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DOE OSTI2018

ANNIE Phase II Reconstruction Techniques

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton Gd-doped water Cherenkov detector installed in the Booster Neutrino Beam at Fermilab. The experiment has two complementary goals: (1) perform the first measurement of the neutron yield from $\nu_{\mu}$ interactions as a function of Q$^2$ in order to constrain neutrino-nucleus interaction theoretical models, and (2) demonstrate the power of new fast-timing, position-sensitive detectors by making the first deployment of Large Area Picosecond PhotoDetectors (LAPPDs) in a physics experiment. In Phase I, ANNIE successfully performed neutron background measurements. To realise the Phase II measurements the ANNIE collaboration has developed several reconstruction techniques using the arrival time and position of the Cherenkov photons in the detector photomultipliers (PMTs) and LAPPDs. A maximum-likelihood fit is used to reconstruct the neutrino interaction vertex and direction. Machine and Deep Learning techniques are used for the muon and neutrino energy reconstruction. We present the results of ANNIE reconstruction techniques and the improvement we can get in resolution with the use of LAPPDs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Research Software Engineering at Oak Ridge National Laboratory

Research software engineers (RSE) play a vital role in scientific discoveries worldwide. They lead the core development of the applications, libraries, and tools that enable today’s supercomputers to process the vast volumes of data generated from the world’s most extensive and complicated scientific instruments and facilities. This article describes the mission, culture, and practices of RSE teams at Oak Ridge National Laboratory (ORNL), including their team dynamics and composition, work ethics, standard practices, and ever-evolving skill sets vital to pursuing scientific innovations and implementing novel ideas. We describe the lessons learned from specific activities that contribute to shaping the identity and growth of RSE roles at ORNL. Lastly, we provide our view for the near future on effective strategies for establishing, leading, and nurturing RSE teams and building a thriving community in collaboration with science stakeholders.

42 ENGINEERING↗

DOE OSTI2020

Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions

In February 2019, the President signed Executive Order 13859, Maintaining American Leadership in Artificial Intelligence. This order launched the American Artificial Intelligence Initiative, a concerted effort to promote and protect AI technology and innovation in the United States. The Initiative implements a government-wide strategy in collaboration and engagement with the private sector, academia, the public, and like-minded international partners. Among other actions, key directives in the Initiative called for Federal agencies to: Prioritize AI research and development investments, Enhance access to high-quality cyberinfrastructure and data, Ensure that the US maintains an international leadership role in the development of technical standards for AI, and Provide education and training opportunities to prepare the American workforce for the new era of AI. The mission of the Department of Energy (DOE) is to ensure America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. In terms of Science and Innovation, the DOE’s mission is to maintain a vibrant US effort in science and engineering as a cornerstone of our economic prosperity with clear leadership in strategic areas. From July to October in 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four AI for Science Town Hall meetings in Chicago, Oak Ridge, Berkeley, and Washington DC. The four meetings were attended by over 1300 scientists from the 17 DOE Labs, 39 companies, and over 90 universities. The goal of the Town Hall series was ‘to examine scientific opportunities in the areas of artificial intelligence, Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these.’ The discussions at the meetings were captured in the final report of the AI for Science Town Hall meetings.

42 ENGINEERING↗

DOE OSTI2025

The Impact of Cultural Values and Organizational Processes on Nuclear Security Operations

Human performance is a pivotal factor in the design, testing, maintenance, and operation of security systems. The effectiveness of these systems relies not only on the capabilities, limitations, motives, and attitudes of the individuals involved, but also on the quality of training, instructional content, and evaluation methods provided. To uphold security standards, seamless integration between technologies and operators necessitates reliable human input. In security operations, human errors, often attributed to blame, sanctions, low motivation, individual accountability, or complacency, are primary causes of system failures. Complacency, characterized by a false sense of security, reflects a lack of awareness of potential threats and is a significant contributing factor to lapses in security. Security incidents arise from various factors, many extend beyond individual control, highlighting the need for a holistic approach to human performance that integrates organizational processes and team collaboration. Historically, errors have been attributed to individual moral or cognitive failures. However, insights from Operational Experiences (OEs) suggest that organizational processes weakness and deficiencies in nuclear cultural values contribute more significantly to security failures than individual mistakes. This paper consolidates lessons learned from diverse international nuclear security cultures and aims to highlight the importance of security culture in shaping global perspectives on nuclear security. It underscores the role of cultural values in shaping nuclear security practices and enhancing the resilience of security systems in the nuclear sector.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗

DOE OSTI2017

URS/WTP's Future Leader Program and AECOM's Early Career Professional Technology Exchange - 17239

