This thesis describes and assesses a simplified tool for modeling underwater explosion shock effects during early naval ship concept design. A simplified fluid model using Taylor flat-plate theory is incorporated directly into the OpenFSI module code in Nastran and used to interface with the structural solver in Nastran to simulate a far-field shockwave impacting the hull. The kick-off velocities and the shock spectra captured in this computationally efficient module is compared to results from a high-fidelity CASE (Cavitating Acoustic Spectral Element) fluid model implemented with the ABAQUS/Nastran structural solver to validate the simplified framework and assess the sufficiency of this very simple but, fast approach for early stage ship design. / Master of Science / This thesis describes and assesses a simplified tool for modeling underwater explosion shock effects during early-stage naval ship design. Far-field explosions have a significant effect in terms of damage to equipment and mission capability of a ship. A simplified fluid-structure interaction model using the concept “Taylor flat-plate theory” is developed to simulate a far-field shockwave impacting the hull. This model is directly incorporated inside ‘OpenFSI’, a module used to couple an external code with the Nastran structural solver software. The initial peak velocity in the time-history and the shock spectra characteristics captured in this computationally efficient module is compared to results from a high-fidelity “CASE” (Cavitating Acoustic Spectral Element) fluid-structure interaction model. The “CASE” model implemented with the ABAQUS/Nastran structural solver is used to validate the simplified framework and assess the sufficiency of this very simple, but fast approach for early stage ship design.

This dissertation describes the development and application of a naval ship distributed system architectural framework, Architecture Flow Optimization (AFO), and Dynamic Architecture Flow Optimization (DAFO) to naval ship Concept and Requirements Exploration (CandRE). The architectural framework decomposes naval ship distributed systems into physical, logical, and operational architectures representing the spatial, functional, and temporal relationships of distributed systems respectively. This decomposition greatly simplifies the Mission, Power, and Energy System (MPES) design process for use in CandRE. AFO and DAFO are a network-based linear programming optimization methods used to design and analyze MPES at a sufficient level of detail to understand system energy flow, define MPES architecture and sizing, model operations, reduce system vulnerability and improve system reliability. 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. DAFO applies the same principles as AFO and adds a second commodity, data flow. DAFO also integrates with a warfighting model, operational model, and capabilities model that quantify tasks and capabilities through system measures of performance at specific capability nodes. This enables the simulation of operational situations including MPES configuration and operation during CandRE. This dissertation provides an overview of design tools developed to implement this process and methods, including objective attribute metrics for cost, effectiveness and risk, ship synthesis model, hullform exploration and MPES explorations using design of experiments (DOEs) and response surface models. / Doctor of Philosophy / This dissertation describes the development and application of a warship system architectural framework, Architecture Flow Optimization (AFO), and Dynamic Architecture Flow Optimization (DAFO) to warship Concept and Requirements Exploration (CandRE). The architectural framework decomposes warship systems into physical, logical, and operational architectures representing the spatial, functional, and time-based relationships of systems respectively. This decomposition greatly simplifies the Mission, Power, and Energy System (MPES) design process for use in CandRE. AFO and DAFO are a network-based linear programming optimization methods used to design and analyze MPES at a sufficient level of detail to understand system energy usage, define MPES connections and sizing, model operations, reduce system vulnerability and improve system reliability. AFO incorporates system templates, simple physics and energy-based component models, preliminary arrangements, and simple undamaged/damaged scenarios to minimize the energy flow usage required to satisfy all operational scenario demands and constraints. DAFO applies the same principles and adds a second commodity, data flow representing system operation. DAFO also integrates with a warfighting model, operational model, and capabilities model that quantify tasks and capabilities through system measures of performance. This enables the simulation of operational situations including MPES configuration and operation during CandRE. This dissertation provides an overview of design tools developed to implement this process and methods, including optimization objective attribute metrics for cost, effectiveness and risk.

