7159, Proceedings of the AIAA Infotech@Aerospace Conference, Arlington, Va., September 26-29. Valasek, J., M.D. Tandale, and J. Rong. 2005. A reinforcement learning-adaptive control architecture for morphing. Journal of Aerospace Computing, Information, and Communication 2(5): 174-195.
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