Our world is filled with large-scale engineering systems, which provide various services and conveniences in our daily life. A distinctive trend in the development of today's large-scale engineering systems is the extensive and aggressive adoption of automation and autonomy that enable the significant improvement of systems' robustness, efficiency, and performance, with considerably reduced manning and maintenance costs, and the U.S. Navy's DD(X), the next-generation destroyer program, is considered as an extreme example of such a trend. This thesis pursues a modeling solution for performing simulation-based analysis in the conceptual or preliminary design stage of an intelligent, self-reconfigurable ship fluid system, which is one of the concepts of DD(X) engineering plant development. Through the investigations on the Navy's approach for designing a more survivable ship system, it is found that the current naval simulation-based analysis environment is limited by the capability gaps in damage modeling, dynamic model reconfiguration, and simulation speed of the domain specific models, especially fluid network models. As enablers of filling these gaps, two essential elements were identified in the formulation of the modeling method. The first one is the graph-based topological modeling method, which will be employed for rapid model reconstruction and damage modeling, and the second one is the recurrent neural network-based, component-level surrogate modeling method, which will be used to improve the affordability and efficiency of the modeling and simulation (M&S) computations. The integration of the two methods can deliver computationally efficient, flexible, and automation-friendly M&S which will create an environment for more rigorous damage analysis and exploration of design alternatives. As a demonstration for evaluating the developed method, a simulation model of a notional ship fluid system was created, and a damage analysis was performed. Next, the models representing different design configurations of the fluid system were created, and damage analyses were performed with them in order to find an optimal design configuration for system survivability. Finally, the benefits and drawbacks of the developed method were discussed based on the result of the demonstration.

In this thesis, a framework is laid for holistic uncertainty management for simulation-based design of multiscale systems. The work is founded on uncertainty management for microstructure mediated design (MMD) of material and product, which is a representative example of a system over multiple length and time scales, i.e., a multiscale system. The characteristics and challenges for uncertainty management for multiscale systems are introduced context of integrated material and product design. This integrated approach results in different kinds of uncertainty, i.e., natural uncertainty (NU), model parameter uncertainty (MPU), model structure uncertainty (MSU) and propagated uncertainty (PU). We use the Inductive Design Exploration Method to reach feasible sets of robust solutions against MPU, NU and PU. MMD of material and product is performed for the product autonomous underwater vehicle (AUV) employing the material in-situ metal matrix composites using IDEM to identify robust ranged solution sets. The multiscale system results in decision nodes for MSU consideration at hierarchical levels, termed as multilevel design. The effectiveness of using game theory to model strategic interaction between the different levels to facilitate decision making for mitigating MSU in multilevel design is illustrated using the compromise decision support problem (cDSP) technique. Information economics is identified as a research gap to address holistic uncertainty management in simulation-based multiscale systems, i.e., to address the reduction or mitigation of uncertainty considering the current design decision and scope for further simulation model refinement in order to reach better robust solutions. It necessitates development of an improvement potential (IP) metric based on value of information which suggests the scope of improvement in a designer's decision making ability against modeled uncertainty (MPU) in simulation models in multilevel design problem. To address the research gap, the integration of robust design (using IDEM), information economics (using IP) and game theoretic constructs (using cDSP) is proposed. Metamodeling techniques and expected value of information are critically reviewed to facilitate efficient integration. Robust design using IDEM and cDSP are integrated to improve MMD of material and product and address all four types of uncertainty simultaneously. Further, IDEM, cDSP and IP are integrated to assist system level designers in allocating resources for simulation model refinement in order to satisfy performance and robust process requirements. The approach for managing MPU, MSU, NU and PU while mitigating MPU is presented using the MMD of material and product. The approach presented in this article can be utilized by system level designers for managing all four types of uncertainty and reducing model parameter uncertainty in any multiscale system.

This thesis introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm relies on an adaptive and multi-resolution mesh to obtain an initial estimation of the objective function surface. Local surrogate models are then constructed to represent the objective function and to generate additional trial points in the vicinity of local minima discovered. The steps of mesh refinement and surrogate modeling continue until convergence criteria are met. An important property of this algorithm is that it produces progressively accurate surrogate models around the local minima; these models can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The thesis also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems.

