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

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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Snowmass 2021 Scintillating Bubble Chambers: Liquid-noble Bubble Chambers for Dark Matter and CE$\nu$NS Detection

The Scintillating Bubble Chamber (SBC) Collaboration is developing liquid-noble bubble chambers for the quasi-background-free detection of low-mass (GeV-scale) dark matter and coherent scattering (CE$\nu$NS) of low-energy (MeV-scale) neutrinos. The first physics-scale demonstrator of this technique, a 10-kg liquid argon bubble chamber dubbed SBC-LAr10, is now being commissioned at Fermilab. This device will calibrate the background discrimination power and sensitivity of superheated argon to nuclear recoils at energies down to 100 eV. A second functionally-identical detector with a focus on radiopure construction is being built for SBC's first dark matter search at SNOLAB. The projected spin-independent sensitivity of this search is approximately $10^{-43}$ cm$^2$ at 1 GeV$/c^2$ dark matter particle mass. The scalability and background discrimination power of the liquid-noble bubble chamber make this technique a compelling candidate for future dark matter searches to the solar neutrino fog at 1 GeV$/c^2$ particle mass (requiring a $\sim$ton-year exposure with non-neutrino backgrounds sub-dominant to the solar CE$\nu$NS signal) and for high-statistics CE$\nu$NS studies at nuclear reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Fast product release requires active-site water dynamics in carbonic anhydrase

Water plays an essential role in enzyme structure, stability, and the substantial rate enhancement of enzyme catalysis. However, direct observations linking enzyme catalysis and active-site water dynamics pose a significant challenge due to experimental difficulties. By integrating an ultraviolet (UV) photolysis technique with temperature-controlled X-ray crystallography, we track the catalytic pathway of carbonic anhydrase II (CAII) at 1.2 Å resolution. This approach enables us to construct molecular movies of CAII catalysis, encompassing substrate (CO 2 ) binding, conversion from substrate to product (bicarbonate), and product release. In the catalytic pathway, we identify an unexpected configuration in product binding and correlate it with sub-nanosecond rearrangement of active-site water. Based on these experimental observations, we propose a comprehensive mechanism of CAII and describe the detailed structure and dynamics of active-site water in CAII. Our findings suggest that CAII has evolved to utilize the structure and fast dynamics of the active-site waters for its diffusion-limited catalytic efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Global gyrokinetic study of shaping effects on electromagnetic modes at NSTX aspect ratio with ad hoc parallel magnetic perturbation effects

Plasma shaping may have a stronger effect on global turbulence in tight-aspect-ratio tokamaks than in conventional-aspect-ratio tokamaks due to the higher toroidicity and more acute poloidal asymmetry in the magnetic field. In addition, previous local gyrokinetic studies have shown that it is necessary to include parallel magnetic field perturbations in order to accurately compute growth rates of electromagnetic modes in tight-aspect-ratio tokamaks. In this work, the effects of elongation and triangularity on global, ion-scale, linear electromagnetic modes are studied at National Spherical Torus Experiment (NSTX) aspect ratio and high plasma β using the global gyrokinetic particle-in-cell code XGC. The effects of compressional magnetic perturbations are approximated via a well-known modification to the particle drifts that was developed for flux-tube simulations [Joiner et al., Phys. Plasmas 17, 072104 (2010)], without proof of its validity in a global simulation, with the gyrokinetic codes GENE and GEM being used for local verification and global cross-verification. Magnetic equilibria are re-constructed for each distinct plasma profile that is used. Coulomb collision effects are not considered. Within the limitations imposed by the present study, it is found that linear growth rates of electromagnetic modes (collisionless microtearing modes and kinetic ballooning modes) are significantly reduced in a high-elongation and high-triangularity NSTX-like geometry compared to a circular NSTX-like geometry. For example, growth rates of kinetic ballooning modes at high- β are reduced to the level of that of collisionless trapped electron modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Enhancement of sub-thermal neutron flux through cold polyethylene

Total thermal neutron cross section measurements serve as the primary means of validation for thermal neutron scattering kernels, an important quantity for neutron transport calculations. In an effort to improve the quality of thermal neutron scattering kernels, researchers at Rensselaer Polytechnic Institute (RPI) designed and constructed a polyethylene based cold moderation system to enhance neutron flux below 10 meV when coupled with the Enhanced Thermal Target (ETT) at the RPI Gaerttner LINAC. The final design yielded an increase in sub-thermal neutron flux (below 10 meV) by a factor of 4.5 for a moderator temperature of 37.5 K relative to the ETT alone. A further increase to a factor of 6 is expected after a minor geometry modification and decrease in polyethylene temperature to 25 K. This novel capability will be used to conduct total thermal neutron cross section measurements from 0.0005–10 eV for different materials including moderator materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Constructing precisely quasi-isodynamic magnetic fields

We present a novel method for numerically finding quasi-isodynamic stellarator magnetic fields with excellent fast-particle confinement and extremely small neoclassical transport. The method works particularly well in configurations with only one field period. We examine the properties of these newfound quasi-isodynamic configurations, including their transport coefficients, particle confinement and available energy for trapped-electron-instability-driven turbulence, as well as the degree to which they change when a finite pressure profile is added. We finally discuss the differences between the magnetic axes of the optimized solutions and their respective initial conditions, and conclude with the prospects for future quasi-isodynamic optimization.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Empirical Validation of UBEM: An Assessment of Bias in Urban Building Energy Modeling for Chicago

Residential and commercial buildings currently account for 30% of total global final energy consumption. Urban-scale building energy modeling (UBEM) can enable scalable investments and unlock building improvements by quantifying energy, demand, emissions, and cost reductions of specific measures or packages for building-specific technologies in large geographic regions. While the sophistication of UBEM data sources and technologies have increased dramatically in the past decade, there remains a knowledge gap for empirical validation and sources of bias between building-specific energy models and measured data at varying geographic scales.As UBEM continues to develop, systemic analysis of accuracy, bias, and limitations of the resulting models is necessary to inform best practices and move toward standardization. These are characterized for the Automatic Building Energy Modeling (AutoBEM) software suite with an initial case study involving metered electricity consumption data from 247,188 buildings in Chicago, Illinois, USA - averaged across years 2019-2021 - compared to the following datasets: (1) the AutoBEM-generated nation-scale Model America version 2 (MAv2) data for 596,064 buildings, (2) tax assessor data for 579,829 buildings, (3) tax assessor data filled with MAv2, and (4) 102 representative dynamic archetypes. The accuracy is reported for every building type and vintage combination, along with multiple sources of bias for unique building descriptors. The AutoBEM simulation workflow produced energy consumption estimates that closely match aggregated metered electricity consumption data for different types of buildings constructed during various time periods at the city scale - with initial normalized mean bias error of 10.9%, and 1.1% after removing outliers. Contribution of statistically significant factors including building type, land use, age, and size to variance in UBEM bias is quantified.

Garg, Ankur↗
DOE OSTI2023

Accurate activity determination of a californium neutron source

Accurately counting analog events requires constructing an electronics chain that produces one count for each input pulse. Here, in this work, we review the use of Nuclear Instrumentation Module electronic units for counting neutron capture events in a 3He tube. We identify two unique types of false trigger events in a leading-edge discriminator and show how a dual timer module can be used to produce a veto window to exclude these events. We use the constructed electronics chain to build an apparatus to measure neutron pulses from a 252 Cf neutron source. We compare the measurements with a Monte Carlo N-Particle (MCNP) model to determine the activity of the neutron source. Furthermore, by making additional measurements with borated polyethylene attenuators between the source and detector, we are able to determine the boron concentration of the polyethylene. This technique provides accurate determination of the source activity to a precision of 2.8% at the k=1 level. The method used is simple, inexpensive, and requires no additional calibrated instruments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure

Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. For chromophores in the condensed phase, the large number of atoms needed to simulate the environment has traditionally prohibited the use of high-level excited-state electronic structure methods. By leveraging transfer learning, we show how to construct machine-learned models to accurately predict the high-level excitation energies of a chromophore in solution from only 400 high-level calculations. Here, we show that when the electronic excitations of the green fluorescent protein chromophore in water are treated using EOM-CCSD embedded in a DFT description of the solvent the optical spectrum is correctly captured and that this improvement arises from correctly treating the coupling of the electronic transition to electric fields, which leads to a larger response upon hydrogen bonding between the chromophore and water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Perspective on Multiscale Modeling of Explicit Solvation-Enabled Simulations of Catalysis at Liquid–Solid Interfaces

Catalysis at liquid-solid interfaces is profoundly influenced by the interfacial solvent structure, which affects catalytic activity, selectivity, and reaction pathways. This perspective discusses state-of-the-art multiscale modeling methods that integrate quantum mechanics and molecular mechanics approaches to apply explicit solvent molecules to capture these interfacial phenomena. Specifically, the construction of multiscale models, the importance of capturing the interfacial solvent structure, and the computational strategies used to achieve this are explored, and the challenges in balancing chemical accuracy with computational expense are highlighted. Additionally, this perspective addresses the limitations of current methods. Opportunities for integrating machine learning are proposed. Here, by advancing the efficiency and user friendliness of multiscale modeling, it is argued that deeper insights into heterogeneous catalysis in liquid phases can be provided, which will ultimately contribute to the development of more efficient catalytic processes.

Ab initio molecular dynamics↗
DOE OSTI2021

Solving the problem of overdetermination of quasisymmetric equilibrium solutions by near-axis expansions. I. Generalized force balance

It is well known that the process of construction of quasisymmetric magnetic fields in magnetostatic equilibrium with isotropic pressure suffers from the problem of overdetermination. This has led to the widespread belief that global quasisymmetric solutions are likely not to exist. Here we develop a general near-axis expansion procedure that does not rely on the assumption of magnetostatic equilibria with isotropic pressure. We then demonstrate that in equilibria with anisotropic pressure, it is possible to circumvent the problem of overdetermination and carry out the power-series solutions to higher order. This suggests, contrary to current belief, that the existence of globally quasisymmetric fields is likely if one relaxes the assumption of magnetostatic equilibria with isotropic pressure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. Here, the architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2020

Hanging by a thread? Forests and drought

Trees are the living foundations on which most terrestrial biodiversity is built. Central to the success of trees are their woody bodies, which connect their elevated photosynthetic canopies with the essential belowground activities of water and nutrient acquisition. The slow construction of these carbon-dense, woody skeletons leads to a slow generation time, leaving trees and forests highly susceptible to rapid changes in climate. Other long-lived, sessile organisms such as corals appear to be poorly equipped to survive rapid changes, which raises questions about the vulnerability of contemporary forests to future climate change. The emerging view that, similar to corals, tree species have rather inflexible damage thresholds, particularly in terms of water stress, is especially concerning. This Review examines recent progress in our understanding of how the future looks for forests growing in a hotter and drier atmosphere.

Science & Technology - Other Topics↗
DOE OSTI2020

Na + -gated water-conducting nanochannels for boosting CO 2 conversion to liquid fuels

Robust, gas-impeding water-conduction nanochannels that can sieve water from small gas molecules such as hydrogen (H 2 ), particularly at high temperature and pressure, are desirable for boosting many important reactions severely restricted by water (the major by-product) both thermodynamically and kinetically. Identifying and constructing such nanochannels into large-area separation membranes without introducing extra defects is challenging. We found that sodium ion (Na + )–gated water-conduction nanochannels could be created by assembling NaA zeolite crystals into a continuous, defect-free separation membrane through a rationally designed method. Highly efficient in situ water removal through water-conduction nanochannels led to a substantial increase in carbon dioxide (CO 2 ) conversion and methanol yield in CO 2 hydrogenation for methanol production.

Science & Technology - Other Topics↗
DOE OSTI2022

Geothermal Hydraulic Stimulation: Overview of Methods and Best Practices

This paper outlines important information related to the hydraulic stimulation of geothermal wells and considerations for regulators responsible for evaluating applications to conduct these operations. Hydraulic stimulation applied to geothermal wells is an evolving technology that is different from the hydraulic fracturing process widely used to complete unconventional oil and gas wells. Important differences relate to operating pressures, variations in rock and fracture processes, and characteristics and disposal of hydraulic fracture fluids. Hydraulic stimulation is not widely used in the geothermal industry; however, future development of enhanced geothermal systems (EGS) will require hydraulic stimulation in order to enhance and create fracture permeability to allow wells to deliver sufficient heat and fluid to power plants. Gigawatts of EGS potential have been identified (U.S. Department of Energy 2019), so it is expected that the use of hydraulic stimulation of geothermal wells will be more common in the future. At present, there are no formal regulations guiding drilling programs or sundry notices that propose hydraulic stimulation of geothermal wells. On federal lands, important constraints and oversight are embedded in the process for obtaining Geothermal Drilling Permits outlined in the Code of Federal Regulations (43 CFR Section 3262.11); in Geothermal Resource Order 2 (GRO 2) guidance for assessment and mitigation of impacts of geothermal operations as well as construction and testing of geothermal wells; and in a Bureau of Land Management (BLM) Induced Seismicity Instruction Memorandum (BLM 2018). Additionally, local knowledge from BLM field offices and expertise of BLM engineers, or that of state regulators for operations on private and state lands, will be applied to fully assess operator applications to conduct geothermal hydraulic stimulation activities. Significant research effort has been directed toward EGS, so geothermal hydraulic stimulation techniques, impacts, and results are evolving and becoming better understood, including adaptation of decades of oil and gas industry experience with hydraulic fracturing of unconventional reservoirs. Recent EGS activities in the United States provide details about hydraulic stimulation with respect to oversight, stimulation design, execution, and results. Based on experience to date, the important issues to address when hydraulically stimulating a geothermal well include the following: 1. Wellbore construction and integrity must be appropriate to protect groundwater and manage stimulation pressures. 2. Understanding of lithology, faults, fractures, and subsurface stress state is necessary to design stimulation plans and predict results. 3. Seismic monitoring allows for observation and mitigation of induced seismicity.

geothermal↗
DOE OSTI2025

A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling

Radiation mapping is a desirable task to automate because of the inherent risks involved and its tedious nature. A novel system was designed to address this by combining various existing technologies, utilizing behavior-based robotics and Bayesian optimization. The system uses a quadruped robot equipped with a manipulator and gamma detector to take measurements at locations that are selected based on the uncertainty of a surrogate model used to estimate the true radiation field. The robot uses input from the world with depth cameras to avoid collisions with the robot’s body, and unreachable points for the end effector are addressed by both allowing for a soft collision with the environment to occur, prompting the system to abandon that point, and varying the exploration tendency of the optimization based on consecutive collisions. This approach provides unique traversability and adaptability over other strategies in the literature. Experiments were performed by placing a Cesium-137 source on the ground and varying geometric setups and an optimization parameter demonstrating the adaptability to diverse environments and the increased robustness resulting from the designed behavior. The results additionally demonstrate that dynamically adjusting the optimization algorithm’s exploration tendency based on the arm’s collision history improves the system’s ability to navigate cluttered environments and construct accurate radiation maps without getting stuck in unreachable areas.

Adams, Joel↗
DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗
DOE OSTI2022

Computational design of mechanically coupled axle-rotor protein assemblies

Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo–electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines.

Science & Technology - Other Topics↗