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)
Three of the most promising multi-level MDO techniques, Bi-Level Integrated System Synthesis (BLISS), Collaborative Optimization (CO) and Modified CollaborativeOptimization (MCO) are applied to the design of a reusable launch vehicle (RLV), evaluated and compared in this study. Apart from comparing the techniques against each other, they are also compared with designs reached via fixed-point iteration (FPI) of disciplines with local optimization and the industry accepted MDO technique, All-at-Once (AAO). The new MDO techniques, particularly BLISS, showed greater ability than FPI to design for a global objective and were more applicable to complex systems than AAO. This study was the first time that the novel MDO methods were compared qualitatively and quantitatively under controlled experimentation practices. It is still impossible to statistically determine if any one of the novel-MDO techniques is better than another as more studies corroborating the conclusions made here are needed. Nomenclaturegrade point average h = equality constraint / enthalpy / altitude INC = incomplete Isp = specific impulse ISS = International Space Station LH2 = liquid hydrogen LOX = liquid oxygen MCO = Modified Collaborative Optimization MDA = multidisciplinary analysis MDO = multidisciplinary optimization MER = mass estimating relationship MoFD = Method of Feasible Directions MR = mass ratio (=W gross /W insertion ) OBD = Optimizer Based Decomposition OMS = orbital maneuvering system P = power p = pressure Perf = Performance POST = Program to Optimize Simulated Trajectories Prop = Propulsion r = fuel to oxidizer mixture ratio (propellant mixture ratio) RCS = reaction control system REDTOP = Rocket Engine Design Tool for Optimal Performance RLV = reusable launch vehicle RSE = response surface equation RSM = response surface model S = wing area SLP = Sequential Linear Programming SQP = Sequential Quadratic Programming SSTO = single stage to orbit T = thrust TRF = technology reduction factor W = weight w = weighting factor (for BLISS) W&S = Weights & Sizing X = input or design variable Y = output or behavior variable ∆V = change in velocity ε = nozzle expansion ratio θ = pitch angle rate Φ = design objective Subscripts c = combustor e = exit eng = engine loc = local o = optimized ref = reference req = required sh = shared input to multiple CA's but not calculated by any CA (for BLISS) SL = sea-level sys = system t = throat vac = vacuum veh = vehicle Superscripts * = output passed to a CA (for BLISS) ^ = output from a CA to system (for BLISS) pf = Performance (for CO and MCO) pp = Propulsion (for CO and MCO) t = target from system optimizer (for CO and MCO) ws = Weights & Sizing (for CO and MCO) American Institute of Aeronautics and Astronautics 2 Downloaded by Stanford University on October 7, 2012 | http://arc.aiaa.org |
The use of commercial-off-the-shelf (COTS) / government-off-the-shelf (GOTS) applications as components in software systems is increasingly prevalent. The critical step of tool selection for an integrated suite is usually based on identifying the tools that best match the functionality requirements needed. Other factors tangential to technical performance are playing a more important role in the tool selection process and making the mapping of customer needs to technical requirements less obvious. This paper suggests a shift from the traditional "best tools" selection approach, where tools are selected for their performance to a more holistic "end-to-end" approach, where customer concerns, business and cost benefits, and technical performance are weighed concurrently.The end-to-end methodology was applied to an integrated suite for the intelligence analysis process and was compared to a theoretical system employing a best tools approach. This showed that the end-to-end approach resulted in significant software related cost reductions.
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