Complex Systems Research

Complex Systems Research focuses on developing design principles and formal methods for designing, modeling, and evaluating complex engineering systems with specific emphasis on mitigation and reduction of risks and uncertainty due to failures. The set of methodologies and tools developed in this lab are in support of concept evaluation and engineering analysis goals for large aerospace organizations (NASA, AFRL, Boeing, Northrop, Lockheed Martin). The lab's expertise areas include design theory and methodology, systems engineering, model based design, failure and risk analysis, failure detection, health management, functional modeling, system level design and optimization, collaborative and concurrent design. 


  • Function and Event-Based Modeling of software-hardware systems

    This research focuses on advancing our understanding of how changes in the individual or subsystem design manifest themselves at the system-level, the consequences of making and reversing decisions during conceptual design, and developing design principles and formal models for capturing, tracking, and evaluating decisions and alternative designs that result from them. The goal is to provide a mathematical framework for understanding and representing designs, design processes, being able to track and retrace design decisions, and change/modify designs without having to start from scratch each time. The proposed research starts from the observation that any design process essentially consists of making design decisions and that any designed artifact is ultimately the embodiment of a set of such design decisions. The research will focus on failures in highly complex software-hardware systems that resulted from design changes and design reuse.
    The project involves:
    • Developing a fundamental understanding of the process of designing based on examples (previous models, designs, products, or analogies with different domains) by bridging the software and engineering design communities,
    • Developing a mathematical framework and systematic methodology to retrace the decisions that lead to new designs based on examples and past designs, so that alternate ideas can be explored without having to start from scratch,
    • Developing a set of sound and validated scientific principles that can be used in general to design complex systems with significant software-hardware interactions.
    • Apply to NASA designs of software-hardware systems, working closely with an advisory board composed of senior researchers from NASA and Industry (NASA Applications).

  • Failure-informed design and analysis of complex software-hardware systems

    This research introduces the Functional Failure Identification and Propagation (FFIP) analysis framework as a means to enable the design of reliable complex software-intensive systems. This framework proposes to use a combination of functional, structural, and behavioral models as represent systems at the early conceptual design phase, and high-level behavioral simulation to estimate potential faults and their propagation under failure scenarios. The goal is to develop a system-level design methodology for assessing and analyzing failure potential of software-intensive systems at the early, functional design stage. By making the exploration of potential failure mechanisms an integral part of system design at the functional level, the proposed approach will enable the exploration of system components and their functionality, and the development of robust system configurations. Using our proposed framework, the design teams will be able to proactively analyze the functionality of the intended systems early in the design process while many decisions and tasks are still open. The method is aimed at providing a means of understanding functional failures (hardware and software) and their propagation paths, to then determine what functions are lost, what the impact to the overall system will be, and what redundancies and safeguards should be added as part of the system design and fault management capability.
    The project involves:
    • Developing system level models to represent function, structure, and behavior as a set of interrelated array of graph-based, elemental component models;
    • Developing and integrating a behavioral simulation capability to determine system behavior under certain conditions;
    • Developing a reasoner about the function-failure mapping to determine the state of each function at any time given the physical state of the system;
    • Integrating these modules software-hardware systems (Air Force applications).

  • Modeling of uncertainty propagation in large engineering organizations

    The design process in large-scale complex systems can be viewed as a series of actions for reducing uncertainty in product or system design specifications. At the beginning of the design process, uncertainty is high because the design space has yet to be explored and decisions have not been made. This uncertainty contributes to design risk, risk due to the engineer's lack of knowledge and/or information. In design teams, design risk takes on added dimensions since knowledge is distributed throughout the team. Uncertainty is propagated from the subsystem to the system level and can ultimately impact system performance, reliability, as well as cost and risk of system development. The goal of this research is to identify opportunities for improving the design process through facilitating team communication and group awareness, resulting in models and a set of guidelines to help streamline the design processes in organizations dealing with large-scale complex systems, and hence reduce design costs, delays, and risk. Multiple methods for data collection/triangulation including surveys, interviews, observation, and document review will be used to complete the analysis. We will compare use of these tools and language across domains to determine if there is similarity and transferability across domains. Finally, we will collect system design outcome data to track impact, effectiveness, and efficiency of the processes used. Using the insights from case studies (large companies and organizations), mathematical models of uncertainty flow and propagation will be developed.
    The project involves:
    • Understanding how is uncertainty dealt with in the design of complex systems. The answer to this critical question will enhance the understanding of how uncertainty propagates in the design process, and result in models and a set of guidelines to help streamline the design processes in organizations dealing with large-scale complex systems, and hence reduce design costs, delays, and risk.
    • Understanding how decision makers (engineers, project managers, technologists, etc) account for uncertainty in their decisions;
    • Understanding how this uncertainty gets communicated to other stakeholders of the design process (and perhaps the end-user/customer).
    • Modeling design decision-making and information flow over time. The initial representation will be a flow diagram, with elements to represent levels of uncertainty, decisions points, information flow and uncertainty reducing tasks.
    • Developing formal mathematical models of that represent and capture how uncertainty flows and propagates in such systems;
    • Identify "best practices" for both identifying and communicating uncertainty in the design of complex engineered systems, based on novel models of uncertainty flow and propagation.

