Airframe Materials and Structures
The most notable developments in UAV airframe structures will be reductions in size (miniaturization) and the use of multifunctional materials. Even though many advances in materials, manufacturing, health monitoring, durability, and smart structures are already enabling technologies for affordable UAVs, not all of the benefits are unique to UAVs. This chapter identifies structures and materials research areas that will have a significant effect on the development of cost-effective UAVs. Like most other next-generation aircraft, UAVs will require low-cost, lightweight materials. The design and construction of any air vehicle is driven by consideration of a range of failure modes, such as excessive elastic deformation, yielding, buckling, fracture, fatigue, corrosion, creep, and impact damage. However, some mission-specific features of UAVs are especially dependent on advances in airframe materials and structures.
The committee identified four areas that will be essential to the further development and evolution of UAVs. All four areas will require research and development ranging from basic science to prototype testing. The four areas (in arbitrary order) are as follows:
defining the design environment in which future UAVs will operate, including loads definition, reliability requirements, and aeroelasticity
reducing manufacturing costs for airframe structural components, including advanced composite materials and multifunctional materials (i.e., structural materials that also serve another primary purpose)
improving design processes to support reduced cycle time, rapid prototyping, and low-cost fabrication
health monitoring, health management, and novel control and sensing technologies, including MEMS, smart materials, new sensors, and actuators
Each of these areas will require a better understanding of the processes and phenomena involved and more reliable prediction of interactions among the elements of the process or device.
The design of aircraft structure considers interactions among many complex processes, including material selection, fabrication, assembly, operations, and maintenance. The primary material parameters that affect a structural design are strength and stiffness, which are traditionally characterized by deterministically derived design property values, or “allowables.” Allowables are statistically reduced values based on experimental data and on assumed statistical models or distributions. Historically, structural design criteria are established by reducing design limits and ultimate conditions by a “safety factor” of 1.5, based on the ratio of ultimate strength to yield strength of common structural metals. In addition to engineering simplicity, the most important reason for using deterministic approaches is that design criteria expressed in terms of a margin of safety are more readily accepted by regulators and customers.
In the deterministic approach, a structure is designed to operate with the simultaneous occurrence of poorest allowable material quality and the most severe operating environment, level of damage, and service load conditions. That is, uncertainties are handled using conservative safety factors, a safe approach when most of the data describing the uncertainties are incomplete. As a result, current structural designs are very conservative and very heavy.
Probabilistic structural design/analysis is based on the principles of structural mechanics and uses conventional structural analysis tools, such as closedform solution of mathematical equations and finite element methods, to solve structural problems involving the variables. In the probabilistic approach, the actual strength or stiffness distributions are developed using analysis models that account for the probability of material defects, dimensional tolerances in structural component fabrication and assembly, variations in operational loads, and the probability of in-service and maintenance damage. Using probabilistic models, the safety and reliability of structures can be assessed over their entire lifetimes. Probabilistic structural design/analysis has been used to solve a variety of engineering problems, including spacecraft engines, durability analyses, and risk assessment of existing structures.
The committee believes that the use of probabilistic design criteria for UAV structural design could result in a lower cost, more efficient structure and could accelerate the maturation and acceptance of probabilistic design approaches for other systems. The probabilistic approach is more suitable for UAV structural design criteria than for inhabited systems for two reasons. First, the uncertainties and difficulties in accurately characterizing the operational environment for UAVs could require an overly conservative statistical safety factor for structural design when using deterministic design criteria. Probabilistic design and analysis would reveal more of the information about a structure to allow for a more realistic assessment of performance and operating life. Second, the design criteria for UAV structures are not well defined, even in the traditional deterministic approach. Thus, in terms of safety or risk of structural failure, there may be fewer objections to a deviation from the conventional arbitrary margin of safety. Customers and designers are reluctant to accept any structure with a level of reliability less than 100 percent or a risk of failure higher than 0 percent for inhabited systems. Risk is more acceptable when human life is not involved.
