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We exist to inspire reliable processes so that people feel safe, secure, and effective every day. Our mission is to utilize Reliability-centered Maintenance and Reliability Engineering principles in partnership with cross-functional cooperation to assure a common goal is safely achieved.
Ensuring the reliability of the NIF, including its support systems, laser systems, target diagnostic systems, and utilities, is essential to the availability of the NIF in its support of NNSA missions. NIF is a key capability in the DOE Stockpile Stewardship Program and supports high energy physics experiments for nuclear weapons, energy, and astrophysics applications. High system reliability provides opportunities for shots and scientific discoveries with opportunities to enhance and upgrade capabilities. This Maintenance Plan (MP) identifies the policies and procedures used to perform and support asset management of the NIF Facility and Infrastructure Systems (FInS), NIF Lasers & Alignment (LASE), NIF Target Experimental Operations (TOPS), NIF Target Area Science and Engineering (TASE), and NIF&PS Control Systems (NCS). The FInS systems include the facility, HVAC, contamination control, beampath, and Line Replaceable Units (LRUs) as well as utilities which create the beampath environments, such as vacuum, argon, or clean dry air. The LASE systems are Programmatic systems which include laser diagnostics, alignment, power conditioning, pulsed power, and input laser systems. The TOPS and TASE systems are also Programmatic systems which include target and diagnostic delivery systems and positioners, many different insertable and fixed target diagnostics, and cryogenic and target gas fill systems. Finally, the NCS systems include both software and hardware for industrial and shot operation control systems. Policies governing administrative and operational practices related to maintenance of FInS, LASE, TOPS, TASE, and NCS systems are described in this plan. In addition, the plan provides processes and procedures for managing, tracking, and documenting the work. This document, the NIF Operations Management Plan, NIF-5020544 (Ref. 1), and NIF Shot Operations Plan, NIF-5018506 (Ref. 2), together satisfy the requirements of the Conduct of Operations. Duties, responsibilities, and reporting requirements of the various positions associated with FInS, LASE, and TOPS maintenance are detailed in this plan. The FInS systems include both Real Property systems with asset management requirements specified in DOE Order 430.1C (Ref. 3) and Programmatic systems. In addition, for FInS, there is a list of the System Level Maintenance Plans (SLMPs) in NIF-1007419198 (Ref. 4) which provide the system descriptions and maintenance plan and schedule. In addition, the list includes the Reliability Centered Maintenance (RCM) and Experience Centered Maintenance (ECM) evaluations that have been performed for applicable FInS systems as well as reliability criticality per Section 3.5. The -AM version of the NIF Maintenance Plan focuses on the reliability program for FInS, LASE, TOPS, TASE, and NCS within the context of the overall NIF Reliability, Availability, and Maintainability (RAM) program and incorporates changes since the -AL version from August 2011 and has been updated to be fully consistent with the updates to Ref. 1. It also includes asset management considerations, updates to the Work Order (WO) process within the NIF Computerized Maintenance Management System (CMMS) which is EAM infor® System Maintenance and Reliability Tracking (SMaRT) (Ref. 5), and updates to metrics and key performance indicators (KPIs).
The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.
The objective of this project was to develop relationships between published scholarship, industry experience, and remanufacturing design rules and identify a clear path for integrating remanufacturing design rules into CAD tools and design practices. To accomplish this, remanufacturing knowledge was compiled, and then the relationships between this knowledge and a prioritized set of product design guidelines was developed. Next, a process to create rules out of guidelines was developed by first defining life extension as a primary function, and remanufacturing as effective tasks to control the failure modes that would restrict the product life. The reliability centered maintenance (RCM) process was used to identify proactive tasks and default actions to mitigate the effects of failure modes. These tasks were developed into expert system rules. The impact of design on remanufacturing was then validated through implementing a prioritized set of design rules for a Heavy Duty Off-Road (HDOR) techno-economic case study. Finally, detailed steps on how to define, develop, and integrate these rules into CAD were created.