Successful future surface combatants in the US Navy must embrace the growing integration and interdependency of propulsive and combat systems. Traditionally, the development of Hull, Mechanical and Electrical systems has been segregated from the development of weapons and sensors. However, with the incorporation of high energy weapons into future ship configurations, ship design processes must evolve to embrace the concept of a System of Systems being the only way to achieve affordable capability in our future fleets. This thesis bridges the gap between the physical architecture of components within a ship and the way in which they are logically connected to model the energy flow through a representative design and provide insight into sizing requirements of both system components and their connections using an Architecture Flow Optimization (AFO). This thesis presents a unique method and tool to optimize naval ship system logical and physical architecture considering necessary operational conditions and possible damage scenarios. The particular and unique contributions of this thesis are: 1) initially only energy flow is considered without explicit consideration of commodity flow (electric, mechanical, chilled water, etc.), which is calculated in post-processing; 2) AFO is applied to a large and complex naval surface combatant system of systems, demonstrating its scalability; 3) data necessary for the AFO is extracted directly from a naval ship synthesis model at a concept exploration level of detail demonstrating its value in early stage design; and 4) it uses network-based methods which make it adaptable to future knowledge-based network analysis methods and approaches. / Master of Science / The US Navy faces a future where their ships will be required to perform a greater number and increasingly more diverse mission set while the resources provided to them dwindle. Traditionally, propulsive, electrical and weapons systems onboard ships have been segregated in their development, however, with the incorporation of high energy weapons into future ship configurations, the ship design processes must evolve to incorporate these interdependent power consumers. To take advantage of emerging technologies in a resource constrained environment, the future fleet of the US Navy must incorporate the concept of a “System of Systems” early in the ship design process. This thesis correlates the energy available onboard a ship to how it can be distributed to components in the execution of required missions. Additionally, this thesis provides insight into the sizing requirements of intermediary and auxiliary components using an Architecture Flow Optimization (AFO) by only analyzing energy flow without considering the commodity flow (electricity, mechanical power, chilled water, etc.) which can be calculated post optimization. Using network-based methods allows the AFO to be adaptable to future knowledge-based network analysis methods and approaches while using data directly from a naval ship synthesis model enables the AFO to be scaled to incorporate a large and complex system of systems proving its value to early stage design.

This thesis describes the refinement of an Architecture Flow Optimization (AFO) tool for naval surface ship design, specifically focusing on the development of new network and matrix-based methods for AFO formulation and their application in Concept Development. The AFO tool analyzes and optimizes the flow of energy through the ship's Vital Components (VCs) interfacing with a Ship Synthesis and Product Model (SSM), ensuring that all physical and operational constraints are satisfied while minimizing system cost across multiple intact and damaged operational scenarios. The total ship system is described by physical and logical architectures in a network structure comprised of vital component nodes and arcs. These elements form the basis of a linear system of equations in matrix form, the manipulation of which relies heavily on linear algebra and matrix operations. The matrix system of equations is solved using linear programming with a significant improvement in computational efficiency. The solution supports the sizing of individual vital components and the refinement of system logical architecture. It also provides the basic AFO engine necessary to support future refinement of a dynamic architecture flow optimization (DAFO) with the computational speed necessary for rapid solution of dynamic mission scenarios insuring optimized and feasible warfighting reconfiguration, with and without damage. / Master of Science / This thesis describes the refinement of an Architecture Flow Optimization (AFO) tool for naval surface ship design, specifically focusing on the development of new network and matrix-based methods for AFO formulation and their application in naval ship Concept Development processes. The Architecture Flow Optimization tool analyzes and optimizes the flow of energy through the ship's Vital Components (VCs). The AFO tool completes this task by interfacing with a Ship Synthesis and Product Model (SSM), ensuring that all of the ship's physical and operational constraints are satisfied. This is done while minimizing the ship system cost across multiple intact and damaged operational scenarios. The total ship system is described by physical and logical architectures in a network structure comprised of vital components (nodes) and their connections (arcs). These elements form the basis of a linear system of equations in matrix form, the manipulation of which relies heavily on linear algebra and matrix operations. The matrix system of equations is solved using a linear programming algorithm with a significant improvement in computational speed. The solution provided from the optimization supports the sizing of individual vital components and the refinement of the ship system logical architecture. It also provides the basic AFO engine necessary to support future refinement of a dynamic architecture flow optimization (DAFO) with the computational speed necessary for rapid solution of dynamic mission scenarios insuring optimized and feasible warfighting reconfiguration, with and without damage.

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. / Doctor of Philosophy / 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) design 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. 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 an overview of the design tools developed to implement these process 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.