  • Developing an Engineering Virtual Organization (EVO) for conceptual design

    This project creates VOICED--a Virtual Organization for Innovation in Conceptual Engineering Design, to synthesize innovative conceptual designs of products and systems through the reuse of existing design knowledge in a cyber-repository. The vision of VOICED is to create an engineering virtual organization that addresses the challenges of synthesizing innovative conceptual designs of increasingly diverse and competitive engineered products and systems through the reuse of existing design knowledge in a cyber repository. As design advancement becomes progressively more difficult and risky to achieve, the ability to efficiently identify and avoid potential failures while archiving and promoting successful novel ideas becomes an invaluable technology that can only be effectively achieved through collaboration of industry and academia. As part of this pilot project, a suite of function based failure analysis tools will be integrated into the web applications of the VOICED cyber repositories. These tools will allow early identification of potential failure modes and needed analyses once functionality is established.
    This project involves:
    • Enabling the generation of a large space of feasible design concepts and quickly explore that space to determine concepts that offer better performance and value;
    • Reducing product risks in the conceptual stage of design, primarily by enabling the rapid evaluation of candidate designs and connecting the methods used to generate them to downstream engineering evaluation and manufacturing assessment;
    • Creating an environment where educators can develop course material and directions by sharing data and input with educators from around the world, in particular providing an open courseware environment that can benefit novice design educators;
    • Creating a community where industry can eavesdrop on design education and both have input on content as well as identify topics of interest for continuing education.

  • System Analysis, Design, and Integration of Health Management into Complex Systems

    This research investigates standard formal practices and methodologies used in system engineering and design and propose a design environment where ISHM systems can be developed in conjunction with the system and subsystem design. Integrated Systems Health Management (ISHM) is an evolving technology used to detect, assess, and isolate faults in complex aerospace systems to improve safety. At the conceptual design level, system-level engineers must make decisions regarding the inclusion of ISHM and the extent and type of the sensing technologies used in various subsystems. Since ISHM provides continuous monitoring of sub-systems for fault conditions, safety will be improved over traditional inspection methods, which can only diagnose faults at the time of inspection. ISHM, however, will add additional cost in the form of hardware and diagnostic/prognostic systems, as well as the potential for false alarms and other ISHM induced faults, such as sensor failures. Therefore, the design of ISHM involves a complex trade-off among performance, reliability, cost, and risk.
    The various parts of this project involve:
    • Development of system-level methodologies to represent the critical functions, flows, and the interactions to accomplish the objectives of the vehicle system' performance alongside the objectives of the health detection and monitoring systems, to map these functions to failure modes;
    • Development of methodologies to design safeguards and/or additional functionality to enable robust ISHM avoiding these failures.
    • Development of automated system analysis and optimization environments to enable vehicle systems designers to perform tradeoff analyses and determine the impact of ISHM Figures of Merit on the vehicle systems performance and risks;
    • Development of methodologies and tools to enable ISHM designers to systematically model and provide metrics of candidate alternatives for the purpose of aiding the decision making process.
    • Development of methodologies to enable selection or rejection of options based upon clearly identified criteria, including quantified safety and reliability performance and cost benefit analysis.

  • Function Based Modeling for Conceptual Design and Systems Engineering

    Early stage design, especially conceptual design, presents the best opportunity to cost effectively catch and prevent potential failures and anomalies. We use a function based modeling approach which enables designers to think through the system layout by following the input and output flows through the main required functions. Specifically, this research looks for improvements over current design methods by providing a standardized functional modeling method that is applicable throughout the design process, conceptual functional models that limit form-specific assumptions and are used for identifying potential solutions to product functionality, form-specific functional models that assist detailed behavioral model identification, behavioral model development based on functional models, well defined methods for identifying, modeling and evaluating solutions and improved identification and representation of auxiliary functions.
    The project involves:
    • Development of a function modeling based failure analysis methodology to map historical and potential failure modes to functions, and search the space of functions and components of similar functionality to generate concepts that eliminate potential failure modes associated with certain functions based on historical data, FMEAs, and expert elicitation.
    • Development of generic and reusable functional models (templates), and a list of standardized failure modes for various domains (Rotorcraft, Spacecraft systems, etc.) using historical data, and building a knowledge base to enable searching through various domains.
    • Mapping of risk to functions to start building the knowledge base for failure rates based on historical data.
    • Formalizing the role of conceptual and form-specific functional models during design
    • Creating a framework for developing the behavioral models used to evaluate a system
    • Improving identification and flowdown of requirements throughout the design process and during critical events while in operation.