Hanford's Tank Farms facilities have been used to safely store waste for over 70 years, with the first single-shell tanks being constructed in 1943. Tank Farm facilities consist of 149 single-shell tanks, 28 double-shell tanks, an evaporator facility, and wastewater treatment facilities. Tank Farm facilities are aging, with a tremendous corrective maintenance burden on the Tank Farm contractor. The mission of Tank Farm facilities is soon changing from waste storage to waste staging for the Hanford Waste Treatment and Immobilization Plant (WTP). WTP operations will demand a significant increase in Tank Farm facility operations, in which corrective maintenance outage windows will shrink drastically. This realization has forced the Tank Farm contractor to consider a paradigm shift in Tank Farm facilities Maintenance planning, and the use of reliability Engineering tools. The Tank Farm Production Operations Engineering Cognizant System Engineering (CSE) organization has led the way in motivating this paradigm shift. This shift has been realized through the use of: 1) technical exchange with other Department of Energy (DOE) contractors to develop improvements in the CSE program, 2) a shift from the use of lagging to leading system health indicators, and 3) a Plant Health Committee to unite Engineering, Operations, and Maintenance personnel toward a productive maintenance strategy. The CSE organization has held several technical exchanges with other DOE contractors to discuss CSE concepts, and how to better maintain aging infrastructure. The technical exchange with other contractors has greatly reduced the time required to make improvements in the Tank Farm CSE program. Other DOE contractors have already faced issues surrounding aging infrastructure, and have vast experience in improving the reliability and usable life of structures and components in nuclear facilities. The past CSE program used lagging health indicators to determine the health of systems. The key lagging indicator used to determine system health was availability, which is the percentage of time that a facility was ready for operation compared to the time the facility was demanded for operation. Availability was a good indicator of health in the waste storage mission of Tank Farms, where safe storage was the most important function of the facility, and where maintenance outage windows were typically long-duration. In current and future operations, outage windows are reducing, resulting in the need for much more reliable systems. Systems that have had high availability may suddenly become inoperable due to a failed component or sub-system. In several instances, the use of availability as an indicator of system health failed to predict system/equipment failure before its occurrence. In discussions with other DOE contractors, a set of reliability tools, including leading indicators of health, has been implemented in the CSE program. This primarily involves the use of failure modes and effects analysis and the study of equipment failure to develop system monitoring plans that focus on trending data to detect oncoming equipment failure ahead of time. In addition, the use of a Plant Health Committee has added significantly to the paradigm shift from a corrective maintenance philosophy to the use of predictive and preventive maintenance. The Plant Health Committee is a chartered team consisting of Engineering, Operations, and Maintenance personnel. CSEs use this forum to present the results of their performance monitoring, including the presentation of health via leading health indicators. The most positive aspect of this committee is the communication that it creates within these critical organizations. The Operations and Maintenance organization benefit from focusing maintenance on the reliability-centered focus provided by Engineering. Engineering benefits from the operational experience of the Operations organization and from the failure data that can be provided by Maintenance personnel. The continued use of the Plant Health Committee is expected to further decrease maintenance outage times, in better support of oncoming 24/7 operations. (authors)
The Reliability and Maintainability team strives to ensure the reliability, availability, and maintainability of plant assets throughout their life cycle. This objective is accomplished by providing world-class maintenance engineering support and maintenance processes. University of Tennessee’s Reliability and Maintainability Center (UT RMC) Partnership – Provided subject matter expert (SME) analysis of current state of both sites integrated work control (IWC) and maintenance execution processes.
In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences and how likely is it to occur. While this definition makes sense in a regulatory based framework to estimate risk associated to power plants (in terms of core damage frequency and large early release frequency), this approach does not provide a useful snapshot of the health of the plant. A possible alternate path can start by redefining the word “risk” to a broader meaning that better reflects the needs of a system health and asset management decision making process. Rather than asking how likely an event can occur (in probabilistic terms), we can ask how far this event is from occurring. We will show how, given the data available from plant equipment reliability and monitoring/diagnostic/prognostic centers, a margin can be described and determined for all type of maintenance approaches (e.g., corrective or predictive maintenance). We will show how to link SSC margin-based reliability models to system reliability models (i.e., fault trees) in order to assess system/plant health and how to perform margin-based system calculations. These calculations are not solved using classical probabilistic calculations applied to sets (as performed by any PRA code) but, instead, through metric spaces operations (i.e., distance/margin based approach).
End goal - The CMMS automatically creates a Maintenance Request when a component was in a degraded condition.
Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield, cost, safety, and reliability of PV plants. Condition monitoring of PV plants is, therefore, a very important approach to estimating the health condition of PV modules and power electronics in the system. However, additional hardware for PV system monitoring adds cost to the system's operation; delayed maintenance service also causes additional energy production loss. The Center for Power Electronics Systems (CPES) at the Virginia Polytechnic Institute and State University and Siemens Cooperate Research developed the online impedance measurement for a PV panel self-monitoring and diagnosing technology using the DC-DC converter connected to the panel. Small-signal impedances of a monocrystalline silicon PV panel were modeled and simulated to reflect fault conditions such as the short-circuit, hot-spot, and junction box faults. Modeling and simulation results were validated firstly using a test setup consisting of a solar simulator, a network analyzer, small-signal injectors, and a monocrystalline PV panel rated at 300 W.
High Energy Physics (HEP) experiments entail an abundance of computing resources, i.e. sites, to run simulations and analyses by processing data. This requirement is fulfilled by local batch farms, grid sites, private/commercial clouds, and supercomputing centers via High Throughput Computing (HTC). The growing needs of such experiments and resources being prone to trends of heterogeneity make it difficult for physicists to handle these resources directly. Additionally, HEP collaborations heavily rely on data and software releases, typically in the order of tens of gigabytes, while conducting simulations and analyses. Hence, aspects of scalability, reliability, and maintenance become crucial with regards to the distribution of the necessary data and software stack. The GlideinWMS [4] framework helps with the resource management problem by using pilot jobs, aka Glideins, to provision reliable elastic virtual clusters. Glideins are submitted to unreliable heterogeneous resources which are validated and customized by the Glideins to make the worker nodes available for end-user job execution. On the other hand, the CernVM File System (CernVM-FS or CVMFS) [1] helps with data distribution. It is a write-once, read-everywhere filesystem used to deploy scientific software to thousands of nodes on a worldwide distributed computing infrastructure. CVMFS is based on the Hyper Text Transfer Protocol and has been widely used within the particle physics community for (1) distributing experiment software and data such as calibrations, and (2) facilitating containerization by efficiently hosting container images along with providing containerization software, especially Singularity [3] GlideinWMS relies on CVMFS installed locally on the computing resources to satisfy the experiments' software needs. This requires system administrators' effort to install and maintain CVMFS at the sites and limits the use of sites, especially HPC resources, that do not have CVMFS installed. This poster presents a solution, taking advantage of Glideins to provide CVMFS at most sites without the need for a local installation. Doing so expands the pool of resources available for HEP experiments and reduces the effort of system administrators for current resources. Additionally, the proposed solution allows GlideinWMS to also start Singularity [3], a containerization software that can run unprivileged, on sites where neither CVMFS nor Singularity are available, including HPC sites. The benefits provided by this solution are: (1) lower overhead for site administrators in that they have less software to install, (2) an expanded pool of resources that run user jobs with easy access to software and data provided by CVMFS, thus making life easier for the scientists, and (3) improved flexibility to use HPC resources by enabling GlideinWMS pilot jobs to support HPC sites.
