45 The above data was used to derive the total hardware manufacturing, technology development, and mission management costs incurred for each design reference. Estimating costs for manufacturing SBSP module hardware: Aerospace derived mass by taking the total system mass and averaging out components by the number of modules. This “bottoms-up” approach includes the following hardware subsystems: power, structure, attitude and determination control, propulsion, telemetry tracking and command, command and data handling, and thermal. As previously mentioned, Aerospace used a combination of cost models for hardware costs due to the maturity of both design references and the timeframe for implementation. (Note that this is an idealized decomposition of module subsystems for a comparative analysis. Mankins proposes separate modules for different functions, including assembly. Sasaki only modularizes the array for efficient terrestrial manufacturing.) We referenced Mankins and Sasaki for number of modules in one system and applied the scaling factor to calculate the number of modules needed for 2 GW of output. Using the module mass and the number of modules needed, we estimated the total in-space mass, accounting for technology advances since Sasaki’s original 2006 concept paper for the Planar Array design reference with Pellegrino et al.’s more recent technology demonstration. Crawford model learning curve formula: cumulative cost of production = first unit cost * (learning rate * (natural log(total number of units produced) / natural log(2))) We used Aerospace’s learning curve estimate of 75% to account for efficiencies over time due to volume, arriving at final module development costs. We use the Crawford model learning curve formula instead of the Cumulative Average approach used by Aerospace. There are many other learning curve models, such as Stanford-B, Plateau, and Dejong’s, and the study team encourages those seeking to repeat this analysis to experiment with alternatives. We assume an initial module cost of $1M. To arrive at final module development costs with our updated number of modules (taking the scaling factor into account), we developed a python script to apply the learning curve and deliver the cost of producing each individual module, a running average cost of all modules, and a running cumulative cost of all modules. The module manufacturing cost presented here is the cumulative cost of all modules. The following code was used to develop these estimates: import numpy as np import pandas as pd import matplotlib.pyplot as plt # Input variables learning_rate = 0.75 first_unit_cost = 1000000 units_produced = list(range(1,6000001))
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