Urban studies - An opportunity for systems engineering
Systems engineering and urban transportation
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Systems engineering and urban transportation
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To facilitate the development of a space nuclear thermal propulsion engine for manned flights to Mars, requirements must be established early in the technology development cycle. The long lead times for the acquisition of the engine system and nuclear test facilities demands that the engine system size, performance and safety goals be defined at the earliest possible time. These systems are highly complex and require a large multidisciplinary systems engineering team to develop and track requirements, and to ensure that the as-built system reflects the intent of the mission. A methodology has been devised which uses sophisticated computer tools to effectively develop and interpret functional requirements, and furnish these to the specification level for implementation.
In addition to but separate from the Red and Blue Teams commissioned by the NASA Administrator, NASA's Associate Administrator for Space Communications commissioned a Blue Team to review the Office of Space Communications (Code O) Core Program and determine how the program could be conducted faster, better, and cheaper, without compromising safety. Since there was no corresponding Red Team for the Code O Blue Team, the Blue Team assumed a Red Team independent attitude and challenged the status quo. The Blue Team process and results are summarized. The Associate Administrator for Space Communications subsequently convened a special management session to discuss the significance and implications of the Blue Team's report and to lay the groundwork and teamwork for the next steps, including the transition from engineering systems to systems engineering. The methodology and progress toward realizing the Code O Family vision and accomplishing the systems engineering initiative for NASA's space communications are presented.
The past several decades have seen the rise of large, highly interactive systems that are on the forward edge of technology. As a result of this growth and the increased usage of digital systems (computers and software), the concept of systems engineering has gained increasing attention. Some of this attention is no doubt due to large program failures which possibly could have been avoided, or at least mitigated, through the use of systems engineering principles. The complexity of modern day weapon systems requires conscious application of systems engineering concepts to ensure producible, operable and supportable systems that satisfy mission requirements. Although many authors have traced the roots of systems engineering to earlier dates, the initial formalization of the systems engineering process for military development began to surface in the mid-1950s on the ballistic missile programs. These early ballistic missile development programs marked the emergence of engineering discipline 'specialists' which has since continued to grow. Each of these specialties not only has a need to take data from the overall development process, but also to supply data, in the form of requirements and analysis results, to the process. A number of technical instructions, military standards and specifications, and manuals were developed as a result of these development programs. In particular, MILSTD-499 was issued in 1969 to assist both government and contractor personnel in defining the systems engineering effort in support of defense acquisition programs. This standard was updated to MIL-STD499A in 1974, and formed the foundation for current application of systems engineering principles to military development programs.
A model of the human as an 'engineered' system provides a starting point for determining human requirements and performance on an equivalent basis with technological systems. The human as an engineered system with performance requirements is defined to consist of four subsystems: cognitive, psychological, biomechanical, and biomedical. It is suggested that the treatment of the psychological subsystem as one that modulates the efficiency and quality of human performance offers a particular approach for examining and characterizing psychological effects.
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world’s energy demands and build energy security.
Systems engineering is of utmost importance for the success of high-cost, high-complexity megaprojects, which are common in the energy sector. However, the traditional document-centric systems engineering approach tends to be labor-intensive and time-consuming, which has inhibited its full adoption despite proven metrics on its return on investment. However, with the modern approach of digital engineering and technological advancements in artificial intelligence (AI) technologies, the barriers to systems engineering adoption can finally be broken. This paper goes through the systems engineering V-model for lifecycle management and assesses the current state of implementation of digital engineering (especially, mod-el-based systems engineering, digital twins, and digital threads) and AI for each step. It was observed that a combination of digital engineering and AI is being used across different industries to accelerate and optimize systems engineering processes such as concept development, requirements management, architecture definition, system development, verification and validation, operations, and maintenance. Specifically in the energy sector, AI-augmented digital engineering has shown initial potential in accelerated development and deployment, performance optimization, anomaly detection, predictive maintenance, and configuration management. However, challenges remain in integrating DE and AI into an end-to-end system lifecycle management ecosystem safely and reliably. Addressing these challenges and continuously developing impactful tools will enable fast, efficient, and high-frequency deployment of power generation capabilities to keep up with the world?s energy demands and build energy security.
Systems engineering is the application of mathematical and scientific principles to practical ends in the life-cycle of a system. A methodology for systems engineering is a carefully developed, relatively complex procedure or process for applying these mathematical and scientific principles. There are many systems engineering methodologies (or possibly many versions of a few methodologies) currently in use in government and industry. These methodologies are usually designed to meet the needs of a particular organization. It has been observed, however, that many technical and non-technical problems arise when inadequate systems engineering methodologies are applied by organizations to their systems development projects. Various criteria for evaluating systems engineering methodologies are discussed. Such criteria are developed to assist methodology-users in identifying and selecting methodologies that best fit the needs of the organization.
