Common Themes and Key Barriers
Chapter 3 describes R&T Thrusts, which are threads of commonality among the R&T Challenges identified by each panel within its own R&T Area. The steering committee also identified threads of commonality among the R&T Thrusts and the R&T Challenges from different R&T Areas and called them Common Themes:
Physics-based analysis tools
Multidisciplinary design tools
Intelligent and adaptive systems
Complex interactive systems
These Themes are not an end in themselves; they are a means to an end. Each Theme describes enabling approaches that will contribute to overcoming multiple Challenges. Exploiting the synergies identified in each Common Theme will enable NASA’s aeronautics program to make the most efficient use of available resources.
Physics-Based Analysis Tools
Physics-based analysis tools attempt to predict the behavior of physical and/or chemical phenomena by solving the fundamental governing conservation, constitutive, and state equations together with appropriate closure equations based on first-principle physical models. In other words, “physics-based” refers to the general use of scientific principles in place of empirical data. The tools can be hierarchal in space and time, and the lowest order model (e.g., zero-dimensional steady state and two-dimensional unsteady state) that predicts a phenomenon to the accuracy desired should be employed. This Theme also includes models derived from other branches of science, such as chemistry, biology, materials science, computer and information science, and cognitive science, though many are not strictly physics-based. For complex problems such as three-dimensional, unsteady, heterogeneous flows, computational simulations that provide numerical solutions must generally be used.
This Theme is particularly applicable to three R&T Areas examined in this study: aerodynamics and aeroacoustics, propulsion and power, and materials and structures. Physics-based analysis tools offer the opportunity to decrease significantly the use of empirical approaches in aeronautics R&T. Empiricism, as defined here, refers to the generation of information through cut-and-try experimentation and testing. It is not inherently bad as long as the results are integrated with models, lead to knowledge and understanding, and are not widely extrapolated beyond the ranges of the test parameters. To a great degree, enlightened empiricism was responsible for many of the aeronautical advances of the previous century. Empiricism, however, can be expensive and time consuming and may not lead to a fundamental understanding of phenomena. From a national perspective, empirical modeling and design can be an inefficient use of resources and may lead to compromised, nonoptimal designs that rely on unnecessarily large design margins.
An important benefit of advances in physics-based analysis tools is the new technology and systems frontiers they open. New concepts often emerge from a greater understanding of the underlying physics offered by new analytical capabilities. In these cases, experimentation might never lead to the level of insight offered by even relatively simplistic physics-based tools. An example of this is sonic-boom mitigation technology. It is highly unlikely that any practical amount of experimentation will lead to a design for a low-sonic-boom aircraft. The development of linear and nonlinear physics-based analysis tools is necessary to mature this technology.
With advances in computational speed, computing power, and the affordability of digital processors in the last two decades, aeronautics researchers in industry, academia, and the government have turned increasingly to computational simulations to model complex physical and chemical phenomena. Industry is motivated by the possibility of using computational simulations to reduce the cost and time of product development, while increasing product reliability. Academic and government researchers also value the ability to attack more complex problems. These computational simulations generally employ a number of physics-based models within the governing conservation and state equations. Examples include models that describe droplet behavior and interactions, particulate matter formation, turbulence, turbulence–chemistry interactions, boundary-layer growth and transition, fracture, crack propagation, and material phase boundaries. A lack of fundamental understanding often requires these models to contain adjustable parameters that are grossly calibrated to empirical data sets. These data sets are often incomplete, which means that untested assumptions must be incorporated in the models. The computational simulations are generally not validated in spatial detail except for comparison of code predictions to input and output measurements. Additionally, the adjustable parameters are tweaked to match predictions with measurements. It is not uncommon to find that the codes do not extrapolate well when the design space changes considerably, prompting more tweaking of the adjustable parameters. Also, when results are presented, details are usually omitted in connection with the use of boundary conditions or how adjustable parameters were set, making it harder for independent researchers to reproduce the results. Thus to a large extent, empiricism has transitioned from the physical to the computational realm, but it persists. Nevertheless, within their applicable ranges, computational simulations have enabled technical progress, as witnessed by the state of aeronautics today. Unfortunately, limits on the use of simulations are often not well understood.
NASA and its academic and industrial partners can make very significant contributions in developing and validating physics-based analysis tools. These are readily assimilated by industry into their proprietary product design codes. NASA, industry, and academia can jointly participate in research into physics-based analysis tools because it is fundamental in nature, publishable, and sharable. This research will take time to mature, yet advances can readily be translated into practice as they occur. Furthermore, given the budget- and schedule-driven nature of the aerospace business, this is the type of work that industry can no longer afford to pursue. Developing physics-based tools whose accuracy and range of applicability limits are well established is a lengthy, iterative process. Validation requires well-designed experiments to elucidate the underlying physics as well as experimental facilities of appropriate scale and advanced, nonperturbing diagnostics to perform detailed, spatially and temporally resolved measurement of parameters.
Benefits of synergy
Advances in physics-based analysis tools would help address R&T Challenges in all of the R&T Areas. For example, turbulence modeling is a key element in the accurate prediction of mixing, which is very important in many aspects of aerodynamics (A2), aeroacoustics (A4a, B1a), and combustion processes (B1b). Accurate predictions of flow separation are a prerequisite to the successful design of both nonreacting (A2, A4a, A4b) and reacting (B1a, B1b, B5, B8) flow devices. Mathematical models of material properties and reactions are essential for structural response (A4a, C4, C10). Droplet–droplet and droplet–flow interactions are important processes in predicting both icing (A6) and combustion behavior (B1b, B8, B10). Modeling flow–structure interactions accurately is an important element of aeroelasticity and noise generation (A4a, B1a, C5, C6b). The development of higher-temperature alloys is key to improving propulsion system fuel efficiency (B4, C6a). Many of the computational science issues associated with large, complex computational simulations, such as automated grid generation, parallelizing codes, and error propagation analyses, are common elements across several R&T Areas.
Relevant R&T Challenges
The following R&T Challenges would benefit significantly from using physics-based analysis tools: A1, B1a, B1b, E1, A2, E2, D3, A4a, A4b, B4, C4, D4, E4, B5, C5, A6, B6a, C6a, C6b, A7b, B8, A9, B10, and C10.
Multidisciplinary Design Tools
Discipline-specific design tools, including optimization and inverse design, have improved the performance of airfoils, wings, structures, control systems, and propulsion systems for many years, and they are now critical parts of the design process. The next step in the design of more complex systems involves more than just combining these disciplinary tools or gluing together discipline-specific analyses and optimization. New multidisciplinary tools are needed to integrate high-fidelity analyses with efficient design methods and to accommodate uncertainty, multiple objectives, and large-scale systems. Research in efficient methods for including large numbers of design variables (e.g., adjoint methods, multifidelity models, and surrogate models), probabilistic design methods, and tools for distributed, complex systems is particularly important to the development of future aeronautical systems.
Methods for simulation-based multidisciplinary design and optimization (MDO) are at the very core of a philosophy that moves away from empirical methods that have proven to be expensive and often have not met expectations in exploring the aeronautical design space. MDO processes bring together people, analytical tools, experimentation, and information to design complex structural components and systems.
The design of aeronautical systems requires a system-level, multidisciplinary approach to assessing potential costs, benefits, and risks. Design tools that couple a small number of disciplines in a restricted design space have reached a level of maturity and fidelity that make them important parts of the design process. For example, aeroelastic design tools are now within reach that couple computational fluid dynamics, multibody dynamics, and finite-element analyses for full aircraft configurations. In structural designs where the topology or outer mold lines are defined, analytical methods such as the structural finite-element technique, coupled with similar analytical tools for load assessment, promise success. More recently, high-fidelity multidisciplinary design tools have begun to incorporate a broader range of disciplines and are starting to be used earlier in the design process.
