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DOE OSTI2022

AI-enhanced Codesign for Next-Generation Neuromorphic Circuits and Systems

This report details work that was completed to address the Fiscal Year 2022 Advanced Science and Technology (AS&T) Laboratory Directed Research and Development (LDRD) call for “AI-enhanced Co-Design of Next Generation Microelectronics.” This project required concurrent contributions from the fields of 1) materials science, 2) devices and circuits, 3) physics of computing, and 4) algorithms and system architectures. During this project, we developed AI-enhanced circuit design methods that relied on reinforcement learning and evolutionary algorithms. The AI-enhanced design methods were tested on neuromorphic circuit design problems that have real-world applications related to Sandia’s mission needs. The developed methods enable the design of circuits, including circuits that are built from emerging devices, and they were also extended to enable novel device discovery. We expect that these AI-enhanced design methods will accelerate progress towards developing next-generation, high-performance neuromorphic computing systems.

42 ENGINEERING↗

DOE OSTI2025

2024 ASER Summary: Annual Site Environmental Report Summary (Rev. 2)

The Laboratory’s Governing Policy for the Environment: (1) We are committed to act as stewards of our environment to achieve our mission in accordance with all applicable environmental requirements. (2) We set continual improvement objectives and targets, measure and document our progress, and share our results with our workforce, sponsors, and the public. (3) We reduce our environmental risk through legacy cleanup, pollution prevention, and long-term management programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2021

A bespoke health risk assessment methodology for the radiation protection of astronauts

An alternative approach that is particularly suitable for the radiation health risk assessment (HRA) of astronauts is presented. The quantity, Radiation Attributed Decrease of Survival (RADS), representing the cumulative decrease in the unknown survival curve at a certain attained age, due to the radiation exposure at an earlier age, forms the basis for this alternative approach. Results are provided for all solid cancer plus leukemia incidence RADS from estimated doses from theoretical radiation exposures accumulated during long-term missions to the Moon or Mars. For example, it is shown that a 1000-day Mars exploration mission with a hypothetical mission effective dose of 1.07 Sv at typical astronaut ages around 40 years old, will result in the probability of surviving free of all types of solid cancer and leukemia until retirement age (65 years) being reduced by 4.2% (95% CI 3.2; 5.3) for males and 5.8% (95% CI 4.8; 7.0) for females. RADS dose–responses are given, for the outcomes for incidence of all solid cancer, leukemia, lung and female breast cancer. Results showing how RADS varies with age at exposure, attained age and other factors are also presented. The advantages of this alternative approach, over currently applied methodologies for the long-term radiation protection of astronauts after mission exposures, are presented with example calculations applicable to European astronaut occupational HRA. Some tentative suggestions for new types of occupational risk limits for space missions are given while acknowledging that the setting of astronaut radiation-related risk limits will ultimately be decided by the Space Agencies. Suggestions are provided for further work which builds on and extends this new HRA approach, e.g., by eventually including non-cancer effects and detailed space dosimetry.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia's Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia's ElectroMagnetic Plasma In Realistic Environments ( EMPIRE ) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia's HPC users.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Integrated System and Application Continuous Performance Monitoring and Analysis Capability (Final)

Scientific applications run on high-performance computing (HPC) systems are critical for many national security missions within Sandia and the NNSA complex. However, these applications often face performance degradation and even failures that are challenging to diagnose. To provide unprecedented insight into these issues, the HPC Development, HPC Systems, Computational Science, and Plasma Theory & Simulation departments at Sandia crafted and completed their FY21 ASC Level 2 milestone entitled "Integrated System and Application Continuous Performance Monitoring and Analysis Capability." The milestone created a novel integrated HPC system and application monitoring and analysis capability by extending Sandia’s Kokkos application portability framework, Lightweight Distributed Metric Service (LDMS) monitoring tool, and scalable storage, analysis, and visualization pipeline. The extensions to Kokkos and LDMS enable collection and storage of application data during run time, as it is generated, with negligible overhead. This data is combined with HPC system data within the extended analysis pipeline to present relevant visualizations of derived system and application metrics that can be viewed at run time or post run. This new capability was evaluated using several week-long, 290-node runs of Sandia’s ElectroMagnetic Plasma In Realistic Environments (EMPIRE) modeling and design tool and resulted in 1TB of application data and 50TB of system data. EMPIRE developers remarked this capability was incredibly helpful for quickly assessing application health and performance alongside system state. In short, this milestone work built the foundation for expansive HPC system and application data collection, storage, analysis, visualization, and feedback framework that will increase total scientific output of Sandia’s HPC users.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Development of a Lower Cost, High Volume, Commercially Available, Precursor for Lower Cost Carbon Fiber for Automotive and Wind Blade Applications

Essentially all carbon fibers used in structural applications are presently manufactured from solution spun polyacrylonitrile (PAN) precursor. Solution spinning is accomplished at relatively low temperatures using very large amounts of environmentally unfriendly solvents that impart major impact to line speed and costs involved with handling and reclaiming the solvents and associated “waste streams” required in spinning and extracting those solvents. Dralon and others have demonstrated the dry spinning of PAN in the production of some acrylic fibers for textile applications, but to this point it has not been demonstrated in the production of carbon fibers. In dry spinning, the PAN polymer is also dissolved into similar solvents, although typically less solvent is utilized to maintain a somewhat higher solids content of 20-50%. The use of lower overall solvent quantities and elimination of the need for solvent/water separation simplifies and reduces the cost of solvent recovery operations. Other portions of the precursor production and carbon fiber manufacturing processes are quite similar. The work described in this report demonstrated that dry spun acrylic fiber can be an attractive candidate for carbon fiber precursor of potential interest to applications looking to capture the specific stiffness (stiffness per unit mass) and specific strength (strength per unit mass) of carbon fiber when produced at a discount relative to commercial carbon fibers in the market today. Dralon has determined that the dry spinning process utilized for producing fibers used as precursors in this project requires 27% less energy versus producing comparable fibers via the industry standard wet spinning process. Identification of carbon fiber manufacturers, as well as applications development and targeted marketing, will be required to achieve significant in-roads into mission areas of interest to DOE, such as automotive applications and wind turbine blades. In support of that activity, initial data produced in this project indicate the innovative approach as described in this report can meet at least the baseline property requirements identified for these applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Achieving Diesel Powertrain Ownership Parity in Battery Electric Heavy Duty Commercial Vehicles Using a Rapid Recurrent Recharging Architecture

Battery electric vehicles (BEV) in heavy duty (HD) commercial freight transport face challenging technoeconomic barriers to adoption. Specifically, beyond safety and compliance, fleet and operational logistics require both high up-time and parity with diesel system productivity/Total Cost of Ownership (TCO) to enable strong adoption of electrified powertrains. At present, relatively high energy storage prices coupled with the increased weight of BEV systems limit the practicality of HD commercial freight transport to shorter range applications, where smaller batteries will suffice for the mission energy requirements (single operational shift). This paper presents an approach to extend the feasibility of BEV HD trucking for a broad range of applications. The concept is based on the leveraging rapid and recurrent recharging of a BEV HD truck that may either already make frequent stops due to shipment drop-offs/reloading or be required to make frequent stops along with longer missions for recharging. While the challenges of the latter are well appreciated, the concept proposed explores making minimal impact to overall mission time by targeting high C-rate charging while optimizing the frequency (miles) through which these events must occur. The concept optimizes battery size and chemistry, such that the expected life (years and total energy throughput) is balanced with the depth of discharge between recharging events, thus making complete use of the energy available through the life of the battery system. The solution is constrained to minimize the impact on payload capacity. The paper analyzes critical levers in achieving diesel price parity (based on a simplified vehicle TCO), achieved through different purchase options (including lease versus buy) and operational models (half-life swap out). Finally, the paper identifies the application design domain where these solutions are viable with limited impact on fleet operations.

Sujan, Vivek↗

DOE OSTI2023

Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

58 GEOSCIENCES↗

DOE OSTI2021

Design Guidelines for Deployable Wind Turbines for Military Operational Energy Applications

This document aims to provide guidance on the design and operation of deployable wind systems that provide maximum value to missions in defense and disaster relief. Common characteristics of these missions are shorter planning and execution time horizons and a global scope of potential locations. Compared to conventional wind turbine applications, defense and disaster response applications place a premium on rapid shipping and installation, short-duration operation (days to months), and quick teardown upon mission completion. Furthermore, defense and disaster response applications are less concerned with cost of energy than conventional wind turbine applications. These factors impart design drivers that depart from the features found in conventional distributed wind turbines, thus necessitating unique design guidance. The supporting information for this guidance comes from available relevant references, technical analyses, and input from industry and military stakeholders. This document is not intended to be a comprehensive, prescriptive design specification. This document is intended to serve as a written record of an ongoing discussion of stakeholders about the best currently available design guidance for deployable wind turbines to help facilitate the effective development and acquisition of technology solutions to support mission success. The document is generally organized to provide high-level, focused guidance in the main body, with more extensive supporting details available in the referenced appendices. Section 2 begins with a brief qualitative description of the design guidelines being considered for the deployable wind turbines. Section 3 provides an overview of the characteristics of the mobile power systems commonly used in U.S. military missions. Section 4 covers current military and industry standards and specifications that are relevant to a deployable wind turbine design. Section 5 presents the deployable turbine design guidelines for the application cases.

17 WIND ENERGY↗

DOE OSTI2025

Full Device Evaluation of Metal Halide Perovskite Solar Cells in Low Earth Orbit Aboard the International Space Station

This study investigates how sustained low Earth orbit (LEO) exposure affects metal halide perovskite (MHP) thin films and perovskite solar cells (PSCs). It examines samples deployed on the Materials International Space Station Experiment (MISSE) 15 and 16 missions. Five methylammonium lead iodide (MAPI) thin films are deployed on MISSE-16, each with distinct UV filters to selectively attenuate AM0 spectral bands. While post-flight optical analysis reveals that the least UV-exposed film exhibits the highest emission and lowest non-radiative recombination rate, no clear correlation is observed among the rest, and all MAPI films maintained excellent integrity throughout the mission. MISSE-15 deployed eight PSCs with diverse structures, MHP compositions, and contact materials. Post-flight analysis reveals stable, highly emissive MHPs, but damaged contacts due to ion migration, which caused loss of electrical response. The MISSE missions demonstrate MHPs' suitability for space applications, while highlighting the need for improved interfacial layers and contact materials to enhance charge carrier mobility, prevent ion migration, and improve charge carrier extraction efficiency.

14 SOLAR ENERGY↗

DOE OSTI2021

Los Alamos LDRD Appraisal Final Report for 20190124ER [Hot Electron Beam Generation and Transport for Fast Ignition]

This project was successful in addressing key technical issues of the cone-guided electron fast ignition concept for ICF. The development of an e-beam to deliver energy of 10’s of kilojoules to HED targets would be useful for myriad experimental applications relevant to the Laboratory’s mission. For example, ICF ignition and the development of a burning plasma is a high priority for the national high energy density physics effort and this work is aligned with that mission. Moreover, other technologies (e.g., MeV laser radiography) would be advanced by the R&D advancements from this project, so this work has broader implications for other LANL missions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A Strategy for NACS investment in Machine Learning

The Nuclear and Chemical Sciences (NACS) Division furnishes the expertise in the scientific areas of chemical, nuclear and isotopic sciences that are foundational in the Laboratory’s national security missions. This expertise is maintained and advanced through identification, development and application of state-of-the-art theoretical, computational and experimental methods and tools. Recent developments in artificial intelligence and machine learning (AI/ML) techniques enabled by advances in computing capabilities and widespread availability of powerful software implementations have made use of these techniques ubiquitous across both science and industry. While the scope of AI/ML applications is incredibly large and evolves very rapidly, the topics most relevant to NACS missions fall into the general category of detecting, categorizing or identifying features in large, complex datasets using either supervised or unsupervised learning. This covers both basic scientific data analysis and the development of efficient surrogate models of real-life technological systems, experimental detectors, or theoretical models. To remain at the forefront of its core scientific disciplines, NACS must both cultivate ML expertise as well as continuously explore applying this expertise to new problems or utilizing new methods. This document identifies the key areas where this support is critical and provides a strategy for investing in them.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Spatio-temporal and weather characterization of road loads of electrified heavy-duty commercial vehicles across U.S. interstate roads

Adoption of battery electric vehicles (BEV) in heavy duty (HD) commercial freight transportation is difficult due to technological and economic hurdles. Beyond safety and compliance, fleet and operational logistics necessitate both high uptime and parity with diesel system productivity/Total Cost of Ownership to support widespread deployment of electric powertrains. However, relatively high energy storage costs, along with the higher weight of BEV systems, limit the viability of HD commercial freight transport to shorter-range applications where smaller batteries will serve for mission energy requirements (single operational shift). Knowing the energy consumption and operating variations of these commercial vehicle systems is crucial for effectively sizing the energy storage systems. This paper is the first in a series of studies to understand the regional specific operating design domain variations of commercial Class 8 HD trucks and the associated impact to their energy requirements. In particular, the local weather conditions are shown to influence the total vehicle energy usage. Further, the impact of temperature, pressure, and humidity changes are shown to impact the local air density. This is needed to calculate aerodynamic drag on vehicles and has been found to play a significant role in the overall performance of a vehicle. Although the methodology of calculating air density varies only slightly throughout the literature, the application to transportation has still been limited. This study provides a means by which air density can be estimated for the contiguous U.S. using the NOAA MADIS dataset. These air density estimates were then used to determine vehicle performance in varying regions of the country to highlight the importance of consideration of weather variables when monitoring vehicle performance, but also to provide recommendations based on these locales upon fleet conversion to BEVs.

Moore, Amy↗

DOE OSTI2020

Proton irradiation damage and annealing effects in ON Semiconductor J-series silicon photomultipliers

Silicon photomultipliers (SiPMs) have become popular light conversion devices in recent years due to their low bias voltage and sensitivity to wavelengths emitted from common scintillating materials. These properties make them particularly attractive for resource-constrained missions such as space-based detector applications. However, the space radiation environment is known to be particularly harsh on semiconductor devices, where high particle fluences can degrade performance over time. The radiation hardness of a particular SiPM, manufactured by ON Semiconductor (formally SensL), has yet to be studied with high energy protons, which are native to the space radiation environment. As such, to study these effects we have irradiated groups of two SiPMs to four different fluences of 800 MeV protons delivered by the accelerator at the Los Alamos Neutron Science Center. Fluences of 1.68 × 10 9 , 1.73 × 10 10 , 6.91 × 10 10 , and 1.73 × 10 11 protons cm –2 , and their corresponding estimated doses of 0.15, 1.55, 6.19, and 15.5 kRad, were chosen based on estimates of the potential exposure a SiPM might receive during an interplanetary space mission lasting 10 years. We report the effects these doses have on dark current and the self-annealing time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Annual Site Environmental Report (CY2019)

The primary purpose of this report is to summarize the performance of Ames Laboratory’s environmental programs, present highlights of significant environmental activities, and confirm compliance with environmental regulations and requirements for calendar year 2019. This report is a working requirement of Department of Energy Order 231.1B, Environment, Safety and Health Reporting. It includes descriptions of the Laboratory’s site, mission, the status of its compliance with applicable environmental regulations, its planning and activities to maintain compliance, and a comprehensive review of its environmental protection, surveillance and monitoring activities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

2021 Idaho National Laboratory Site Environmental Report

The INL Site’s operations, as well as the ongoing cleanup, necessarily involve a commitment to environmental stewardship and full compliance with environmental protection laws. As part of this commitment, the INL Site Environmental Report is prepared annually to inform the public, regulators, stakeholders, and other interested parties of the INL Site’s environmental performance during the year. This report is published for the DOE-ID in compliance with DOE O 231.1B, “Environment, Safety and Health Reporting.” Its purpose is to: • Present the INL Site, mission, and programs • Report compliance status with applicable federal, state, and local regulations • Describe the INL Site environmental programs and activities • Summarize results of environmental monitoring • Discuss potential radiation doses to the public residing in the vicinity of the INL Site • Report on ecological monitoring and research conducted by contractors and affiliated agencies and by independent researchers through the Idaho National Environmental Research Park • Describe quality assurance methods used to ensure confidence in monitoring data • Provide supplemental technical data and reports that support the INL Site Environmental Report (https://idahoeser.inl.gov/publications.html).

99 GENERAL AND MISCELLANEOUS↗