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Model/costanalysis1/costanalysis/cost_buildup.py

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+# Estimate tooling cost of a vehicle by summing tooling costs for all major
+# components
+#
+# Inputs:
+#  rProp        - Prop/rotor radius [m]
+#  cruiseOutput - Structure with cruise performance data
+#
+# Outputs:
+#  toolCostPerVehicle - Estimate of tool cost per vehicle [$]
+#
+# Functions Called:
+#       toolingCost.m
+
+#function toolCostPerVehicle = costBuildup(rProp,cruiseOutput)
+import numpy as np
+from .tooling_cost import ToolingCost
+
+def CostBuildup(r_prop,c_ref, b_ref):
+    # Inputs
+    fuselage_width = 1
+    fuselage_length = 5
+    toc = 0.15  # Wing / canard thickness
+    prop_radius = r_prop
+    prop_chord = 0.15 * prop_radius
+    x_hinge = 0.8
+    winglet =  0.2
+
+    parts_per_tool = 1000
+
+    fuselage_height = 1.3 # Guess
+    span = b_ref
+    chord = c_ref
+
+    # tool_cost_per_vechile = 100 # used as test value
+
+    # Wing Tooling
+    wing_tool_cost = np.array([0, 0, 0, 0])
+    wing_tool_cost[0] = ToolingCost((span - fuselage_width)/2, toc*chord, chord*0.2) # Leading Edge
+    wing_tool_cost[1] = ToolingCost((span - fuselage_width)/2, toc*chord*0.7, chord*0.2) # Aft spar
+    wing_tool_cost[2] = ToolingCost((span - fuselage_width)/2, chord*0.75, 0.02)*2 # Upper/Lower skin
+    wing_tool_cost[3] = ToolingCost(span, toc*chord, chord*.20) # Forward spar
+    wing_tool_cost = 2*(2*sum(wing_tool_cost[0:3])+wing_tool_cost[3]) # Total wing tooling cost (matched tooling)
+
+    # Winglet Tooling
+    winglet_tool_cost = np.array([0, 0, 0, 0])
+    winglet_tool_cost[0] = ToolingCost(winglet*span/2, toc*chord, chord*.2) # Leading edge
+    winglet_tool_cost[1] = ToolingCost(winglet*span/2,toc*chord*0.7,chord*.2) # Aft spar
+    winglet_tool_cost[2] = ToolingCost(winglet*span/2,chord*0.75,0.02)*2 # Upper/Lower skin
+    winglet_tool_cost[3] = ToolingCost(winglet*span/2,toc*chord,chord*.20) # Forward spar
+    winglet_tool_cost = 4*sum(winglet_tool_cost) # Total winglet tooling cost (matched tooling)
+
+    # Canard Tooling
+    canard_tool_cost = wing_tool_cost # Total canard tooling cost
+
+    # Fuselage Tooling
+    fuselage_tool_cost = np.array([0, 0, 0, 0])
+    fuselage_tool_cost[0] = ToolingCost(fuselage_length*.8,fuselage_height,fuselage_width/2)*2 # Right/Left skin
+    fuselage_tool_cost[1] = ToolingCost(fuselage_length*.8,fuselage_width/2,fuselage_height/4) # Keel
+    fuselage_tool_cost[2] = ToolingCost(fuselage_width,fuselage_height,0.02)*2 # Fwd/Aft Bulkhead
+    fuselage_tool_cost[3] = ToolingCost(fuselage_length*.1,fuselage_width,fuselage_height/3) # Nose/Tail cone
+    fuselage_tool_cost = 2*sum(fuselage_tool_cost) # Total fuselage tooling cost (matched tooling)
+
+    # Prop Tooling
+    prop_tool_cost = np.array([0, 0])
+    prop_tool_cost[0] = ToolingCost(prop_radius,prop_chord,prop_chord*toc/2)*2 # Skin
+    prop_tool_cost[1] = ToolingCost(prop_radius,prop_chord*toc,prop_chord/4)*2 # Spar tool
+    prop_tool_cost = 4*sum(prop_tool_cost) # Total propeller tooling cost (left/right hand) (matched tooling)
+
+    # Control Surface (2 tools)
+    control_tool_cost = np.array([0, 0])
+    control_tool_cost[0] = ToolingCost((span-fuselage_width)/2,(1-x_hinge)*chord,chord*toc/4) # Skin
+    control_tool_cost[1] = ToolingCost((span-fuselage_width)/2,(1-x_hinge)*chord,chord*toc/4) # Skin
+    control_tool_cost = 8*sum(control_tool_cost) # Total wing tooling (matched tooling)
+
+    # Total tool cost
+    total_tool_cost = wing_tool_cost + canard_tool_cost + fuselage_tool_cost + prop_tool_cost + control_tool_cost + winglet_tool_cost
+
+    tool_cost_per_vechile = total_tool_cost / parts_per_tool
+
+    return tool_cost_per_vechile
+