However, for designs with a multiplicity of topologies, some of which are not well defined, and for problems where a large number of design parameters and constraints must be considered in the early stages of the design process, the multidisciplinary design process is still underdeveloped. One of the major limitations of past efforts to create MDO tools has been a low level of fidelity, driven by a lack of physics-based models that are efficient and appropriate for system-level design. In addition, MDO tools have often lacked flexibility and have been developed and applied for very specific applications.
Significant new developments are required in both the design strategies and the embedded tools that constitute the multidisciplinary design process. Key issues associated with next-generation multidisciplinary design tools include fidelity, computational efficiency, and the ability to handle uncertainty.
Efficiency and effectiveness continue to be a problem, particularly in large-dimensionality problems and multimodal or disjointed search spaces. Most current approaches to design are ill-equipped to deal with the vast amounts of data associated with the design process. High numerical efficiency is paramount for multidisciplinary design tools, and alternative paradigms that take advantage of a new generation of parallel computational hardware must be sought. Furthermore, not all methods are ideal for all problems. The goal of this Theme is not to generate one perfect, all-encompassing algorithm but to use the most efficient and effective method or combination of methods for each problem. Proper algorithm selection in itself is an important research topic.
Uncertainty modeling in a data-lean environment, which is often the case with new concepts, continues to be an issue, particularly in situations where uncertainty distributions do not conform to standard forms or where components or elements exhibit discrete behavior. The propagation of uncertainty in complex and highly coupled multidisciplinary systems needs to be modeled, and tools for design and optimization in a nondeterministic environment continue to be computationally intractable, especially when applied to design problems involving a large number of nondeterministic variables, parameters, and design constraints.
Methods for distributed design (where the design team is geographically dispersed) and for the design of large-scale distributed systems have achieved some success but have been restricted to special types of problems. Continued development of more general, scalable approaches to this problem is also critical for the design of complex systems.
The practical resolution of these issues will require fundamental research efforts in the development of design-oriented, physics-based models; new design methodologies that can seamlessly manage models of multiple fidelities for the various components of the system; methods to increase the computational efficiency of tools; methods to handle complex interactions with high accuracy; and methods to manage uncertainty in the design process.
Benefits of synergy
Multidisciplinary design processes develop synergistic benefits by integrating people, analytical tools, experimentation, and information to design complex components and systems. Their importance is reflected in the relevant R&T Challenges listed below.
Relevant R&T Challenges
Many Challenges in each R&T Area rely on improved multidisciplinary design tools. These include Challenges A1, A2, A3, A4a, B1a, B1b, B4, B8, C6b, D3, and D4. In addition, many R&T Challenges identify multidisciplinary design tools and design under uncertainty as core technologies, including A11, C3, D2, and E1.
Advanced configurations embody innovative, outside-the-box approaches to better meet the strategic objectives outlined in this report. They serve as technologies in themselves when they represent advancements in system-level
definitions beyond those possible with conventional design tools, methods, and expertise. Examples of advanced configuration technologies include revolutionary aircraft concepts and advanced structural designs.
Integration of innovative technologies into advanced configurations has long been a part of U.S. aeronautical development. For example, the Bell X-1A demonstrated supersonic flight, thus pushing the envelope beyond what was once thought to be an impassable barrier. Other advanced system configurations, such as the X-15, X-29, and X-35, have demonstrated multiple advanced technologies. Other examples, such as the Gossamer-Condor, Voyager, and Helios aircraft, demonstrated advanced vehicle configurations that were groundbreaking innovations and went beyond validated analytical methodologies.
Creativity and good ideas have been at the center of revolutionary advances in aeronautics. One of NASA’s roles is to foster the implementation of innovative solutions to challenging technological barriers. The development of innovative concepts needs to include freedom to innovate as well as physics and engineering checks. It is implicit that available design tools (from component-level, physics-based tools to MDO models) and empirical knowledge will be used to screen concepts before new tools and models are created.
Innovation is not possible, however, without tolerance for failure. The progression from technology identification, maturation, and demonstration to implementation is rarely linear. Advanced configurations, regardless of “success,” form the basis for validating component-level and system-level physics-based models and MDO approaches. For example, the development programs for the SR-71 and NASA’s XV-15 tilt-rotor had technology problems, but both produced functional aircraft. Even today, technical gaps persist in modeling the high-speed flight aerodynamics and combustion processes of the SR-71. However, by having a good balance between innovation, tolerance of failure, sound technical knowledge and judgment, and engineering analysis, the SR-71 came to fruition and became the fastest and highest-flying production aircraft ever built. The XV-15 demonstrated V/STOL capabilities, and programs such as the V-22 Osprey and the Bell/Agusta BA609 civil tilt-rotor aircraft have greatly benefited from and advanced the concepts demonstrated by the XV-15. Design and testing of advanced configurations should continue to have an important presence in civil aeronautics R&T.
Another aspect of innovation on advanced configurations is the process of integration itself. Oftentimes, outside-the-box thinking is needed to seamlessly integrate technologies that have been optimized individually but not yet integrated into a system. How to best integrate different technologies is a topic common to many R&T Challenges.
Benefits of synergy
Many synergies arise when developing advanced system configurations that integrate diverse technologies. For example, there is a direct synergy between advances in variable-cycle engines and the development of supersonic aircraft. Similarly, research on sonic boom mitigation is integral to the design of engines and propulsion systems for supersonic civil aircraft. In addition, for hypersonic vehicles (e.g., scramjet), the propulsion system cannot be designed separately from the rest of the vehicle. In this case, technologies that support the engine and vehicle often mature hand-in-hand as the systems are integrated. Many advanced configurations would also benefit from new sets of active control techniques and smart components (engines, materials, structures) that can self-diagnose and repair. Moreover, from the operational point of view, advanced (and even current) configurations benefit from a change in paradigm in the way that guidance, control, and real-time weather information is shared and used by pilots, controllers, and air traffic managers.
Relevant R&T Challenges
The following R&T Challenges are closely related to advanced configurations: A1, C1, C2, E2, A3, B3, D3, A4a, C4, D4, B5, C5, D5, B6a, B6b, C6a, E6, A7a, E7, B8, C8, A9, A10, B9, B10, and C10.
Intelligent and Adaptive Systems
When an emerging detailed knowledge of physical phenomena is combined with the development of miniaturized sensors, compact actuators, and powerful computational capabilities, the potential exists to develop intelligent and adaptive systems with significantly improved performance and robustness. This Common Theme encompasses aircraft-level R&T Challenges aimed at sensing the operational environment, actively responding to that environment, and learning from the resulting interactions. Examples include (1) flow control techniques for improving aerodynamic performance, reducing noise, increasing maneuverability, and making aircraft robust to atmospheric disturbances and adverse weather conditions and (2) methods for improving the interaction of humans with aircraft systems. This Theme also involves technologies aiming at development of smart engines and mechanical power systems, adaptive materials and morphing structures, load suppression, and vibration and aeromechanical stability control.
The development of innovative classes of aircraft and complex systems will be facilitated by techniques to over-
come the design and operational constraints and the physical limits of current systems. Each of the R&T Challenges related to this Theme involves the measurement of physical characteristics of a system in an effort to develop responsive and flexible schemes to improve system performance, robustness, efficiency, and safety.
