Requirements Engineering Magic Practical Application of Systems Engineering Alchemy.
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The presentation was delivered at the 2022 Smart Manufacturing Experience event in Pittsburgh, PA.
Potassium copper hexacyanoferrate-cellulose hydrogel composites (HCF-gels) and potassium copper hexacyanoferrate-embedded poly-citric acid hydrogel film (HPC) were synthesized. In Cs{sup +} removal experiments, HCF-gels exhibited exceptional Cs{sup +} adsorption capacities 2.06-2.32 mmol g{sup -1}. HPC showed a very high Cs{sup +} uptake of 667 mg/g KCuHCF. The adsorbents revealed stable and high Cs{sup +} removal efficiency across a wide pH range. Notably, the adsorbents could adsorb Cs{sup +} selectively in seawater containing Cs{sup +}. The combination of testing and radiation stability results indicated that the performance of the materials are maintained whether irradiated or not. The cost of the HCF-gels and HPC were estimated approximately 13.0055 $/kg and 38.2490 $/kg respectively. This is reasonable cost compared to commercialized Cs{sup +} adsorbents. New pool scrubbing system is designed for engineering system design to apply novel Cs{sup +} adsorbents under severe accident condition. (authors)
The Weapon Systems Engineering Material Database, often called ‘Granta’ (due to the use of Granta MI software by Granta Design) but referred to in this document as ‘the database’, stores and distributes material information to W-division at Los Alamos National Laboratory (LANL). The database is constantly evolving as data is imported and methods of retrieving data are developed. Because limited resources necessitate prioritization of data to be imported and retrieval method development, a definition is needed of how the database is to be used, both currently and in the future. This document, to be used by the database developers, is intended to provide that definition in the form of requirements for the database.
Operating a gaseous fuel engine system includes controlling at least one of a delivery location, a delivery timing, or in situ mixing of a gaseous fuel with air, based on at least one engine system parameter upon the basis of which a blowby amount of a gaseous fuel to a crankcase varies. Crankcase accumulation of the gaseous fuel is maintained below a flammability limit. Related apparatus and control logic is also disclosed.
This paper addresses the systems engineering (SE) processes used for the National Spherical Torus Experiment-Upgrade (NSTX-U). It focuses on SE across the life cycle of the system, including requirements management, interface control, risk management, integration, and verification/validation. This is particularly significant as NSTX-U includes new systems and an existing plant and reused systems from past projects such as the Tokamak Fusion Test Reactor (TFTR). The implementation of SE provides the ability to control complexity, improve communications, identify risks early, and prevent defects. Systems engineering principles are applied to enhance the integration while maintaining relevance in plasma research. Furthermore, these principles define a deliberate process to identify and resolve issues early in the development cycle, thus reducing risks and optimizing outputs. They also establish relationships to gather knowledge from experts and stakeholders, supporting the continued ability of NSTX-U in building and maintaining an operational system able to adapt to changing environments and emerging requirements.
Abstract Deep learning has significantly improved the state-of-the-art in computer vision and natural language processing, and holds great potential to design effective tools for predicting and simulating complex engineering systems. In particular, scientific machine learning seeks to apply the power of deep learning to scientific and engineering tasks, with operator learning (OL) emerging as a particularly effective tool. OL can approximate nonlinear operators arising in complex engineering systems, making it useful for simulating, designing, and controlling those systems. In this position paper, we provide a comprehensive overview of OL, including its potential applications to complex engineering domains. We cover three variations of OL approaches: deterministic OL for modeling nonautonomous systems, OL with uncertainty quantification (UQ) capabilities, and multifidelity OL. For each variation, we discuss drawbacks and potential applications to engineering, in addition to providing a detailed explanation. We also highlight how multifidelity OL approaches with UQ capabilities can be used to design, optimize, and control engineering systems. Finally, we outline some potential challenges for OL within the engineering domain.
There is growing interest in the development of small modular reactors (SMRs) as a cost-effective renewable source of energy. Most of the current conceptual designs propose advanced and automated control and monitoring technologies which would require complex human system interfaces. The proposals also include a reduction in the number of staff, and partial or full remote control of the facilities. The human factors challenges of such a design philosophy range from identification of information requirements of users to assessing workload to deciding on best information visualization techniques to ensure situation awareness is maintained in a remote control facility. Addressing these challenges will require a systems engineering approach where user requirements are identified through a systematic approach for various contexts, including normal, abnormal, and accident conditions. A systems engineering process will integrate human factors as well as other specialty disciplines such as reliability and design optimization in a coherent fashion that would lead to realization of a successful system that addresses user requirements. The licensing model in Canada is based upon a graded approach and is largely technology neutral. A key factor in ensuring effective licensing effort is identification of regulatory requirements. This paper discusses how adopting a systems engineering approach and early consideration of human factors principles and requirements will not only ensure an efficient design process, but will also help with developing a design that meets the regulatory requirements. (authors)
Early Systems Research and Development (ESR&D) is one of the most crucial phases in the product development process. It both blends and blurs the lines between science and engineering, and requires a risk-based, disciplined, and graded approach to effectively manage scope, cost, and complexity of the final product. Many leaders, program managers, and scientists are unwilling to involve systems engineering because of the perception that systems engineering is heavily process oriented, adds unnecessary costs, and should be applied only to mature technologies. The value of systems engineering as applied to ESR&D is unclear to these key individuals. The unfortunate result is that system engineering is not applied to ESR&D. This results in R&D efforts that may have solved the wrong problem, selected the wrong architecture, require technical rework, have difficulty transitioning later maturity levels, and result in higher R&D costs and extended development timelines. This work discusses the difficulty of introducing systems engineering to the research and early development process and their inclination perspectives of researchers, engineers, and managers. The article shall offer potential means to manage the cultural transformation of early adoption of right-sized systems engineering in ESR&D and reverse the attitudinal positions.