This thesis introduces a new optimization algorithm for simulation-based design of systems with multi-modal, nonlinear, black box objective functions. The algorithm extends the recently introduced adaptive multi-modal optimization by incorporating surrogate modeling features similar to response surface methods (RSM). The resulting optimization algorithm has reduced computational intensity and is therefore well-suited for optimization of expensive black box objective functions. The algorithm relies on an adaptive and multi-resolution mesh to obtain an initial estimation of the objective function surface. Local surrogate models are then constructed to represent the objective function and to generate additional trial points in the vicinity of local minima discovered. The steps of mesh refinement and surrogate modeling continue until convergence criteria are met. An important property of this algorithm is that it produces progressively accurate surrogate models around the local minima; these models can be used for post-optimization studies such as sensitivity and tolerance analyses with minimal computational effort. This algorithm is suitable for optimal design of complex engineering systems and enhances the design cycle by enabling computationally affordable uncertainty analysis. The mathematical basis of the algorithm is explained in detail. The thesis also demonstrates the effectiveness of the algorithm using comparative optimization of several multi-modal objective functions. It also shows several practical applications of the algorithm in the design of complex power and power-electronic systems.

This document summarizes a research effort focused on improving live-to-virtual (L-V) communication systems. The purpose of this work is to address a significant challenge facing the tactical communications training community through the development of the Live-to-Virtual Relay Radio Prediction Algorithm and implementation of the algorithm into an Integrated Live-to-Virtual Communications Server prototype device. The motivation for the work and the challenges of integrating live and virtual communications are presented. Details surrounding the formulation of the prediction algorithm and a description of the prototype system, hardware, and software architectures are shared. Experimental results from discrete event simulation analysis and prototype functionality testing accompany recommendations for future investigation. If the methods and technologies summarized are implemented, an estimated equipment savings of 25%-53% and an estimated cost savings of $150,000.00 - $630,000.00 per site are anticipated. Thus, a solution to a critical tactical communications training problem is presented through the research discussed.

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A vast array of computer-based simulation tools are used to support engineering design and analysis activities. Several such activities call for the simulation of various coupled sub-systems in parallel, typically to study the emergent behavior of large, complex systems. Most sub-systems have their own simulation models associated with them, which need to interoperate with each other in a federated fashion to simulate system-level behavior. The run-time exchange of information between federate simulations requires a common information model that defines the representation of simulation concepts shared between federates. However, most federate simulations employ disparate representations of shared concepts. Therefore, it is often necessary to implement transformation stubs that convert concepts between their common representation to those used in federate simulations. The tasks of defining a common representation for shared simulation concepts and building translation stubs around them adds to the cost of performing a system-level simulation. In this thesis, a framework to support automation and reuse in the process of achieving interoperability between federate simulations is developed. This framework uses ontologies as a means to capture the semantics of different simulation concepts shared in a federation in a formal, reusable fashion. Using these semantics, a common representation for shared simulation entities, and a corresponding set of transformation stubs to convert entities from their federate to common representations (and vice-versa) are derived automatically. As a foundation to this framework, a schema to enable the capture of simulation concepts in an ontology is specified. Also, a graph-based algorithm is developed to extract the appropriate common information model and transformation procedures between federate and common simulation entities. As a proof of concept, this framework is applied to support the development of a federated air traffic simulation. To progress with the design of an airport, the combined operation of its individual systems (air traffic control, ground traffic control, and ground-based aircraft services) in handling varying volumes of aircraft traffic is to be studied. To do so, the individual simulation models corresponding to the different sub-systems of the airport need to be federated, for which the ontology-based framework is applied.

