Bridging Systems Engineering Theory and Application in Undergraduate Curricula

Systems engineering undergraduate curricula are typically divided into foundational, methodology, and application courses. The United States Military Academy, Systems Engineering program primary application course, often referred to as a Capstone project, involves teams of students performing client-based work to solve complex real-world problems. Existing foundational and methodology courses tend to emphasize engineering management processes and operations research techniques at the expense of systems engineering technical processes. As such, students often do not have the requisite knowledge base necessary for their Capstone, reducing their self-efficiency, decision-making, overall project interest, and quality of technical artifacts. In an attempt to bridge this gap, the United States Military Academy, Systems Engineering program introduced a cornerstone course to teach system engineering design and system engineering technical processes as practiced in industry and documented in the INCOSE handbook. The course structure follows the system engineering V methodology and uses a realistic, but constrained, design project to teach and apply systems engineering skills. The introduction of this new course was found to increase the overall knowledge-base of the students entering their Capstone project, allowing them to be more self-efficient and capable of making informed engineering design decisions

Experimental Space-Filling Designs For Complicated Simulation Outpts

MOVES Research & Education Systems Seminar: Presentation; Session 5: M&S standards and methodologies; Moderator: Curtis Blais; Experimental Space-filling Designs for Complicated Simulation Outputs; speaker: LTC Alex MacCalman, PhD Student Candidat

A Case Study in Developing an Integrated Data and Model Management System for the Development of a Complex Engineered System

Ahstract- This paper presents a case study in implementing an integrated data and model management system in the development of a complex system. Joint Acquisition Task Force (JATF) Tactical Light Operator Suit (TALOS), a subordinate organization of U.S. Special Operations Command, is attempting to build a powered exoskeleton to support dismounted special operators. The project is, at its core, a research and development project, but it is challenged by the fact that it is also a Department of Defense acquisition project that must balance the requirements of cost, schedule, and performance. To balance these competing needs, the team has developed an integrated approach to sharing information among the models necessary to support cost, schedule, and performance analysis. Notably, the team makes significant use of model-based systems engineering (MBSE) to support information integration. This paper presents the challenges associated with developing TALOS, its approach to information management, and ongoing findings on how this approach has improved its performance

Selecting Robotic Power Solutions : A Case Study of Stochastic Value Modeling

Value modeling is a powerful tool that allows system engineers to ensure that a design will meet the operator’s needs. The process involves developing value measures, the criteria that is valued by the user, for a design and using these measures to derive a score that allows for an easy comparison between different alternatives. These value scores can be plotted with the cost of a design alternative to create a cost-value diagram. An inherent issue with value modeling is that it requires precise knowledge of each design alternative. However, early in the design process, precise data is typically not readily available to inform the value model or cost approximations. As such, approximations must be made, introducing uncertainty and risk into the model. This uncertainty and risk can be captured through the use of stochastic value modeling, which allows for uncertainty in the raw data to propagate through the model, creating a distribution of value scores and costs. In this paper, a case study of stochastic value modeling is presented through the selection of a power source for a robotic application. The selection of the power source is a critical design decision, since failure to select an adequate power source can result in a failed development effort. However, selection of a power system is made difficult by the number of different options and the unpredictable advancement in the technology. As such, the selection of an appropriate power system is an ideal application for a stochastic value model. This paper presents a stochastic value model for selecting a power source for a robotic application comparing different solutions, including: batteries, fuel cells, internal combustion engines, photovoltaics, and thermoelectric generators. The analysis includes value measures associated with power output, weight, noise, and thermal signature. The value model facilitates development of a cost-benefit graph that captures the uncertainty in each design alternative with respect to value and cost. The outputs of the model are then used to demonstrate a technique for risk management throughout the development of the system

Tradespace analysis for multiple performance measures

To meet the changing demands of operational environments, future Department of Defense solutions require the engineering of resilient systems. Scientists, engineers and analysts rely on modeling, simulation, and tradespace analysis to design future resilient systems. During conceptual system design, high performance computing clusters and models from multiple domains are leveraged to conduct large-scale simulation experiments that generate multi-dimensional data for tradespace exploration. Despite recent breakthroughs in computation capabilities, the world’s most powerful computers cannot effectively explore a high-dimensional tradespace using a brute-force approach. This paper outlines a viable methodology and process to generate large numbers of variant solutions for tradeoff analysis. Design of experiments is used to efficiently explore a high-dimensional tradespace and identify system design drivers. These drivers are used to identify model inputs that help focus tradespace generation in areas that promise viable solutions. A dashboard illustrates how viable variant exploration can be conducted to illuminate trade decisions

Model-based Systems Engineering: Application and Lessons from a Technology Maturation Project

The Tactical Assault Light Operator Suit (TALOS) project is a Special Operations Command (SOCOM) initiative to enhance operator performance, situational awareness, survivability, and lethality. The project adopted a Model-Based Systems Engineering (MBSE) approach to manage the structural system configuration and support the test and integration plan. This approach relies on a unified model in the Systems Modeling Language (SysML) to capture the logical and physical aspects of the system design. The MBSE approach was used to develop a test and evaluation framework that allows for traceability of test plans back to performance requirements. Additionally, the model supported the integration of hardware and software as well as the design of wire harnesses; the MBSE approach provided benefits over more traditional integration techniques. This paper provides lessons learned including the need to balance requirements analysis with functional characterization and the products that were generated from the model. Overall the adoption of the MBSE approach provided lessons on managing a system’s configuration among a distributed team

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables