摘要 :
On-demand aviation refers to an envisaged air taxi service, using small, autonomous, vertical-takeoff-and-landing, battery-powered electric aircraft. A conceptual design and optimization tool for on-demand aviation is presented in...
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On-demand aviation refers to an envisaged air taxi service, using small, autonomous, vertical-takeoff-and-landing, battery-powered electric aircraft. A conceptual design and optimization tool for on-demand aviation is presented in this paper. The tool uses Geometric Programming, a class of optimization problems with extremely fast solve times and for which global optimality is guaranteed. The optimization model consists of a vehicle, a sizing mission, a revenue-generating mission, and a deadhead (non-passenger-carrying) mission. Cost per trip, including the additional cost due to the deadhead mission, is used as the objective function. Vehicle noise is computed during post-processing using a semi-empirical method. The tool is used to conduct a trade study between several different on-demand aircraft configurations. Four case studies are presented: one on a sizing plot useful for vehicle preliminary design; one on New York City airport transfers; one on technological assumptions in the near-and long-term; and one on low-noise design techniques. A series of sensitivity studies are also performed. Vehicle configurations with a higher lift-to-drag ratio, but a higher disk loading, generally weigh less and cost less to operate; configurations with a lower lift-to-drag ratio, but a lower disk loading, are quieter. An on-demand air service, even in the near term, is far superior in terms of cost per trip as compared to current helicopter air taxi operations. In the long term, costs become competitive with current car ridesharing services, indicating that on-demand aviation may one day become a widespread commute system for the masses. Technological assumptions and vehicle requirements, especially mission range, battery energy density, vehicle autonomy level, battery manufacturing cost, and reserve requirements, have significant impacts on vehicle weight and cost. Vehicle noise can be reduced through the careful selection of key design parameters. However, envisaged noise requirements cannot easily be met, even with the most generous long-term technological assumptions. Vehicle noise is therefore a critical issue for on-demand aviation; substantial engineering effort to reduce noise will be required.
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摘要 :
On-demand aviation refers to an envisaged air taxi service, using small, autonomous, vertical-takeoff-and-landing, battery-powered electric aircraft. A conceptual design and optimization tool for on-demand aviation is presented in...
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On-demand aviation refers to an envisaged air taxi service, using small, autonomous, vertical-takeoff-and-landing, battery-powered electric aircraft. A conceptual design and optimization tool for on-demand aviation is presented in this paper. The tool uses Geometric Programming, a class of optimization problems with extremely fast solve times and for which global optimality is guaranteed. The optimization model consists of a vehicle, a sizing mission, a revenue-generating mission, and a deadhead (non-passenger-carrying) mission. Cost per trip, including the additional cost due to the deadhead mission, is used as the objective function. Vehicle noise is computed during post-processing using a semi-empirical method. The tool is used to conduct a trade study between several different on-demand aircraft configurations. Four case studies are presented: one on a sizing plot useful for vehicle preliminary design; one on New York City airport transfers; one on technological assumptions in the near-and long-term; and one on low-noise design techniques. A series of sensitivity studies are also performed. Vehicle configurations with a higher lift-to-drag ratio, but a higher disk loading, generally weigh less and cost less to operate; configurations with a lower lift-to-drag ratio, but a lower disk loading, are quieter. An on-demand air service, even in the near term, is far superior in terms of cost per trip as compared to current helicopter air taxi operations. In the long term, costs become competitive with current car ridesharing services, indicating that on-demand aviation may one day become a widespread commute system for the masses. Technological assumptions and vehicle requirements, especially mission range, battery energy density, vehicle autonomy level, battery manufacturing cost, and reserve requirements, have significant impacts on vehicle weight and cost. Vehicle noise can be reduced through the careful selection of key design parameters. However, envisaged noise requirements cannot easily be met, even with the most generous long-term technological assumptions. Vehicle noise is therefore a critical issue for on-demand aviation; substantial engineering effort to reduce noise will be required.
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The traditional aircraft design process is typically split into three stages: conceptual, preliminary, and detailed design. This three-stage process usually proceeds sequentially from stage to stage, and major design decisions are...
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The traditional aircraft design process is typically split into three stages: conceptual, preliminary, and detailed design. This three-stage process usually proceeds sequentially from stage to stage, and major design decisions are frozen between stage transitions. But this model does not capture the more complex reality, where design decisions are subject to frequent iteration at all stages of the process. This paper presents the Engineering Sketch Pad (ESP) Phasing capability that captures this more complex design workflow by decomposing the process into atomic portions called Phases. Each phase is intended to branch from any completed phase and answer a specific design question, allowing the designer to make design decisions non-sequentially. The use of this Phasing capability is demonstrated with the sizing of an aircraft wing while simultaneously optimizing an airfoil with increasing aerodynamic and geometric model fidelity. A number of cases are presented, beginning with a low fidelity aerodynamic model and a NACA 24XX airfoil geometry, and culminating in a Kulfan CST4 representation of geometry with MSES to perform the airfoil analysis.
