FieldView-VisIt: A Modern Engineering Post-Processing System for Ultra-Scale Physics Based Simulations
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Slides from seminar to the Texas Water Energy Institute on July 30, 2021
We present mixed-integer linear programming models for utility targeting and heat exchanger network synthesis that account for variable stream temperatures and flow rates as well as unclassified streams and thus can be combined with process synthesis optimization models. By projecting temperature information onto a fixed discrete temperature grid, the model remains linear even under variable temperatures and flow rates, leading to a more tractable optimization model. The proposed model is well suited to address problems in the area of process intensification where the close interaction between different phenomena naturally leads to process synthesis problems with unclassified process streams. Several extensions are presented, including nonisothermal mixing as well as phase changes. Moreover, we show how a nonuniform grid can be adopted to reduce the complexity of the model for medium and large-size problems.
Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.
Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.
Abstract not provided.
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 primary objective of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is to sustain operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). The Advanced Instrumentation, Information, and Control (II&C) System Technologies pathway conducts targeted R&D to address aging and reliability concerns with legacy instrumentation and control (I&C) and other information systems in existing U.S. NPPs. Control room modernization is an important part following this pathway, and human factors experts at Idaho National Laboratory (INL) have been involved in conducting R&D to support migration of new digital main control room (MCR) technologies from legacy analog and legacy digital I&C. This paper describes a human factors engineering (HFE) process that supports human-system interface (HSI) design in MCR modernization activities, particularly with migration of old digital to new digital I&C. The process described in this work is an expansion from the LWRS Report INL/EXT-16-38576, and is a requirements-driven approach that aligns with NUREG-0711 requirements. The work described builds upon the existing literature by adding more detail around key tasks and decisions to make when transitioning from HSI Design into Verification and Validation (V&V). The overall objective of this process is to inform HSI design and elicit specific, measurable, and achievable human factors criteria for new digital technologies. Upon following this process, utilities should have greater confidence with transitioning from HSI design into V&V.
The primary objective of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is to sustain operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R&D). The Advanced Instrumentation, Information, and Control (II&C) System Technologies pathway conducts targeted R&D to address aging and reliability concerns with legacy instrumentation and control (I&C) and other information systems in existing U.S. NPPs. Control room modernization is an important part following this pathway, and human factors experts at Idaho National Laboratory (INL) have been involved in conducting R&D to support migration of new digital main control room (MCR) technologies from legacy analog and legacy digital I&C. This paper describes a human factors engineering (HFE) process that supports human-system interface (HSI) design in MCR modernization activities, particularly with migration of old digital to new digital I&C. The process described in this work is an expansion from the LWRS Report INL/EXT-16-38576, and is a requirements-driven approach that aligns with NUREG-0711 requirements. The work described builds upon the existing literature by adding more detail around key tasks and decisions to make when transitioning from HSI Design into Verification and Validation (V&V). The overall objective of this process is to inform HSI design and elicit specific, measurable, and achievable human factors criteria for new digital technologies. Upon following this process, utilities should have greater confidence with transitioning from HSI design into V&V.
The Federal Project Director (FPD) for the U.S. Department of Energy (DOE), Richland Operations Office (RL) Waste Management and D&D Division (WMD) requested a Technology Readiness Assessment (TRA) for the Management of the Cesium/Strontium Capsule Storage Project (MCSCP) at the Waste Encapsulation and Storage Facility (WESF) on the Hanford Site in Washington State. The MCSCP CD-1 TRA was performed by a team selected in collaboration between the Office of Environmental Management (EM) Chief Engineer (EM-3.3) and RL, WMD FPD. The TRA Team included subject matter and technical experts having experience in cask storage, process engineering, and system design who were independent of the MCSCP, and the team was led by the Director of Operations and Processes from the EM Chief Engineer's Office (EM-3.32). Movement of the Cs/Sr capsules to dry storage, based on information from the conceptual design, involves (1) capsule packaging, (2) capsule transfer, and (3) capsule storage. The project has developed a conceptual process, described in 30059-R-02, "NAC Conceptual Design Report for the Management of the Cesium and Strontium Capsules Project", which identifies the five major activities in the process to complete the transfer from storage pool to pad-mounted cask storage. The process, shown schematically in Figure 1, is comprised of the following process steps: (1) loading capsules into the UCS; (2) UCS processing; (3) UCS insertion into the TSC Basket; (4) cask transport from WESF to CSA and (5) extended storage at the CSA.