While some technologies encompassed by this Theme are relatively immature, significant performance improvements can be expected through development and execution of a coordinated research plan. Many promising flow control techniques have already been developed, such as micro-fluidic injectors, piezoelectric synthetic jets, voice-coil actuators, dielectric barrier discharges, and surface plasma discharges. These techniques have shown promise in the laboratory with limited, scaled flight testing. Adaptive materials with the ability to radically change their properties are also being explored, with the goal of affecting load-carrying capability and allowing large variations in wing area or shape. In the past 2 years, prototypes from DARPA’s Morphing Aircraft Structures program have been demonstrated at transonic speeds in the Transonic Dynamics Tunnel at NASA Langley. Significant advancements in sensing techniques have also been realized with respect to miniaturization, frequency response, and allowable environmental operating conditions. Techniques currently exist to measure both surface and in-stream properties useful for adaptive control techniques. Finally, basic control techniques are available, but research is needed in the flight control laws for systems with a large number of highly distributed sensors and actuators, nonlinear adaptive control techniques, and adaptive techniques compatible with the failure of distributed sensors and actuators.
To fully realize the benefits of the research within this Theme, cross-disciplinary teams will be required. Coordination across the R&T Challenges should be pursued to leverage promising developments in overlapping technologies. Efforts aimed at improving aircraft performance will require people with detailed knowledge in the following areas:
The fundamental physical processes being controlled.
Novel actuator designs, including material and structural response and electronics.
Innovative sensing techniques.
The cross-disciplinary teams should interact frequently with designers and operators of current systems to clearly understand evolving constraints of existing systems. In addition, control of one parameter may have unexpected consequences for other parameters. These trade-offs must be identified and understood before they can be addressed.
Benefits of synergy
Integration of the R&T Challenges within the Common Theme on intelligent and adaptive systems would facilitate the cross-pollination of ideas and techniques. For example, flow control actuators developed for improving external aerodynamics may well find application in propulsion systems, while adaptive materials and structures developed for morphing aircraft may find application in noise reduction efforts. With this research conducted as an integrated Theme, rapid and effective implementation of advancements could be realized across historically disparate domains.
Synergies also exist between this Theme (which focuses on aircraft R&T) and the Common Theme on complex interactive systems (which focuses on the air transportation system as a whole). Intelligent and adaptive systems developed for use on aircraft potentially provide information useful in the operation of larger, more complex air transportation systems. For example, sensors incorporated into an aircraft to detect icing may well provide information useful to the ATM system.
Relevant R&T Challenges
The following R&T Challenges are closely related to intelligent and adaptive systems: E1, C1, A2, C2, D2, E2, B3, D3, E3, A4a, D4, E4, C5, D5, A6, C6b, D6, E6, A7b, D8, E8b, E8c, D9, and C10.
Complex Interactive Systems
As noted in Chapter 1, as used in this report, a complex interactive system (also known as a system of systems) refers to an adaptive system consisting of a large, widespread collection or network of independent systems functioning together to achieve a common purpose. Complex interactive systems are distinguished from large, monolithic systems by the independent functioning of their components, which provides freedom for existing components to evolve and new components to emerge independent of a central configuration control authority. Complex interactive systems also tend to be distributed over a large geographic area and require effective communications and coordination protocols for the various components to interact efficiently (Maier, 2006).
To achieve the Strategic Objectives, the air transportation system must be understood as a complex interactive system, because its performance emerges from collective interactions among many independent systems and organizations, including aircraft of many different types, capabilities, and mis-
sions; pilots; air traffic controllers and air traffic flow managers; communication, navigation, and surveillance systems; airline operation control centers; manufacturers; labor organizations; and air carriers of many different sizes, capabilities, and operating philosophies. All of these “components” of the air transportation system loosely operate under a set of operating agreements, rules, regulations, and communications protocols established by international, national, and local government and nongovernmental organizations.
As aeronautic systems become more complex, the following systems issues become more critical and more difficult to examine:
When system performance is itself a complex, nondeterministic phenomenon emerging from the interaction of independent system components with stochastic behaviors, it may not be feasible to develop an analytical model of the entire system, making it difficult to describe, explain, and predict the system performance resulting from changes to any system component.
