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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Measurement of off-shell Higgs boson production in the $H^*\rightarrow ZZ\rightarrow 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector

A measurement of off-shell Higgs boson production in the $H^*\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb −1 of proton–proton collisions at $\sqrt{s} = 13$ TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$ decay channel at 68% CL is $0.87^{+0.75}_{-0.54}$ ($1.00^{+1.04}_{-0.95}$ ). The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$ decay channel has an observed (expected) significance of 2.5σ (1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell 2\nu$ decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ (2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is $4.3^{+2.7}_{-1.9}$ ($4.1^{+3.5}_{-3.4}$ ) MeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2012

Integrated Analysis of Interferometric SAR, Satellite Altimetry and Hydraulic Modeling to Quantify Louisiana Wetland Dynamics

Wetland loss in Louisiana has been accelerating due primarily to anthropogenic and nature processes, and is being advocated as a problem with national importance. Accurate measurement or modeling of wetland-wide water level changes, its varying extent, its storage and discharge changes resulting in part from sediment loads, erosion and subsidence are fundamental to assessment of hurricane-induced flood hazards and wetland ecology. Here, we use innovative method to integrate interferometric SAR (InSAR) and satellite radar altimetry for measuring absolute or geocentric water level changes and applied the methodology to remote areas of swamp forest in coastal Louisiana. Coherence analysis of InSAR pairs suggested that the HH polarization is preferred for this type of observation, and polarimetric analysis can help to identi:fy double-bonnce backscattering areas in the wetland. Envisat radar altimeter-measured 18- Hz (along-track sampling of 417 m) water level data processed with regional stackfile method have been used to provide vertical references for water bodies separated by levees. The high-resolution (approx.40 m) relative water changes measured from ALOS PALSAR L-band and Radarsat-l C-band InSAR are then integrated with Envisat radar altimetry to obtain absolute water level. The resulting water level time series were validated with in situ gauge observations within the swamp forest. Furthermore, we compare our water elevation changes with 2D flood modeling from LISFLOOD hydrodynamic model. Our study demonstrates that this new technique allows retrospective reconstruction and concurrent monitoring of water conditions and flow dynamics in wetlands, especially those lacking gauge networks.

Lee, Hyongki↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
DOE OSTI2024

Anomaly detection with flow-based fast calorimeter simulators

Recently, several normalizing flow-based deep generative models have been proposed to accelerate the simulation of calorimeter showers. Using caloflow as an example, we show that these models can simultaneously perform unsupervised anomaly detection with no additional training cost. As a demonstration, we consider electromagnetic showers initiated by one (background) or multiple (signal) photons. The caloflow model is designed to generate single-photon showers, but it also provides access to the shower likelihood. We use this likelihood as an anomaly score and study the showers tagged as being unlikely. As expected, the tagger struggles when the signal photons are nearly collinear but is otherwise effective. This approach is complementary to a supervised classifier trained on only specific signal models using the same low-level calorimeter inputs. While the supervised classifier is also highly effective at unseen signal models, the unsupervised method is more sensitive in certain regions, and, thus, we expect that the ultimate performance will require a combination of these approaches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Nuclear Responses with Neural-Network Quantum States

We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique to compute the dynamical properties of self-bound quantum many-body systems in continuous Hilbert spaces. While broadly applicable to various quantum systems, including atoms and molecules, in this initial application we focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available. Our accurate theoretical predictions are complemented by robust uncertainty quantification, enabling meaningful comparisons with experiments. Here, we demonstrate that a relatively simple nuclear Hamiltonian—based on a leading-order pionless EFT expansion and known to accurately reproduce ground-state energies of nuclei with 𝐴 ≤ 40—also provides a reliable description of the photoabsorption cross section.

Ab initio calculations↗
DOE OSTI2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Utilization of the Critic Subnetwork of a Generative Adversarial Network as Detector of Morphological Material Change in Image Data

The resolution of computed tomography (CT) has become high enough to monitor morphological changes due to aging in materials in long-term applications. For this work, we explored the utility of the critic of a generative adversarial network (GAN) to automatically detect such changes. The GAN was trained with images of pristine Pharmatose, which is used as a surrogate energetic material. It is important to note that images of the material with altered morphology were only used during the test phase. The GAN-generated images reproduced the microstructure of Pharmatose well, although some unrealistic particle fusion was seen. Calculated morphological metrics (volume fraction, interfacial line length, and local thickness) for the synthetic images also showed good agreement with the training data, albeit with signs of mode collapse in the interfacial line length. While the critic exposed changes in particle size, it showed limited ability to distinguish images by particle shape. The detection of shape differences was also a more challenging task for the selected morphological metrics that related to energetic material performance. We further tested the critic with images of aged Pharmatose. Subtle changes due to aging are difficult for the human analyst to detect; but both critic and morphological metrics analysis showed image differentiation.

