Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Paredis, Chris; Committee Member: McGinnis, Leon; Committee Member: Schaefer, Dirk

To develop competitive vehicles with ever increasing complexity, automotive designers need to improve their ability to explore a broad range of system architectures efficiently and effectively. Whereas traditional vehicle systems are based upon internal combustion (IC) engines, today’s environmentally conscious vehicle manufacturers must consider alternatives to the IC engine-only systems such as hybrid or electric systems. To help the engineers to model these multiple alternatives, it would be ideal to start from a base vehicle architecture. To design a good vehicle, it is necessary for each of these system architectures to be analyzed from a variety of attributes including performance, fuel economy, or even thermal behavior. Creating the necessary analysis models for each system architecture would be time-consuming, expensive, and could be error prone. To aid in overcoming such challenges, we have developed an approach for supporting the generation of subsystem model templates to support the integration of analysis models. The approach is based on formally modeling the system architecture in the Systems Modeling Language (OMG SysML) and then using model transformations to generate stubs for corresponding analysis models in Modelica and Simulink. In this manner, we assist designers in managing large systems with multiple analyses, ensuring that the systems remain consistent, and enabling the reuse of generic architectures through specialization and redefinition. The starting point is a reference architecture, called the Vehicle Model Architecture or VMA, in which all the key subsystems and interactions between subsystems are formally modeled. In addition, we have created a generic template that is a specialized version of the VMA. This specialized template can then be adapted by the systems engineer to represent a specific vehicle program. In addition, pre-defined, generic analysis templates can be redefined for the specific vehicle program under analysis. The SysML VMA system model is transformed through two model transformations, one that translates the physical portion of the system to Modelica, and one that transforms the logical controls portion of the system to Simulink. By automating these transformations and reusing a set of fixed templates for further specialized architectures, this approach helps to manage the complexity, reduces modeling time by enabling system model reuse. The entire approach taken in this thesis has been named the Vehicle Architecture Modeling Framework, VAMF, which includes the SysML VMA, the corresponding analysis templates, and the tools developed to support this approach. Throughout this thesis, the specific (fictitious) vehicle program “C100” and a 0-to-100 kph performance analysis test are used as examples for demonstration.

Today s market is driven by the desire for increasingly complex products that perform well from manufacturing to disposal. Designing these products for multiple lifecycle phases requires effective management of engineering knowledge and integration of this knowledge across multiple disciplines. By managing this knowledge, products can be realized faster, perform better and be more complex. However, management techniques are often very costly and managers can easily become bogged down with large quantities of information, slowing the design process and degrading knowledge transfer. Thus, a need exists for effective yet inexpensive knowledge management. One approach for decreasing the costs associated with generating design knowledge is to reuse modules of existing knowledge. In Model-Based Systems Engineering (MBSE), information about a design is stored formally in many knowledge structures, or models, including requirements, stakeholders, and analyses. To support the reuse of the existing knowledge in design, MBSE is used as a basis for integrating engineering analysis models. In this thesis, a framework is presented for model classification that organizes models by components and aspects. This scheme is found to be useful in classifying engineering analysis models for reuse by storing them as a set in containers known as Multi-Aspect Component Models (MAsCoMs). Each model in a MAsCoM is related to the formal structure model of a physical component, and to the many aspects of the component that the model represents. The Systems Modeling Language, OMG SysML, is used to implement MAsCoMs and support MBSE. Validation of the MAsCoM concept is performed with fluid-power design examples, including a log splitter, scissor lift, and hydraulic excavator. In these examples, MAsCoMs improve design value by 1) Classifying modular and composable engineering analysis models for reuse in multiple disciplines, and 2) Providing knowledge modules to computer-automated algorithms for the future automated composition of component models into system models to perform system-level analyses.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Chris Paredis; Committee Member: Dirk Schaefer; Committee Member: Russell Peak.

At the present time, most systems engineers do not have access to cognitive work analysis information or training in terms they can understand. This may lead to a disregard of the cognitive aspect of system design. The impact of this issue is system requirements that do not account for the cognitive strengths and limitations of users. Systems engineers cannot design effective decision support systems without defining cognitive work requirements. In order to improve system requirements, integration of cognitive work requirements into the systems engineering process has to be improved. One option to address this gap is the development of a Cognitive Work Analysis (CWA) framework using Systems Modeling Language (SysML). The study had two phases. The first involved aligning the CWA terminology with the SysML to produce a CWA framework using SysML. The second was the creation of an instruction using SysML to inform systems engineers of the process of integrating cognitive work requirements into the systems engineering process. This methodology provides a structured framework to define, manage, organize, and model cognitive work requirements. Additionally, it provides a tool for systems engineers to use in system design which supports a user’s cognitive functions, such as situational awareness, problem solving, and decision making.