Model/costanalysis1/costanalysis/tooling_cost.py

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+# Estimates tooling cost based on part dimensions including material, rough
+# machining, and finishing machining
+#
+# Inputs:
+# length - part length [m]
+# width  - part width [m]
+# depth  - part depth [m]
+#
+# Output:
+# cost - Tool cost [$]
+#
+import math 
+
+def ToolingCost(length, width, depth):
+    # Material
+    tool_side_offset = 0.09 # Offset on each side of tool
+    tool_depth_offset = 0.03 # Offset at bottom of tool
+    tool_cost = 10000 # Cost be m^3 of tooling material [$/m^3]
+
+    tool_volume = (length + 2 * tool_side_offset) * (width + 2 * tool_side_offset) * (depth + tool_depth_offset)
+    material_cost = tool_cost * tool_volume # Tooling material costs
+
+    # Machining (Rough Pass)
+    rough_SFM = 200 # Roughing surface feet per minute
+    rough_FPT = 0.003 # Roughing feed per tooth [in]
+    rough_cost_rate = 150/3600 # Cost to rough [$/s]
+    rough_bit_diam = 0.05 # Rougher diameter [m]
+
+    rough_bit_depth = rough_bit_diam/4 # Rougher cut depth [m]
+    rough_RPM = 3.82 * rough_SFM / (39.37 * rough_bit_diam) # Roughing RPM
+    rough_feed = rough_FPT * rough_RPM * 2 * 0.00042 # Roughing Feed [m/s]
+    rough_bit_step = 0.8 * rough_bit_diam # Rougher step size
+
+    cut_volume = length * math.pi * depth * width / 4 # Amount of material to rough out
+    rough_time = cut_volume / (rough_feed * rough_bit_step * rough_bit_depth) # Time for roughing
+    rough_cost = rough_time * rough_cost_rate # Roughing cost
+
+    # Machining (Finish Pass)
+    finish_SFM = 400 # Roughing surface feet per minute
+    finish_FPT = 0.004 # Roughing feed per tooth [in]
+    finish_cost_rate = 175/3600 # Cost to finish [$/s]
+    finish_bit_diam = 0.006 # Finish diameter [m]
+    finish_passes = 5 # Number of surface passes
+
+    finish_RPM = 3.82 * finish_SFM / (39.37 * finish_bit_diam) # Roughing RPM
+    finish_feed = finish_FPT * finish_RPM * 2 * 0.00042 # Roughing Feed [m/s]
+
+    def EllipsePerimeter(a, b):
+        h = (a - b)**2 / (a + b)**2
+        p = math.pi*(a+b)*(1+3*h/(10+(4-3*h)**0.5))
+        return p
+
+    finish_bit_step = 0.8 * finish_bit_diam # Rougher step size
+    cut_area = length * EllipsePerimeter(width/2,depth)/2; # Amount of material to rough out
+    finish_time = cut_area / (finish_feed * finish_bit_step) * finish_passes; # Time for roughing
+    finish_cost = finish_time * finish_cost_rate; # Roughing cost
+
+    # Total Cost
+    cost = material_cost + rough_cost + finish_cost
+
+    return cost
+

Model/costanalysis1/operating_cost.py

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+from openmdao.api import ExplicitComponent
+from .costanalysis.cost_buildup import CostBuildup
+
+class OperatingCost(ExplicitComponent):
+
+
+    def setup(self):
+        self.add_input('r_prop', desc='Radius Propeller [m]')   # 1 
+        self.add_input('cruise_speed', desc='Cruise Speed [m/s]')   # 102.82
+        self.add_input('avg_dist', desc='Average Distance of trip [m]') # 193121
+        self.add_input('shaft_power', desc='Shaft Power [kW]' ) # 156
+        self.add_input('We/W0', desc = 'Empty Weight Fraction')
+        self.add_input('W0', desc = 'Gross Weight')
+        #self.add_input('mass_struct', desc='Mass of Structure [kg]')    # 180
+        self.add_input('mass_batt', desc='Mass of Battery [kg]')    # 627.66
+        self.add_input('mass_motor',desc = 'Mass of Motor [kg]')    # 635.8
+        self.add_input('Neg',desc = 'Number of motors') 
+        #self.add_input('b_ref', desc= 'Span of one wing [m]') # 13.21 / 2
+        #self.add_input('c_ref', desc= 'Ref Chord [m]') # 1 
+        self.add_input('S', desc = 'Wing Reference Area (both) [m^2]')
+        self.add_input('AR', desc = 'Aspect Ratio')
+        self.add_input('P_C', desc= 'Power at Cruise [kW]') # power to weight 0.22
+
+        self.add_output('Cost', desc = 'Cost per trip [$]')
+
+        self.declare_partials(of='*', wrt='*', method='fd')
+
+    def compute(self, inputs, outputs):
+
+        r_prop = inputs['r_prop']
+        cruise_speed = inputs['cruise_speed']
+        avg_dist = inputs['avg_dist']
+        shaft_power = inputs['shaft_power']
+        mass_struct = inputs['We/W0']*inputs['W0'] - inputs['mass_motor']
+        mass_batt = inputs['mass_batt']
+        mass_motor1 = inputs['mass_motor']
+        mass_motor = mass_motor1*inputs['Neg']
+        S_single = inputs['S']/2
+        b_ref = (inputs['AR'] * S_single)**0.5
+        c_ref = S_single / b_ref
+        power_to_weight = inputs['P_C'] / inputs['W0']
+
+        # Assumptions
+        flight_hours_per_year = 600
+        flight_time = avg_dist/cruise_speed
+        flights_per_year = flight_hours_per_year / (flight_time / 3600)
+        vehicle_life_years = 5
+        n_vehicles_per_facility = 50
+
+        # Tooling Cost
+        tool_cost_per_vehicle = CostBuildup(r_prop,b_ref, c_ref)
+
+        # Material Cost
+        material_cost_per_kg = 700
+        material_cost = material_cost_per_kg * mass_struct
+        # Battery Cost
+        power_to_weight_hr = power_to_weight * flight_time / 3600
+        battery_cost_per_kg = 700 * power_to_weight_hr   # Roughly $700/kW-hr * P/W [kW-hr /kg]
+        battery_cost = battery_cost_per_kg * mass_batt
+        # Motor Cost
+        motor_cost_per_kg = 500   # Approx $1500 for 10 kg motor +  controller ?
+        motor_cost = motor_cost_per_kg * mass_motor
+
+        # Servo cost
+        n_servo = 6 + inputs['Neg']   # 8 props, 4 surfaces, 2 tilt
+        servo_cost = n_servo * 800 # Estimate $800 per servo in large quantities
+
+        # Avionics cost
+        avionics_cost = 60000
+
+        # BRS (Ballistics Recovery System) cost
+        BRS_cost = 5200*2
+
+        # Total acquisition cost
+        acquisition_cost = battery_cost + motor_cost + servo_cost + avionics_cost + BRS_cost + material_cost + tool_cost_per_vehicle
+        acquisition_cost_per_flight = acquisition_cost/(flights_per_year * vehicle_life_years)
+
+        # Insurance Cost
+        # Follow R22 for estimate of 6.5# of acquisition cost
+        insurance_cost_per_year = acquisition_cost * 0.065
+        insurance_cost_per_flight = insurance_cost_per_year/flights_per_year
+
+        # Facility rental cost
+        vehicle_footprint = 1.2 * (8 * r_prop + 1) * (4 * r_prop + 3) # m^2, 20# for movement around aircraft for maintenance, etc.
+        area_cost = 10.7639 * 20 * 12 # $/m^2, $2/ft^2 per month assumed
+
+        # Facility Cost = Vehicle footprint + 10x footprint for operations
+        # averaged over # of vehicles at each facility
+        facility_cost_per_year = (vehicle_footprint + 10*vehicle_footprint/n_vehicles_per_facility) * area_cost
+        facility_cost_per_flight_hour = facility_cost_per_year/flight_hours_per_year
+        facility_cost_per_flight = facility_cost_per_flight_hour * flight_time / 3600
+
+        # Electricity Cost
+        # E * $/kWhr including 90# charging efficiency
+        # Average US electricity cost is $0.12 per kW-hr, up to $0.20 per kW-hr in CA
+        E = shaft_power * flight_time / 3600
+        energy_cost_per_flight = 0.12 * E / 0.9
+
+        # Battery Replacement Cost
+        batt_life_cycles = 2000
+        battery_repl_cost_per_flight = battery_cost/batt_life_cycles
+
+        # Motor Replacement Cost
+        motor_life_hrs = 6000
+        motor_repl_cost_per_flight = flight_time / 3600 / motor_life_hrs * motor_cost
+
+        # Servo Replacement Cost
+        servo_life_hrs = 6000
+        servo_repl_cost_per_flight = flight_time / 3600 / servo_life_hrs * servo_cost
+
+        # Maintenace Cost
+        human_cost_per_hour = 85*2
+        man_hr_per_flight_hour = 0.10 # periodic maintenance estimate
+        man_hr_per_flight = 0.2 # Inspection, battery swap estimate
+        labor_cost_per_flight = (man_hr_per_flight_hour * flight_time / 3600 + man_hr_per_flight) * human_cost_per_hour
+
+        # Cost Per Flight
+        cost_per_flight = acquisition_cost_per_flight + insurance_cost_per_flight + facility_cost_per_flight + \
+        energy_cost_per_flight + battery_repl_cost_per_flight + motor_repl_cost_per_flight + \
+        servo_repl_cost_per_flight + labor_cost_per_flight
+
+        outputs['Cost'] = cost_per_flight
+