36 MATERIALS SCIENCE↗
NASA NTRS2021

NASA Electric Vertical Takeoff and Landing (eVTOL) Aircraft Technology for Public Services – A White Paper

History has shown that our personal life is highly dependent on the technology that people have developed. A strategic scan of the aerospace environment at the beginning of the 21st century strongly suggests that the world might be approaching a new age of airpower—the era of electrified/hybrid aircraft propulsion. Undeniably, starting from the Montgolfier Brothers balloon flight in 1783, to the Wright Brothers piston engine flight in 1903, and the jet engine of the 1960s, or the space age of today, one can say that leaps in propulsion technology have marked the different ages of human flight. The technological advancements, brought at the beginning of 21st century by the revolution in data exchange, computational power, sensors, wireless communication, internet, and autonomy, contributed to the vision of this new age of propulsion we are approaching. Historically, conventional vertical takeoff and landing (VTOL) aircraft have been equipped with propulsion units relying on complex internal combustion machines (turbines, piston engines, for example), and complex mechanical arrangements (gearboxes, shafts, variable pitch propeller). By contrast, electric VTOL aircraft (eVTOL)1 rely on simpler propulsion units (electric motors and in some cases fixed-pitch propellers). This promotes redundancy and improves tolerance to failures, in turn improving safety. The use of simpler electric propulsion units should also allow significant acquisition and operating cost reductions. Whether full-electric (relying solely on batteries) or hybrid-electric (relying on a combination of batteries, fuel-powered engines, and generators.), eVTOLs are also expected to generate less noise and air pollution than conventional aircraft with similar payloads. According to the 2019 Annual Review of IATA (International Air Transport Association) [ref.1], due to an expected increase in air transport traffic by 5% every year and a doubling of air transport passenger numbers to 8.2 billion by 2037 significant challenges are posed to the aviation industry. Furthermore, this report does not factor in the expected demand for short-range (intra-city) air transportation, which is in development and yet to be operational. The increased demand to fly creates a responsibility to expand in a sustainable manner and an endeavor to develop more environmentally-friendly aircraft. eVTOL aircraft, either piloted or autonomous, is gathering considerable interest worldwide. Modern and novel full-electric or hybrid-electric eVTOL configurations enable a new paradigm shift in air transportation as the aviation industry remains committed to its goals of carbon-neutral growth from 2020 onwards and cutting CO2 emissions to half 2005 levels by 2050. While electric power has been used for decades, recent developments in mobile electric/hybrid propulsion coupled with advanced materials and autonomous systems may create the possibility to transition into the next age of air mobility propelled by electric/hybrid VTOL aircraft technology. Although eVTOL aircraft might seem like an incremental improvement or even a counterintuitive regression with regard to past VTOL development, it has in fact the potential to transform air mobility across a wide range of government applications. Previous transformations in aviation generated dramatic leaps in performance, but the cost was commensurate with performance, limiting quantity produced. This next age appears to take a different approach. Performance may not increase, but at this moment technology is poised for future urban mobility that will spawn commercial passenger drone services, that is, autonomous (pilotless) air taxis and thereby add a new dimension to the urban transportation mix of the future [ref. 2]. Advances in electric propulsion, autonomous flight technology, and 5G communication networks will enable this fast new-growing market to become a reality. It is now time to envision the introduction of electric/hybrid eVTOL aircraft for Public Services2. We believe that in the next decades eVTOL aircraft will have the potential to become an essential tool to Public Service agencies around the world in applications such as firefighting, public safety, search and rescue, disaster relief and law enforcement. This is due to several major factors. • First, with the increasing popularity of small, unmanned aircraft vehicles (UAVs) or drones, many companies today are focusing on the development of passenger UAVs designed to accommodate up to five passengers or equivalent cargo payload. Many such configurations are electric or hybrid-electric designs with VTOL capabilities. Several of these projects have started a flight test program and many more are expected to be in the experimental and development phase in 2020. Such revolutionary vehicles could be in commercial operations by 2030. These eVTOL systems could be ready for selected Public Services missions even sooner. • Second, although these advanced eVTOL vehicles under development still need access to fuel (hybrid) and/or electric charging capability, they can take off and land from almost anywhere. Therefore, such vehicles, both manned and unmanned can be successfully integrated for the critical missions of the Public Services with extra deployment flexibilities. • Third, advancement in electric propulsion systems in the automotive industry together with NASA’s leading efforts in electrification of aircraft propulsion systems, FAA’s ongoing active eVTOL certification programs, and EASA’s proposed framework for the certification of electric/hybrid small category VTOL aircraft in Europe [ref. 3] will help accelerate industry electric propulsion system development and integration. • Finally, eVTOL vehicles could be deployed for Public Services sooner than air taxi or other commercial applications, since Public Services missions may be more easily approved based on specific mission criteria, localized airworthiness authority for public-use aircraft3, and are normally operating under centralized airspace management and control by the theater command. Moreover, public perception and acceptance are generally less of a concern when operations save lives and benefit the wider community. The prioritized introduction of eVTOL aircraft in Public Services is ambitious, but we believe it is achievable in the coming decades if fundamental enablers (people and technologies) are engaged in defining the objectives and needs of these missions. The revolution that is currently taking place in eVTOL aircraft represents an unprecedented opportunity to develop a safer, more affordable, more available and more environmentally friendly future of vertical flight. To ensure that these novel aircraft meet the future expectations of Public Services, it is essential to take a collaborative and multi-disciplinary approach to their development, across engineering disciplines, policy-making, program management, business case development, manufacturing, and flight demonstrations. It should be noted that the term eVTOL (in the near term) used throughout this publication implies aircraft capable of transporting up to 5 persons which may or may not include a pilot if operated fully autonomously, assuming an average of 200 pounds (91 kg) per person or equivalent payload and a range up to 60 miles plus a suitable reserve. Hybrid or hydrogen powered eVTOLs would have greater range. For example, a “3-seat” eVTOL aircraft may only be able to carry two fully equipped firemen, and payload capacity is more relevant when used for the supply mission. Moreover, this paper concentrates on the “last-mile” solutions with a deployment time of no more than 6 hours. Although not specifically discussed in this document, it is understood that the future of Transformative Vertical Flight in general and Public Services, in particular, will also involve smaller UAVs that will undoubtedly play a crucial role in future aerial operations. For example, smaller unmanned aircraft may be used to dispatch medical supplies, portable filtration systems or perform the Search task of future Search and Rescue (SAR) operations. Close collaboration between the aircraft industry, the Civil Aviation Authorities (CAA), e.g., Federal Aviation Administration (FAA), European Aviation Safety Agency (EASA), Transport Canada Civil Aviation (TCCA) and the Department of Defense (DoD) certifiers, will help identify Public Services requirements, define expectations and limit development cost and timescales. Take the US Air Force Agility Prime as an example, the majority of the eVTOL application opportunities and mission elements identified are in line with the NASA TVF WG-4 objectives and use cases. Together, it forms a strong partnership to accelerate the development, certification, and practical deployment for public service missions. The US Air Force Agility Prime has been a collaboration partner on this white paper, and provided valuable input and recommendations. Most of the eVTOL public service mission elements discussed in this paper and additional use cases envisioned by the NASA TVF WG-4 team are shared by the Agility Prime program. The focus and efforts of the Agility Prime in product and system development, industry and government partnership, accelerated certifications as well as early test and deployment are totally in sync with the path forward recommended by this white paper. This kind of collaboration and partnership will help enable the practical use of the eVTOL for public service missions, benefit the eVTOL public acceptance, and accelerate the eVTOL industry revolution.

