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The main motivation behind a physics-based conceptual design approach is the focus on unconventional systems for which no "canned" design programs exist. Currently, NASA has identified several classes of unconventional systems tha...
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The main motivation behind a physics-based conceptual design approach is the focus on unconventional systems for which no "canned" design programs exist. Currently, NASA has identified several classes of unconventional systems that it wishes to examine over the next several years. Some of those configurations are highlighted here. Unfortunately, since these systems are extremely unconventional, reliance upon historical data is often inappropriate. As such, efforts are underway at Georgia Tech to more fully understand these systems and model them in a variable-fidelity physics-based design environment.
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High-fidelity, physics-based modeling and simulation have become integral to the design of aircraft, but can have intractably high computational costs when used for uncertainty quantification. This study presents a non-intrusive, ...
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High-fidelity, physics-based modeling and simulation have become integral to the design of aircraft, but can have intractably high computational costs when used for uncertainty quantification. This study presents a non-intrusive, parametric reduced order modeling method to enable the prediction of uncertain high-dimensional outputs with complex, nonlinear features and limited sampling budgets. A Proper Orthogonal Decomposition (POD) procedure is utilized to reduce the dimensionality of the high-dimensional space and identify a low-dimensional latent space. A sparse regression-based polynomial chaos expansion (PCE) is then used to construct a mapping between the uncertain input parameters and the latent space coordinates. The methodology is assessed on three test cases, including two-dimensional transonic flow around the RAE2822 airfoil with geometric uncertainties and several canonical problems with varying input and output space dimensionality. The study focuses on problems with strong nonlinearities and discontinuities, such as shocks, to investigate the effectiveness of the ROM in predicting high-speed aerodynamic fields. The performance is assessed by comparing the uncertain mean, variance, point predictions, and integrated quantities of interest obtained using the ROMs to Monte Carlo simulations.
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Drastic changes in aircraft operational requirements and the emergence of new enabling technologies often occur symbiotically with advances in technology inducing new requirements and vice versa. These changes sometimes lead to th...
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Drastic changes in aircraft operational requirements and the emergence of new enabling technologies often occur symbiotically with advances in technology inducing new requirements and vice versa. These changes sometimes lead to the design of vehicle concepts for which no prior art exists. They lead to revolutionary concepts. In such cases the basic form of the vehicle geometry can no longer be determined through an ex ante survey of prior art as depicted by aircraft concepts in the historical domain. Ideally, baseline geometries for revolutionary concepts would be the result of exhaustive conguration (or subsystem layout) space exploration and optimization. Numerous component layouts and their implications for the minimum external dimensions of the resultant vehicle would be evaluated. The dimensions of the minimum enclosing envelope for the best component layout ( s) (as per the design need) would then be used as a basis for the selection of a baseline geometry. Unfortunately layout design spaces are inherently large. The process must thus be automated. Automation makes a key contributing analysis i.e. automated detection of collisions between subsystems imperative. This key analysis can be very expensive. When an appropriate baseline geometry has been identied, another hurdle i.e. vehicle scaling has to be overcome. Through the design of a notional Cessna C-172R powered by a liquid hydrogen Proton Exchange Membrane (PEM) fuel cell, it was demonstrated that the various approaches to vehicle scaling i.e. photographic and historical-data-based regression can result in highly sub-optimal results even for very small O(103) scale factors. Building complete CAD mock-ups for each of what could be thousands of designs and then analysing the scaling behavior of each can also be computationally prohibitive. Therefore, there is a need for higher delity and relatively inexpensive vehicle scaling laws especially since emergent technologies tend to be volumetrically and/or gravimetrically constrained when compared to incumbents. The Conguration-space Exploration and Scaling Methodology (CESM) is postulated herein as a solution to the above-mentioned challenges. The methodology