Using domain specific languages to capture design knowledge for model-based systems engineering

Design synthesis is a fundamental engineering task that involves the creation of structure from a desired functional specification; it involves both creating a system topology as well as sizing the system's components. Although the use of computer tools is common throughout the design process, design synthesis is often a task left to the designer. At the synthesis stage of the design process, designers have an extensive choice of design alternatives that need to be considered and evaluated. Designers can benefit from computational synthesis methods in the creative phase of the design process. Recent increases in computational power allow automated synthesis methods for rapidly generating a large number of design solutions. Combining an automated synthesis method with an evaluation framework allows for a more thorough exploration of the design space as well as for a reduction of the time and cost needed to design a system. To facilitate computational synthesis, knowledge about feasible system configurations must be captured. Since it is difficult to capture such synthesis knowledge about any possible system, a design domain must be chosen. In this thesis, the design domain is hydraulic systems. In this thesis, Model-Driven Software Development concepts are leveraged to create a framework to automate the synthesis of hydraulic systems will be presented and demonstrated. This includes the presentation of a domain specific language to describe the function and structure of hydraulic systems as well as a framework for synthesizing hydraulic systems using graph grammars to generate system topologies. Also, a method using graph grammars for generating analysis models from the described structural system representations is presented. This approach fits in the context of Model-Based Systems Engineering where a variety of formal models are used to represent knowledge about a system. It uses the Systems Modeling Language developed by The Object Management Group (OMG SysML™) as a unifying language for model definition.M.S.Committee Chair: Paredis, Chris; Committee Member: McGinnis, Leon; Committee Member: Schaefer, Dir

Combining SysML and Model Transformations to Support Systems Engineering Analysis

As modern systems become increasingly complex, there is a growing need to support the systems engineering process with a variety of formal models, such that the team of experts involved in the process can express and share knowledge precisely, succinctly and unambiguously. However, creating such formal models can be expensive and time-consuming, making a broad exploration of different system architectures cost-prohibitive. In this paper, we investigate an approach for reducing such costs and hence enabling broader architecture space exploration through the use of model transformations. Specifically, a method is presented for verifying design alternatives with respect to design requirements through automated generation of analyses from formal models of the systems engineering problem. Formal models are used to express the structure of design alternatives, the system requirements, and experiments to verify the requirements as well as the relationships between the models. These formal models are all represented in a common modeling language, the Object Management Group’s Systems Modeling Language (OMG SysMLTM). To then translate descriptive models of system alternatives into a set of corresponding analysis models, a model transformation approach is used to combine knowledge from the experiment models with knowledge from reusable model libraries. This set of analysis models is subsequently transformed into executable simulations, which are used to guide the search for suitable system alternatives. To facilitate performing this search using commercially available optimization tools, the analyses are represented using the General Algebraic Modeling System (GAMS). The approach is demonstrated on the design of a hydraulic subsystem for a log splitter

Using logic-based approaches to explore system architectures for systems engineering

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.PhDCommittee Chair: Paredis, Christiaan; Committee Member: Augenbroe, Godfried; Committee Member: Bras, Berdinus; Committee Member: Eastman, Charles; Committee Member: McGinnis, Leo

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

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