During my experience of teaching aircraft structures I have felt the need for a textbook written specifically for students of aeronautical engineering. Although there. Following topics will be covered in this session. A l i f i f i bli b. • Analysis of aircraft structure, its assemblies, sub- assemblies and the types of loading these are. A Brief History of Aircraft Structures. The history of aircraft structures underlies the history of aviation in general. Advances in materials and processes used to.
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the main parts of an aircraft. A knowledge of the basic stresses on aircraft structures will help you understand why aircraft are built the way they are. The fuselage. Aircraft Structures for engineering students This page intentionally left blank Aircraft Structures for engineering students Fourth Edition T. H. G. Megson. BASIC AIRCRAFT STRUCTURES. The basic aircraft structure serves multiple purposes. Such as aircraft aerodynamics; which indicates how smooth the aircraft .
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The gap- and edge-free deformation of aerodynamic surfaces known as conformal morphing has gained previously unrealized capabilities such as inherent de-icing, erosion protection and lightning strike protection, while at the same time the technological risk has been greatly reduced.
And nanocomposites no longer lose their improved properties when trying to upscale from neat resin testing to full laminate testing at element level. Skip to main content Skip to table of contents. Advertisement Hide. Conference proceedings. Successful application of continuum damage mechanics is dependent upon the formulation of the evolution equation for the state variables.
Void growth models are the most common damage evolution models [ 32 ], but continuum damage mechanics has been applied to subcritical crack growth under monotonic loading [ 33 ]. While the evolution equations can be formulated using a phenomenological procedure, a multiscale homogenization procedure using the physics-based damage models discussed above would be preferred.
For instance, these methods allow the representation of discontinuities and singularities via geometric descriptions of the crack surfaces, which are independent of the volume mesh, coupled with suitable enrichment functions [ 34 ]. In other words, a single finite element mesh suffices for modeling as well as capturing the evolution of material boundaries and cracks because the finite element mesh does not need to conform to internal boundaries.
Both approaches have been successful at integrating damage into a structural FEM when there is a single dominant damage driver, typically stress or strain. Simulation of damage development in more complex situations involving varying temperature, environment, and stresses is currently limited by the development of suitable damage state evolution equations.
Uncertainty Quantification, Modeling, and Control The purpose of quantifying uncertainty in a model is to exercise some control over the magnitude of the uncertainty [ 35 , 36 ].
The magnitude of the uncertainty can be controlled by the choice of model fidelity and scale. It is not given that the finest analysis scale and highest fidelity models decrease uncertainty in the simulation results sufficiently to justify their cost. There may be input parameters, such as the applied loading, whose uncertainty overrides all of the other uncertainties in the model.
Higher fidelity models and grain-scale analyses will not decrease the uncertainty associated with the applied loading. Computational cost increases as finer scales are analyzed and higher fidelity models are used. The choice of what fidelity and scale to use in a simulation should be based upon the acceptable level of uncertainty and the computational cost to achieve it. The ideal situation is to know the impact of scale and fidelity choices on uncertainty prior to performing any simulations.
Determining, after the effort, that a simulation did not provide an acceptable level of uncertainty is of less benefit. The cost of one simulation has already been incurred, and now another simulation of finer scale and higher fidelity will need to be performed with no guarantee that it will provide an acceptable level of uncertainty either. However, performing Monte Carlo simulations with different realizations of a model of an entire aircraft over a complete flight brings significant computational cost.
A detailed, nonlinear simulation of an entire flight for a complete airframe amplifies the computational time by orders of magnitude. And, while sampling methods are readily parallelized, more sophisticated probabilistic methods such as stochastic finite element methods SFEMs might be better than sampling methods.
SFEM comprises three basic steps: discretization of the stochastic fields representing the uncertain input parameters, formulation of the stochastic matrix at the element and then at the global level, and, finally, the response variability calculation [ 37 ].
There are two main variants of SFEM: the perturbation approach based on a Taylor series expansion of the response vector and the spectral stochastic finite element method, where each response quantity is represented by a series of random Hermite polynomials.
Each of these variants has issues in terms of computation effort that make application to large-scale non-linear systems, such as an aircraft, currently prohibitive.
