Three multilevel multidisciplinary optimization techniques, Bi-Level Integrated System Synthesis, Collaborative Optimization, and Modified Collaborative Optimization, are applied to the design of a reusable launch vehicle, evaluated, and compared in this study. In addition to comparing the techniques against each other, they are also compared with designs reached via fixed-point iteration of disciplines with local optimization and the industry accepted multidisciplinary optimization technique, All-at-Once. The new multidisciplinary optimization techniques, particularly Bi-Level Integrated System Synthesis, showed greater ability than fixed-point iteration to design for a global objective and were more applicable to complex systems than All-at-Once. This study was the first time that the novel multidisciplinary optimization methods were compared qualitatively and quantitatively under controlled experimentation practices. It is still impossible to statistically determine whether any one of the novel multidisciplinary optimization techniques is better than another, because more studies using different test problems corroborating the conclusions made here are needed. Nomenclature A = area c = characteristic exhaust velocity c F = thrust coefficient g = inequality constraint h = equality constraint/enthalpy/altitude Isp = specific impulse _ m = mass rate MR = mass ratio, W gross =W insertion P = power p = pressure Perf = performance Prop = propulsion r = fuel to oxidizer mixture ratio (propellant mixture ratio) S = wing area SF = scale factor T = thrust W = weight w = weighting factor (for BLISS) W&S = weights and sizing X = input or design variable Y = output or behavior variable V = change in velocity " = nozzle expansion ratio _ = pitch angle rate = design objective Subscripts c = combustor e = exit eng = engine loc = local o = optimized ref = reference req = required sh = shared input to multiple CAs but not calculated by any CA (for BLISS) SL = sea-level sys = system t = throat vac = vacuum veh = vehicle Superscripts pf = performance (for CO and MCO) pp = propulsion (for CO and MCO) t = target from system optimizer (for CO and MCO) ws = weights and sizing (for CO and MCO) = output passed to a CA (for BLISS) = output from a CA to system (for BLISS)

Aerospace vehicle conceptual design is dominated by interactions among various traditional engineering disciplines. Aerodynamics, propulsion, performance, weights, sizing, and others are usually highly coupled, and complete vehicle analysis requires an iterative process with efficient methods of communication among the disciplines. Progress to computerize the analysis process has been fast in recent years, producing analysis tools such as NASA-Langley's AVID and EASIE. Given a configuration, the capability exists to quickly analyze it in order to determine its overall characteristics and performance. However, the vehicle designer/ integrator still largely depends on intuition to make systems level changes to the configuration and components in order to improve or optimize the overall design. "What if" studies are typically performed by perturbing the design variables one at a time in an attempt to locate a better design. A complete reanalysis of the entire system is then required for each variable change. This method is a time consuming process that may or may not lead to a more desirable vehicle design. Several mathematically based design techniques have recently emerged that could help the system designer make necessary improvements. These new methods serve to bridge the gap between analysis and design. This paper attempts to give a brief overview of four such techniques, system decomposition, sensitivity analysis, Taguchi methods, and for comparison, classical optimization. References to examples of successful uses of each technique are provided. The goal of this paper is to assess the pros and cons of each technique and their applicability to aerospace vehicle conceptual design. *-graduate student, mech. and aero eng. dept.

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Future space transportation vehicles may well rely on high speed airbreathing propulsion (ramjets and scramjets) to supply much of their motive power. Because of the tradeoff relationship between engine thrust and vehicle airframe weight, ascent trajectories are typically simulated using a constant dynamic pressure phase during airbreathing acceleration. That is, dynamic pressure is increased to benefit vehicle thrust up to some fixed limit imposed by the vehicle structure. The constant dynamic pressure portion of the trajectory typically begins around Mach 2 or 3 and continues to the maximum airbreathing Mach number or until some convective aeroheating limit is reached.

Rocket-based combined-cycle engines are currently under consideration for use on future, reusable launch vehicles. By combining traditional rocket and airbreathing operating modes into a single engine, multi-mode RBCC engines offer a number of advantages for launch vehicle designers including higher trajectory averaged I sp than pure rockets and higher installed thrust-to-weight ratios than pure airbreathers.