Results for “System Engineering Processes” — Page 25

DOE OSTI2020

Innovations in Sustainable Passive Wastewater Treatment - 20236

Sustainable water treatment systems, engineered to mimic natural processes and utilize natural materials, yield economic and environmental benefits through reductions in long term operational inputs, including: power, chemicals, and labor. Golder is an industry leader in the development and implementation of sustainable technologies at operating and closed mines. We continue to innovate these technologies and customize solutions to meet client needs by applying past experience and focused research. Sustainable (or passive) water treatment systems include ponds, wetlands, flow-through natural media beds, and limestone drains. These systems can blend in with a natural environment, are highly cost-effective for long-term water treatment, and can outcompete traditional active treatment technologies. Application of sustainable technologies relative to active technologies can be influenced by the influent water matrix, effluent limitations, flow rate of water requiring treatment, and available land area. In this presentation, four Golder-developed systems are reviewed as case studies, demonstrating our continued progressive approach to advancing sustainable water treatment technologies to meet industry needs. These case studies focus on design innovations and operational performance from data collected from the operational systems. Resources (power, chemicals, labor) required for the operation of each case study are compared with those that would be required for operation of their active treatment technology alternatives. - Case Study 1 presents an anaerobic biochemical reactor (BCR), a technology typically implemented for metals treatment and acid neutralization, for the treatment of sulfate to low concentrations (<250 mg/L). Active treatment via reverse osmosis (RO) membrane filtration is typically required for treatment of sulfate to similar concentrations, this innovation provides potential for sustainable treatment to replace the need for energy, chemical, labor, and waste (brine) intensive RO treatment for specific applications. Magnetite waste rock materials from the site were upcycled for use in this system to provide sequestration of the sulfide generated in the BCR. - Case Study 2 examines another novel application of the conventional BCR, modified for the removal of radionuclides and metals (uranium, radium, and selenium) at a closed uranium mine. - Case Study 3 presents implementation of sustainable treatment for mine drain water from a historic mine site, now a state park with around 100,000 visitors per year. The system includes a pond, wetland, and manganese removal bed surrounded by public trails designed to maintain the character of the park. The system treats for arsenic, iron, and manganese at relatively high flows (up to 1,200 gpm), with large seasonal fluctuations in flow and quality. - Case Study 4 is a unique project for which Golder is currently designing a sustainable treatment system, with no energy input, to treat nitrate in mine water post-closure at a site near the arctic circle. In this unprecedented application, climate, management of freshet, and space availability presented substantial technical challenges. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2011

Fault Management Guiding Principles

Regardless of the mission type: deep space or low Earth orbit, robotic or human spaceflight, Fault Management (FM) is a critical aspect of NASA space missions. As the complexity of space missions grows, the complexity of supporting FM systems increase in turn. Data on recent NASA missions show that development of FM capabilities is a common driver for significant cost overruns late in the project development cycle. Efforts to understand the drivers behind these cost overruns, spearheaded by NASA's Science Mission Directorate (SMD), indicate that they are primarily caused by the growing complexity of FM systems and the lack of maturity of FM as an engineering discipline. NASA can and does develop FM systems that effectively protect mission functionality and assets. The cost growth results from a lack of FM planning and emphasis by project management, as well the maturity of FM as an engineering discipline, which lags behind the maturity of other engineering disciplines. As a step towards controlling the cost growth associated with FM development, SMD has commissioned a multi-institution team to develop a practitioner's handbook representing best practices for the end-to-end processes involved in engineering FM systems. While currently concentrating primarily on FM for science missions, the expectation is that this handbook will grow into a NASA-wide handbook, serving as a companion to the NASA Systems Engineering Handbook. This paper presents a snapshot of the principles that have been identified to guide FM development from cradle to grave. The principles range from considerations for integrating FM into the project and SE organizational structure, the relationship between FM designs and mission risk, and the use of the various tools of FM (e.g., redundancy) to meet the FM goal of protecting mission functionality and assets.

principles↗

NASA NTRS1994

Ordering design tasks based on coupling strengths

The design process associated with large engineering systems requires an initial decomposition of the complex system into modules of design tasks which are coupled through the transference of output data. In analyzing or optimizing such a coupled system, it is essential to be able to determine which interactions figure prominently enough to significantly affect the accuracy of the system solution. Many decomposition approaches assume the capability is available to determine what design tasks and interactions exist and what order of execution will be imposed during the analysis process. Unfortunately, this is often a complex problem and beyond the capabilities of a human design manager. A new feature for DeMAID (Design Manager's Aid for Intelligent Decomposition) will allow the design manager to use coupling strength information to find a proper sequence for ordering the design tasks. In addition, these coupling strengths aid in deciding if certain tasks or couplings could be removed (or temporarily suspended) from consideration to achieve computational savings without a significant loss of system accuracy. New rules are presented and two small test cases are used to show the effects of using coupling strengths in this manner.