The Systems Analysis & Decision Support group (2150) at Sandia National Laboratories (SNL) led a study from 2015 to 2017 to investigate the utility of various options to achieve systematic and comprehensive incorporation of the human component of a system throughout the product lifecycle. Although technology readiness levels (TRL) are widely used in the systems engineering process to address technical maturity throughout design and development, a comparable technique to estimate the readiness of a technology for human operator use is lacking at SNL. The present study was designed to investigate the utility of a human readiness levels (HRL) scale to complement the TRL scale for the types of nuclear deterrence work conducted at SNL. The study team conducted 24 interviews with 26 individuals to (1) understand the extent to which current baseline design and development approaches at SNL incorporate the human dimension and (2) estimate the utility of various options to fully incorporate the human element in SNL work. Results confirmed that current processes do not manage the human component of a system systematically or comprehensively across programs. Although multiple options were seen to have some utility to address this shortcoming, an approach combining human readiness assessments within the existing TRL scale was perceived as relatively more useful for SNL. The study team created a proposed path forward to progress from the current ad hoc, reactive approach to a systematic, comprehensive, and rigorous approach characterized by full consideration of the human component of a system within and across programs and throughout the entire product lifecycle.
This presentation was given at the third Wind Energy Systems Engineering Workshop in Boulder, Colorado, and focused on wind plant controls research, combined optimization, a case study on the Princess Amalia Wind Park, results from the case study, and future work.
The second National Renewable Energy Laboratory (NREL) Wind Energy Systems Engineering Workshop was held in Broomfield, Colorado, from January 29 to February 1, 2013. The event included a day-and-a-half workshop exploring a wide variety of topics related to system modeling and design of wind turbines and plants. Following the workshop, 2 days of tutorials were held at NREL, showcasing software developed at Sandia National Laboratories, the National Aeronautics and Space Administration's Glenn Laboratories, and NREL. This document provides a brief summary of the various workshop activities and includes a review of the content and evaluation results from attendees.
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.
The purpose of this paper is to explore the concept of ‘enterprise’ in the context of Systems Engineering (SE). The term ‘enterprise’ has been used extensively to generally describe large complex entities that have an extensive scope of operations. However, a deeper examination of ‘enterprise’ significance for SE can provide insights as our challenges continue with increasingly complex, uncertain, ambiguous, and integrated entities struggling to thrive in the future. The paper explores three central topics. First, the concept of enterprise is introduced as a central aspect of the future focus for SE, as recognized in the INCOSE SE Vision 2035. Second, a more detailed examination of the enterprise concept is developed in relationship to SE. The thrust of this examination is to understand the nature and role of ‘enterprise’ across a broad spectrum of literature and knowledge, ultimately providing a more informed perspective of enterprise for SE. As part of this exploration, a bibliometric analysis of the term ‘enterprise’ is performed. This exploration extracts key themes (clusters) in the ‘enterprise’ literature. Third, challenges for further development and inculcation of ‘enterprise’ within the SE discipline and support for realization of the SE 2035 Vision are suggested. These challenges point out the need to ‘think differently’ about ‘enterprise’ within the SE context. ‘Enterprise’ is proposed as a central, albeit different, perspective for the SE discipline. Finally, the paper closes with a first–generation perspective for ‘enterprise’ in pursuit of the SE Vision 2035.
The near-term objective of the EC team is to establish an operating, commercially scalable Recompression Closed Brayton Cycle (RCBC) to be constructed for the NE - STEP demonstration system (demo) with the lowest risk possible. A systems engineering approach is recommended to ensure adequate requirements gathering, documentation, and mode ling that supports technology development relevant to advanced reactors while supporting crosscut interests in potential applications. A holistic systems engineering model was designed for the ART Energy Conversion program by leveraging Concurrent Engineering, Balance Model, Simplified V Model, and Project Management principles. The resulting model supports the identification and validation of lifecycle Brayton systems requirements, and allows designers to detail system-specific components relevant to the current stage in the lifecycle, while maintaining a holistic view of all system elements.
The purpose of this project is to apply a systems engineering method to a programmatic improvement or implementation of a system found within the Weapons and Complex Integration (WCI) Nuclear Materials Technology Program (NMTP) at Lawrence Livermore National Laboratory (LLNL). This project identifies an opportunity for improvement of the Tritium Processing Station (TPS) based on key stakeholder requirements. This document will cover the generation of the selected concept, system operational context and use cases, the production of verifiable engineering requirements, functional decomposition, and an assessment of risk via a failure mode and effect analysis (FMEA).
Model Biomass Feedstock National User Facility (BFNUF) to reduce cost and speed up the development to meet Bioenergy Technologies Office (BETO) 2030 mission. Model Based System Engineering (MBSE) detects problems early in the system which reduces cost and speeds up the development and is used for modeling BFNUF.
Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. Herein, this Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. Resulting from this, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.