The costs associated with the increasing maintenance and surveillance needs of aging structures are rising at an unexpected rate. Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today’s extensive fleet of aging aircraft and space vehicles. Aircraft maintenance and repairs represent about a quarter of a commercial fleet’s operating costs. The application of Structural Health Monitoring (SHM) systems using distributed sensor networks can reduce these costs by facilitating rapid and global assessments of structural integrity. The use of in-situ sensors for real-time health monitoring can overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. The ease of monitoring an entire on-board network of distributed sensors means that structural health assessments can occur more often, allowing operators to be even more vigilant with respect to flaw onset. SHM systems also allow for condition-based maintenance practices to be substituted for the current time-based or cycle-based maintenance approach thus optimizing maintenance labor. The Federal Aviation Administration has conducted a series of SHM validation and certification programs intended to comprehensively support the evolution and adoption of SHM practices into routine aircraft maintenance practices. This report presents one of those programs involving a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Sikorsky, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp., Acellent Technologies Inc., and the Federal Aviation Administration (FAA) carried out a trial validation and certification program to evaluate Comparative Vacuum Monitoring (CVM) and Piezoelectric Transducers (PZT) as a structural health monitoring solution to specific rotorcraft applications. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM and PZT systems into rotorcraft maintenance programs and the document revisions necessary to allow for their routine use as an alternate means of performing periodic structural inspections. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically relevant experimental effort to quantify the reliability of a CVM system applied to Sikorsky S-92 fuselage frame application and a PZT system applied to an S-92 main gearbox mount beam application. The applications included both local and global damage detection assessments. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for SHM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and is acceptable to both the aviation industry and regulatory bodies. The validation work completed in this program demonstrated the ability of both CVM and PZT SHM systems to detect cracks in rotorcraft components. It proved the ability to use final system response parameters to provide a Green Light/Red Light (“GO” – “NO GO”) decision on the presence of damage. In additional to quantifying the performance of each SHM system for the trial applications on the S-92 platform, this study also identified specific methods that can be used to optimize damage detection, guidance on deployment scenarios that can affect performance and considerations that must be made to properly apply CVM and PZT sensors. These results support the main goal of safely integrating SHM sensors into rotorcraft maintenance programs. Additional benefits from deploying rotorcraft Health and Usage Monitoring Systems (HUMS) may be realized when structural assessment data, collected by an SHM system, is also used to detect structural damage to compliment the operational environment monitoring. The use of in-situ sensors for health monitoring of rotorcraft structures can be a viable option for both flaw detection and maintenance planning activities. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM and PZT technology. It will also streamline future regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft and space vehicles. The use of in-situ sensors for real-time health monitoring of aircraft structures are a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, structural health monitoring systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems are able to detect incipient damage before catastrophic failures occurs. Condition-based maintenance practices could be substituted for the current time-based maintenance approach. Other advantages of on-board distributed sensor systems are that they can eliminate costly, and potentially damaging, disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time- consuming manual inspections. This report presents a Sandia Labs-aviation industry effort to move SHM into routine use for aircraft maintenance. This program addressed formal SHM technology validation and certification issues so that the full spectrum of concerns, including design, deployment, performance and certification were appropriately considered. The Airworthiness Assurance NDI Validation Center (AANC) at Sandia Labs, in conjunction with Boeing, Delta Air Lines, Structural Monitoring Systems Ltd., Anodyne Electronics Manufacturing Corp. and the Federal Aviation Administration (FAA) carried out a certification program to formally introduce Comparative Vacuum Monitoring (CVM) as a structural health monitoring solution to a specific aircraft wing box application. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, the potential inspector population, adoption of CVM into an airline maintenance program and the document revisions necessary to allow for routine use of CVM as an alternate means of performing periodic structural inspects. To carry out the validation process, knowledge of aircraft maintenance practices was coupled with an unbiased, independent evaluation. Sandia Labs designed, implemented, and analyzed the results from a focused and statistically-relevant experimental effort to quantify the reliability of the CVM system applied to the Boeing 737 Wing Box fitting application. All factors that affect SHM sensitivity were included in this program: flaw size, shape, orientation and location relative to the sensors, as well as operational and environmental variables. Statistical methods were applied to performance data to derive Probability of Detection (POD) values for CVM sensors in a manner that agrees with current nondestructive inspection (NDI) validation requirements and also is acceptable to both the aviation industry and regulatory bodies. This report presents the use of several different statistical methods, some of them adapted from NDI performance assessments and some proposed to address the unique nature of damage detection via SHM systems, and discusses how they can converge to produce a confident quantification of SHM performance An important element in developing SHM validation processes is a clear understanding of the regulatory measures needed to adopt SHM solutions along with the knowledge of the structural and maintenance characteristics that may impact the operational performance of an SHM system. This report describes the major elements of an SHM validation approach and differentiates the SHM elements from those found in NDI validation. The activities conducted in this program demonstrated the feasibility of routine SHM usage in general and CVM in particular for the application selected. They also helped establish an optimum OEM-airline-regulator process and determined how to safely adopt SHM solutions. This formal SHM validation will allow aircraft manufacturers and airlines to confidently make informed decisions about the proper utilization of CVM technology. It will also streamline the regulatory actions and formal certification measures needed to assure the safe application of SHM solutions.
