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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