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Recent years have seen a push to use explicit consideration of "value" in order to drive design. This paper conveys the need to explicitly align perspectives on "value" with the method used to quantify "value." Various concepts of...
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Recent years have seen a push to use explicit consideration of "value" in order to drive design. This paper conveys the need to explicitly align perspectives on "value" with the method used to quantify "value." Various concepts of value are introduced in the context of its evolution within economics in order to propose a holistic definition of value. Operationalization of value is discussed, including possible assumption violations in the aerospace domain. A series of prominent Value-Centric Design Methodologies for valuation are introduced, including Net Present Value, Multi-Attribute Utility Theory, and Cost-Benefit Analysis. These methods are compared in terms of the assumptions they make with regard to operationalizing value. It is shown that no method is fully complete in capturing the definition of value, but selecting the most appropriate one involves matching the particular system application being valued with acceptable assumptions for valuation. Two case studies, a telecommunications mission and a deep-space observation mission, are used to illustrate application of the three prior mentioned valuation methods. The results of the studies show that depending on method used for valuation, very different conclusions and insights will be derived, therefore an explicit consideration of the appropriate definition of value is necessary in order to align a chosen method with desired valuation insights.
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摘要 :
Recent years have seen a push to use explicit consideration of "value" in order to drive design. This paper conveys the need to explicitly align perspectives on "value" with the method used to quantify "value." Various concepts of...
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Recent years have seen a push to use explicit consideration of "value" in order to drive design. This paper conveys the need to explicitly align perspectives on "value" with the method used to quantify "value." Various concepts of value are introduced in the context of its evolution within economics in order to propose a holistic definition of value. Operationalization of value is discussed, including possible assumption violations in the aerospace domain. A series of prominent Value-Centric Design Methodologies for valuation are introduced, including Net Present Value, Multi-Attribute Utility Theory, and Cost-Benefit Analysis. These methods are compared in terms of the assumptions they make with regard to operationalizing value. It is shown that no method is fully complete in capturing the definition of value, but selecting the most appropriate one involves matching the particular system application being valued with acceptable assumptions for valuation. Two case studies, a telecommunications mission and a deep-space observation mission, are used to illustrate application of the three prior mentioned valuation methods. The results of the studies show that depending on method used for valuation, very different conclusions and insights will be derived, therefore an explicit consideration of the appropriate definition of value is necessary in order to align a chosen method with desired valuation insights.
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Aircraft design benefits from optimization under uncertainty, since design feasibility and performance can have large sensitivities to uncertain parameters. Legacy methods of protecting against uncertainty do not adequately explai...
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Aircraft design benefits from optimization under uncertainty, since design feasibility and performance can have large sensitivities to uncertain parameters. Legacy methods of protecting against uncertainty do not adequately explain the trade-offs between feasibility and optimality, and require prior engineering knowledge which may not be available for novel aerospace vehicle concepts. This paper proposes a solution method for engineering design optimization problems under uncertainty using robust signomial programs (RSPs). The method transforms stochastic optimization problems to deterministic problems by considering the worst-case robust counterpart of each design constraint. The formulation leverages an existing approximate robust geometric program (RGP) formulation and extends it by allowing difference-of-log-convex constraints that appear in many design problems. Signomial programs have demonstrated potential in the solution of multidisciplinary non-convex optimization problems such as aircraft design, and the formulation of RSPs allows for conceptual engineering design that captures parametric uncertainty with probabilistic guarantees of design feasibility. The paper details a method based on solving a sequence of RGPs, where each RGP is a local approximation of the RSP. Then it explores the trade-off between robustness and optimality rigorously by implementing RSPs on an unmanned aircraft design problem, and evaluates the effect of robustness requirements on aircraft design decisions.
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摘要 :
Aircraft design benefits from optimization under uncertainty, since design feasibility and performance can have large sensitivities to uncertain parameters. Legacy methods of protecting against uncertainty do not adequately explai...
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Aircraft design benefits from optimization under uncertainty, since design feasibility and performance can have large sensitivities to uncertain parameters. Legacy methods of protecting against uncertainty do not adequately explain the trade-offs between feasibility and optimality, and require prior engineering knowledge which may not be available for novel aerospace vehicle concepts. This paper proposes a solution method for engineering design optimization problems under uncertainty using robust signomial programs (RSPs). The method transforms stochastic optimization problems to deterministic problems by considering the worst-case robust counterpart of each design constraint. The formulation leverages an existing approximate robust geometric program (RGP) formulation and extends it by allowing difference-of-log-convex constraints that appear in many design problems. Signomial programs have demonstrated potential in the solution of multidisciplinary non-convex optimization problems such as aircraft design, and the formulation of RSPs allows for conceptual engineering design that captures parametric uncertainty with probabilistic guarantees of design feasibility. The paper details a method based on solving a sequence of RGPs, where each RGP is a local approximation of the RSP. Then it explores the trade-off between robustness and optimality rigorously by implementing RSPs on an unmanned aircraft design problem, and evaluates the effect of robustness requirements on aircraft design decisions.
