Formulation and Test of an Object-Oriented Approach to Aircraft Sizing

Lu, Zhijie; Yang, Eun-Suk; DeLaurentis, Daniel; Mavris, Dimitri

doi:10.2514/6.2004-4302

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…6 An example of such physics-based analyses is the framework developed at Linköping University. 24,25 Two major limitations of current conceptual design environments were identified by Lu et al: 26 1. The application of design tools is limited to a specific vehicle type, because the sizing method is fixed…”

Section: Figure 2 Technology Evaluation and Selection Under Uncertaintymentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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Section: Figure 2 Technology Evaluation and Selection Under Uncertaintymentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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“…Often, the aerodynamics and propulsion models are imbedded within the sizing algorithm, which makes it difficult to modify or change the analysis tool. The authors of [14] are focused on the first limitation; however, they did not fully address the scalability analysis issue, which is required for a variable fidelity environment.…”

Section: Armentioning

confidence: 98%

“…The exploration of different systems alternatives can be achieved through a flexible and modular design environment [14]. For example, the design team may consider different propulsion architectures like an internal combustion engine with a propeller, a turbofan engine, a ducted fan, or a liquid hydrogen engine.…”

Section: Unmanned Aerial Vehicle Design Challengesmentioning

confidence: 99%

“…A partial list of currently available design environments for fixed wing aircraft includes the flight optimization systems (FLOPS) developed at NASA Langley Research Center [10], aircraft synthesis (ACSYNT) developed by the NASA Ames Research Center and Virginia Polytechnic Institute and State University [11,12], and the general aviation synthesis program (GASP) developed at the NASA Ames Research Center [13]. Lu et al discussed the applications and limitations of these conceptual design environments and identified two major limitations [14]. The first limitation is that the applications of the design tools are generally limited to a specific type of vehicle, because the sizing algorithm is hard coded in the model, which also has the consequence of making it difficult to model a wide variety of missions.…”

Section: Armentioning

confidence: 99%

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Variable Fidelity Conceptual Design Environment for Revolutionary Unmanned Aerial Vehicles

Dufresne

Johnson

Mavris

2008

Journal of Aircraft

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This paper describes the development of an unmanned aerial vehicle's conceptual design environment. A review of current unmanned aerial vehicle design challenges is conducted, which leads to the description of the desired capabilities for the environment. The key characteristics of the design environment include the capability of integrating variable fidelity, modular, and flexible disciplinary analysis tools. The environment is tested against available data from the AeroVironment Pathfinder Plus vehicle. In addition to the Pathfinder Plus mission analysis, aerodynamics, propulsion, and structures performance results are discussed and then applied to a hurricane-tracker mission. This case study demonstrates the environment's capability to perform and explore new types of missions. A further exploration of the Pathfinder Plus design space is conducted using response surface methodology. This investigation provides valuable information regarding design tradeoffs, which are essential for the selection of a final vehicle architecture. Nomenclature AR= wing aspect ratioairfoil maximum lift coefficient D = vehicle drag, N ezV = electrolyzer voltage, V E ez = solar energy consumed to power the electrolyzer, W E ez = energy consumed by the electrolyzer per mass of hydrogen, W=kg E fc = total fuel cell energy production, W E fc = energy produced by the fuel cell per mass of hydrogen, W=kg E solar = solar energy consumed to power the vehicle, W E total = total energy consumed to power the vehicle, W fcV = fuel cell voltage, V Kv = voltage constant kgH2 = mass of hydrogen, kg m prop = total propulsion system mass, kg n days = number of days the vehicle flew S = wing planform area, m 2 sa eff = solar cell efficiency T A = thrust available, N R = response of the response surface equation V = vehicle velocity, m=s W i = vehicle actual weight, N Wpld = payload mass, kg x i = design variable of the response surface equation = coefficients of the response surface equation ez = electrolyzer efficiency

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Aeroelastic Analysis Using Deforming Cartesian Grids

Boschitsch

Whitehouse

2023

AIAA Journal

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Ongoing work in air-vehicle design illustrates the potential of advanced concepts to provide significant improvements in efficiency; but with their incorporation of lightweight flexible structures, such configurations may require active control systems to ensure reliability and safety. However, many contemporary analysis methods are inefficient for aeroelastic analysis and design of such configurations. This paper describes the development of a new approach that automates the geometry setup, mesh generation, and assembly of fluid–structural coupling interfaces to enable efficient aeroelastic and aeroservoelastic analysis of advanced concepts. The core elements for this approach are a cut-cell Cartesian grid-based computational fluid dynamics solver, a nonlinear beam element structural model, a conservative fluid–structural interface treatment, and the formulation and implementation of a new deforming grid capability within the cut-cell Cartesian grid solver. Herein, emphasis is on this latter component with detailed description given of the mesh motion strategy, evaluation of fluxes and structural loads at the surface, and computation of geometrical properties such as cell volume, directed face areas, centroids, and motion-induced fluxes for deforming Cartesian grids required to advance the flow states. Aeroelastic simulations exercising the capability show favorable agreement with data and predictions in the literature for subsonic and supersonic applications.

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Formulation and Test of an Object-Oriented Approach to Aircraft Sizing

Cited by 6 publications

References 6 publications

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Variable Fidelity Conceptual Design Environment for Revolutionary Unmanned Aerial Vehicles

Aeroelastic Analysis Using Deforming Cartesian Grids

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