The highest payoff from using the probabilistic design/analysis approach will be the potential to meet required safety goals with an optimized structural design that reduces both weight and cost. Based on operational experience with a range of aircraft, industry has obtained a substantial amount of data to simulate the probabilistic occurrence of individual events. Probabilistic design and analysis can use this information to design more efficient structures. In addition, because structures will be designed to meet a discrete safety goal, new approaches for planning structural testing will be necessary to generate experimental data to characterize materials and structures. Once basic statistical relationships between key structural parameters have been determined, simulations could replace many materials and structures tests. Thus, less expensive and more accurate assessments of structural performance could be obtained with less testing.
Research should be initiated to integrate design and analysis models and methods into a versatile engineering tool. Current analytical models, such as the finite element codes developed by the National Aeronautics and Space Administration (NASA) and structural evaluation codes developed by industry, will have to be modified and improved for structural design and analysis in a production environment. The development of analytical tools should also include the development of procedures for assessing the accuracy and reliability of model predictions.
Several probabilistic approximation methods are available, the most reliable of which is the Monte Carlo simulation method. However, faster and more efficient methods are needed.
Characterization and Testing
Fundamental research should be undertaken to establish potential failure modes and performance levels for materials and structures to support probabilistic analysis methods. Current approaches for testing materials are based on deterministic design methods and rely on extensive testing at the subelement, element, subcomponent, component, and full-scale levels, using a “building block” approach (NRC, 1996). The development of basic property relationships and potential failure modes are needed for implementing probabilistic design approaches and reducing the amount of large-scale verification testing required.
Techniques and software codes should be developed for computational simulations of structural responses to operational environments throughout the structure’s lifetime at both the material and structural levels. Effective analytical simulations would enable designers to model design alternatives without developing and testing expensive prototypes, resulting in potentially significant reductions in developmental costs.
Fundamental research on critical failure modes and property relationships to establish meaningful design criteria for probabilistic methods should be undertaken. Criteria could be established based on the results of studies on the relationship between the conventional safety factor and the probabilistic reliability of a structure, along with an in-depth survey of existing structures.
Recommendation. To support the development and introduction of probabilistic methods for UAVs, the U.S. Air Force should sponsor research on (1) analytical tools, (2) characterization and testing, (3) simulation methods, and (4) design criteria.
Aeroelasticity is the interaction between mechanical and aerodynamic forces. Unstable aeroelastic interactions can lead to flutter, buffeting, and ultimately catastrophic failure. As described in Chapter 3, high aspect ratios and low structural weight fractions for HALE UAVs can lead to structural flexibility and potential problems with aeroelastic stability. The large displacements inherent in flexible structures can result in nonlinear aeroelasticity, which substantially complicates structural analysis and design.
Aeroelastic tailoring of composite structures could significantly reduce aeroelastic instability. Aeroelastic tailoring is accomplished using directional structural stiffness. Structural laminate tailoring has many potential benefits, including the potential to increase flutter speed and improve effectiveness. The location of the primary stiffness direction (i.e., the locus of points where the structure exhibits the greatest resistance to bending deformation) can be tailored by laying out stiffeners, ribs, or skin structures in a way that shifts the axis fore or aft of the conventional elastic axis. Although structures optimized for aeroelastic interactions may not represent the lightest weight or lowest cost configuration, the benefits to dynamic stability and control often outweigh these penalties.
Recommendation. As part of an integrated approach to vehicle configuration and structural design, the U.S. Air Force should conduct research to develop a fundamental understanding of design and analysis methods for aeroelastic tailoring of composite structures. This capability will be especially important for high-altitude, long-endurance configurations.
