A classifier-guided sampling method for early-stage design of shipboard energy systems
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The United States Navy is committed to developing technology for an All-Electric Ship (AES) that promises to improve the affordability and capability of its next-generation warships. With the addition of power-intensive 21st century electrical systems, future thermal loads are projected to exceed current heat removal capacity. Furthermore, rising fuel costs necessitate a careful approach to total-ship energy management. Accordingly, the aim of this research is to develop computer tools for early-stage design of shipboard energy distribution systems. A system-level model is developed that enables ship designers to assess the effects of thermal and electrical system configurations on fuel efficiency and survivability. System-level optimization and design exploration, based on these energy system models, is challenging because the models are sometimes computationally expensive and characterized by discrete design variables and discontinuous responses. To address this challenge, a classifier-guided sampling (CGS) method is developed that uses a Bayesian classifier to pursue solutions with desirable performance characteristics. The CGS method is tested on a set of example problems and applied to the AES energy system model. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms.