Weights Analysis of Space Launch Vehicles in an Advanced Engineering Environment

“…Together with the TPS sizing obtained from TPSSIZER and the propellant mass estimate from the trajectory tool POST2, the weight/sizing vehicle closure analysis code, XWAT/XClosure [16] provides an estimate of the delivered payload for the particular vehicle shape parameters selected by the MDO analysis. XWAT, the XML based Weight/Mass Analysis Tool, is a C++ application to compute mass/sizing of any space vehicle concept.…”

Section: F Vehicle Mass and Sizingmentioning

confidence: 99%

Co-Optimization of Mid Lift to Drag Vehicle Concepts for Mars Atmospheric Entry

Garcia

Brown

Kinney

et al. 2010

10th AIAA/ASME Joint Thermophysics and Heat Transfer Conference

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As NASA continues to make plans for future robotic precursor and eventual human missions to Mars, the need to characterize and develop designs for entry vehicles capable of delivering large masses to the surface of Mars will persist. In combination with this, NASA has recognized that the current heritage technology for Mars' Entry Decent and Landing (EDL) does not have the capability to land the required payload masses. Both the Thermal Protection System (TPS) and the Descent/Landing systems require new design approaches. Because of these needs, NASA has performed an Entry, Descent and Landing Systems Analysis (EDL-SA) study for high mass exploration and science missions to identify key enabling technology areas for further investment. One key technology area identified includes rigid aeroshell shapes for aerodynamic performance and controllability.In this investigation, a system optimization study of alternative aeroshell shapes for Mars exploration class payloads of approximately 40 metric tons has been conducted. This system optimization is accomplished using a Multi-disciplinary Design Optimization (MDO) framework which accounts for the aeroshell shape, trajectory, thermal protection system, and vehicle subsystem closure along with a Multi Objective Genetic Algorithm (MOGA) for the initial shape exploration. This is accomplished using a combination of engineering analysis and higher-fidelity physics based tools along with optimization methods and engineering judgment. The results of this process have shown that a proposed family of optimized mid lift-to-drag aeroshell shapes exhibit a significant improvement to the current reference entry rigid aeroshell configuration. Furthermore, a trade-off between the vehicle TPS and structural mass is identified for this class of aeroshell shapes and their corresponding trajectories. Balancing this trade-off can yield an overall decrease in the total vehicle mass or corresponding increase in landed payload, as compared to the current reference configuration. NomenclatureA = Effective projected vehicle area CD = Drag coefficient CDa = Drag Area, m 2 CL = Lift coefficient Cm = Pitching moment coefficient Cm α = Pitching moment coefficient curve slope variation with angle-of-attack, /deg. 0 = Earth's gravity constant (g 0 = 9.80665 m/s2) Isp = Specific impulse, sec (on earth) L B = Body Length, m L B_REF = Reference Body Length, m L/D = Lift-to-drag ratio m_Entry = Entry Mass, kg m_Entry REF = Reference Entry Mass, kg m payload = Payload mass, kg MaxQdot = Maximum Heat flux on the body, W/cm 2 qmax = Maximum dynamic pressure, Pa hload = Integrated heatload, J/cm 2 Re = Reynolds Number RCS = Reaction Control System RSS = Root Sum Square Swet = Wetted vehicle surface area, m 2 TPS = Thermal Protection System UWT_Body REF = Reference body structural areal mass M ∞ = Free-stream Mach number αtrim = Trim angle-of-attack, deg. ρ ∞ = Free-stream density, kg/m 3

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“…The weights and sizing tool, XWAT, also has adopted XML for the same reason. 17 The XML schema defines element tags and content such that they model the tree structure of the mission being analyzed. Figure 3 and Table 1 Figure 4 shows one method of marking up with XML the data that describe the generic RLV mission presented in Fig.…”

Section: Input File Data "Marked Up" With Xmlmentioning

confidence: 99%

Aerospace Vehicle Trajectory Design and Optimization Within a Multi-Disciplinary Environment

Windhorst

Galloway

Lau

et al. 2004

42nd AIAA Aerospace Sciences Meeting and Exhibit

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A trajectory design and optimization tool named Mission is developed to operate within a multi-disciplinary analysis tool environment for conceptual design of aerospace vehicles. Mission possesses several features designed to facilitate its set-up and operation within the environment. It receives input via an Extensible Markup Language tagged data file. The tree structure of the tags reflects that of a branched and multi-segmented trajectory, aiding parsing and editing for set-up of data exchange within the environment. Mission uses Xerces, a free library developed by Apache, for parsing the data file. In addition, it is linked to both gradient-based and genetic algorithmbased optimizers, allowing a choice depending on the amount of a priori information available. When little is known about the control variables, an initial guess of their histories may be forgone by initializing the optimization process with the genetic algorithm. Once a solution is approached, the gradient-based method is used. This strategy increases the robustness and autonomy of the trajectory tool operation within the multi-disciplinary environment. Finally, Mission's trajectory integration process is coded for parallel execution via the Message Passing Interface standard. The resultant execution speed increase reduces the relative expense of operating Mission within the multidisciplinary environment. The results of a Crew Transfer Vehicle "abort from ascent" problem are presented to demonstrate and quantify Mission's features. NOMENCLATURE a = base area of nozzle * c = fuel mass flow rate C D = drag coefficient C L = lift coefficient D = drag g = local gravitational acceleration g o = sea level gravitational acceleration h = altitude I sp = specific impulse L = lift m = mass n dv = number of design variables n g = number of trajectory integrations needed to calculate a gradient n gen = number of generations per execution n l = number of trajectory integrations needed to perform a line search n i = number of major iterations per execution n p = number of processors n pop = number of genetic strings in a population M = Mach number p = local atmospheric pressure p c = probability of a gene being crossed over * Member AIAA, Aerospace Engineer. † Research Scientist. ‡ Member AIAA, Senior Research Scientist. § Associate Fellow AIAA, Technical Director. p m = probability of a gene being mutated p sl = sea level atmospheric pressure q = dynamic pressure r = radial coordinate r e = radius of earth r s = execution speed ratio of two parallel runs S = side force S ref = vehicle reference area t = time t i = time required to perform a gradient-based optimization iteration t g = time required to evaluate fitness function values for all genetic strings in population t s = time required to integrate a single trajectory T = thrust v = relative speed of vehicle = angle of attack = side slip angle = heading angle = nozzle pitch angle = longitude = flight path angle feas = SNOPT feasibility criterion opt = SNOPT optimality criterion = latitude µ = throttle se...

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Weights Analysis of Space Launch Vehicles in an Advanced Engineering Environment

Abstract: This paper describes the development of an XML-based Weights Analysis Tool (XWAT)using the Launch Vehicle Language in the Advanced Engineering Environment, which demonstrates a methodology that readily accommodates weights analysis of disparate vehicles.

Cited by 3 publications

References 6 publications

LVL Editor: A Graphical Tool for Visualizing and Managing Data in an Engineering Component Hierarchy

LVL Editor: A Graphical Tool for Visualizing and Managing Data in an Engineering Component Hierarchy

Co-Optimization of Mid Lift to Drag Vehicle Concepts for Mars Atmospheric Entry

Aerospace Vehicle Trajectory Design and Optimization Within a Multi-Disciplinary Environment

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