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At least 127 records · Page 7

Reports From The Frontier: Overcoming Limitations for Pure-water Anion-exchange-membrane Electrolysis

Anion-exchange-membrane electrolysis is positioned to play a key role in the predicted exponential growth of green hydrogen technology with essential R&D advances. We reveal key design parameters essential to commercialization. First, stable alkaline oxygen-evolution reaction catalysts with high electronic conductivity and minimal surface reconstruction during operation must be designed. Alkaline catalyst layers must also be applied to the membrane electrode assembly with scalable, industrially relevant techniques. Second, ionomer oxidation mitigation strategies must be developed. Furthermore, this approach could also target other creative catalyst layer design, such as phase-separation control to protect oxidation-prone organic components or catalyst engineering to direct selectivity for hydroxide over polymer oxidation. If competitive efficiency and durability can be achieved in pure water, AEM electrolysis has the potential to become a dominant electrolyzer technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Grand-scale Atmospheric Imaging Apparatus (GAIA) and Wind LiDAR Multi-scale Measurements in the Atmospheric Surface Layer

Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale ($\mathcal{O}$(mm)) up to large energy-containing structures ($\mathcal{O}$(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system.

17 WIND ENERGY↗

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. In conclusion, our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Workflows↗

Opinion: Lighting Research During and After the Pandemic

For many lighting researchers, the recent restrictions related to COVID-19 have paused most in-person laboratory or field experiments. In certain countries, regulatory institutions have allowed such experiments to resume but issued safety recommendations like pre-checking participants for disease symptoms and excluding those that are especially vulnerable. To avoid potential complications and delays, researchers are likely to resort to other research designs that enable remote research — eliminating the need for the experimenter and subjects to be in the same room. While unanticipated, this shift to remote research methods is an opportunity to revisit and further develop remote experimental techniques, which can have beneficial long-lasting implications on lighting research during and after the pandemic. Perhaps the most common type of remote research is online experiments; which can greatly benefit from improved techniques to allow for greater control or characterization of experimental conditions, like those proposed by Villa and Labayrade . These techniques require validation specific to the type of response collected and context, e.g. space type and attributes. New research techniques continue to emerge and will enable remote collection of objective measures such as gaze direction and high dynamic range images (HDRIs) without specialized equipment. In fact, many of these methods and techniques are commonly used in other disciplines, like vision science and psychology, and can be adapted for lighting research. Not all lighting studies can be conducted online, but in the age of ubiquitous and connected technologies, there is a largely missed opportunity to utilize crowdsourced responses to lighting conditions in real environments where people live and go about their daily lives without the research being limited to a certain time and laboratory locations. For instance, outdoor street luminaires can be rated by passersby using ecological momentary assessments on mobile phones. While this approach does not provide laboratory-level control over experimental conditions, it can provide more contextual responses resulting from ‘normal’ dynamic gaze, behavior, and interactions. Further, it can also reduce bias related to the experimental setting. Further development of remote research methods and their use in lighting research can improve the quality of lighting research. Given that sample sizes in most lighting research are relatively small, remote research can substantially increase sample sizes, reduce research costs, and shorten study duration, all while reducing interaction between experimenter and subjects. Another benefit is reaching a wider audience and including under-represented individuals who typically are not reachable using common recruitment methods. The pandemic has set back our plans for in-lab experiments, but can it inspire us to think of new and creative ways to expand lighting research?

Abboushi, Belal K.↗

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version 0.2. is created to correspond to data updates in the USGS 1/3 arcsec DEM, the USGS National Hydrography Dataset, including its Water Boundary Dataset, and the NHDPlus medium resolution dataset. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS - v0.1, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.21 (20200601)

The continental flood inundation mapping (CFIM) framework is a high-performance computing (HPC)-based computational framework for the Height Above Nearest Drainage (HAND)-based inundation mapping methodology. Using the 10m Digital Elevation Model (DEM) data produced by U.S. Geological Survey (USGS) 3DEP (the 3-D Elevation Program) and the NHDPlus hydrography dataset produced by USGS and the U.S. Environmental Protection Agency (EPA), a hydrological terrain raster called HAND is computed for HUC6 units in the conterminous U.S. (CONUS). The value of each raster cell in HAND is an approximation of the relative elevation between the cell and its nearest water stream. Derived from HAND, a hydraulic property table is established to calculate river geometry properties for each of the 2.7 million river reaches covered by NHDPlus (5.5 million kilometers in total length). This table is a lookup table for water depth given an input stream flow value. Such lookup is available between water depth 0m and 25m at 1-foot interval. The flood inundation map is then computed by using HAND and this lookup table based on the near real-time water forecast from the National Water Model (NWM) at the National Oceanic and Atmospheric Administration (NOAA). HAND and the Hydraulic Property Table version v0.21 is an upgrade to v0.2. Version 0.21 applies NHD HR etching to DEMs for all HUC6 units before running the HAND workflow. This new version also fixed the issue of void areas along HUC boundary. This dataset comprises 331 HUC6 units for CONUS (excluding the five great lakes units), each is a downloadable zip file. Version 0.1 was computed at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urban-Champaign in 2016 and is currently hosted at the Texas Advanced Computing Center (TACC). Please cite this data DOI and the following publications and data DOIs when you use HAND and the hydraulic property table: Liu, Yan Y. Height Above Nearest Drainage (HAND) and Hydraulic Property Table for CONUS - Version 0.2. (20200301). Oak Ridge Leadership Computing Facility. (2020). DOI: 10.13139/ORNLNCCS/1608331 Liu, Yan Y., David R. Maidment, David G. Tarboton, Xing Zheng, and Shaowen Wang. 'A CyberGIS integration and computation framework for high-resolution continental-scale flood inundation mapping.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 770-784. DOI: 10.1111/1752-1688.12660 Zheng, Xing, David G. Tarboton, David R. Maidment, Yan Y. Liu, and Paola Passalacqua. 'River channel geometry and rating curve estimation using height above the nearest drainage.' JAWRA Journal of the American Water Resources Association 54, no. 4 (2018): 785-806. DOI: 10.1111/1752-1688.12661 Liu, Y. (2018). Height Above Nearest Drainage (HAND) for CONUS, HydroShare, https://doi.org/10.4211/hs.69f7d237675c4c73938481904358c789 LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗

Back to Basics: First Principles of HRA

Human Reliability Analysis (HRA) continues to suffer from a myriad of technical gaps in its foundations. Core issues have been widely discussed but remain largely unaddressed, although the path taken to this point was paved with good intentions. Now, while this characterization may seem dramatic it is not intended to throw doubt onto HRA as a discipline or to diminish the value it has on increasing safety and resilience in nuclear power. Rather, it is intended to communicate that HRA has some serious challenges and is desperately in need of a critical evaluation. To quote Dougherty [1], “the misrepresentations that will undoubtedly arise may serve to provoke the necessary creativity.” What bears noting is that in the last thirty years since Alan Swain declared that “no adequate HRA had ever been done,” there has been little effort to correct the issues he highlighted. Human factors practice is still poorly implemented, many complex systems are fundamentally incompatible with reliable human performance, and there remains poor foundational support for many of the assumptions within HRA. We propose three categories of issues in HRA, philosophical, theoretical, and empirical and potential solutions to these issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Evaluating and Explaining Natural Language Generation with GenX

Current methods for evaluation of natural language generation models focus on measuring text quality but fail to probe the model creativity, i.e., its ability to generate novel but coherent text sequences not seen in the training corpus. We present the GenX tool which is designed to enable interactive exploration and explanation of natural language generation outputs with a focus on the detection of memorization. We demonstrate the utility of the tool on two domain-conditioned generation use cases - phishing emails and ACL abstracts.

Duskin, Kayla R.↗

An August boost for key roles in Weapons Production

In August, the Associate Laboratory Directorate of Weapons Production (ALDWP) hosted two virtual hiring events and welcomed the first cohort of a new college pipeline program. With hiring steadily on the increase in support of the national security mission, creative methods like these have been key to finding successful candidates.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

2020 LLNL Nuclear Science and Security Summer Internship Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Science and Security Summer Internship Program (NS 3 IP) is designed to give graduate students an opportunity to come to LLNL for 8–10 weeks of hands-on research. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry), and gain a better understanding of the various science programs at LLNL. Due to the travel and access restrictions imposed by the COVID-19 pandemic, the 2020 NS 3 IP was organized as an “all-virtual” internship program. With LLNL’s extensive institutional support, students accessed the laboratory’s cyberinfrastructure through a secure virtual desktop environment and all seminars, mentor interactions, summer presentations, and laboratory tours were performed remotely. While this virtual internship format did not allow for hands-on laboratory research projects, both the interns and their mentors constructed creative research projects that maximized student exposure to nuclear science research that is relevant to DTRA and LLNL interests in nuclear security.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Tribal Revegetation Project Final Project Report: 92-Acre Area, Area 5 Radioactive Waste Management Complex, Nevada National Security Site, Nevada

Nuwu (Southern Paiute), Newe (Western Shoshone), and Nuumu (Owens Valley Paiute) are linguistically related, Numic-speaking peoples who are part of the broader Uto-Aztecan language group. Numic peoples view the land as a holistic, living, sentient being with feelings and purpose. The land is personified with human characteristics and it needs to be experienced to be understood through “learning by doing.” Numic peoples do not support ground disturbing activities within their ancestral lands, including activities tied to the storage of low-level radioactive waste or classified materials on the NNSS, which they view as culturally inappropriate. These deep-rooted ancestral connections are the impetus for reinforcing Numic responsibility for healing disturbed areas by integrating respect and patience with consistent Tribal interaction. Tribal Ecological Knowledge (TEK) is the science of describing Tribal approaches for understanding natural resources. Numic TEK is embedded in traditional teachings learned incrementally over time though experience and it evolves through lessons learned and responses to environmental changes over millennia. Therefore, TEK can broaden and enhance Western scientific knowledge associated with revegetation, especially in highly disturbed areas. The project blended TEK with Western scientific ecological methods to create a vegetative cover within test plots on the 92-Acre Area located at the Radioactive Waste Management Complex (RWMC) located in Area 5 on the Nevada National Security Site (NNSS). The vegetated test plots were systematically created for the Department of Energy (DOE) in tandem with the existing Federal Facilities Agreement and Consent Order (FFACO) with the Nevada Department of Environmental Protection (NDEP). Three previous contractor-lead attempts at revegetation, one targeting full cover revegetation and two targeting test plot revegetation, did not achieve the anticipated results at this location. When presented to the 16 American Indian Tribal nations and affiliated groups with cultural and historical ties to the NNSS, the group appointed a Tribal Revegetation Committee (TRC) that included six expert Tribal knowledge holders to collaborate with an ethnoecologist/cultural anthropologist and two biologists. Project design, planning, seed and outplant selection, spiritual land preparation, and methodology were guided by the TRC and an ethnoecologist/cultural anthropologist from Portland State University (PSU) and biologists from Desert Research Institute (DRI). Using TEK, the TRC recommended a specific seed mixture that contained nine native plant species and three species of outplants. The revegetation effort included preparing and planting thirty 10 m × 10 m (32.8 ft × 32.8 ft) seeded plots, twelve of which also included outplants; and eight 10 m × 100 m (32.8 ft × 328 ft) plots that only received outplants, all atop a waste cell cover. The TRC and the project team creatively adapted TEK with Western scientific methods so that the planned revegetation efforts could occur within the safety and security parameters of the RWMC. Test plots were subjected to one of five soil treatments with varying combinations of straw or mulch applications, soil amendment, and/or outplant planting and one of two watering treatments (watered or unwatered). Planting was divided into two events: one in the fall season and another during the subsequent spring season based on TEK and a corresponding recommendation from the TRC. Monitoring and spiritual management was conducted by the TRC to evaluate plant progress on a monthly basis (in conjunction with the biologist and anthropologist) during each respective growing season for a period of three years after planting. This approach allowed Tribal members the opportunity to conduct traditional blessings and other culturally appropriate activities to restore balance to the land in accordance with Tribal protocols. Following TEK-guided methods, successful plant establishment from seed stock and outplants was observed in many plots. Overall, plots planted in the spring, as recommended by the TRC, showed higher rates of outplant survival and native seedling emergence than those planted in the fall. This finding is significant because it is contrary to the original guidance and previous approaches provided for planting in this region. The TRC believes the frequent co-occurrence of native seedlings near surviving outplants indicates an important symbiotic relationship understood by Tribal communities and overlooked by others. Watered outplants displayed much higher survivability than unwatered plants, even after watering was reduced after the plants were established. Soil amendments and mulch created higher densities of native plants from seed. Many native seedlings showed significant delays in germination, which is considered a normal adaptation to desert climates. Some native plants did not germinate until the third growing year, whereas others germinated during the first growing year, which demonstrates the complexity of the desert environment. Evidence of native insects, reptiles, mammals, and birds, as well as native plants that were not part of the planted species, were noted and considered culturally significant. Despite the presence of non-native plants, native outplants continued to thrive and the incidence of native plant germination from seed increased over time. These successful revegetation results where previous efforts were unsuccessful reinforce the importance of integrating regionally appropriate, TEK-guided methodology with Western science to achieve positive results and the necessity of integrating Tribal involvement in all stages of the revegetation effort. Expanded approaches coupled with Tribal knowledge and tools from Western science addressed a complex problem tied to revegetating atop a low-level radioactive waste cell. The level of Tribal participation serves as a progressive model for building collaborative relationships and addressing ecological challenges on the NNSS.

54 ENVIRONMENTAL SCIENCES↗

Commuting again to work on-site? Lab’s first drivers faced their own challenges decades ago

After a long pause in commuting, more and more Lab staff are filling travel mugs, topping off their tanks, and heading up the hill for on-site work. As long as the Lab has existed, workers have snaked through tight mountain roads to get to their jobs. And, back in the early 1940s, drivers faced even more challenges. In the Lab’s earliest years, drivers likely worried about getting stuck in the mud, navigating intense switchbacks, or making sure important equipment, such as a disassembled power plant, didn’t fall off the back of the truck that was hauling it. Creative solutions could go a long way to get out of a jam. As many of us return onsite and get reacquainted with commuting, we look back at how transportation has changed since the Manhattan Project, the U.S. government’s top secret effort at Los Alamos to create the first atomic bombs to help end World War II. These stories and photos are part of the collections in the National Security Research Center, which is the Lab’s classified library and also houses unclassified artifacts from our past.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

PPPL Laboratory Directed Research and Development (Project Final Reports, FY2018 - FY2020)

The U.S. Department of Energy’s (DOE) Princeton Plasma Physics Laboratory (PPPL) is a collaborative national center for fusion energy science, basic sciences, and advanced technology. The Laboratory has three major missions: (1) to develop the scientific knowledge and advanced engineering to enable fusion to power the U.S. and the world; (2) to advance the science of nanoscale fabrication for technologies of tomorrow; and (3) to further the development of the scientific understanding of the plasma universe from laboratory to astrophysical scales. PPPL’s Laboratory Directed Research and Development (LDRD) program supports and encourages creativity and innovation and contributes to its long-term viability. New scientific and technical research areas emerge and are nurtured through the program. Furthermore, new capabilities are developed to enable the Laboratory to meet its and DOE’s missions. The program is used to systematically diversify the Laboratory’s programs and mission. In the last few years, the program has started projects in nanomaterial synthesis, microelectronics, advanced x-ray spectroscopy, high-energy-density physics, superconducting magnet technology, machine learning and artificial intelligence, 3D magnetic fields to optimize fusion plasmas, integration of permanent magnets with simple high-field magnets to reduce the cost of producing complex 3D magnetic fields, advanced computational methods for predictive understanding and control of fusion plasma, development of quantum computing algorithms for plasma physics, liquid metal plasma-facing components for fusion reactors, virtual engineering, and plasma-based space propulsion. The program is also the vehicle to recruit and train talented scientists and engineers with the new skills needed to perform the Laboratory’s mission. Many of the new hires through the program go on to become world-class scientists and engineers in their fields. This report provides descriptions and accomplishments of those LDRD projects that were completed during fiscal years 2018 through 2020.

36 MATERIALS SCIENCE↗

2021 LLNL Nuclear Science and Security Summer Internship Program

The Lawrence Livermore National Laboratory (LLNL) Nuclear Science and Security Summer Internship Program (NS 3 IP) is designed to give graduate students an opportunity to come to LLNL for 8–10 weeks of hands-on research. Students conduct research under the supervision of a staff scientist, attend a weekly lecture series, interact with other students, and present their work in poster format at the end of the program. Students also have the opportunity to meet staff scientists one-on-one, participate in LLNL facility tours (e.g., the National Ignition Facility and Center for Accelerator Mass Spectrometry), and gain a better understanding of the various science programs at LLNL. Due to the travel and access restrictions imposed by the COVID-19 pandemic, the 2021 NS 3 IP was organized as an “all-virtual” internship program. With LLNL’s extensive institutional support, students accessed the laboratory’s cyberinfrastructure through a secure virtual desktop environment and all seminars, mentor interactions, summer presentations, and laboratory tours were performed remotely. While this virtual internship format did not allow for hands-on laboratory research projects, both the interns and their mentors constructed creative research projects that maximized student exposure to nuclear science research that is relevant to DTRA and LLNL interests in nuclear security. We anticipate a return to an in-person internship format for the summer of 2022.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

In Situ Thermodynamics and Kinetics of Mixed-Valence Inorganic Crystal Formation

We have found that the established methods to discover and produce earth-abundant iron-containing active layers are very inefficient. The prospective new material iron silicon sulfide, Fe 2 SiS 4 , was previously only synthesized by slow reactions of metallic iron, silicon, and sulfur powders, resulting in poor yield. We found that using an ordered iron-silicon alloy as a feedstock results in a much faster reaction at a lower temperature. We could track this reaction using our unique in-situ X-ray diffractometer, which allows us to heat our encapsulated material up to 1000°C while providing a simultaneous picture of how the atoms in the structure rearrange. We are using this powerful new technique to search for new active layers and optimize their formation by creative use of more complex reagents. Our key accomplishments are mechanisms for improving synthesis, and new materials that result from them. We have demonstrated the utility of high-throughput, DOE-developed infrastructure, such as the Materials Project, to accelerate materials discovery and synthesis. We have pioneered new capabilities of user facilities, such as beamlines 6-BM, 17-BM, and 11-BM at the Advanced Photon Source (APS) and POWGEN and NOMAD at the Spallation Neutron Source.

36 MATERIALS SCIENCE↗

The False Dilemma: Rethinking AF Science and Technology Officer Talent Management

In 2019, Secretary of the Air Force (SECAF) Heather Wilson launched the 2030 Science and Technology (S&T) Strategy by stating, “The advantage will go to those who create the best technologies and who integrate and field them in creative operational ways that provide military advantages.” In 2021, 15% of Air Force general officers responsible for creating and integrating this technological edge had a science, technology, engineering, and math (STEM) graduate degree, and less than 1% of generals had a STEM doctorate. As one point of comparison, at least 32% and 13% of founding CEOs of Fortune 500 technology companies had a master’s and doctoral STEM degree, respectively. The gulf is larger when you consider that these CEOs used their technical degrees throughout their careers whereas most general officers do not. In large organizations, if the leader does not possess technical knowledge, it is difficult to drive innovation systems and connect ideas to reality. The evidence for this disconnect is abundant in acquisition challenges for high-tech systems, significant pushback to the “revolution in military affairs”, and failure of the “Third Offset” to take hold and deliver capabilities to offset Chinese and Russian capabilities. Indeed, when the former Air Force and Space Force Chief Software Officer, Nicolas M. Chaillan, offered his resignation, one of the main reasons for leaving was “the failure of OSD and the Joint Staff to deliver on their own alleged top ‘priority’, JADC2 – they couldn’t ‘walk the walk.’” Senior leaders have recognized this disconnect, and several inquiries and studies regarding STEM competency have been conducted. Many have posed a false dilemma: a large, STEM-cognizant or small, STEM-expert force. However, current and future STEM human capital needs are managed at the unit and functional area level. This leads to “silos” and the tactical “needs of the present” dominating the strategic “needs of the future”, highlighted by only two functional areas systematically tracking future STEM needs. Therefore, the decision on force structure appears to have been historically made by default through these and other perceived structural realities, and a vast majority of recommendations have focused on the small STEM community – an exercise in diminishing returns. In essence, the solution often boils down to finding unicorns – officers that have chartered the unforgiving pathway through the traditional “gates” to general officer while obtaining sufficient STEM proficiency along the way. Fortunately for the Air Force, these officers exist, albeit at a rate below what the evidence would suggest is necessary. However, this approach fundamentally limits the ability to develop a deep pool of officers with the ability to create and integrate technologies and ensures the Dunning-Kruger effect is prevalent. The Dunning-Kruger effect is a cognitive bias where those with limited knowledge in an area lack the expertise necessary to recognize their lack of expertise and consequently are prone to overestimate their knowledge and performance. In short, we need less leaders overestimating their STEM knowledge and more with hard-earned STEM competency required to “walk the walk” to create and integrate technologies for military advantage. This will not be accomplished by restricting the emphasis to the traditional scientists and engineering (S&E) career fields – the pool, only 10% of line officers, and general officer progression is just too small. Fortunately, the recent SECAF’s Management Initiatives and Chief of Staff’s (CSAF) Action Orders, both containing emphasis on organic expertise to “accrue advantage in military-technological competition,” present an opportunity to truly develop a framework for the force of the future.

99 GENERAL AND MISCELLANEOUS↗

Peak Demand Limiting and Optimization of HVAC Systems for Common Areas in High-Rise Multi-Family Housing (CRADA 518)

This project aims to create an innovative energy saving solution that is affordable and easy to implement. Initially, the proposed solution will fulfill an existing need for HVAC systems that serve multi-family housing. However, the solution can be easily expanded to other markets such as large hospitals, hotels, and commercial buildings. The proposed solution will be creatively packaged so that it can work where peak demand charge is high or where goal is to conserve energy and reduce carbon footprint. Of course, both Peak Demand Limiting (PDL) and Set-point optimization (SPO) can also operate simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