consists of a configuration space exploration approach and a scaling law derivation approach. This paper focuses on the former aspect. This bottom-up methodology entails the representation of component or sub-system geometries as matrices of points in 3D space. These typically large matrices are reduced using minimal convex sets or convex hulls. This reduction leads to signicant gains in collision detection speed at minimal approximation expense. (The Gilbert-Johnson-Keerthi algorithm is used for collision detection purposes in this methodology.) Once the components are laid out, their collective convex hull (from here on out referred to as the super-hull) is used to approximate the inner mold line of the minimum enclosing envelope of the vehicle concept. A sectional slicing algorithm is used to extract the sectional dimensions of this envelope. An offset is added to these dimensions in order to come up with the sectional fuselage dimensions. Once the lift and control surfaces are added, vehicle level objective functions can be evaluated and compared to other designs. The size of the design space coupled with the fact that some key constraints such as the number of collisions are discontinuous, dictate that a domain-spanning optimization routine be used. Also, as this is a conceptual design tool, the goal is to provide the designer with a diverse baseline geometry space from which to chose. For these reasons, a domain-spanning algorithm with counter-measures against speciation and genetic drift is the recommended optimization approach. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is shown to work well for the proof of concept study. There are two major reasons why the need to evaluate higher fidelity, custom geometric scaling laws became a part of this body of work. First of all, historical-data based regressions become implicitly unreliable when the vehicle concept in question is designed around a disruptive technology. Second, it was shown that simpler approaches such as photographic scaling can result in highly suboptimal concepts even for very small scaling factors. Yet good scaling information is critical to the success of any conceptual design process. In the CESM methodology, it is assumed that the new technology has matured enough to permit the prediction of the scaling behavior of the various subsystems in response to requirement changes. Updated subsystem geometry data is generated by applying the new requirement settings to the aected subsystems. AH collisions are then eliminated using the NSGA-II algorithm. This is done while minimizing the adverse impact on the vehicle packing density. Once all collisions are eliminated, the vehicle geometry is reconstructed and system level data such as fuselage volume can be harvested. This process is repeated for all requirement settings. CESM enables the designer to maintain design freedom by portably carrying multiple designs deeper into the design process. Also since CESM is a bottom-up approach, all proposed baseline concepts are implicitly volumetrically feasible. System level geometry parameters become fall-outs as opposed to inputs. This is a critical attribute as, without the benefit of experience, a designer would be hard pressed to set the appropriate ranges for such parameters for a vehicle built around a disruptive technology. As a proof of concept, a set of baseline geometries for a fuel cell powered general aviation aircraft are derived using the methodology.The NSGA-II algorithm was run with a population of 20 configurations over 200 generations. Each configuration consisted of 12 components with up to 6 degrees of freedom depending on the subsystem. The instrumentation box for example had no rotational degrees of freedom whereas the cylindrical fuel tank had two rotational degrees of freedom. Seven objectives were used in the optimization process namely : packing efficiency, maximum lift-to-drag ratio, static margin (target 5%), number of collisions, a connectivity metric, estimated take-off gross weight and superhull symmetry based on the Balinsky distance. The total time expense on this test bed was 4413 minutes. This equates to 1.47 minutes per case. When compared to the results of a conceptual design tools/ methods run time survey results, this approach compares very favorably to the other disciplines. These candidate geometries can at this point be fine-tuned manually if necessary. The key contribution of this approach is that the designer now can establish reliable volumetric visibility early on in the design. It is this result that enables the rapid derivation of vehicle scaling laws as requirements change. These laws significantly improve the fidelity of any optimization approach while at the same accelerating the process. This second aspect will be tackled in a future publication.
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摘要 :
Drastic changes in aircraft operational requirements and the emergence of new enabling technologies often occur symbiotically with advances in technology inducing new requirements and vice versa. These changes sometimes lead to th...
