Development and Application of Dynamic Architecture Flow Optimization to Assess the Impact of Energy Storage on Naval Ship Mission Effectiveness, System Vulnerability and Recoverability
This dissertation presents the development and application of a naval ship distributed system architecture framework, Architecture Flow Optimization (AFO), Dynamic Architecture Flow Optimization (DAFO), and Energy Storage System (ESS) model in naval ship Concept and Requirements Exploration (CandRE). The particular objective of this dissertation is to determine and assess Energy Storage System (ESS) capacity, charging and discharging capabilities in a complex naval ship system of systems to minimize vulnerability and maximize recoverability and effectiveness. The architecture framework is implemented through integrated Ship Behavior Interaction Models (SBIMs) that include the following: Warfighting Model (WM), Ship Operational Model (OM), Capability Model (CM), and Dynamic Architecture Flow Optimization (DAFO). These models provide a critical interface between logical, physical, and operational architectures, quantifying warfighting and propulsion capabilities through system measures of performance at specific capability nodes. This decomposition greatly simplifies the Mission, Power, and Energy System (MPES) design process for use in CandRE. AFO and DAFO are network-based, linear programming optimization methods used to design and analyze MPESs at a sufficient level of detail to understand system energy flow, define MPES architecture and sizing, model operations, reduce system vulnerability and improve system effectiveness and recoverability with ESS capabilities. AFO incorporates system topologies, energy coefficient component models, preliminary arrangements, and (nominal and damaged) steady state scenarios to minimize the energy flow cost required to satisfy all operational scenario demands and constraints. The refined DAFO applies the same principles as AFO, but adds two more capabilities, Propulsion and ESS charging, and maximizes effectiveness at each scenario timestep. DAFO also integrates with a warfighting model, operational model, and capabilities model that quantify the performance of tasks enabled by capabilities through system measures of performance at specific capability nodes. This dissertation provides a description of the design tools developed to implement these processes and methods, including a ship synthesis model, hullform exploration, MPES explorations and objective attribute metrics for cost, effectiveness and risk, using design of experiments (DOEs) response surface models (RSMs) and Energy Storage System (ESS) applications.