This system engineering toolbox is designed to provide tools and methodologies to the design-oriented systems engineer. A tool is defined as a set of procedures to accomplish a specific function. A methodology is defined as a collection of tools, rules, and postulates to accomplish a purpose. For each concept addressed in the toolbox, the following information is provided: (1) description, (2) application, (3) procedures, (4) examples, if practical, (5) advantages, (6) limitations, and (7) bibliography and/or references. The scope of the document includes concept development tools, system safety and reliability tools, design-related analytical tools, graphical data interpretation tools, a brief description of common statistical tools and methodologies, so-called total quality management tools, and trend analysis tools. Both relationship to project phase and primary functional usage of the tools are also delineated. The toolbox also includes a case study for illustrative purposes. Fifty-five tools are delineated in the text.
A system engineering methodology is described which enables users, particulary NASA and DOD, to accommodate changing needs; incorporate emerging technologies; identify, quantify, and manage system risks; manage evolving functional requirements; track the changing environment; and reduce system life-cycle costs. The approach is a concurrent, dynamic one which starts by constructing a performance model defining the required system functions and the interrelationships. A detailed probabilistic risk assessment of the system elements and their interrelationships is performed, and quantitative analysis of the reliability and maintainability of an engineering system allows its different technical and process failure modes to be identified and their probabilities to be computed. Decision makers can choose technical solutions that maximize an objective function and minimize the probability of failure under resource constraints.
Complex systems improvement by integration of reliability and systems engineering techniques
Systems engineering is examined in terms of functional activities that are performed in the conduct of a system definition/design, and system development is described in a parametric analysis that combines functions, performance, and design variables. Emphasis is placed on identification of activities performed by design organizations, design specialty groups, as well as a central systems engineering organizational element. Identification of specific roles and responsibilities for doing functions, and monitoring and controlling activities within the system development operation are also emphasized.
Systems engineering implementation phase interwoven with primary management tasks to assure overall system integrity and compatibility
Very large integrated systems have always posed special problems for engineers. Whether they are power generation systems, computer networks or space vehicles, whenever there are multiple interfaces, complex technologies or just demanding customers, the challenges are unique. 'Systems engineering' has evolved as a discipline in order to meet these challenges by providing a structured, top-down design and development methodology for the engineer. This paper attempts to define the general class of problems requiring the complete systems engineering treatment and to show how systems engineering can be utilized to improve customer satisfaction and profit ability. Specifically, this work will focus on a design methodology for the largest of systems, not necessarily in terms of physical size, but in terms of complexity and interconnectivity.
User Engineering is a new System Engineering perspective responsible for defining and maintaining the user view of the system. Its elements are a process to guide the project and customer, a multidisciplinary team including hard and soft sciences, rapid prototyping tools to build user interfaces quickly and modify them frequently at low cost, and a prototyping center for involving users and designers in an iterative way. The main consideration is reducing the risk that the end user will not or cannot effectively use the system. The process begins with user analysis to produce cognitive and work style models, and task analysis to produce user work functions and scenarios. These become major drivers of the human computer interface design which is presented and reviewed as an interactive prototype by users. Feedback is rapid and productive, and user effectiveness can be measured and observed before the system is built and fielded. Requirements are derived via the prototype and baselined early to serve as an input to the architecture and software design.
An effective systems engineering approach applied through the project life cycle can help Langley produce a better product. This paper demonstrates how an enhanced systems engineering process for in-house flight projects assures that each system will achieve its goals with quality performance and within planned budgets and schedules. This paper also describes how the systems engineering process can be used in combination with available software tools.
Formal model-based systems engineering (MBSE) combines a model, systems thinking, and systems engineering to visually depict the boundaries, context, and behavior of interconnected systems, facilitating effective design, development, and utilization of engineered systems throughout the systems engineering lifecycle. Although nuclear reactor vendors employ these tools to integrate functionality, performance, and safety, they are not yet addressing digital risk concerns introduced by use of operational technology, such as digital instrumentation and control systems. To accomplish this objective, the Microreactor Applications Research Validation and EvaLuation (MARVEL) microreactor was used as an MBSE case study. This real-world application provides a first-of-a-kind opportunity to demonstrate the benefits of integrating digital risk and cybersecurity into the MBSE design process of a nuclear reactor. This paper provides an update of the ongoing MARVEL Cyber MBSE project as it specifically relates to the integration of digital risk management and cybersecurity by design.