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Drastic changes in aircraft operational requirements and the emergence of new enabling technologies often occur symbiotically with advances in technology inducing new requirements and vice versa. These changes sometimes lead to the design of vehicle concepts for which no prior art exists. They lead to revolutionary concepts. In such cases the basic form of the vehicle geometry can no longer be determined through an ex ante survey of prior art as depicted by aircraft concepts in the historical domain. Ideally, baseline geometries for revolutionary concepts would be the result of exhaustive conguration (or subsystem layout) space exploration and optimization. Numerous component layouts and their implications for the minimum external dimensions of the resultant vehicle would be evaluated. The dimensions of the minimum enclosing envelope for the best component layout ( s) (as per the design need) would then be used as a basis for the selection of a baseline geometry. Unfortunately layout design spaces are inherently large. The process must thus be automated. Automation makes a key contributing analysis i.e. automated detection of collisions between subsystems imperative. This key analysis can be very expensive. When an appropriate baseline geometry has been identied, another hurdle i.e. vehicle scaling has to be overcome. Through the design of a notional Cessna C-172R powered by a liquid hydrogen Proton Exchange Membrane (PEM) fuel cell, it was demonstrated that the various approaches to vehicle scaling i.e. photographic and historical-data-based regression can result in highly sub-optimal results even for very small O(103) scale factors. Building complete CAD mock-ups for each of what could be thousands of designs and then analysing the scaling behavior of each can also be computationally prohibitive. Therefore, there is a need for higher delity and relatively inexpensive vehicle scaling laws especially since emergent technologies tend to be volumetrically and/or gravimetrically constrained when compared to incumbents. The Conguration-space Exploration and Scaling Methodology (CESM) is postulated herein as a solution to the above-mentioned challenges. The methodology consists of a configuration space exploration approach and a scaling law derivation approach. This paper focuses on the former aspect. This bottom-up methodology entails the representation of component or sub-system geometries as matrices of points in 3D space. These typically large matrices are reduced using minimal convex sets or convex hulls. This reduction leads to signicant gains in collision detection speed at minimal approximation expense. (The Gilbert-Johnson-Keerthi algorithm is used for collision detection purposes in this methodology.) Once the components are laid out, their collective convex hull (from here on out referred to as the super-hull) is used to approximate the inner mold line of the minimum enclosing envelope of the vehicle concept. A sectional slicing algorithm is used to extract the sectional dimensions of this envelope. An offset is added to these dimensions in order to come up with the sectional fuselage dimensions. Once the lift and control surfaces are added, vehicle level objective functions can be evaluated and compared to other designs. The size of the design space coupled with the fact that some key constraints such as the number of collisions are discontinuous, dictate that a domain-spanning optimization routine be used. Also, as this is a conceptual design tool, the goal is to provide the designer with a diverse baseline geometry space from which to chose. For these reasons, a domain-spanning algorithm with counter-measures against speciation and genetic drift is the recommended optimization approach. The Non-dominated Sorting Genetic Algorithm (NSGA-II) is shown to work well for the proof of concept study. There are two major reasons why the need to evaluate higher fidelity, custom geometric scaling laws became a part of this body
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This paper is an initial contribution to the aerospace audience intended to disseminate copulas theory and showcase its capabilities. The applications demonstrate how the quantification of uncertainty in design can be improved usi...
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This paper is an initial contribution to the aerospace audience intended to disseminate copulas theory and showcase its capabilities. The applications demonstrate how the quantification of uncertainty in design can be improved using copulas to correlate variables that are traditionally and arbitrarily treated as independent or deterministic. Our canonical problem and subsequent applications only address a few technology impact factors applied to the aircraft wing tracked through a single aircraft level response metric, yet we have shown that inclusion of copulas theory has substantial impact on our results. There are many other engineering problems with a large variety of technologies and impacts on the entire aircraft that must be investigated and correlated as appropriate between themselves, across technologies, and with design variables to fully understand the implications of using copulas theory in the design of aircraft.
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摘要 :
This paper is an initial contribution to the aerospace audience intended to disseminate copulas theory and showcase its capabilities. The applications demonstrate how the quantification of uncertainty in design can be improved usi...
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This paper is an initial contribution to the aerospace audience intended to disseminate copulas theory and showcase its capabilities. The applications demonstrate how the quantification of uncertainty in design can be improved using copulas to correlate variables that are traditionally and arbitrarily treated as independent or deterministic. Our canonical problem and subsequent applications only address a few technology impact factors applied to the aircraft wing tracked through a single aircraft level response metric, yet we have shown that inclusion of copulas theory has substantial impact on our results. There are many other engineering problems with a large variety of technologies and impacts on the entire aircraft that must be investigated and correlated as appropriate between themselves, across technologies, and with design variables to fully understand the implications of using copulas theory in the design of aircraft.
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Uncertainty quantification (UQ) in transonic and supersonic flows is difficult due to the presence of strong nonlinearities and discontinuities. This challenge is further exacerbated by limited training data and the high cost of c...
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Uncertainty quantification (UQ) in transonic and supersonic flows is difficult due to the presence of strong nonlinearities and discontinuities. This challenge is further exacerbated by limited training data and the high cost of computational fluid dynamics simulations. This study presents a non-intrusive, nonlinear UQ method called proISOMAP to efficiently propagate uncertainty in problems with high-dimensionality and Shockwaves. The objective is to identify a low-dimensional manifold that best captures the dynamics of the high-dimensional data and nonlinear features of the problem. This manifold is then parameterized by mapping the variations in the uncertain inputs to changes in the projected data in the latent space. Specifically, proISOMAP combines a data-driven global manifold learning procedure with an adaptive polynomial chaos expansion (PCE) to develop a probabilistic approach on manifolds for rapid UQ. The performance of the proposed methodology is compared to state-of-the-art linear and local manifold learning approaches for UQ on examples using wedges and nozzles under uncertain geometric and flow conditions. The accuracy and robustness of the method is assessed as polynomial chaos order and the number of training samples are varied. Several scalar and field-level error metrics are introduced to measure both global and local predictive performance.
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Current space suit technologies rely on gas-retaining liners to maintain their breathing atmosphere and proper body pressurization. Planetary environments will be much more demanding, with surface suits requiring greater flexibili...
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Current space suit technologies rely on gas-retaining liners to maintain their breathing atmosphere and proper body pressurization. Planetary environments will be much more demanding, with surface suits requiring greater flexibility for the wearer, higher durability in harsh environments, and lower energy expenditure during use. Although some solutions have been proposed to improve the effectiveness of contemporary suits for long-term surface operations, there remains a risk of asphyxiation due to accidental suit puncture, especially along the limbs. Much of this risk can be mitigated through the implementation of two systems: a sensor array that is able to detect the location of a suit puncture along limb sections; and a series of emergency inflatable cuffs capable of producing air-tight seals at strategic points along the body. A solution for active puncture mitigation is presented within a larger framework of exploration-system management that leveraging the capabilities of commercially available IoT devices and services.
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While several studies have explored the mild hybrid turbofan idea, there is currently a lack of parametric representation of these types of systems in the conceptual literature. Parts of the mild hybridization problem have been st...
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While several studies have explored the mild hybrid turbofan idea, there is currently a lack of parametric representation of these types of systems in the conceptual literature. Parts of the mild hybridization problem have been studied by other authors, and some integrated system studies attempted, but there remains a lack of fully-integrated aircraft and propulsion system models and modeling capabilities which can facilitate parametric design space exploration. This paper extends, integrates, and synthesizes previously-developed ideas into a parametric system-level analysis. A unique methodology is developed for modeling and simulating a mild hybrid turbofan architecture, and a test case is proposed and explored for an advanced tube-and-wing aircraft with targeted entry into service in the 2030-2035 time frame. The model developed will be used to optimize the system utilizing a surrogate model approach, and key sensitivities will be identified with respect to design variables, technology parameters, and other key modeling assumptions. Potential fuel burn reduction benefits of the system are identified along with areas of future work, including model fidelity improvements and system uncertainty reduction.
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This paper is one of a series being presented at this conference summarizing the technical outcomes of NATO AVT-297. It presents the framework for a process that may be used to identify the requirements for physical referent data ...
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This paper is one of a series being presented at this conference summarizing the technical outcomes of NATO AVT-297. It presents the framework for a process that may be used to identify the requirements for physical referent data in support of computational model validation. This relies upon applying the principles of systems engineering to develop systematic abstractions and decompositions of the problem space. Rather than contriving to prescribe a specific, structured series of activities a priori, the framework relies on empirical observation and learning to facilitate the identification of candidate Points of Entry into model validation experiments. In order to avoid the inappropriate conflation of one form of abstraction with another, the framework builds on three architectures: functional, physical and modeling. Guidelines are provided as to how these may be distinguished and traversed consistently as they, themselves, mature. Particular attention is drawn to (i) the challenges that may be faced when handling mismatches in abstraction typical of those encountered in multidisciplinary modeling scenarios, and (ⅱ) the potential utility of multi-fidelity analyses as mechanisms for estimating model form uncertainty and assessing the suitability of candidate Points of Entry into model validation experiments. The importance of technique verification and validation dialog is reinforced throughout, highlighting the mutual accountability of those engaged in the computational and physical sciences to provide the learning and, subsequently, the capabilities that will be required to realize our digital transformation ambitions.
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