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Humans in extreme environments, regardless of whether in space or deep in the oceans ofthe Earth, rely on life support systems to be kept alive and perform their explorationmissions. Diving is similar to extravehicular activities ...
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Humans in extreme environments, regardless of whether in space or deep in the oceans ofthe Earth, rely on life support systems to be kept alive and perform their explorationmissions. Diving is similar to extravehicular activities in its duration and the need for humanrespiratory sustaining. This paper presents the development of an analytical rebreathermodel, which is the system that recirculates and conditions the air the diver is breathingduring a dive. The capability of simulating rebreather performance is currently lacking inthe diving commercial and military industry. We believe that the advantages of having sucha model are multi-fold: it can be used for mission planning, evaluating the impact of addinga new technology or modifying existing parameters or operational regime on an hardwareconfiguration without performing expensive and time consuming hardware tests. Ananalytical model, like the one developed in this paper, can also be used in complement withhardware testing to fine tune systems and increase resource endurance through theapplication of different electronic control strategies. The developed Matlab/Simulink modelof this rebreather is modular and can be generalized to study open, semi-closed or closedcircuits, in which the breathing gas used is air, oxygen, nitrox or heliox. The system’soperational environment can be the ocean’s surface (1 atmosphere), space (less than 1atmosphere pressure) or deep underwater (more than 1 atmosphere pressure). Afterintroducing the analytical modeling process for the rebreather, this paper goes on to explorethe model’s applications for the study of different oxygen control strategies in order tomaximize the oxygen lifetime during a dive, as well as the model’s applicability as an aid inaccident investigations. We aim to determine what is the maximum endurance of arebreather system, given a particular, set configuration of components, as well as to studythe reverse problem: if we set a mission endurance, what architectures would be able toachieve this level? Additionally, we are interested in studying how the tradespace of divingdepth versus the diving systems’ endurance looks like and how more complex controlmethods can help in pushing the existent boundary toward higher endurance limits. Weshow that more complex control algorithms can extend the duration of the oxygen tanks in arebreather by a factor of 6.35, and, when given a set endurance level, control can help lowerthe tank sizes by a factor of 4.
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In this paper, we present a wall model based upon a modified version of the Spalart-Allmaras turbulence model. Unlike typical wall models, this method avoids the need to query the interior solution by utilizing solution informatio...
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In this paper, we present a wall model based upon a modified version of the Spalart-Allmaras turbulence model. Unlike typical wall models, this method avoids the need to query the interior solution by utilizing solution information solely at the boundary, making it well-suited for unstructured grids and facilitating adjoint consistent discretizations-key features that enable mesh adaptation and higher-order finite element methods. This wall-modeling method is formulated to lessen the near-wall grid resolution requirements by, below the log layer, making the eddy viscosity approach a constant, non-zero value and both the velocity and SA working variable become approximately linear. Using this new wall-modeling method, the impacts of output-based mesh adaptation and higher-order methods are assessed and compared to standard RANS-SA. It is shown that this new wall-modeling approach results in accurate predictions of quantities of interest such as aerodynamic coefficients, surface pressure, and skin friction compared with RANS-SA, while requiring substantially less near-wall grid.
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In the hypersonic flow regime, it is difficult and expensive to obtain experimental data that is representative of flight conditions. This leads to a reliance on numerical simulation for the design and analysis of vehicles operati...
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In the hypersonic flow regime, it is difficult and expensive to obtain experimental data that is representative of flight conditions. This leads to a reliance on numerical simulation for the design and analysis of vehicles operating in this regime. One possible source of error for numerical results is modeling error due to the uncertainty in the mechanisms underlying certain physical processes. This can be compounded by the discretization error, i.e. the error induced from solving the governing equations in an approximate sense on a grid. The Mesh Optimization via Error Sampling and Synthesis (MOESS) algorithm is a method for controlling a local indicator of the discretization error in an output functional through anisotropic grid adaptation. We examine several thermochemical models that are commonly seen throughout the literature, including vibrational energy exchange models and chemical kinetics model. We apply the MOESS algorithm to each of the different calculations to control the discretization error in the results for various output functionals. We then compare the differences in grids and output quantities due to the physical modeling choices and output functional choices with the discretization error in those results.
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