This thesis proposes a framework to design and analyze sustainability within complex urban transportation systems. Urban transit systems have large variability in temporal and spatial resolution, and are common in lifecycle analyses and sustainability studies. Unlike analyses with smaller scope or broader resolution, these systems are composed of numerous interacting layers, each intricate enough to be a complete system on its own. In addition, detailed interaction with the system environment is often not accounted for in lifecycle studies, despite its strong potential effects on the problem domain. To manage such complexity, this thesis suggests a methodology that focuses on integrating existing modeling constructs in a transparent manner, and capturing structural and functional relationships for efficient model reuse. The Systems Modeling Language (OMG SysML ) is used to formally implement the modeling framework. To demonstrate the method, it is applied to a large scale multi-modal transportation network. Analysis of key network parameters such as emissions output, well-to-wheel energy use, and system capacity are presented in a case study of the Atlanta, Georgia metropolitan area. Results of the case study highlight several areas that differ from more traditional lifecycle analysis research. External influences such as regional electricity generation are found to have extremely large effects on environmental impact of a regional mobility system. The model is used to evaluate various future scenarios and finds that existing policy measures for curbing energy use and emissions are insufficient for reducing impact in a growing urban region.

This research is focused on helping engineers design better systems by supporting their decision making. When engineers design a system, they have an almost unlimited number of possible system alternatives to consider. Modern systems are difficult to design because of a need to satisfy many different stakeholder concerns from a number of domains which requires a large amount of expert knowledge. Current systems engineering practices try to simplify the design process by providing practical approaches to managing the large amount of knowledge and information needed during the process. Although these methods make designing a system more practical, they do not support a structured decision making process, especially at early stages when designers are selecting the appropriate system architecture, and instead rely on designers using ad hoc frameworks that are often self-contradictory. In this dissertation, a framework for performing architecture exploration at early stages of the design process is presented. The goal is to support more rational and self-consistent decision making by allowing designers to explicitly represent their architecture exploration problem and then use computational tools to perform this exploration. To represent the architecture exploration problem, a modeling language is presented which explicitly models the problem as an architecture selection decision. This language is based on the principles of decision-based design and decision theory, where decisions are made by picking the alternative that results in the most preferred expected outcome. The language is designed to capture potential alternatives in a compact form, analysis knowledge used to predict the quality of a particular alternative, and evaluation criteria to differentiate and rank outcomes. This language is based on the Object Management Group's System Modeling Language (SysML). Where possible, existing SysML constructs are used; when additional constructs are needed, SysML's profile mechanism is used to extend the language. Simply modeling the selection decision explicitly is not sufficient, computational tools are also needed to explore the space of possible solutions and inform designers about the selection of the appropriate alternative. In this investigation, computational tools from the mathematical programming domain are considered for this purpose. A framework for modeling an architecture selection decision in mixed-integer linear programming (MIP) is presented. MIP solvers can then solve the MIP problem to identify promising candidate architectures at early stages of the design process. Mathematical programming is a common optimization domain, but it is rarely used in this context because of the difficulty of manually formulating an architecture selection or exploration problem as a mathematical programming optimization problem. The formulation is presented in a modular fashion; this enables the definition of a model transformation that can be applied to transform the more compact SysML representation into the mathematical programming problem, which is also presented. A modular superstructure representation is used to model the design space; in a superstructure a union of all potential architectures is represented as a set of discrete and continuous variables. Algebraic constraints are added to describe both acceptable variable combinations and system behavior to allow the solver to eliminate clearly poor alternatives and identify promising alternatives. The overall framework is demonstrated on the selection of an actuation subsystem for a hydraulic excavator. This example is chosen because of the variety of potential architecture embodiments and also a plethora of well-known configurations which can be used to verify the results.

Contemporary systems engineering problems are becoming increasingly complex as they are handled by geographically distributed design teams, constrained by the objectives of multiple stakeholders, and inundated by large quantities of design information. According to the principles of model-based systems engineering (MBSE), engineers can effectively manage increasing complexity by replacing document-centric design methods with computerized, model-based approaches. In this thesis, modeling constructs from SysML and Modelica are integrated to improve support for MBSE. The Object Management Group has recently developed the Systems Modeling Language (OMG SysML ) to provide a comprehensive set constructs for modeling many common aspects of systems engineering problems (e.g. system requirements, structures, functions). Complementing these SysML constructs, the Modelica language has emerged as a standard for modeling the continuous dynamics (CD) of systems in terms of hybrid discrete- event and differential algebraic equation systems. The integration of SysML and Modelica is explored from three different perspectives: the definition of CD models in SysML; the use of graph transformations to automate the transformation of SysML CD models into Modelica models; and the integration of CD models and other SysML models (e.g. structural, requirements) through the depiction of simulation experiments and engineering analyses. Throughout the thesis, example models of a car suspension and a hydraulically-powered excavator are used for demonstration. The core result of this work is the provision of modeling abilities that do not exist independently in SysML or Modelica. These abilities allow systems engineers to prescribe necessary system analyses and relate them to stakeholder concerns and other system aspects. Moreover, this work provides a basis for model integration which can be generalized and re-specialized for integrating other modeling formalisms into SysML.