Fission Batteries (FBs) are nuclear reactors defined by five characteristics which enable large-scale deployment: cost competitive, standardized sizes for economic mass production, easy installation and removal, secure and safe unattended operation with high reliability. FBs are not defined by technology or power level. Technical and market considerations suggest that most FBs will produce 20 to 30 MWt. This proceedings reports on the outcomes of two workshops that were held in January 2021 to better define markets and economic challenges for FBs. Three major markets were identified. The largest market is the industrial and commercial heat market. There are about 4000 industrial users (excluding utilities) that require more than one megawatt of heat. The number of customers versus size of heat demand was determined. In a low-carbon world there is the potential for many additional customers—including expanded biofuels production and district heat. The second market is for non-grid electricity. This includes co-generation plants that produce heat and electricity for a single customer. The third market is the maritime market with ~100,000 ships worldwide. In the United States, natural gas is the low-cost energy option today and will remain so unless constraints or taxes impact its use. If restrictions on greenhouse gas emissions, the FB competition includes natural gas with carbon capture, biofuels, hydrogen and grid electricity. Natural gas with carbon capture is not economically viable on a small scale. Biofuels may be expensive but may be the economically preferred option for locations with small energy demands of a few megawatts. Hydrogen is a potential competitor with many of the characteristics of natural gas. Grid electricity is not a competitive source of heat. For FBs to be economically competitive, the price of delivered heat must be $20-50/MWh ($6-15/million BTU). The economically competitive range for non-grid electricity is estimated at $70-100/MWh. These electricity prices are competitive with the retail prices of electricity in many parts of the United States for the customer. FBs are not expected to be competitive selling wholesale electricity to the grid. To achieve the aforementioned cost targets for heat and electricity markets, FB designers must (1) maximize the power output within the constraints of a FB (e.g., truck transportability, passive decay heat removal), (2) drastically reduce the size of onsite staff, (3) adopt core designs with low fuel costs (enrichment and fabrication), and (4) develop a system design that is efficiently manufactured in factories. The business case depends upon more than being just a replacement for natural gas. The largest incentives for adoption of FBs is where they create new markets and new sources of revenue. An example is the paper and pulp industry that burns biomass wastes to provide heat and electricity to make paper. An external heat source could meet the demand for heat and electricity by the paper process and enable converting waste biomass into liquid biofuels rather than burning to provide heat. Other markets, such as data centers, are driven by special energy requirements such as extreme reliability. Most customers are not in the energy business but need heat and electricity to produce a product—a manufactured good, education, retail sales (shopping malls), marine transport or some other product. As a consequence, there will be large incentives to lease rather than own FBs. Leasing avoids the regulatory challenges that remain with the owner of the FB. Leasing creates large incentives for FP standardization of sizes and transportability to maintain the value of the FB at the end of the lease—similar to the leasing of jet engines and aircraft. The economic constraints combined with technical constraints suggest competitive FBs will likely have outputs exceeding 10 MWt. There appear to be little incentives for very long-lived reactor cores because such machines require much larger inventories of fuel. Maintenance requirements and the options to provide technology updates may favor shorter lifetimes (~5 years). The assessment is that there is the potential for FBs to be economically viable and play a major role in global decarbonization in three markets: heat, non-grid electricity and maritime applications.
The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER’s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use.
The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER?s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use. PowerPoint for conference that was reviewed in PRS and LRS PRS/CON-25-05379 and INL/CON-25-82946
This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.