Rogers, James L., Jr.↗

NASA NTRS1994

Ordering Design Tasks Based on Coupling Strengths

The design process associated with large engineering systems requires an initial decomposition of the complex system into modules of design tasks which are coupled through the transference of output data. In analyzing or optimizing such a coupled system, it is essential to be able to determine which interactions figure prominently enough to significantly affect the accuracy of the system solution. Many decomposition approaches assume the capability is available to determine what design tasks and interactions exist and what order of execution will be imposed during the analysis process. Unfortunately, this is often a complex problem and beyond the capabilities of a human design manager. A new feature for DeMAID (Design Manager's Aid for Intelligent Decomposition) will allow the design manager to use coupling strength information to find a proper sequence for ordering the design tasks. In addition, these coupling strengths aid in deciding if certain tasks or couplings could be removed (or temporarily suspended) from consideration to achieve computational savings without a significant loss of system accuracy. New rules are presented and two small test cases are used to show the effects of using coupling strengths in this manner.

Rogers, J. L.↗

NASA NTRS1991

The development of a post-test diagnostic system for rocket engines

An effort was undertaken by NASA to develop an automated post-test, post-flight diagnostic system for rocket engines. The automated system is designed to be generic and to automate the rocket engine data review process. A modular, distributed architecture with a generic software core was chosen to meet the design requirements. The diagnostic system is initially being applied to the Space Shuttle Main Engine data review process. The system modules currently under development are the session/message manager, and portions of the applications section, the component analysis section, and the intelligent knowledge server. An overview is presented of a rocket engine data review process, the design requirements and guidelines, the architecture and modules, and the projected benefits of the automated diagnostic system.

Zakrajsek, June F.↗

NASA NTRS1991

The development of a post-test diagnostic system for rocket engines

An effort was undertaken by NASA to develop an automated post-test, post-flight diagnostic system for rocket engines. The automated system is designed to be generic and to automate the rocket engine data review process. A modular, distributed architecture with a generic software core was chosen to meet the design requirements. The diagnostic system is initially being applied to the Space Shuttle Main Engine data review process. The system modules currently under development are the session/message manager, and portions of the applications section, the component analysis section, and the intelligent knowledge server. An overview is presented of a rocket engine data review process, the design requirements and guidelines, the architecture and modules, and the projected benefits of the automated diagnostic system.

Zakrajsek, June F.↗

NASA NTRS2004

How Systems Engineering and Risk Management Defend Against Murphy's Law and Human Error

Systems Engineering and Risk Management processes can work synergistically to defend against the causes of many mission ending failures. Defending against mission ending failures is facilitated by fostering a team that has a healthy respect for Murphy's Law and a team with a of curiosity for how things work, how they can fail, and what they need to know. This curiosity is channeled into making the unknowns known or what is uncertain more certain. Efforts to assure mission success require the expenditure of energy in the following areas: 1. Understanding what defines Mission Success as guided by the customer's needs, objectives and constraints. 2. Understanding how the system is supposed to work and how the system is to be produced, fueled by the curiosity of how the system should work and how it should be produced. 3. Understanding how the system can fail and how the system might not be produced on time and within cost, fueled by the curiosity of how the system might fail and how production might be difficult. 4. Understanding what we need to know and what we need learn for proper completion of the above three items, fueled by the curiosity of what we might not know in order to make the best decisions.