URS Energy and Construction, Inc. (URS), a subsidiary of AECOM, is the Bechtel National, Inc. (BNI) teaming subcontractor on the Hanford Tank Waste Treatment and Immobilization Plant (WTP) Project. Recently, the WTP Project has begun a transition toward facility turnover and commissioning. With that transition came the hiring of staff for the startup and commissioning workforce. With many of these individuals being Early Career Professionals (ECPs), URS recognized a need to create and implement a workforce development program to give these professionals the skills to thrive in a professional environment and provide a sense of community to improve talent retention. The result of this recognition of need was the creation of URS/WTP Future Leaders. This paper will share the successes and lessons learned related to founding the URS/WTP Future Leaders. This includes establishing programs such as the Continuous Learning Workshops, Rotational Program, and implementing a mentor program. In addition, establishing a budget and obtaining support from Human Resources will be discussed. By March 2017, the ECP program will have been in effect for approximately two years. As a separate effort, many of AECOM's major waste remediation contractors have started having Early Career Professional Technology Exchanges (ECP TE) in an effort to create collaboration between sites and to establish industry contacts that will continue to be a resource for years to come. Washington River Protection Solutions, LLC; URS/WTP; Savannah River Remediation, LLC; and Nuclear Waste Partnership, LLC have conducted ECP TE, with each site hosting an event. For these events, each participant prepares a presentation and answers questions on the presentation topic. This allows ECPs to practice presentation skills, develop skills in answering questions from their peers and management, and be the expert in the room on a particular topic. Tours of the onsite facilities also are given as a way for ECPs to gain a broader understanding of the waste remediation industry and the challenges that other sites are facing. Accomplishments, lessons learned, and future plans for ECP TE will be discussed. The ECP TE events allow AECOM, as the parent company, to provide a unique learning opportunity to the next generation of engineers within the DOE nuclear field and to ensure the safe operations of these nuclear sites for years to come. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

AI@DOE Interim Executive Report

Interim executive report for DOE’s Office of Science, NNSA, and Applied Energy Offices, in collaboration with the Artificial Intelligence and Technology Office, DOE AI roundtable workshops (December 2021 through February 2022) (“AI@DOE”). These workshops were used to help identify AI research requirements and priorities for the next decade for Department of Energy in developing machine-learning-based prediction and decision-support capabilities that address and anticipate DOE mission challenges. These missions’ challenges for AI include: AI to advance scientific discovery and technological innovation AI to support high consequence decisions while managing risk AI to improve DOE’s responsiveness to national & global issues AI to assist stewardship of the environment and national critical infrastructure These will be addressed in large part through the development and application of new and powerful artificial intelligence and/or machine learning algorithms and strategies. https://web.cvent.com/event/fc3922f8-fc75-4041-a317-f13a1da44f7c/summary?locale=en-US&i=2Wx2tjbStkydRFq_N2whPw

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Exploring the Feasibility of INCONEL® ALLOY 740H® for Power Plant Headers: Integrating Machine Learning with Computational Fluid Dynamics (CFD)

This keynote presentation explores the behavior of headers—essential components of pipeline systems—using ANSYS simulation software and machine learning techniques. The study aims to predict the thermal and mechanical performance of headers under diverse conditions through both steady-state and transient simulations. We investigate critical parameters such as heat transfer coefficient, fluid velocity, and temperature to optimize header design. Conducted as part of a DOE project led by NCAT in collaboration with UNC Charlotte, this research encompasses multiple key topics. The initial section focuses on the behavior of header systems under steady-state conditions using ANSYS simulation. It underscores the importance of headers in industrial infrastructure, especially in the energy sector, and examines the implications of material selection and flow direction on heat transfer dynamics. Methodologically, we employ Computational Fluid Dynamics (CFD) analysis through ANSYS, detailing the development of models, material properties, geometry specifications, boundary conditions, and meshing strategies. Our simulations explore various operational parameters, including temperature and mass flow rates, crucial for predicting heat transfer coefficients and enhancing header design. Results from the study include parametric investigations into mesh sensitivity, viscosity model evaluations, and the effects of heat transfer locations, all validated against theoretical calculations. We conclude with insights on mesh optimization, the suitability of viscosity models, and recommendations for future research aimed at improving header system efficiency and sustainability in industrial applications.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Abstraction hierarchy to define biofoundry workflows and operations for interoperable synthetic biology research and applications

Lack of standardization in biofoundries limits the scalability and efficiency of synthetic biology research. Here, we propose an abstraction hierarchy that organizes biofoundry activities into four interoperable levels: Project, Service/Capability, Workflow, and Unit Operation, effectively streamlining the Design‑Build‑Test‑Learn (DBTL) cycle. This framework enables more modular, flexible, and automated experimental workflows. It improves communication between researchers and systems, supports reproducibility, and facilitates better integration of software tools and artificial intelligence. Our approach lays the foundation for a globally interoperable biofoundry network, advancing collaborative synthetic biology and accelerating innovation in response to scientific and societal challenges.