Increasingly powerful and affordable computing has revolutionized scientific and scholarly discovery across a broad range of fields. Computing relies on software, which has been rapidly growing in scope, diversity, and complexity. At the same time, the methods, processes, and tools used to produce and utilize this essential software are often ad hoc, and the study and improvement of them are often done without the benefit of direct funding or prioritization. Consequently, concerns are growing about the productivity of the developers and users of scientific software, its sustainability, and the trustworthiness of the results that it produces. Increased investment, especially in the characterization and improvement of how scientific software is developed and used, is important for sustaining and improving the impact of software as the scope and complexity of scientific efforts expand. Without this investment, we face the risk of diminishing returns on our software investments because the demands for increased functionality, usability, reliability, and more will not be sufficiently met. The US Department of Energy Office of Science (DOE/SC) is at the forefront of modern software-enabled scientific discovery across numerous areas of computational, experimental, and observational science, including major investments in national user facilities that support these activities. For many years, DOE/SC software investments have provided tremendous value to the scientific community. We want to continue and further improve the value of DOE/SC software efforts by using a scientific approach to understanding and improving how scientific software is developed and used. In December 2021, the DOE/SC Office of Advanced Scientific Computing Research (ASCR) convened a workshop on basic research needs for the Science of Scientific-Software Development and Use (SSSDU). Through keynote presentations, lightning talks, and breakout groups, which built on insights from 124 pre-workshop position papers, participants discussed the current practice of software development, maintenance, evolution, and use, and considered how the scientific method could be used to examine these practices and develop more evidence-based approaches to enhance the impact of software and computing on all areas of science. Workshop participants identified three priority research directions (PRDs) and three important crosscutting themes that center on the following overarching insight: Software has become an essential part of modern science, impacting discoveries, policy, and technological development. To maintain and improve confidence in science delivered via software, we must improve the processes and tools that help us create and use software, and this enhancement requires a deep understanding of the diverse array of teams and individuals doing the work.
Nuclear energy is recognized as the most feasible energy source towards achieving nation-wide net-zero goals (COP 208, MIT). Additionally, the demand for reliable, consistent energy supply is soaring with the construction of energy hungry data centers and AI tools. This means the construction of new nuclear and the continuation of current nuclear are a high national priority. However, this leaves the nuclear industry with a workforce challenge. The rising demand for nuclear power is threatened by an underpopulated workforce. There exist a few contributors to the decreased workforce such as skewed workforce demographics resulting in large-scale workforce retirement. Also, some plants have observed difficulty hiring and retaining skill sets that typically comprise the nuclear power workforce – specifically skilled craftsman. Those who are graduating with desirable skills are seeking employment in sectors that are more modern and culturally more aligned with younger generational values. Lastly, within the nuclear sector, skilled employees will be a competitive commodity as advanced plants come online offering work environments that incorporate modern technologies, skill sets, and opportunities for career advancement. These factors emphasize the need for legacy plants to modernize with a focus on the workplace environment and culture that meets younger generations’ skills, cultural expectations, and desire for advancement opportunities. Nuclear plants are actively engaged in deploying advancements that improve the management of systems, structures, and components as well as process improvements and technologies that can reduce the costs of operation and maintenance. However, the advancements must also be analyzed, reviewed and deployed in a manner that considers the impact on how people within the organization collaborate, communicate, make decisions, and solve problems. Neglecting to consider the cultural impact of modernization risks poor adoption, unrealized opportunities, or insignificant change toward helping attract new employees. The traditional way of working is not necessarily the way new generations want to engage with their employer. For instance, in a survey performed by North American Young Generation in Nuclear the top three reasons for younger nuclear employees to seek other job opportunities was seeking better work-life balance, a lack of advancement opportunities, and work culture and leadership style differences (Smyth et al. 2022). Integrated Operations for Nuclear (ION) is an approach for the nuclear industry to create long-term strategic modernization plans and analyze each advancement for the impact on people and processes and measure how those impacts flow up to support high-level, long-term plant goals and requirements. One principle of ION is to replace the current labor-centric operation style in legacy nuclear plants with data-centric collaborative operation styles. This paper will explain how transitioning operations to leverage centralized skills and responsibility, multi-skilled teams, and collaborative decision making can flatten the hierarchical structure in plants, enable greater individual efficacy while also offering more experiences and advancement opportunities to staff. Adopting the ION way of working shifts the industry mind-set towards more networked, collaborative, and modern way of working that will attract skilled, next-generation workers to operate the legacy nuclear fleet.