Correspondingly, when a change to system behavior is desired, translation of this system-level representation into specific requirements for components can be difficult.
Unlike centrally organized systems, which may be decomposed according to a hierarchy of control, decomposing the system model into design-manageable elements may be impossible when many different components interact in many different ways.
The components’ behaviors (especially human behaviors) will often be context dependent, especially when they are attempting to meet several competing objectives. Thus, a small change in one part of the system may change the operating context of several components, generating broader, unanticipated effects.
The types of behaviors that can significantly impact system performance include not only the physical functioning of technologies but also the cognitive behaviors of humans in the systems; social and organizational dynamics; and economic dynamics.
Complex interactive systems are typically collabora-tive—that is, they allow component systems to more or less voluntarily collaborate to fulfill agreed-upon central purposes. Agreements among the central players on service provision and rejection provide a primary enforcement mechanism to maintain standards.
The linkages between components are typically created through communication and coordination protocols rather than mechanical linkages or command structures.
Looking specifically at the air transportation system, much of its structure has evolved over time, with each independent entity finding methods of operation that satisfy its own goals as much as possible within the overall constraints that are imposed upon them. Human–machine and machine– machine interfaces are often created after the development of the technologies and operating concepts, sometimes leading to problems when interface design is unduly difficult or expensive. Aircraft have been developed to meet market demands without full consideration of overall impact on the system of variant performance characteristics, which may reduce system capacity and efficiency. System models typically examine isolated effects or components within the system, and few models attempt to examine a large range of complex, interactive system effects, especially those involving nondeterministic behaviors. Additionally, current system models are not easily reconfigured or adaptable to real-time analysis.
Key to analyzing a complex interactive system such as the air transportation system is developing a suite of interacting models with comprehensive simulation and analysis capabilities. Such an interactive system of models should be capable of (1) assessing impacts locally within system components as well as globally across the system and (2) introducing new systems, operating procedures, and protocols for information transfer, communication, coordination, and collaboration. In addition, models suited to complex interactive systems are needed early in the conception and design of any technology intended to function within such a system, including systems intended to support human activity. This process will be facilitated by explicitly representing the anticipated contributions of the technology to the larger system.
The need for clear communication and coordination protocols within the system is another critical design consideration. System designs should also consider the need for collaborative decision making, the relative roles and authority of the components (including organizational structures and the role of technologies in mediating human interactions), and their information needs.
Benefits of synergy
The ability to analyze complex interactive systems is relevant to many R&T Areas, and methods of modeling and analyzing such systems can be broadly applicable. Redesigning the air transportation system will be difficult, but the ability to accurately and efficiently model it as a complex interactive system will help reduce program risk and allow coordinating design efforts across multiple agencies.
Relevant R&T Challenges
The following R&T Challenges are closely related to complex interactive systems: C1, E1, D2, E2, B3, C3, E3, E4, E6, A7b, E8a, E8b, E5, and D10.
The steering committee identified two key barriers to achieving the six Strategic Objectives that should guide civil aeronautics R&T: (1) certification and (2) change management, internal and external. If these barriers are not addressed, the Strategic Objectives will not be accomplished, even if individual R&T Challenges are successfully overcome. Although these barriers may not appear to be explicitly part of NASA’s mission, if they are considered from the earliest stages of research, the civil aeronautics community will be more likely to use the results of NASA R&T in developing operational products and procedures. Furthermore, the barriers have technical aspects, which the R&T Challenges will address.
Certification is the demonstration of a design’s compliance with regulations. For example, before it can be operated by U.S. airlines, a new aircraft must be shown to comply with U.S. federal aviation regulations. As systems become more complex and nondeterministic, methods to certify new technologies become more difficult to validate. Core research in methods and models for assessing the performance of large-scale systems, human-interactive systems, nondeterministic systems, and complex, software-intensive systems, including safety and reliability in all relevant operating conditions, is essential for NASA, because such research is currently beyond the capabilities of regulators such as the FAA. The ultimate utility of this research will be significantly enhanced through early and consistent coordination of technology maturation with the FAA and other organizations responsible for certification of operational systems. Furthermore, this research would be facilitated by collaboration with other organizations involved in advanced software development methods.