Kim, Haseong↗

DOE OSTI2021

NMSBA Sustainable Engineering (Final Report)

This report summarizes the guidance provided to Sustainable Engineering to help them learn about equation-oriented optimization and the Sandia-developed software packages Pyomo and IDAESPSE. This was a short 10-week project (October 2021 – December 2021) and the goal was to help the company learn about the IDAES framework and how it could be used for their future projects. The company submitted an SBIR proposal related to developing a green ammonia process model with IDAES and if that proposal is successful this NMSBA project could lead to future collaboration opportunities.

42 ENGINEERING↗

DOE OSTI2025

Annual Report on NCSP Technical Support task in BNL during FY25

This work reports on the investigations done during FY 25 in collaboration with one 2025 Summer undergraduate intern through the DOE SULI program. These efforts were partially funded by the Nuclear Criticality Safety Program through the Technical Support Succession Plan task. This project, developed by Matthew Molnar, aimed to apply the BNL’s machine-learning code to correct spin mis-assignments in neutron resonances, the Bayesian Resonance Reclassifier (BRR), to the odd-even nucleus 143 Nd. For this, the handling of spingroup classification had to be further investigated and improved.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Detecting and Characterizing Fracture Zones Using a Convolutional Neural Network

This project directly supports the Geothermal Technologies Office (GTO) objectives outlined in the Multi-Year Program Plan (MYPP) by advancing two key research areas: “Exploration and Characterization” and “Data, Modeling, and Analysis.” This project has successfully demonstrated a pre-drilling ability to image and characterize the distribution and connectivity of subsurface faults and fractures, key parameters for identifying permeable pathways that enable geothermal fluids to circulate and produce energy. Specifically, we developed and implemented innovative machine learning methodologies to enhance geothermal exploration. Large-scale faults were detected using a Convolutional Neural Network (CNN), while small-scale fractures were characterized using a novel Double-Beam Neural Network (DBNN). These tools have proven both technically effective and cost-efficient by reducing reliance on expensive exploratory drilling. Through collaboration with our geothermal industry partner, this research has significantly advanced techniques for identifying hidden geothermal systems and extending the productive lifespan of existing geothermal fields. We applied our methods to two geothermal fields—Soda Lake (Nevada) and Lightning Dock (New Mexico)—to identify shallow steam-charged fracture zones and characterize deep faults at depths of 1.5-2 km. The steam zone identified at the Soda Lake geothermal field showed excellent agreement with prior drilling data, validating the effectiveness of our approaches. In addition, the analysis revealed three new prospective drilling targets for further development and verification. The outcomes of this project improve our scientific understanding of geothermal reservoir behavior, enhance exploration efficiency, extend the economic life of existing geothermal plants. Ultimately, these advancements contribute to GTO’s goal of achieving more sustainable, affordable, and data-driven geothermal energy development across the United States.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Safety-Related Instrumentation and Control Pilot Upgrade: Initial Scoping Phase Implementation and Lessons Learned

In May 2016, the U.S. Nuclear Regulatory Commission (NRC) staff provided a digital instrumentation and control (I&C) regulatory infrastructure integrated action plan to the NRC for approval. One of the objectives of that plan was to establish a clear regulatory structure with reduced regulatory uncertainty to enable the expanded safe use of digital I&C in commercial nuclear reactors while continuing to ensure safety and security. To achieve this end, the NRC, with collaboration from industry, developed a streamlined License Amendment Request Alternate Review (AR) process for safety-related (SR) digital I&C upgrades. In spite of this effort, the industry has remained reluctant to perform such I&C upgrades because of perceived regulatory and financial risks associated with being the first or an early adopter of the AR process for SR I&C upgrades. The U.S. Department of Energy Light Water Reactor Sustainability Program at the Idaho National Laboratory performed Initial Scoping Phase research to help break this impasse by supporting a SR I&C Pilot Upgrade, working with MPR Associates, ScottMadden Inc., and Exelon Generation. Exelon’s Limerick Generating Station (LGS) was selected as the target for this research. This paper summarizes the Initial Scoping Phase engineering and operations, licensing, and project management activities necessary to bound the scope, schedule, and estimated cost of the project sufficiently to enable utility management authorization of Conceptual Design Phase activities. These efforts and associated products are intended to provide a template to support larger industry efforts to perform similar upgrades as a foundation stone for a digital transformation that will improve plant safety, reliability, and operational performance while lowering plant total cost of ownership. As a result of the combined effort of Exelon Generation and research participants, Conceptual Design Phase activities for the subject upgrade at LGS were approved by Exelon. Further, the U.S. Department of Energy also awarded a $50 million cost share award to Exelon in order to pave the way for SR I&C modernization and associated control room upgrades across the U.S. nuclear fleet. Additional research reports are planned for the Conceptual Design Phase, Detailed Design Phase, and the Implementation Phase of the LGS project to document the process followed and promulgate lessons learned to industry.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

High-throughput virtual laboratory for drug discovery using massive datasets

Time-to-solution for structure-based screening of massive chemical databases for COVID-19 drug discovery has been decreased by an order of magnitude, and a virtual laboratory has been deployed at scale on up to 27,612 GPUs on the Summit supercomputer, allowing an average molecular docking of 19,028 compounds per second. Over one billion compounds were docked to two SARS-CoV-2 protein structures with full optimization of ligand position and 20 poses per docking, each in under 24 hours. GPU acceleration and high-throughput optimizations of the docking program produced 350× mean speedup over the CPU version (50× speedup per node). GPU acceleration of both feature calculation for machine-learning based scoring and distributed database queries reduced processing of the 2.4 TB output by orders of magnitude. The resulting 50× speedup for the full pipeline reduces an initial 43 day runtime to 21 hours per protein for providing high-scoring compounds to experimental collaborators for validation assays.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Dynamical Inference and 3D Imaging of Magnetized Dusty Plasmas

This is the final technical report for this DOE award. The motion of dust particles in a laboratory plasma has been studied for nearly 30 years and has led to the discovery of strongly-coupled crystalline structures and nonequilibrium dynamics governed by gravitational, hydrodynamic, and electrostatic forces. Many of the inter-particle forces are complex; they can be non-reciprocal, and non-additive. Deciphering the mélange of particle interactions has been a challenging task, nevertheless, magnetized dusty plasmas remains an open challenge with limited understanding. With applications ranging from magnetically-confined plasmas for fusion to near-surface planetary environments, this research field is ripe for well-controlled, laboratory experiments and new theoretical tools. In collaboration with the MDPX experiment at Auburn University, this proposal aims to tease apart both the known and unknown forces that drive dusty plasmas in magnetized environments using high-resolution, three-dimensional imaging and particle tracking coupled with modern dynamical inference and machine learning techniques.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Technical Assistance for Digital Assurance: DistribuTECH Workshop

Idaho National Laboratory, the Department of Energy’s Grid Deployment Office, and other national laboratories are collaborating to ensure the U.S. energy infrastructure is reliable, resilient, and secure. This involves strategically leveraging digital technologies to modernize the grid, enhance its resilience against all hazards and disruptions, and fortify national energy security across a diverse energy portfolio. In this workshop you will learn about Idaho National Lab’s Technical Assistance programs, where organizations will be matched with a national laboratory subject matter expert to focus on their key topical area. The technical assistance offered through this track is designed to be responsive to a rapidly changing regulatory landscape and cutting-edge technologies that enhance grid reliability and efficiency. Users will be guided through a tailored analysis and mitigation program to determine their current security posture and given assistance in evaluating supply chain and protection choices against potential consequences.

Digital assurance↗

DOE OSTI2024

OPEN-Augmented Reality GUI for Bioenergy Crop Phenotyping and Precision Agriculture (Donald Danforth Plant Science Center Final Scientific Technical Report)

The project led by the Donald Danforth Plant Science Center, in collaboration with Arizona State University, George Washington University, and Saint Louis University, has made significant strides in advancing the phenotypic analysis of bioenergy crops through the development of an innovative AI processing pipeline. This initiative was primarily funded by ARPA-E, with additional cost-sharing provided by the participating institutions. The project successfully utilized a variety of sensors—3D scanners, thermal, RGB, and hyperspectral—to refine algorithms for data-driven trait signature identification and improve the classification and visualization of plant traits. The developed AI processing pipeline is capable of handling the complex, multidimensional data characteristic of dynamic agricultural environments. 1) Contributions to understanding: The research has advanced the field of plant phenomics by showcasing the synergistic use of various sensor data to enhance the precision of trait analysis in bioenergy crops. Through the integration of 3D scanners, thermal, RGB, and hyperspectral sensors, the project has developed robust data-driven trait signature algorithms and visualization techniques. These innovations have facilitated detailed monitoring and management of plant traits, providing vital insights into plant growth dynamics and stress responses. Further, the project has broadened our understanding of how machine learning can be effectively applied in multi-sensor environments to refine trait analysis. By leveraging diverse datasets, the research has not only improved the accuracy of phenotypic assessments but also established a versatile methodological framework that can be extended beyond agriculture to other fields requiring detailed phenotypic analysis. 2) Technical effectiveness and economic feasibility: The AI processing pipeline developed in this project demonstrated significant technical effectiveness, achieving high throughput analysis of extensive phenotypic data and meeting targeted accuracies. This system exemplified the capability of advanced machine learning technologies to efficiently manage and analyze large, complex datasets. Economically, the implementation of the project-developed pipelines may offer substantial cost savings across multiple sectors. It enhances data analysis processes and significantly reduces the need for manual data interpretation, thereby decreasing both the time and resources required. 3) Public benefit: The project has significantly broadened the scope of agricultural methodologies to enhance phenotypic analysis, with potential applications in various sectors beyond agriculture. Additionally, the initiative fostered an enriching educational and collaborative environment, significantly enhancing the technical skills of participants. It also made substantial contributions to the scientific community by providing open-access data sets and tools, encouraging ongoing research and development across various disciplines. Overall, the project not only met its scientific goals but also showcased the extensive utility of integrating advanced machine learning and sensor data analysis technologies. These advancements have proven instrumental in driving forward both theoretical research and practical applications, setting a strong foundation for future explorations and innovations in data-driven science.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Workspace and Office Design: A Review of the Literature

The Systems Analysis & Decision Support (02150) group completed a review of the research literature on workspace and office design in 2016 for the Asset Management Department (04853). The goal was to characterize results and lessons learned from existing research to understand the effectiveness of current workspaces at Sandia National Laboratories and inform guidance for future workspace design. The study team reviewed 96 documents, published primarily since the year 2000, covering a range of factors associated with workspace design - workspace costs, acoustics, collaboration and privacy, generational preferences, employee health, performance and productivity, organizational retention, and workspace satisfaction. The research literature consistently highlighted the relative deficiencies of open-plan office spaces as compared to traditional private enclosed offices for knowledge workers. While open-plan offices can provide some cost savings, they may not be cost effective in the long term due to future hidden costs incurred by degradations in employee productivity, increased attrition, and increased sickness absences as well as any post-construction modifications needed to resolve emerging workspace issues. The chief deficiencies of open-plan offices include lower levels of employee satisfaction due to reduced visual and auditory privacy, increased interruptions, distractions from irrelevant background speech, less physical space, and more ambient noise. The drawbacks reported in the literature tend to outweigh any benefits associated with potential facilitation of coworker interactions and collaboration. Key suggestions identified in the literature to guide and optimize workspace and office design are provided.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2018

Perovskite Solar Cells: Addressing Low Cost, High Efficiency, and Reliability Through Novel Hole-Transport Materials

The ultimate goal of this project is to develop low-cost and scalable organic-based hole transport materials that will lead to thin film perovskite solar cells with high efficiencies (>25%) and long lifetimes (>20 years). A very important component of a perovskite solar cell is the hole transport layer (HTL). This layer is also generally the most expensive and problematic component of the device. Currently the state-of-the-art HTL is based on a lithium salt doped aromatic amine termed N 2 ,N 2 ,N 2' ,N 2' ,N 7 ,N 7 ,N 7' ,N 7' -octakis(4-methoxyphenyl)-9,9'-spirobi[9H-fluorene]-2,2',7,7'-tetramine (spiro-OMeTAD) that is very difficult to prepare with no paths for becoming cost effective at high volume. This project will directly prepare perovskite solar cells more easily commercialized by designing and preparing new HTL materials with properties that address the current bottlenecks, such as cost, tunable conductivity and energy levels, hydrophobicity, Li free dopants, and stability to name a few. We have preliminary results from new cost-effective HTLs and dopants that have led to high efficiency (>19%) and long operating lifetime devices without encapsulation – significantly improved over spiro-OMeTAD based devices prepared in our labs as controls. We will focus on using the lessons learned from that first departure from the norm to design even more ideal and scalable HTLs. We believe the outcomes of this proposal will lead to a substantial impact for the perovskite community to develop a low cost, scalable and high performing HTL. This will be a collaborative effort where our synthetic chemists will perform HTL design, chemical synthesis, and materials characterization, while our device engineers will perform device physics, fabrication/processing, and characterization.

14 SOLAR ENERGY↗