Bay, Michael↗

NASA NTRS2007

NASA Systems Engineering Handbook

This handbook is intended to provide general guidance and information on systems engineering that will be useful to the NASA community. It provides a generic description of Systems Engineering (SE) as it should be applied throughout NASA. A goal of the handbook is to increase awareness and consistency across the Agency and advance the practice of SE. This handbook provides perspectives relevant to NASA and data particular to NASA. The coverage in this handbook is limited to general concepts and generic descriptions of processes, tools, and techniques. It provides information on systems engineering best practices and pitfalls to avoid. There are many Center-specific handbooks and directives as well as textbooks that can be consulted for in-depth tutorials. This handbook describes systems engineering as it should be applied to the development and implementation of large and small NASA programs and projects. NASA has defined different life cycles that specifically address the major project categories, or product lines, which are: Flight Systems and Ground Support (FS&GS), Research and Technology (R&T), Construction of Facilities (CoF), and Environmental Compliance and Restoration (ECR). The technical content of the handbook provides systems engineering best practices that should be incorporated into all NASA product lines. (Check the NASA On-Line Directives Information System (NODIS) electronic document library for applicable NASA directives on topics such as product lines.) For simplicity this handbook uses the FS&GS product line as an example. The specifics of FS&GS can be seen in the description of the life cycle and the details of the milestone reviews. Each product line will vary in these two areas; therefore, the reader should refer to the applicable NASA procedural requirements for the specific requirements for their life cycle and reviews. The engineering of NASA systems requires a systematic and disciplined set of processes that are applied recursively and iteratively for the design, development, operation, maintenance, and closeout of systems throughout the life cycle of the programs and projects.

Source record↗

DOE OSTI2023

Characterizing the Impacts of Multi-Scale Heterogeneity on Solute Transport in Fracture Networks

We model flow and transport in fracture networks with varying degrees of fracture-to-fracture aperture heterogeneity and network intensity to show how changes in these properties can cause the emergence of anomalous flow and transport behavior. If fracture-to-fracture aperture heterogeneity is increased in sparse networks, velocity fluctuations inhibit high flow rates and solute transport can be delayed. Surprisingly, transport can be slowed even in cases where hydraulic aperture is monotonically increased. As the intensity of the networks is increased, more connected pathways allow for particles to bypass these effects. There exists transition behavior where with relatively few connected pathways in a network, first arrival times of particles are not heavily affected by fracture-to-fracture aperture heterogeneity, but the scaling behavior of the tails is strongly influenced. These results reinforce the importance of considering multi-scale effects in fractured systems and can inform flow and transport processes in both natural and engineered fractured systems.

58 GEOSCIENCES↗

NASA NTRS2016

Systems Engineering Awareness

The presentation will provide an overview of the fundamentals and principles of Systems Engineering (SE). This includes understanding the processes that are used to assist the engineer in a successful design, build and implementation of solutions. The context of this presentation will be to describe the involvement of SE throughout the life-cycle of a project from cradle to grave. Due to the ever growing number of complex technical problems facing our world, a Systems Engineering approach is desirable for many reasons. The interdisciplinary technical structure of current systems, technical processes representing System Design, Technical Management and Product Realization are instrumental in the development and integration of new technologies into mainstream applications. This tutorial will demonstrate the application of SE tools to these types of problems..

technical management↗

NASA NTRS1989

Suggested criteria for evaluating systems engineering methodologies

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.

Gates, Audrey↗

NASA NTRS1996

Operations engineering: Applying hands-on experience to the development process

The concepts behind operations engineering as applied to the requirements, design, development and testing of data processing systems are presented, together with the associated benefits. The objective of operations engineering is to reduce the overall life cycle costs by integrating operations experience with the development process. To achieve this goal, operations engineering seeks to reduce the development costs by that assuring operational requirements are incorporated into the design and development process as early as possible, and reduce the operational costs by decreasing operations staffing requirements and other related costs through improved system capabilities. The areas for improved system capabilities include: system recovery; data recovery; fault isolation; system operability; system flexibility; system automation; and system reporting. It is described how operations engineering is integrated with the development process, and the difficulties and misconceptions experienced in using operations engineering are discussed.

Alcott, Gary↗

DOE OSTI2022

A Causal Approach to Integrate Component Health Data into System Reliability Models

Two of the challenges of current plant reliability approaches are the ability to integrate plant health data, and to support decision making. Condition based data and diagnostic/prognostic information are in fact not considered into plant reliability models to inform system engineers on the most critical components. Currently, the propagation of quantitative health data from the component to the system level is a challenge given the diverse nature/structure of the data. On the other hand, plant reliability methods (which are typically based on fault-trees or reliability block diagrams) can effectively propagate data from the component to the system level, but values of failure rates or failure probabilities are an approximated integral representation of the past industry-wide operational experience, and it neglects the present component health status (e.g., diagnostic and condition-based data) and health projection (when available from prognostic data). Our first claim is that system reliability models should propagate health information from the component to the system/plant level in order to provide a quantitative snapshot of system/plant health and identify the most critical components. Our second claim is that component health should be informed solely by that specific component current and historical performance data and should not be an approximated integral representation of the past industry-wide operational experience. This paper is directly supporting these two claims by proposing a different approach to perform reliability modeling which relies on available component diagnostic, prognostic and condition-based data to measure component health, and it propagates this information through fault tree models. The propagation of health data from the component to the system level is performed not in terms of probability, but in terms of margins where margin is defined as the “distance” between the present actual status and an undesired event (e.g., failure or unacceptable performance). Through a cause-effect lens, while classical reliability models target the effect associated to a component performance, a margin-based approach focuses on the cause of an undesired component performance (i.e., component health). Hence, thinking of reliability in terms of margins implies decision making based on causal reasoning. We will show how fault tree models can be solved using a margin language and how this process can effectively assist system engineers to identify the most critical components.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2011