Johnny T. Doo↗
DOE OSTI2025

Hydrotrope-enabled high concentration aqueous electrolytes for reversible and sustainable iron metal anodes

Iron metal-based energy storage devices hold great potential in stationary grid-scale sustainable energy due to the high theoretical specific capacity, ultralow cost, and abundance of iron. However, their practical deployment is limited by the poor reversibility of iron plating and stripping, as well as competitive hydrogen evolution. Here we introduce the concept of hydrotropy into iron electrolytes by developing an environmentally friendly and cost-effective high-concentration ferrous sulfate electrolyte using urea as a hydrotropic agent. The designed electrolyte increases the Coulombic efficiency of iron metal electrodes to approximately 96.5%, compared with ~84.6% for the dilute electrolyte. Molecular dynamics simulations and Raman spectroscopy illustrate that urea regulates the competitive coordination of anions and urea in the iron solvation sheath, while reconstructing the hydrogen-bond network in free water molecules. This reduces the activity of both solvated and free water, thereby alleviating hydrogen evolution. Moreover, the coordinated anions and urea molecules facilitate the in-situ formation of an organic-inorganic hybrid protective layer on the metallic iron, establishing a physical barrier against water and promoting homogeneous interfacial reactions. This work demonstrates an appealing opportunity to design cost-effective and high-performance electrolytes and propels the practical application of iron metal-based energy storage devices.

Feng, Guangxia [Stanford Univ., CA (United States)↗
DOE OSTI2022

Energy Resilient Recovery in Puerto Rico: Hurricane Maria Recovery Support

This report covers microgrids as a special application of distributed energy resources (DERs) in Puerto Rico. This is one of a series of reports describing the U.S. Department of Energy (DOE) multi-laboratory efforts undertaken. To ensure the sustainable, long-term recovery of Puerto Rico's electric power grid from hurricanes Maria and Irma and to build capacity to manage future potential natural disasters in the most secure and resilient way, DOE convened experts from multiple national laboratories to develop a comprehensive set of data, models, analytic tools, and studies, considering inputs from a wide variety of stakeholder groups, to support technically sound recommendations for Puerto Rico's energy investment decisions. In Phase 1 of the multi-laboratory effort to support Puerto Rico's recovery, the National Renewable Energy Laboratory (NREL) provided the utility company Puerto Rico Electric Power Authority (PREPA) recommendations for a new framework of interconnection standards to accelerate the integration of utility-scale, transmission-connected, renewable electrical generation and energy storage that ensure cross-technology compatibility and enable high deployment levels without compromising grid reliability, safety, or security (https://www.nrel.gov/docs/fy19osti/73848.pdf). In Phase 2, NREL published a report focused on the interconnection of distributed energy resources (DERs) to the electric distribution system in Puerto Rico (https://www.nrel.gov/docs/fy21osti/77127.pdf). This report familiarized the reader with Puerto Rico's distribution infrastructure and operational practices and procedures that are relevant to DER interconnection. The report also provided considerations for streamlining the interconnection process given the expected increase in deployments resulting from Puerto Rico's renewable portfolio standard goal of 100% renewables by 2050. Accordingly, the report identifies considerations and concerns associated with the increase in intermittent generation, strategies for DER interconnection best practices, and the potential use of the latest technological solutions identified in the latest revision of the Institute of Electrical and Electronics Engineers 1547-2018 interconnection standard. Additionally, the report identified ways to improve the physical resiliency of installed DERs. This task describes how distributed energy systems may participate in and contribute to improving the resiliency of Puerto Rico's energy infrastructure in the future. This report presents an analysis of the Puerto Rico grid to illustrate the possible different microgrid and minigrid scenarios. The capability to form sustained microgrids and minigrids has the potential to provide a high level of resiliency during disturbances on the grid. DERs, on the other hand, have the capability to aid in the microgrid operation by providing grid support as well as grid-forming functionality. Other tasks in Phase 3 include voltage regulation in distribution networks using DERs and transmission-and-distribution co-simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Cryo-EM structures of the small-conductance Ca 2+ -activated K Ca 2.2 channel

Small-conductance Ca 2+ -activated K + (K Ca 2.1-K Ca 2.3) channels modulate neuronal and cardiac excitability. We report cryo-electron microscopy structures of the K Ca 2.2 channel in complex with calmodulin and Ca 2+ , alone or bound to two small molecule inhibitors, at 3.18, 3.50, 2.99 and 2.97 angstrom resolution, respectively. Extracellular S3-S4 loops in β-hairpin configuration form an outer canopy over the pore with an aromatic box at the canopy’s center. Each S3-S4 β-hairpin is tethered to the selectivity filter in the neighboring subunit by inter-subunit hydrogen bonds. This hydrogen bond network flips the aromatic residue (Tyr362) in the filter’s GYG signature by 180°, causing the outer selectivity filter to widen and water to enter the filter. Disruption of the tether by a mutation narrows the outer selectivity filter, realigns Tyr362 to the position seen in other K + channels, and significantly increases unitary conductance. UCL1684, a mimetic of the bee venom peptide apamin, sits atop the canopy and occludes the opening in the aromatic box. AP14145, an analogue of a therapeutic for atrial fibrillation, binds in the central cavity below the selectivity filter and induces closure of the inner gate. These structures provide a basis for understanding the small unitary conductance and pharmacology of K Ca 2.x channels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Mechanisms of Ethylene Epoxidation over Silver from Machine Learning-Accelerated First-Principles Modeling and Microkinetic Simulations

In this work we employed machine learning-augmented density functional theory (DFT) thermodynamic calculations to assess the stability of different AgO x structures under catalytic ethylene epoxidation reaction conditions. We found that there are multiple AgO x surface motifs that could co-exist under the relevant conditions. These included Ag surface oxides (e.g., AgO_p(4 × 4) and Ag 1.83 O) and atomic oxygen-covered Ag(111) surfaces. Furthermore, we employed DFT calculations to evaluate the energetics of different reaction mechanisms by which ethylene and oxygen can react on these surfaces. These studies revealed several energetically viable reaction pathways for ethylene epoxidation. Microkinetic modeling analyses, based on the DFT-calculated reaction pathways, showed that ethylene epoxidation can proceed on all surfaces and that multiple pathways, including those involving Langmuir–Hinshelwood and Eley–Rideal mechanisms, could be involved in selective and unselective reactions. The diversity of mechanisms that we discovered in the context of the relatively simple ethylene epoxidation reaction on Ag suggests that the richness and complexity of surface chemistry are most likely a rule rather than an exception in heterogeneous catalytic chemical transformations on metal surfaces and that the concept of a single or even a dominant mechanism and reaction intermediates might need to be revisited for many reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

SCaN Testbed Software Development and Lessons Learned

National Aeronautics and Space Administration (NASA) has developed an on-orbit, adaptable, Software Defined Radio (SDR)Space Telecommunications Radio System (STRS)-based testbed facility to conduct a suite of experiments to advance technologies, reduce risk, and enable future mission capabilities on the International Space Station (ISS). The SCAN Testbed Project will provide NASA, industry, other Government agencies, and academic partners the opportunity to develop and field communications, navigation, and networking technologies in the laboratory and space environment based on reconfigurable, SDR platforms and the STRS Architecture.The SDRs are a new technology for NASA, and the support infrastructure they require is different from legacy, fixed function radios. SDRs offer the ability to reconfigure on-orbit communications by changing software for new waveforms and operating systems to enable new capabilities or fix any anomalies, which was not a previous option. They are not stand alone devices, but required a new approach to effectively control them and flow data. This requires extensive software to be developed to utilize the full potential of these reconfigurable platforms. The paper focuses on development, integration and testing as related to the avionics processor system, and the software required to command, control, monitor, and interact with the SDRs, as well as the other communication payload elements. An extensive effort was required to develop the flight software and meet the NASA requirements for software quality and safety. The flight avionics must be radiation tolerant, and these processors have limited capability in comparison to terrestrial counterparts. A big challenge was that there are three SDRs onboard, and interfacing with multiple SDRs simultaneously complicatesd the effort. The effort also includes ground software, which is a key element for both the command of the payload, and displaying data created by the payload. The verification of the software was an extensive effort. The challenges of specifying a suitable test matrix with reconfigurable systems that offer numerous configurations is highlighted. Since the flight system testing requires methodical, controlled testing that limits risk, a nearly identical ground system to the on-orbit flight system was required to develop the software and write verification procedures before it was installed and tested on the flight system. The development of the SCAN testbed was an accelerated effort to meet launch constraints, and this paper discusses tradeoffs made to balance needed software functionality and still maintain the schedule. Future upgrades are discussed that optimize the avionics and allow experimenters to utilize the SCAN testbed potential.

radio communication↗
DOE OSTI2021

Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction

This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Decoding Zeolite Crystallization and Stage III in Nuclear Waste Glasses by Coupled Modeling and Experiments

Under specific conditions of pH and temperature, nuclear waste immobilization borosilicate glasses may exhibit a sudden acceleration in their corrosion kinetics (stage III)—a behavior that has been associated with the formation of zeolite crystals. Such accelerated dissolution may compromise the integrity of nuclear wasteforms placed in geological depositories. However, thus far, none of the available models is able to predict the thermodynamic propensity and kinetics of zeolite precipitation as a function of the solution conditions due to (i) a lack of fundamental knowledge regarding the nucleation & growth mechanisms of zeolitic phases, (ii) uncertainty regarding the compositions (types) of zeolites that may form and the rate-limiting step in their precipitation as a function of the solution conditions, and (iii) the complexities that arise due to the vast parametric space (i.e., solution chemistry, temperature, number of secondary phases, etc.) that encompass these systems under conditions of environmental exposure. To resolve these challenges, this project aimed to unambiguously identify the thermodynamic propensity for zeolite precipitation and the kinetics thereof as a function of the solution conditions (composition, pH, and temperature). To achieve this goal: 1) We identified the solution conditions and zeolite phases relevant to nuclear glass dissolution. 2) We performed a series of ab initio molecular dynamics (AIMD) simulations to compute the thermodynamic properties of a group of characteristic zeolites that features a large range of compositions, various hydration levels, a wide range of framework structures, and partial atomic site occupancies. 3) We released a first-of-a-kind self-consistent thermodynamic database that can be used to assess the kinetics and the stability fields of zeolitic phases within a Gibbs energy minimization (GEM) framework. 4) We developed a robust geochemical modeling method allowing us to predict the stability of secondary phases (including zeolites, calcium–silicate–hydrate gels, and clays) upon the dissolution of nuclear waste immobilization glasses. 5) We introduced a model that predicts the dissolution kinetics of a series of borosilicate nuclear waste immobilization glasses in terms of the topology of their atomic network. 6) We investigated the roles of the solution composition on the crystallization kinetics of phillipsite zeolites and tobermorite silicate hydrates. Via PNNL’s collaboration and engagement, this project directly supports DOE’s nuclear waste immobilization activities by offering a technical, science-based foundation that will (i) facilitate predictions of the long-term corrosion rates and extents of existing nuclear waste immobilization glasses to help ensure safe and successful vitrification operations, (ii) inform the development of advanced glass formulations with enhanced durability, and, (iii) enable cost-savings that result from making more decisive and hence less conservative predictions while offering higher levels of nuclear waste embedment in smaller, more compact glass volumes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

De-noising drift chambers in CLAS12 using convolutional auto encoders

Modern Nuclear Physics experimental setups run experiments with higher beam intensity resulting in increased noise in detector components used for particle track reconstruction. Increased uncorrelated signals (noise) result in decreased particle reconstruction efficiency. In this paper, we investigate the usage of Machine Learning, specifically Convolutional Neural Network Auto-Encoders (CAE), for de-noising raw hits from drift chambers in the CLAS12 detector. To the best of our knowledge, this is the first time CAE is employed to perform such an operation in this field. During the de-noising phase, it is important to remove as much noise as possible while retaining the valid hits to avoid losing crucial information about the experiment. Here, we show that using CAE, it is possible to remove noise hits while retaining up to 94% of valid tracks for a beam current of 110nA while for lower beam currents (45-55nA), we get up to 98% efficiency. Studies on experimental conditions with increasing noise show that CAE performs better than conventional tracking algorithms in isolating hits belonging to tracks. Specifically, the de-noising algorithm results in tracking efficiency improvements greater than 15%, in real data production procedures with nominal conditions, and up to two times better efficiency in synthetically generated data with high luminosity conditions (90-110nA), indicating that machine learning can lead to significantly shorter times for conducting physics experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Strategies for machine-learning (ML)-accelerated discovery that are general across material composition spaces are essential, but demonstrations of ML have been primarily limited to narrow composition variations. By addressing the scarcity of data in promising regions of chemical space for challenging targets such as open-shell transition-metal complexes, general representations and transferable ML models that leverage known relationships in existing data will accelerate discovery. Over a large set (~1000) of isovalent transition-metal complexes, we quantify evident relationships for different properties (i.e., spin-splitting and ligand dissociation) between rows of the Periodic Table (i.e., 3d/4d metals and 2p/3p ligands). We demonstrate an extension to the graph-based revised autocorrelation (RAC) representation (i.e., eRAC) that incorporates the group number alongside the nuclear charge heuristic that otherwise overestimates dissimilarity of isovalent complexes. To address the common challenge of discovery in a new space where data are limited, we introduce a transfer learning approach in which we seed models trained on a large amount of data from one row of the Periodic Table with a small number of data points from the additional row. We demonstrate the synergistic value of the eRACs alongside this transfer learning strategy to consistently improve model performance. Analysis of these models highlights how the approach succeeds by reordering the distances between complexes to be more consistent with the Periodic Table, a property we expect to be broadly useful for other material domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