An examination of vehicle design tradeoffs and trajectory optimization for trimmed scramjet-powered hypersonic vehicles on ascent. Mbagwu, C., C. & Driscoll, J., F. In AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2018, 2018. American Institute of Aeronautics and Astronautics Inc, AIAA.
An examination of vehicle design tradeoffs and trajectory optimization for trimmed scramjet-powered hypersonic vehicles on ascent [pdf]Paper  An examination of vehicle design tradeoffs and trajectory optimization for trimmed scramjet-powered hypersonic vehicles on ascent [link]Website  doi  abstract   bibtex   
Several design rules are discussed that can be helpful in optimizing the design and the ascent trajectory of a generic hypersonic vehicle that is powered by a dual-mode ramjet-scramjet engine. The focus is on “vehicle integration” design rules, which differ from the “propulsion-oriented” optimization that has been discussed in certain textbooks. Vehicle-integration rules account for realistic constraints, including the requirement that the vehicle must be trimmed at all points along an ascent trajectory and that additive drag and vehicle stability are considered. A hypersonic waverider does not follow rules for a conventional airplane, where the goal is a large ratio of wing area to frontal area in order to maximize Lift/Drag ratio. Nor does a waverider follow rules for a rocket (where the goal is to maximize the Thrust/Drag ratio, requiring a small ratio of wing area to frontal area). Instead a waverider requires an optimization of both T/D and L/D, which introduces certain challenges. Governing parameters that were varied were: aspect ratio (b/c), engine inlet width (W), root chord length (c), acceleration profiles (a), and flight Mach number (M). The output parameters selected for optimization were thrust-to-drag (T/D) and lift-to-drag (L/D). Trends for auxiliary parameters such as angle-of-attack (α), elevon deflection angle (δ), and equivalence ratio (φ) were examined. A surrogate-based optimization algorithm was applied. The advantages of selecting the largest possible dynamic pressure are discussed. Trajectory optimization was also performed to minimize fuel burn mf and maximize (T/D) along an ascent trajectory.

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