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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Review of Reactor Facilities without Main Control Rooms

Small, advanced reactors may require few, if any, safety-related human actions (HAs) and fewer HAs to monitor and control the plant. In comparison to large light water reactors, these are significant changes that have implications for many aspects of an applicant's human factors engineering (HFE), including control room design, plant staffing, and the management of safety functions. To support the Nuclear Regulatory Commission's (NRC) ability to evaluate these changes, information needs to be developed addressing the characteristics and potential issues. The objectives of our research were to (1) identify when a traditional main control room (MCR) may not be necessary, (2) identify workplace design alternatives to traditional MCRs, and (3) develop guidance for reviewing an applicant's workplace designs with and without a MCR. We determined that the safety question isn't so much justifying why a design has no MCR, but rather verifying that important human actions can be accurately and reliably performed under a range of challenging conditions using the HSIs provided regardless of their location. We developed guidance to review alternatives to MCRs based on HFE analyses for determining workplace location and design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Linking human-building interactions in shared offices with personality traits

Occupant behavior influences office building energy performance. The level of human-building interactions (HBIs) in shared offices strongly influences building energy use and occupant well-being. This study explored the link between occupant personality types and their behaviors of sharing energy and environment control systems and interactions with their colleagues. Inspired by the Five-Factor Model (FFM), we classified HBI behaviors into four dimensions: willingness to share control, knowledge of control, group decision behavior, and adaptive strategies. These four variables can be mapped to the four personality traits proposed by the FFM: agreeableness, openness, extraversion, and conscientiousness. Our cluster analysis identified six behavioral patterns: average (17.7%), reserved (15.3%), environmentally friendly (16.6%), role model (24.2%), self-centered (17.2%), and mechanist (9.0%). We further applied association rules, a widely utilized machine learning technique, to discover how demographics, building-related contextual factors, and perception-attitudinal factors influence HBI behaviors. Country, control feature accessibility, and group dynamics were found to be the three most influential factors that determine occupants’ HBI behaviors. Finally, the study provides insights about building design and operation, as well as policy to promote socially and environmentally desirable HBI behaviors in a shared office environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0

Abstract. Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Interpretable Machine Learning Models for Practical Antimonate Electrocatalyst Performance

Computationally predicting the performance of catalysts under reaction conditions is a challenging task due to the complexity of catalytic surfaces and their evolution in situ, different reaction paths, and the presence of solid-liquid interfaces in the case of electrochemistry. We demonstrate here how relatively simple machine learning models can be found that enable prediction of experimentally observed onset potentials. Inputs to our model are comprised of data from the oxygen reduction reaction on non-precious transition-metal antimony oxide nanoparticulate catalysts with a combination of experimental conditions and computationally affordable bulk atomic and electronic structural descriptors from density functional theory simulations. From human-interpretable genetic programming models, we identify key experimental descriptors and key supplemental bulk electronic and atomic structural descriptors that govern trends in onset potentials for these oxides and deduce how these descriptors should be tuned to increase onset potentials. Here, we finally validate these machine learning predictions by experimentally confirming that scandium as a dopant in nickel antimony oxide leads to a desired onset potential increase. Macroscopic experimental factors are found to be crucially important descriptors to be considered for models of catalytic performance, highlighting the important role machine learning can play here even in the presence of small datasets.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Occupant-Centric key performance indicators to inform building design and operations

Building performance indicators are widely used to guide building design and track and benchmark operational performance. Traditional building performance indicators mostly focus on the energy efficiency perspective. As occupants are the primary building service recipients in residential and most commercial buildings, their comfort and wellbeing are crucial. As such, this study first identified significant attributes of occupant-centric key performance indicators (KPIs) and analyzed the diverse factors that should be considered in formulating an occupant-centric KPI. Then a suite of occupant-centric KPIs were synthesized from the review and enhancement of existing occupant-related performance metrics. The proposed occupant KPIs represent the occupant lens on three integrative aspects of building performance: resource use (including energy and water), indoor environmental quality, and human–building interactions. A simulation-based case study was conducted to demonstrate how occupant-centric KPIs can be used to quantify the impacts of building operation changes from the occupants’ point of view. Highlights: Occupant-centric metrics are currently ad hoc and limited, yet crucial to inform building design and operations. Literature was reviewed to reveal the state-of-the-art and gaps of occupant-centric metrics. A suite of occupant-centric key performance indicators (KPIs) covering five groups of building services were synthesized. Proposed occupant KPIs represent three aspects of performance: resource use and demand, occupant comfort and health, and human–building interactions. A case study using whole building simulation was conducted to demonstrate the use of occupant-centric KPIs in evaluating building operations during a power outage.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The drivers and predictability of wildfire re-burns in the western United States (US)

Evidence is mounting that the effectiveness of using prescribed burns as a management tactic may be diminishing due to the higher incidence of wildfire re-burns. The development of predictive models of re-burns is thus essential to better understand their primary drivers so that forest management practices can be updated to account for these events. First, we assess the potential for human activity as a driver of re-burns by evaluating re-burn trends both within and outside of the wildland–urban interface (WUI) of the western US. Next, we investigate the predictability of re-burns through the application of both random forest and the explanatory machine learning non-negative matrix factorization using k-means clustering (NMFk) algorithms to predict re-burn occurrence over California based on a number of climate factors. Our findings indicate that while most states showed increasing trends within the WUI when trends were conducted over longer moving windows (e.g. 20 years), California was the only state where the rate of increase was consistently higher in the WUI, indicating a stronger potential for human activity as a driver in that location. Furthermore, we find model performance was found to be robust over most of California (Testing F1 scores = 0.688), although results were highly variable based on EPA level III Ecoregion (F1 scores = 0.0–0.778). Insights provided from this study will lead to a better understanding of climate and human activity drivers of re-burns and how these vary at broad spatial scales so that improvements in forest management practices can be tuned according to the level of change that is expected for a given region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of color in digital nuclear power plant control room displays

Human system interface design in industrial process control is guided by industry standards, human factors best practices, and domain-specific conventions, and often there is a conflict between one or more of the sources of design input for specific design elements. In the nuclear domain, one design element for which conflict arises is the use of color to represent equipment state. Here, this study evaluates the tradeoffs associated with using color in a process control display versus using white and shades of gray. The performance metrics were response time, accuracy, and eye movement metrics using a simplified experimental task and professional operators. Results revealed that adhering to color conventions in nuclear power yielded small advantages in simple tasks, but did not exist for more complex tasks. The results did not provide strong evidence for or against using a particular color scheme and revealed the need for further research on the use of color for commercial nuclear power plants and other process control industries.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

NPP Simulators for Coupled Thermal and Electric Power Dispatch

The Light Water Reactor Sustainability (LWRS) program within the United States Department of Energy supports extending the operation of the U.S. commercial nuclear power plant (NPP) fleet. Within the LWRS program, the Flexible Plant Operation and Generation (FPOG) Pathway works to diversify the revenue streams of light water reactors (LWRs) by opening opportunities for the co-generation of non-electric products in addition to supplying electrical power to the grid. Recent events have added greater motivation to these efforts. For example, the recent Inflation Reduction Act (IRA) passed by the U.S. federal government offers substantial tax incentives for producing clean hydrogen, the technology readiness level of dispatchable and high-efficiency hydrogen production has dramatically increased in a short time, and societal response to world climate change is driving a transition away from fossil fuels. Producing hydrogen with maximum efficiency using nuclear power requires dispatching both electrical and thermal power from the nuclear plant to the hydrogen plant, so testing concepts of operations for combined electrical and thermal power dispatch (TPD) from an NNP to a hydrogen plant is of interest. This report documents achievement of the Light Water Reactor Sustainability (LWRS) program milestone “Install and demonstrate a vendor-developed simulator on the Human Systems Simulation Laboratory (HSS) for dispatch of LWR electrical power to a close-coupled electrolysis plant” with a due date of Dec. 22, 2022. Several factors provide motivation for this effort. Coupling the power generation deck of a nuclear power plant to a hydrogen production facility introduces new possibilities for operational transients that must be addressed. In particular, the performance of the integrated system during startup and shutdown of the hydrogen production facility, as well as offnormal conditions, need to be evaluated to ensure there are no adverse effects on the operation of the existing NPP. The concept of operations involving the NPP, the hydrogen plant, and the electric power grid must be tested using NPP simulators and operating procedures that have been modified for TPD operations. These tests must also include dynamic simulations of the coupled tertiary thermal and electric loads as well as coordinated activities with NPP operators, tertiary load operators and grid power coordinators. The report summarizes progress in developing and testing full-scope NPP simulators at the HSSL, including a generic BWR simulator from GSE Systems, Inc. and generic PWR simulator from Westinghouse. In the case of the TPD-GBWR Simulator from GSE Systems, Inc., a BWR is thermally coupled to a high temperature electrolysis (HTE) plant that produces hydrogen and oxygen from de-ionized water. The hydrogen plant is not explicitly simulated but only included as a transient heat sink. A thermal power dispatch (TPD) system transfers heat between the steam systems at the BWR and the hydrogen plant. Operational results from two versions of the modified simulator are presented. The first version uses synthetic oil as a heat transfer fluid in a closed delivery heat loop (DHL) that generates steam at the hydrogen plant. The second version uses steam as the heat transfer fluid in a delivery steam line (DSL) to provide steam to the hydrogen plant. For both versions, the estimated thermal power delivery distance is approximately one kilometer. The amount of thermal power dispatched in the simulators is 15% of the total reactor thermal power such that the simulators provide a tool to study the feasibility of coupling a BWR to industrial processes that benefit from a combination electrical and thermal power dispatch. Ongoing work within a CRADA is also developing a full-scope PWR simulator provided by Westinghouse for both thermal and electric power coupling. This simulator is based on a PWR plant with two three-loop Westinghouse reactors. Westinghouse PWRs are sufficiently similar that a simulator of a three-loop reactor is an appropriate representation for two-loop and four-loop PWR reactors. The three-loop simulator will initially be modified for close-coupling to a 100 MW HTE hydrogen production plant that will require approximately 25 MW of thermal power while operating at its maximum rated capacity. The simulator testing will include full coupling to dynamic simulations of a hydrogen production plant and a representative bulk electric grid. The simulator provided by Westinghouse is similar to the GPWR simulator that INL has already obtained from GSE Systems but has a few important added benefits. First, the Westinghouse simulator is based on digital controls and has additional screens that can be called up to show parameter trends to assist operators in decision-making. The Westinghouse simulator also has upgrades to the controls and hardware representations, such as valve actuators, that make it more realistic and flexible in terms of accurately sim

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗
DOE OSTI2024

Regulation of β-cell death by ADP-ribosylhydrolase ARH3 via lipid signaling in insulitis

Lipids are regulators of insulitis and β-cell death in type 1 diabetes development, but the underlying mechanisms are poorly understood. Here, we investigated how the islet lipid composition and downstream signaling regulate β-cell death. We performed lipidomics using three models of insulitis: human islets and EndoC-βH1 β cells treated with the pro-inflammatory cytokines interlukine-1β and interferon-γ, and islets from pre-diabetic non-obese mice. We also performed mass spectrometry and fluorescence imaging to determine the localization of lipids and enzyme in islets. RNAi, apoptotic assay, and qPCR were performed to determine the role of a specific factor in lipid-mediated cytokine signaling. Across all three models, lipidomic analyses showed a consistent increase of lysophosphatidylcholine species and phosphatidylcholines with polyunsaturated fatty acids and a reduction of triacylglycerol species. Imaging assays showed that phosphatidylcholines with polyunsaturated fatty acids and their hydrolyzing enzyme phospholipase PLA2G6 are enriched in islets. In downstream signaling, omega-3 fatty acids reduce cytokine-induced β-cell death by improving the expression of ADP-ribosylhydrolase ARH3. The mechanism involves omega-3 fatty acid-mediated reduction of the histone methylation polycomb complex PRC2 component Suz12, upregulating the expression of Arh3, which in turn decreases cell apoptosis. Our data provide insights into the change of lipidomics landscape in β cells during insulitis and identify a protective mechanism by omega-3 fatty acids.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Improved Creep Testing Approach for Bentonite EBS

In the United States, the nuclear reactor contributes 18.7 percent of the nation’s total electricity generation and produces a significant amount of spent nuclear fuel (SNF) and high-level waste. Because SNF is being stored for longer periods than initially envisioned, the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Science and Technology is assessing the technical performance of the SNF storage systems after extended durations. The concept of a sustainable geologic repository for SNF disposal relies heavily on the safety provided by multiple barriers, such as repository host rock, overlying rock formation, and human-made engineered barrier systems (EBS). One of the major components of EBS is bentonite-based buffer materials that isolate the nuclear-waste canisters from the host rock and fill the void left in the horizontally drilled boreholes. The resaturated bentonite has a swelling characteristic, which is useful for self-sealing the microcracks in the host rock and supporting the canister’s heavy weight. However, factors such as nonuniformity in the saturation, swelling pressure of bentonite, and the high temperature at the canister-bentonite interface may lead to instability of the waste canister inside the borehole. Therefore, evaluating the long-term deformation of bentonite-based material is necessary. RESPEC Company, LLC recently completed a preliminary study for the U.S. Department of Energy (DE SC0022804) that is attempting to understand the time-dependent deformation in the consolidated sand/bentonite (SB) specimens through triaxial creep experiments and improve the understanding of EBS performance under anticipated repository conditions. The project included developing a standard testing procedure for preparing the consolidated core specimen from a mixture of sand and bentonite in a 50:50 ratio by weight, fabricating a new pressure vessel in-house, modifying existing creep equipment to perform triaxial creep experiments on consolidated SB specimens, and performing six long-term triaxial creep experiments under low-deviatoric stress at two different temperature conditions, which is representative of hypothetical conditions that might be encountered in a geologic repository within a reasonable rate of success in completing creep experiments. The consolidated SB specimen was prepared using the novel specimen preparation procedure and had physical characteristics (e.g., moisture content and bulk density) reasonably comparable with the properties of bentonite-based buffer, which is proposed to be used in the EBS in the Swedish KBS-3H design and the Swiss design of SNF disposal in the horizontal drifts. The newly fabricated pressure vessel could withstand the confining pressure of up to 15 megapascals (MPa) while maintaining the test temperature of up to 90 degrees Celsius (°C) for long-term creep experiments. The preliminary results from the triaxial creep experiments suggested that the consolidated SB specimen may experience time-dependent deformation at a low-deviatoric stress state and room temperature condition. Based on the deformation recorded in the SB specimen from axial and radial linear variable differential transformers (LVDTs) and physical characteristic changes, it is apparent that the time-dependent deformation is a combination of consolidation and creep, which is influenced by the level of deviatoric stresses and temperature. The systematic creep testing of bentonite-based specimens helped develop a standard testing procedure applicable for analyzing the similar characteristics of shale or clay-like materials in different geologic conditions outside repository science, including in civil engineering and infrastructure projects, underground and open pit mining, and deep drilling.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluation of DNA extraction protocols from liquid-based cytology specimens for studying cervical microbiota

Cervical microbiota (CM) are considered an important factor affecting the progression of cervical intraepithelial neoplasia (CIN) and are implicated in the persistence of human papillomavirus (HPV). Collection of liquid-based cytology (LBC) samples is routine for cervical cancer screening and HPV genotyping and can be used for long-term cytological biobanking. We sought to determine whether it is possible to access microbial DNA from LBC specimens, and compared the performance of four different extraction protocols: (ZymoBIOMICS DNA Miniprep Kit; QIAamp PowerFecal Pro DNA Kit; QIAamp DNA Mini Kit; and IndiSpin Pathogen Kit) and their ability to capture the diversity of CM from LBC specimens. LBC specimens from 20 patients (stored for 716 ± 105 days) with CIN values of 2 or 3 were each aliquoted for each of the four kits. Loss of microbial diversity due to long-term LBC storage could not be assessed due to lack of fresh LBC samples. Comparisons with other types of cervical sampling were not performed. We observed that all DNA extraction kits provided equivalent accessibility to the cervical microbial DNA within stored LBC samples. Approximately 80% microbial genera were shared among all DNA extraction protocols. Potential kit contaminants were observed as well. Variation between individuals was a significantly greater influence on the observed microbial composition than was the method of DNA extraction. We also observed that HPV16 was significantly associated with community types that were not dominated by Lactobacillus iners.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Increasing Industry Involvement: A Solution from a Student's Perspective

Many engineering employers are finding that graduates are not sufficiently equipped to begin contributing productively when they exit a 4-year institution and enter the workforce. The Intern Professional Development Center (IPDC) is a proposed business model that exposes engineering undergraduates to industry-relevant knowledge, while providing them with working experience during their degree. Using a tri-stage approach, the IPDC engages with students early in their academic careers, provides continuous training and development opportunities, and finally connects the students with potential employers. The two topics most commonly under-represented in engineering programs are: - Familiarity with drafting software - Knowledge of relevant codes and standards. The Intern Professional Development Center (IPDC): A tri-stage solution for recruiting, training, and encouraging the workforce of tomorrow. An IPDC allows students to be active members of industry during their undergraduate careers, leading to: - Improved local talent retention; - Improved graduation and academic performance statistics; - Near-guarantee for post-graduation employment. In March of 2019, the first IPDC prototype began operating in the Tri-Cities, Washington. Since then, 3 undergraduate students have entered the IPDC at Stage 2, beginning with the standard training and professional development. Of those 3 students, 2 have already moved on to Stage 3, with successful ongoing internships with local employers. At a performance review for one of the IPDC's Stage 3 students after 90 days of employment, the direct manager said: 'How soon can we get another one?'.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Digitalization mapping and assessment process supporting ION strategic transformation activities

The existing fleet of commercial nuclear power plants (NPPs) are an important asset in the nation’s portfolio of electrical generating resources. Their continued safe and reliable operation are critical to providing a large source of carbon-free electricity to power the nation’s economy. The United States Department of Energy’s (DOE) Light Water Reactor Sustainability (LWRS) Program develops the scientific bases, methods, and tools, for the continued safe and economical operation of the nation's commercial NPPs. The Plant Modernization Pathway within LWRS Program focuses on providing guidance to industry on the full-scale implementation of modernization solutions for NPPs that significantly reduce the technical and financial risks associated with modernization. This research is focused on helping the nuclear industry understand how to digitize and digitalize their NPPs so that they can design their modernization solutions to be scalable, sustainable, and integrated both laterally and horizontally within their organization. That is, this research creates a digital transformation in NPPs by reshaping cultural mindsets and by identifying business efficiencies. In partnership with industry, and using four previously established guiding principles for digitalization, this research supported NPP modernization through assessing readiness for digitalization as a means to achieve integrated operations for nuclear. Specifically, this research created an assessment to review an entire organization’s work processes to gather information about the digitalization health of the plant. The assessment tools were administered to plant employees, and the results were used to develop a digitalization plan. The survey assessment identified the optimal candidate processes that would most benefit from a digitalization initiative which were revealed through analytical frameworks. One analysis calculated mean digitalization health indicator scores for all endorsed activities which allowed the researchers to rank and color code the results for easy identification. Individual health indicator scores are also provided, should our industry partner wish to understand these findings according to their own organizational priorities, business considerations and desired end-state. The results were also analyzed from the perspective that organizations are comprised of different types of innovators (e.g., generators, optimizers, conceptualizers, and implementers), which differentially affects the organization’s ability to comprehend and adapt to change (i.e., opportunities to innovate). Understanding the relative composition of innovator types at an NPP allows them to gather insights into the strengths and weaknesses they have in innovating how work is performed. For the utility that partnered with this research team, the results showed that implementers make up the largest portion of respondents and conceptualizers the smallest portion. Knowing the proportion of innovator types gave this organization insights on how they can effectively implement their innovation solutions. Additionally, the results were analyzed from a technical, economic, and risk perspective to identify and quantify work reduction opportunities (WROs). Recognizing that not all cost-saving opportunities are the same, a Technical, Economic and Risk Assessment (TERA) was performed to evaluate WROs to identify areas of greatest potential and lowest risk. The key results from TERA included a digitalization opportunity score for each activity, and a calculation of potential cost savings. These two outputs formed the bases for calculating a priority index and rank for the activities/processes assessed. From the prioritization calculations, TERA can then help the utility 1) decide what digitalization priorities to invest money in implementing and then 2) calculates how much should be invested in the digitalization initiatives selected to achieve cost savings and/or an acceptable return on investment. Last, onsite interviews revealed several inefficiencies in the standard work processes that occur cross-departmentally that are due to the absence of digitized and digitalized processes. Examples of these include time spent scanning paper documents and then uploading the documents electronically, obtaining signatures, and searching for desired information. This represents a digital but not digitalized process. Over 15 opportunities to improve work processes were identified through this multi-method digitalization assessment. The various analytical assessments used (e.g., TERA, digitalization health indicator scores), as well as discussions with the utility partner, corroborated that all the opportunities identified had a strong potential to make work processes more efficient and to improve overall performance of the NPP.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