Certification can also be a major barrier to the ultimate implementation of new technologies and operating concepts. In some cases, such as low-cost avionics for general aviation, the cost of certification can be several times greater than the cost of developing and manufacturing the product itself. Furthermore, relying on empirical testing to demonstrate compliance with certification standards may not be feasible for large-scale systems (including complex, software-intensive systems and air traffic operating concepts) and human-in-the-loop behaviors, which are not the same in different operating contexts; in these cases, certification will be substantially aided by the use of design tools and design processes developed to mitigate concerns about design validity, safety, and reliability. Certification issues can be showstoppers if not addressed early in the R&T process. Thus, NASA should address the following concerns in its aeronautics R&T program:
Systematic documentation and publication of model and design assumptions from the earliest stage of R&T development, to aid in a technology’s ultimate certification.
Ongoing iterative validation of models and design tools—and their specifications—during their development, and verification of models and design tools relative to their specifications.
Generation of databases and models from empirical data to provide a basis for validation and certification.
Establishment of community-accepted metrics, criteria, and methods for validation and certification.
Change Management, Internal and External
The air transportation system includes large organizations with long-standing institutional cultures and business concerns that are impacted by—and sometimes resist—the introduction of new technologies. These organizations must be motivated to participate in new operating concepts and to accept the risk of change to improve performance. Changing an interactive system as complex as the air transportation system is difficult because it involves changing a large number of individual elements, including equipment of many different kinds, personnel training, institutional organization, and business models. Additionally, the end state of the air transportation system remains undefined, so R&T should create and maintain the flexibility to steer the system in any of several different directions. This requires interdisciplinary applications of large-scale systems engineering, organization design, economics, and financial analysis, an approach which in some ways is beyond the current state of knowledge. Even so, improved change management techniques are vital to a cost-effective, noncontentious, and safe transition to the air transportation system of the future.
Change management within the federal government is particularly important because of the major impact that federal agencies, regulations, and funding have on the operation of the air transportation system and the development of new aeronautical technologies. In addition, change management within the federal government is particularly difficult because of the complex internal organization of the federal government, with multiple independent agencies, competing national priorities, and political factors that are beyond the control of any one person or agency. One way to facilitate change in the midst of such complexity is to establish strong, focused leadership that establishes a public/private process for change that defines air transportation as a national priority, produces a widely endorsed long-term vision of the air transportation system, and coordinates action by interested organizations. The process should be carefully structured to accommodate the increasing complexity of the air transportation system, competing national and organizational priorities, and fiscal limitations. The process should produce validated R&T requirements, a clear understanding of government and industry roles, and a plan to implement new technologies, operational concepts, and system archi-
tectures (NRC, 2003). The establishment of the Next Generation Air Transportation System (NGATS) Joint Planning and Development Office (JPDO) is an example of federal efforts to change interagency relationships to improve change management in civil aviation.
The issues related to change management transcend NASA’s role as a single agency. The federal government should continue to support the work of the JPDO while conducting a high-level review of organizational options for ensuring U.S. leadership in civil aeronautics.1
Maier, M. 2006. Architecting Principles for Systems-of-Systems. The Information Architects Cooperative (TiAC) White Paper Repository. Available online at <www.infoed.com/Open/PAPERS/systems.htm>.
National Research Council (NRC). 2003. Securing the Future of U.S. Air Transportation: A System in Peril. Washington, D.C.: The National Academies Press. Available online at <http://fermat.nap.edu/catalog/10815.html>.
NRC. 2006. Aeronautics Innovation: NASA’s Challenges and Opportunities. Washington, D.C.: The National Academies Press. Available online at <http://fermat.nap.edu/catalog/11645.html>.