Product Lifecycle Management and the Quest for Sustainable Space Exploration Solutions

Product Lifecycle Management (PLM) is an outcome of lean thinking to eliminate waste and increase productivity. PLM is inextricably tied to the systems engineering business philosophy, coupled with a methodology by which personnel, processes and practices, and information technology combine to form an architecture platform for product design, development, manufacturing, operations, and decommissioning. In this model, which is being implemented by the Marshall Space Flight Center (MSFC) Engineering Directorate, total lifecycle costs are important variables for critical decision-making. With the ultimate goal to deliver quality products that meet or exceed requirements on time and within budget, PLM is a powerful concept to shape everything from engineering trade studies and testing goals, to integrated vehicle operations and retirement scenarios. This briefing will demonstrate how the MSFC Engineering Directorate is implementing PLM as part of an overall strategy to deliver safe, reliable, and affordable space exploration solutions and how that strategy aligns with the Agency and Center systems engineering policies and processes. Sustainable space exploration solutions demand that all lifecycle phases be optimized, and engineering the next generation space transportation system requires a paradigm shift such that digital tools and knowledge management, which are central elements of PLM, are used consistently to maximum effect. Adopting PLM, which has been used by the aerospace and automotive industry for many years, for spacecraft applications provides a foundation for strong, disciplined systems engineering and accountable return on investment. PLM enables better solutions using fewer resources by making lifecycle considerations in an integrative decision-making process.

Caruso, Pamela W.↗

NASA NTRSDate not supplied

Digital Assistance for System Requirement Discovery and Analysis using Machine Learning Natural Language Processing Algorithm

NASA’s Air Traffic Management-Exploration (ATM-X) Urban Air Mobility (UAM) Airspace Subproject is conducting research that evolves UAM airspace towards a highly automated and operationally flexible system of the future (see https://www.nasa.gov/uam-overview/ for more information). The complexity of UAM airspace, and its evolution through a series of transformative epochs, requires a planning tool to effectively organize, integrate, and communicate the research that will guide the evolution of UAM operations in the National Airspace System (NAS). The planning tool, called the UAM airspace research roadmap (or just roadmap), is being developed as a new system engineering methodology leveraging model based system engineering (MBSE) and machine learning natural language processing (ML NLP, or just NLP) capabilities. This presentation gives an overview of the NLP application within this system engineering methodology and will describe how it is being used to meet the ATM-X UAM Airspace Subproject’s overarching research goals.

ATM↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System

NASA↗

NASA NTRSDate not supplied

System Safety Analysis of Complex NASA Systems with Model Based Engineering

The emergence of model-based engineering is transforming design and analysis methodologies [5]. A recognized benefit of model-based engineering is the existence of a “single source of truth” about the system that becomes the authoritative source of data and information for designers, analysts, and developers. This promotes consistency and efficiency as the design emerges and can be used to further optimize the design. Integrating System Safety Engineers to the “single source of truth” will ensure that the outputs of their assessments and analyses are relevant to the design as it evolves. Use of an integrated system model enables near immediate evaluation of a design change as well as development of operational processes for risk assessment and communication. Such models can enable efficient and timely analysis of system hazards (e.g., hazard fault tree analysis and procedure simulations) and produce complete, accurate, and more consistent products (e.g., hazard reports and safety requirement evaluations). Therefore, an agency-sponsored team at Goddard Space Flight Center (GSFC) recently completed a System Safety Study of modeling and testing capabilities as part of a Model-Based Safety and Mission Assurance Initiative (MBSMAI). Using an existing model developed for reliability analyses [1], GSFC modeling and system safety experts performed system safety analysis/modeling and produced safety products. The team evaluated model-based feasibility to support System Safety Engineering, developed safety analysis modeling processes, and identified tool capability advancement/development needs. These study results indicate model-based engineering is valid and useable for System

Model Based Engineering↗

DOE OSTI2023

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