CUVX Design Report - the AOE home page - Virginia Tech

CUVX Design – VT Team 2 Page 35Required/Minimal Available ErrorWeight: W T = 2.582× 10 4 MTW FL = 2.584× 10 4 MT ERR = 7.214×10 − 4Arrangeable area: A TR = 1.846× 10 5 ft 2A TA = 2.057× 10 5 ft 2 ERR A = 0.114Hangar Area: A HangarReq = 8.273× 10 3 m 2 A HANGmax = 1.089× 10 4 m 2Deckhouse area: A DR = 545.731m 2A DA = 671.705m 2 V D ≡ C vd ⋅V FLPropulsion power: P IREQ = 4.114× 10 4 hpP IPRP = 4.16× 10 4 hp N T = 658Electrical plant: KW GREQmech = 2.15× 10 3 kW KW G = 2.5 × 10 3 kWMach. box height: H MBreq = 6.67mH MB = 7.63mDepth: D 10MIN = 84.219ftD 10 = 87.369ftSustained Speed: V e = 20kntV S ≡ 21.4⋅kntStability: .07-.2 C GMB = 0.155Length of Flight Deck: L FltReq = 100mL Flt = 150mBreadth of Flight Deck: B FltReq = 25mB = 29.54mWeight and displacement are iterated until convergence. A design is considered feasible if feasibility requirementsand thresholds are satisfied simultaneously. If a design is feasible, the synthesis model continues to calculate cost,effectiveness and risk as described in Sections 3.5.1, 3.5.2 and 3.5.3. These characteristics are the objectiveattributes for a Multi-Objective Genetic Optimization (MOGO) that is used to search the design space and identifynon-dominated designs as described in Section 3.5.3.5 Multi-Objective OptimizationObjective attributes for this optimization are cost (lead ship acquisition cost and mean follow ship acquisitioncost, performed separately), risk (technology cost, schedule and performance risk) and military effectiveness. Aflow chart for the Multi-Objective Genetic Optimization (MOGO) is shown in Figure 20. In the first designgeneration, the optimizer randomly defines 1200 balanced ships using the ship synthesis model to balance eachship and to calculate cost, effectiveness and risk. Each of these designs is ranked based on their fitness ordominance in effectiveness, cost and risk relative to the other designs in the population. Penalties are applied forinfeasibility and niching or bunching-up in the design space. The second generation of the optimization is randomlyselected from the first generation, with higher probabilities of selection assigned to designs with higher fitness.Twenty-five percent of these are selected for crossover or swapping of some of their design variable values. Avery small percentage of randomly selected design variable values are mutated or replaced with a new randomvalue. As each generation of ships is selected, the ships spread across the effectiveness/cost/risk design space andfrontier. After 300 generations of evolution, the non-dominated frontier (or surface) of designs is defined as shownin Figure 29 and Figure 30. Each ship on the non-dominated frontier provides the highest effectiveness for a givencost and risk compared to other designs in the design space. The “best” design is determined by the customer’spreferences for effectiveness, cost and risk.In order to perform the optimization, quantitative objective functions are developed for each objectiveattribute. Effectiveness and risk are quantified using overall measures of effectiveness and risk developed asillustrated in Figure 21 and described in Sections 3.5.1 and 3.5.2. Cost is calculated using a modified weight-basedregression approach as described in Section 3.5.3.Feasible?DefineSolutionSpaceRandomPopulationShipSynthesisRiskCostFitness -DominanceLayersSelectionCrossoverMutationNiche?Figure 20. Multi-Objective Genetic Optimization