There are other developments in SFEM that hold promise. The first is stochastic reduced basis methods SRBMs where the response process is represented using basis vectors that span the preconditioned, stochastic, Krylov subspace [ 38 ]. SRBMs are computationally efficient as compared to polynomial chaos expansions PCEs at a comparable level of accuracy, and so are better suited for solving large-scale problems. The computational costs of simulations using PCEs are such that solutions have been limited to uncertain systems with a small number of degrees of freedom.
Furthermore, the basis vectors are problem dependent which limit the ability to develop a general approach. The second development is nonintrusive SFEM approaches [ 39 ].
These approaches take advantage of powerful existing deterministic FE codes by building a surrogate response surface model using PCEs. The investments in deterministic FE codes can be leveraged. Multiscale SFEM seeks to propagate uncertainty information in fine scale quantities, such as microstructure, to coarse scale quantities, such as stiffness and strength that are functions of the fine scale quantities.
X-SFEM provides for the propagation of geometric uncertainties in the solution of partial differential equations, that is, PDEs defined on random domains.
Manipulation of Large, Shared Databases A model of an entire airframe is, by itself, an enormous database that is difficult to input, maintain the integrity of, and manipulate.
The basic geometry and assembly of components for the airframe can be established with a CAD system. Discretization of the individual components can be challenging, especially for large, detailed structural components. The integrity of the geometry and the discretization of this large complex model must be established and maintained over the life of the model. The discretization must be adaptable in order to adequately model the insertion of unexpected damage and subsequent repairs that occur during the service life of an aircraft.
These tasks will likely need to be automated as current manual methods are not up to the Digital Twin challenge. The information generated by performing flight-by-flight simulations over the entire design or service, life of an aircraft will be voluminous. In fact, manipulation of just the information from the simulation of a single flight for an entire air vehicle stretches current capabilities.
For the Digital Twin, the results from the simulation of every flight during the life of the aircraft must be kept available.
In order to use this information for making decisions about the continuing airworthiness of the vehicle, all of the information contained in the simulation results must be accessible. Rapid, focused interrogation of the database to support specific decisions must be possible. Some interrogations will need to be automated. For instance, it is not practical to manually search through an entire aircraft, physically or virtually, to locate damage hot spots.
The Digital Twin will need to automatically identify locations with prescribed levels of damage and present this information in a user-friendly way. High-Resolution Structural Analysis Capability Simulation-based design and certification will require very high performance computing, performance far beyond what is commonly used for aircraft structural analyses today. As envisioned, the Digital Twin of a complete airframe will have on the order of degrees of freedom.
If multiscale models of the microstructure, such as in Figure 7 , are required at some locations, these models will have on the order of degrees of freedom at each location [ 26 ]. Despite its large size, the Digital Twin must execute with sufficient speed so that the modeling and simulation can keep up with the actual usage of the aircraft, that is, a 1-hour flight must be simulated in 1-hour of clock time or less.
If simulations are unable to stay ahead of the actual aircraft, the power of the Digital Twin for life prediction and decision making is lost. Clearly, very high performance computing will be needed to meet the vision of the Digital Twin. But high-performance computing HPC is a relative term. In the next several years, petaflop-per-second power will become available. The teraflop-per-second scale computing of today is effectively infinite computing power by the standard of many engineers.
The problem is not the availability of high-performance computing hardware, but rather the usability of it through the appropriate codes and other software tools. A recent U. Department of Defense survey [ 42 ] found that the average age of commercially available finite element software is about 20 years and that the typical maximum number of processors such tools could effectively access was about !
The required advances are driven by the increased complexity of the problems, involving multiple and coupled physics models, high dimensionality described by large numbers of equations PDEs, ODEs, DAE, geometric descriptions and boundary conditions, optimization, etc.
The HPC community has identified certain application characteristics and math and algorithms needs Figure 8 which have already been discussed for the Digital Twin. Within the context of the Digital Twin, there is a need to solve coupled PDEs, quantify uncertainty, design and optimize the structure, while handling large and noisy data.