This paper introduces how an integrated set of Systems Engineering (SE) and Project Management (PM) processes were applied in a practical way, to address the challenges on a complex, highly technical, Engineering Procurement Construction project within the nuclear field. This approach was inspired by: - the principles contained in a Community of Practice on Lean in Program Management jointly established in May 2012 by MIT, PMI and INCOSE; - an opportunity to apply international standard ISO/IEC 15288:2008(E) Systems and software engineering - System life cycle processes on a real-life situation; - and the belief that there is great value in applying Systems Engineering and Project Management in an integrated manner on a project. The project offering this opportunity was the Detail Engineering, Procurement, Construction and Cold Commissioning (EPC) of the Silo Direct-encapsulation Plant (SDP). This project was undertaken by the Joint Venture (JV) formed by Areva, Mace and Atkins (a.m.a. Nuclear Ltd). Unfortunately, early termination of the project, at a stage when only a part of the processes and tools were being implemented, did not allow this integrated approach to be demonstrated in full. However, the benefits achieved in the early stages in aligning and forming an integrated project execution team, showed potential for great benefits. This paper covers an introductory analysis of the situation, the successes and benefits achieved from implementing an integrated SE - PM approach up to the stage when the project was terminated, and possible path forward had the project continued. (authors)
The primary objective of the United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program is to sustain operation of the existing commercial nuclear power plants (NPPs) through a multi-pathway approach in conducting research and development (R and D). The Advanced Instrumentation, Information, and Control (II and C) System Technologies pathway conducts targeted R and D to address aging and reliability concerns with legacy instrumentation and control (I and C) and other information systems in existing U.S. NPPs. Control room modernization is an important part following this pathway, and human factors experts at Idaho National Laboratory (INL) have been involved in conducting R and D to support migration of new digital main control room (MCR) technologies from legacy analog and legacy digital I and C. This paper describes a human factors engineering (HFE) process that supports human-system interface (HSI) design in MCR modernization activities, particularly with migration of old digital to new digital I and C. The process described in this work is an expansion from the LWRS Report INL/EXT-16-38576, and is a requirements-driven approach that aligns with NUREG-0711 requirements. The work described builds upon the existing literature by adding more detail around key tasks and decisions to make when transitioning from HSI Design into Verification and Validation (V and V). The overall objective of this process is to inform HSI design and elicit specific, measurable, and achievable human factors criteria for new digital technologies. Upon following this process, utilities should have greater confidence with transitioning from HSI design into V and V. (authors)
Here we present a novel application of reinforcement learning (RL) for online dynamic tuning of model predictive controllers (MPC). Applying a state-action-reward-state-action (SARSA) algorithm for temporal difference learning with a control-specific reward function improves the error tracking performance of a standard MPC formulation. The proposed RL approach is also readily adaptable to other MPCs, or entirely different control approaches. Practical details for the implementation of the RL-MPC algorithm are also presented. The proposed algorithm is applied to a case study of controlling nitrogen oxide (NO x ) emissions in an industrial selective catalytic reduction (SCR) unit, a control problem characterized by significant nonlinearity and time delay. Along with an RL-MPC formulation for NOx control, another MPC is proposed to mitigate ammonia slip and decrease ammonia consumption in the SCR. Results showing the efficacy of the RL-MPC for NO x control through learning and implementation on the nonlinear SCR dynamic model are presented.
The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.
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)
Algal biofuels are increasingly important as a source of renewable energy. The absence of reliable thermodynamic and other property data, and the large amount of kinetic data that would normally be required have created a major barrier to simulation. Additionally, the absence of a generally accepted flowsheet for biofuel production means that detailed simulation of the wrong approach is a real possibility. This model of algal biofuel production estimates the necessary data and places it into a heuristic model using a commercial simulator that back-calculates the process structure required. Furthermore, complex kinetics can be obviated for now by putting the simulator into energy limitation and forcing it to solve for the missing design variables, such as bioreactor surface area, productivity, and oil content. The model does not attempt to prescribe a particular approach, but provides a guide towards a sound engineering approach to this challenging and important problem.
Abstract A physical understanding of the progression from flow‐driven (quasi‐static) poromechanical deformation to dynamic fault rupture is critical to the resilient operations of several engineering systems. These processes are bridged by a progression from fault reactivation to the spontaneous nucleation of unstable sliding. Toward addressing this challenge, novel automatic time step size control methods are developed to enable accurate and efficient simulation of these dynamics and transitions from the first principles. The controllers combine local models for discretization error and Coulomb failure conditions to automatically adjust the time step size across several orders of magnitude. The methods do not require additional empirical or theoretical input and can resolve the pre‐rupture, interseismic, and seismic periods to the allowed accuracy. The computational results reveal that the proposed methods automatically capture the onset of reactivation and nucleation for homogeneous and heterogeneous fields. Hydrodynamic and structural heterogeneity lead to disparate critical nucleation sizes compared to those predicted by theory. The results highlight its potential in predicting induced seismicity in realistic subsurface engineering systems and at practical scales.
The combustion of middle distillates (diesel and jet fuel) is responsible for the emission of more than 2 GTon of CO 2 per year worldwide. While sustainable alternatives exist for gasoline and jet fuel, we still lack sustainable alternatives for diesel. This fact is especially relevant if we consider that electrification of sectors where diesel is used is challenging. One sustainable approach for diesel production is the catalytic upgrading of ethanol. While most work in this field has focused on the dehydration/oligomerization of ethanol, this approach is limited to producing fuels that have a high degree of branching and low cetane number. Another approach is the sequential use of ethanol Guerbet coupling, leading to higher alcohols, followed by etherification, leading to large ethers which, importantly, result in a product with high cetane number. In this work, we explore the catalytic upgrading of ethanol into diesel following an approach based on the initial Guerbet coupling of ethanol followed by etherification. The results presented are a collaborative and synergic effort among process and systems engineers, experimentalist in the area of catalysis, and fuel property modelers. Here, we demonstrate experimentally the feasibility of upgrading ethanol into a diesel fuel with properties that surpass its fossil counterpart. The diesel produced has a predicted cetane number of ~70 and outstanding cold flow properties, while maintaining other properties (viscosity, density, and flash point) within expected ranges. A technoeconomic analysis performed based on a detailed biorefinery model shows that the MFSP is ~5.89$ per Gal in 2021 dollars when lignocellulosic ethanol is used, with the most relevant economic driver the cost of the ethanol feedstock. The upgrading process can be performed with a net energy gain (EROI = 1.49 > 1). An LCA analysis of greenhouse gas (GHG) emissions reveals that the use of lignocellulosic ethanol may lead to more than 50% reduction in GHG emissions compared to fossil diesel. Depending on the CO 2 emissions associated with the production of ethanol we show that in some instances the production of carbon neutral diesel fuel is possible.