Simulation-based design optimization and finite element method are used in this research to investigate weight reduction of car body structures made of metallic and composite materials under different design criteria. Besides crashworthiness in full frontal, offset frontal, and side impact scenarios, vibration frequencies, static stiffness, and joint rigidity are also considered. Energy absorption at the component level is used to study the effectiveness of carbon fiber reinforced polymer (CFRP) composite material with consideration of different failure criteria. A global-local design strategy is introduced and applied to multi-objective optimization of car body structures with CFRP components. Multiple example problems involving the analysis of full-vehicle crash and body-in-white models are used to examine the effect of material substitution and the choice of design criteria on weight reduction. The results of this study show that car body structures that are optimized for crashworthiness alone may not meet the vibration criterion. Moreover, optimized car body structures with CFRP components can be lighter with superior crashworthiness than the baseline and optimized metallic structures.

In simulation-based design, a key challenge is to formulate and solve analysis problems efficiently to evaluate a large variety of design alternatives. The solution of analysis problems has benefited from advancements in commercial off-the-shelf math solvers and computational capabilities. However, the formulation of analysis problems is often a costly and laborious process. Traditional simulation templates used for representing analysis problems are typically brittle with respect to variations in artifact topology and the idealization decisions taken by analysts. These templates often require manual updates and "re-wiring" of the analysis knowledge embodied in them. This makes the use of traditional simulation templates ineffective for multi-disciplinary design and optimization problems. Based on these issues, this dissertation defines a special class of problems known as variable topology multi-body (VTMB) problems that characterizes the types of variations seen in design-analysis interoperability. This research thus primarily answers the following question: How can we improve the effectiveness of the analysis problem formulation process for VTMB problems? The knowledge composition methodology (KCM) presented in this dissertation answers this question by addressing the following research gaps: (1) the lack of formalization of the knowledge used by analysts in formulating simulation templates, and (2) the inability to leverage this knowledge to define model composition methods for formulating simulation templates. KCM overcomes these gaps by providing: (1) formal representation of analysis knowledge as modular, reusable, analyst-intelligible building blocks, (2) graph transformation-based methods to automatically compose simulation templates from these building blocks based on analyst idealization decisions, and (3) meta-models for representing advanced simulation templates VTMB design models, analysis models, and the idealization relationships between them. Applications of the KCM to thermo-mechanical analysis of multi-stratum printed wiring boards and multi-component chip packages demonstrate its effectiveness handling VTMB and idealization variations with significantly enhanced formulation efficiency (from several hours in existing methods to few minutes). In addition to enhancing the effectiveness of analysis problem formulation, KCM is envisioned to provide a foundational approach to model formulation for generalized variable topology problems.

Problem: Engineering design decisions require the integration of information from multiple and disparate sources. However, this information is often independently created, limited to a single perspective, and not formally represented, thus making it difficult to formulate decisions. Hence, the primary challenge is the development of computational representations that facilitate the exchange of information for decision support. Approach: First, the scope of this research is limited to representing design decisions as compromise decision support problems (cDSP). To address this challenge, the primary hypothesis is that a formal language will enable the semantics of cDSP to be captured, thus providing a digital interface through which design information can be exchanged. The primary hypothesis is answered through the development of a description logic (DL) based formal language. The primary research question is addressed in four sub-questions. The first two research questions relate to the development of a vocabulary for representing the semantics of the cDSP. The first hypothesis used to answer this question is that formal information modeling techniques can be used to explicitly capture the semantics and structure of the cDSP. The second research question is focused on the realization of a computer-processible representation. The hypothesis used to answer this question is that DL can be used for developing computational-based representations. The third research question is related to the organization and retrieval of decision information. The hypothesis used to answer this question is DL reasoning algorithms can be used to support organization and retrieval. Validation: The formal language developed in this dissertation is theoretically and empirically validated using the validation square approach. Validation of the hypotheses is achieved by systematically building confidence through example problems. Examples include the cDSP construct, analysis support models, the design of a cantilever beam, and design of a structural fin array heat sink. Contributions: The primary contribution from this dissertation is a formal language for capturing the semantics of cDSPs and analysis support models comprised of: (1) a systematic methodology for decision formulation, (2) a cDSP vocabulary, (3) a graphical information model, and (4) a DL-based representation. The components, collectively, provide a means for exchanging cDSP information.