Figure 8: Common applications characteristics and math and algorithms needs for problems like high-resolution structural simulation [ 4 ]. Specifically the air vehicle portion has developed a fixed wing virtual aircraft simulation tool called Kestrel [ 43 ]. Kestrel integrates multiple single executable modules.
The most significant for the purposes of the Digital Twin are a CFD solver and a linear modal representation of the aircraft along with fluid-structure interfacing operations.
Aerodynamic loads are applied directly to the structure. However, the stresses in the structural components still need to be determined to enable life prediction. Developing the Digital Twin There are many challenges that must be overcome in developing the Digital Twin. It is difficult to put together a comprehensive Digital Twin development plan that covers a decade or more of activities. However, the initial, path-finding work that has been done and that is planned for the near future will be discussed.
The Air Vehicles Directorate at the U. Air Force Research Laboratory has been investigating a ROM for obtaining aerodynamic loads on the aircraft or internal stresses, from pilot inputs either in the actual aircraft or in a flight simulator. This activity developed out of work to streamline the clearance for external stores on an aircraft [ 21 , 44 ].
The integration of this stick-to-stress ROM into structural life prediction will be investigated as part of a program to demonstrate the potential of higher fidelity stress history, structural reliability analysis, and structural health monitoring for improving the management of airframes.
Two full scale fatigue tests of aircraft assemblies will serve as surrogates for actual flying aircraft in this program. Additional spirals of development will increase the fidelity of this Digital Twin by incorporating new technologies as they mature. Another may be the coupling of different physics models, that is, thermal, dynamic, and stress. A third technology is the digital thread manufacturing technology [ 46 ] for the F The digital thread makes it easier to see how the information necessary to construct tail number-specific structural models can be collected.
In the digital thread, the same 3D solid models from engineering design are used in manufacturing for numerically controlled programming, coordinate measurement machine inspections. Laser measurements are used with the digital thread to virtually mate parts in order to identify potential fit-up problems prior to actually mating the parts. The digital thread, in addition to the F production rates, has enabled Lockheed to use automated hole drilling in many places.
Therefore, to within the accuracy of the production measurement systems, the dimensions of many of the detail parts and the location of many of the fastener holes were known at one time during production.
It becomes a matter of supplying that information as initial conditions to the Digital Twin for an aircraft as it enters service. Advantages of a Digital Twin In the current life prediction process for aircraft, each type of physics has its own separate model.
Computational capabilities have restricted what physics and damage models are considered during the life prediction process. Information is passed between the physics models by writing the results from one model to a file, translating that output file into an input file for the other model, and finally reading the input file into the second model.
This process makes it difficult to develop a synchronized stress-temperature-chemical STC loading spectrum.
Furthermore, the effect of damage development on the stress or temperature history is not considered. The approach has been to assume some appropriately severe conditions during the design and subsequent usage tracking for an aircraft.
Such an approach is usually conservative, but leads to an air vehicle that is heavier than it may need to be and inspections more frequently than may be needed. The physics involved would be seamlessly linked, the way that physics is linked in the physical structure. The joint STC loading history for the aircraft will directly result from the simulation of the flight. This joint spectrum can be found for any location in the structure and will not rely on an idealized transfer function.
As damage develops within the structure, the local STC spectrum will naturally adjust for the presence of damage. It will not be necessary to assume the repetition of a statistically representative spectrum over the lifetime of the vehicle either; the STC spectrum can evolve as the usage of the vehicle and the age of the structure dictate. Damage findings, repairs, replacements, and structural modifications, if they are recorded at all, are presently maintained in a database separate from the structural analysis models.
It is not clear that this database is consulted when updating the remaining useful life of an aircraft. Such information is certainly not stored in a format that facilitates its use in a structural analysis model. The Digital Twin would provide a visual database that is directly related to both the structural model and the physical aircraft.
Therefore, in addition to providing a structural life prediction tool, the Digital Twin also facilitates configuration control for an individual aircraft. The Digital Twin will enable better management of an aircraft throughout its service life.
Engineers will have more information about the condition of the aircraft and have it sooner. This will allow better maintenance decisions to be made in a timely manner. References B.
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