For more than 25 years, structural materials for military aircraft were selected and structural components were designed and fabricated to provide maximum performance with relatively little concern for the manufactured cost of the structures. Reductions in weapon acquisition budgets in the past decade have focused attention on the life-cycle costs (including acquisition, operational, maintenance, and disposal costs) of structural components. The need for low-cost aircraft and the differences in structural configurations and design criteria for UAVs should encourage the introduction of new structural concepts and innovative manufacturing processes. In addition to modular structural designs and reduced size and weight discussed in Chapter 2, advances in low-cost materials and processes will provide opportunities for reducing the cost of airframe structural components.
The implementation of innovative, low-cost manufacturing processes, along with consideration of manufacturing costs and sustainment throughout the design process, will be key to the development of cost-effective UAV airframes. Processes that reduce the number of parts, simplify tooling, reduce energy requirements, and minimize waste will be preferred. Complicating the need for low-cost processes is that production quantities for UAVs will be small. Therefore, primary criterion for the expanded use of polymeric composites in structural applications is the potential for low-cost manufacturing processes (NRC, 1996).
An important program is already under way to reduce the processing costs of high-performance composites for aircraft. The Composite Affordability Initiative (CAI) is jointly funded by the Air Force, the Navy, and industry (Boeing, Lockheed Martin, and Northrop Grumman). The objective of CAI is to “develop
the tools, methodologies, and technologies necessary to design and manufacture a composite airframe utilizing revolutionary design and manufacturing practices to enable breakthrough reductions in cost, schedule, and weight” (DOD, 1999). CAI benefits government and industry by developing technology applicable to a variety of aircraft. The program includes (1) design integration, (2) design and manufacturing concepts, (3) fabrication technologies for unitized structures, (4) assembly processes for unitized structures, (5) development of performance standards for analysis methods, (6) element and subcomponent design and testing, (7) cost data, modeling, and analysis, (8) development of quality methods, (9) component scale-up and process validation, and (10) long-term technology development.
Analysis tools and design methodologies are being developed to automate and improve predictions of the characteristics of composite components so that designs can be less conservative and the excess weight associated with overdesign can be avoided. The CAI is investigating a range of innovative composite processes, including the following:
resin transfer molding (and vacuum-assisted resin transfer molding)
low-temperature/vacuum bag curing
through-thickness reinforcement (e.g., stitching/3-D weaving/Z pinning)
electron beam curing
The low-cost, high-performance structures developed for CAI would be of particular interest for HSM-type vehicles.
Recommendation. The U.S. Air Force should monitor the progress of the Composites Affordability Initiative and conduct research to develop a fundamental understanding of processes with promise for UAV structures.
Although polymeric composite structures will dominate future UAVs, significant advances in the processing of high-performance metallic alloys will also be required. Although metallic structures will continue to be driven by traditional weight and durability considerations, cost is expected to become an even greater issue. Net-shape processing and integrated manufacturing techniques have the potential to reduce costs (Theibert and Semiatin, 1998). Promising processes for producing metal airframe structures in small quantities at reduced cost include the following:
solid free-form fabrication
electron-beam physical vapor deposition
advanced sheet metal processes
Reducing the number of parts and lowering cost may also be aided by more common materials, processes, and design features.
Recommendation. The U.S. Air Force should conduct research to develop a fundamental understanding of metals processes applicable to UAV structures, such as research on low-cost processing of UAV airframe components.
Finally, for the low-cost vehicle type, the suite of airframe materials should be expanded beyond those used for conventional aircraft. For example, the MALD Program took a CAIV approach to design by trading off performance for cost reduction (Price, 1998). MALD is a small, inexpensive, modular vehicle that will replicate a jet aircraft kinematically and in terms of radar cross section on the battlefield. In addition to modular design and extensive use of existing commercialoff-the-shelf components, the MALD program used very low-cost materials and processes to meet its cost targets. A key manufacturing technology used by the MALD program was compression molding of sheet-molding compounds to produce discontinuously reinforced composite components. These materials and processes are similar to those widely used in the automotive industry.
The committee believes that very low-cost materials and processing can also be used for small, expendable UAVs, especially for components substructures, such as ribs and bulkheads, because of the shorter service life and lower reliability requirements of these UAVs. Materials and processes, such as aluminum casting, high-speed machining of integral metal structures, and compression molding of low-cost materials (e.g., automotive sheet molding compounds), should be considered.
Recommendation. The U.S. Air Force should expand the suite of materials and processes for use in small, low-cost vehicles to include very low-cost, commoditygrade materials that are not used in conventional aircraft constructions.
COMPUTATIONAL DESIGN PROCESSES
A number of analytic tools have been developed to model and simulate environments and reduce the amount of testing required to qualify structures for aerospace applications. These tools have shortened the design process and permitted more iteration during product development. However, extensive empirical testing and data reduction are still required to establish mechanical, chemical, and thermal properties and the effects of process variations. Basic research is still
required to develop the fundamental effects of alloy composition and heat treatment for metals; and resin behavior, interface properties, and fiber chemistry for composites.
Modeling so far has been of little use for identifying new compositions. Although modeling at the first-principles level can provide useful information on thermodynamic stability and structure, many key aspects of materials cannot be adequately simulated. Modeling has had a significant impact on materials processing, however, where macroscopic predictions and trends have been useful for optimizing processes. Key barriers to implementation of computational tools include the following (Srolovitz, 1998):
complexity of bridging between atomistic models and engineering components (which involves a variation of 22 orders of magnitude in time scales and 9 orders of magnitude in spatial scales)
basis in principles versus experimental knowledge (i.e., heuristic materials models)
model verification (because models will only be trusted if they have been verified)
The objective of process design is for a small team to be able to design and produce a quality product quickly and efficiently. Process design will enable teams to simulate processes and conduct cost trade-offs for materials and processes.
Recommendation. The U.S. Air Force should develop computational models for new materials and processes and apply them to UAVs.
HEALTH MONITORING AND HEALTH MANAGEMENT
Prognostics and health monitoring are being used today to assess air vehicle systems. Systems such as engines, auxiliary power units (APUs), computers, and avionics packages contain sensors and self-diagnostic software to evaluate their performance in real time. Onboard computing has increased significantly, and shared networks are technically feasible. For UAVs, diagnostic capabilities will have to be extended to the airframe structure to evaluate load cycles, damage conditions, corrosion, and fatigue.
UAVs that operate with minimal human intervention will require self-monitoring. UAVs that must function reliably after long-term storage will require a nervous system integral to the airplane, which will add both complexity and cost.
Sensors developed for one purpose can often be adapted to serve other sensory functions. In some cases, they can also serve as actuators. Low-cost UAVs
will require materials and devices that can control smaller vehicles without using hydraulic systems. Smart structures technologies, such as piezoelectrics and neural networks, can improve load and health monitoring capabilities, as well as alleviate dynamic loads (Geng et al., 1994; Kim and Stubbs, 1995). Neural networks can potentially monitor many locations on an aircraft and reduce the number of sensors required. Piezoelectric-based health monitoring systems have been demonstrated in the laboratory for integrated damage detection of both metallic and composite structures (Lichtenwalner et al., 1997).
Along with a mix of sensors (e.g., accelerometers; pressure transducers; or piezoelectric sensors, actuators, or strain gages) that can sense the environment and determine desired vehicle response, an ideal system would be able to locate and assess damage rapidly on the ground or in the air.
Recommendation. The U.S. Air Force should develop improved health monitoring technologies that take advantage of recent advances in sensors, controls, and computational capabilities. Specific opportunities include the following:
MEMS and mesoscale technologies for integrated sensor-actuationcontrol devices
improved load and condition-monitoring capabilities that use piezoelectric sensors and neural networks for data analysis
active flutter suppression and buffet load suppression systems that link condition-monitoring capabilities with piezoelectric transducers/actuators and intelligent controls