刀(考古)
用户友好型
制造工程
工程类
系统工程
计算机科学
机械工程
操作系统
作者
Stephen Johnson,Aadi Kothari,D. Todd Griffith,James A. Sherwood
出处
期刊:Sampe Journal
日期:2025-05-01
卷期号:61 (3): 28-42
标识
DOI:10.33599/sj.v61no3.03
摘要
The wind industry is constantly introducing new wind turbines with ever larger rotor diameters while at the same facing enormous pressure from competitors and customers to reduce the cost of wind turbine energy. As a result, blade makers are constantly looking for new ideas to reduce costs. The use of automation to reduce labor costs is one cost-cutting option, but blade makers are reluctant to invest in automation in absence of a cost modeling tool that can evaluate the potential return on investment (ROI). As a result, the blade makers are cautiously conservative to make any changes to a process that works. The aim of this research is to fill the cost-modeling void by delivering a user-friendly high-fidelity techno-economic costing tool which can rapidly deliver accurate cost estimates on new wind blade designs and manufacturing process innovations to enable wind blade designers and builders to identify changes that can lower their wind blade costs. Data and methodology for modeling the labor hours needed to make blades and for modeling the material and overhead costs were shared in previous papers. In the current work, a software tool that uses these models is developed and demonstrated to predict wind blade costs in a user-friendly fashion. This software tool is developed in such a way to interface seamlessly with existing detailed blade design and modeling tools such as NuMAD. Excel sheets are used for input and output data; however, all calculations are done in VBA which enables complex scenarios and alternatives to be quickly examined. The tool incorporates a graphical user interface to allow user selection of alternative costing scenarios and data analysis algorithms. Analysis options include learning-curve impact and dynamic factory sizing with net blade cost recursively derived from the business multi-year cash flows and user-defined ROI. The resulting tool can be used by industry and academia to explore new design options and manufacturing processes that will help the industry continue to lower the cost of wind energy and to increase the market penetration of wind energy.
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