Multi-objective optimization techniques that result in the generation of a Pareto frontier

have allowed decision makers to investigate optimal tradeoffs between a set of designs. Ideally, the designs present in the Pareto frontier are acceptable candidates in the design process. However, what if none of the designs in the Pareto frontier meets the design requirements? Or, what if the desire is to find designs that are beyond the Pareto frontier?

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This unevaluated or undiscovered area beyond the Pareto frontier can be described as

“whitespace”. The process of whitespace exploration is the systematic process of exploring

and investigating areas of interest previously not evaluated. A methodology for whitespace exploration is presented, which involves revisiting the assumptions and factors implicit in the generation of a Pareto frontier. The methodology presented is tested against two design problems to show efficacy. Additionally, a prototype whitespace exploration tool was developed to aid in the testing, development and demonstration of the process.

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This paper proposes a total-ship system design and requirements definition methodology that includes important components necessary for a systematic approach to naval ship concept design. The methodology is described in the context of an Offshore Patrol Vessel (OPV) project conducted by senior undergraduate design students at Virginia Tech. Concept Exploration trade-off studies and design space exploration are accomplished using a Multi-Objective Genetic Optimization (MOGO) after significant technology research and definition. Objective attributes for this optimization are cost, risk (technology, cost, schedule and performance) and mission effectiveness. The product of this optimization is a series of cost-risk-effectiveness frontiers which are used to select alternative de-signs and define Operational Requirements based on the customer’s preference for cost, risk and effectiveness.

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The notional OPV requirement is based on an OPV Initial Capabilities Document and Virginia Tech OPV Acquisition Decision Memorandum (ADM). The OPV is intended to replace the in-service US Coast Guard (USCG) Medium Endurance Cutters (MECs) including 13 Famous Class cutters (82.3 meters, built in 1980s), 16 Reliance Class cutters (64 meters, built in 1960s) and 2 single cutters built in 1944 and 1968. These vessels are reaching or past their safe service life with capabilities that are inadequate for current USCG missions. The primary OPV mission functional areas and capabilities include: Port, Waterway and Coastal Security (PWCS); Search And Rescue (SAR); Drug Interdiction (DRUG); Migrant Interdiction (AMIO); Protect Living Marine Resources (LMR); Other Law Enforcement (OLE); and Defense Readiness (DR).

The selected OPV alternative is a low risk, low cost, knee-in-the-curve displacement monohull design on the cost-risk-effectiveness frontier. This design was chosen because it provides a sharp increase in effectiveness with a minimal increase in cost at a low cost and risk level based on the MOGO results.

The emphasis of this paper is on the concept exploration design and requirements process.

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In many engineering application the need arises to solve multi-objective optimiza-tion problems that involve computationally expensive functions. Our approach for multi-objective optimization is Normal-Boundary Intersection (NBI) by Das and Dennis. We can

run NBI either on surrogate models of the simulations or directly on the simulations. Nei-ther of these alternatives is effective on complex functions; the surrogates are not accurate enough, and using the simulations directly is too costly. We have found a unique approach that solves this dilemma. Our approach combines the use of surrogates and simulations in

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a way similar to the general surrogate management framework by iteratively using both

the surrogate models and the simulations.

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This paper describes ongoing research to develop a flexible 3D hullform design process and modules with associated performance models, and integrate these modules into an existing ship synthesis model (SSM) in a multi-objective optimization approach to perform Naval Ship Concept and Requirements Exploration (C&RE). Effectiveness is initially based on seakeeping indices and resistance, and then extended to a multi-objective genetic optimization of an Offshore Patrol Vessel (OPV) total ship design.

Solving multi-objective optimization problems that involve computationally expensive functions is a normal part of many engineering applications. Runtime issues are magnified when one moves from a single discipline to multiple disciplines in a multidisciplinary design optimization setting. The tools selected to perform a design space exploration within this setting must be chosen with care.

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Normal-Boundary Intersection (NBI) is a robust general purpose multi-objective opti-mization method for design problems. In the setting of computationally expensive func-tions, running NBI directly using the simulation requires a prohibitive amount of compu-tational cost. Alternatively, running on a surrogate model approximation to the simulation fails to produce sufficiently accurate solutions. Our approach combines the use of mod-

els and simulations in a way similar to the general surrogate management framework by iteratively using both the models and the simulations. While this approach can be ex-ecuted with the push of a button, it is best used, especially at the beginning of a new problem, as one step in a larger design space exploration process. The process includes

the design, execution and analysis of computer experiments, surrogate model development, single objective optimization and problem refinement.

This paper outlines the surrogate model management framework approach to solving multi-objective optimization problems within the context of a general design exploration process. The importance of the use of data visualization techniques will be highlighted

in the discussion. We conclude with a representative aircraft design problem from the aerospace industry.

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Digital twins are virtual representations of subsystems within a system of systems. They can be utilized to model and predict performance and condition degradation throughout a system’s life cycle. Condition based mainte-nance, or the performance of system maintenance based on the subsystem states, is often facilitated by the implementation of digital twins. An open challenge is selecting the subsystems that require digital twins. We establish a generic process for determining a set of priority-based system components requiring digital twin development for condition based maintenance purposes. The priority set, which we term the “triage” set, rep-resents the set of components that when monitored through a digital twin lead to the greatest increase in total system reliability and simultaneously represent the minimal cost set of components for implementing a digital twin. While we focus our process on an unmanned underwater vehicle (UUV), where we frame the design problem as a multiobjective optimization problem utilizing experimentally determined data and metrics from the model of a real UUV system, the process is generic enough that it could be utilized by any system looking at cost and reliability estimates for leveraging digital twin technology.