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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Recent developments in 3D-printed membranes for water desalination

The recognition of membrane separations as a vital technology platform for enhancing the efficiency of separation processes has been steadily increasing. Concurrently, 3D printing has emerged as an innovative approach to fabricating reverse osmosis membranes for water desalination and treatment purposes. This method provides a high degree of control over membrane chemistry and structural properties. In particular, when compared to traditional manufacturing techniques, 3D printing holds the potential to expedite customization, a feat that is typically achieved through conventional manufacturing methods but often involves numerous processes and significant costs. This review aims to present the current advancements in membrane manufacturing technology specifically tailored for water desalination purposes, with a particular focus on the development of 3D-printed membranes. A comprehensive analysis of recent progress in 3D-printed membranes is provided. However, conducting experimental work to investigate various influential factors while ensuring consistent results poses a significant challenge. To address this, we explore how membrane manufacturing processes and performance can be effectively pre-designed and guided through the use of molecular dynamics simulations. Finally, this review outlines the challenges faced and presents future perspectives to shed light on research directions for optimizing membrane manufacturing processes and achieving optimal membrane performance.

3D printing membranes↗
DOE OSTI2023

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Applications which fuse machine learning and simulation are rarely best served by a single computing resource. Highly parallel simulation codes are best deployed on super- computers, while AI tasks used to decide which simulations to perform may be best suited to specialized accelerators. Here we present a Function-as-a-Service (FaaS) system for executing complex, distributed computational campaigns that achieves performance parity with conventional workflow systems without the complexities of secure network connections between compute providers. One innovation enabling high performance is a subsystem that directly moves task data between sites, separate from the cloud-hosted FaaS system used to distribute task instructions. We also introduce a flexible scheduling system that allows us access factor of 2 trade offs between the amount of resources required to solve a problem at each compute site. We anticipate that this system will upgrade multi-site applications from demonstration projects to routine practice in computational science.

Ward, Logan↗
DOE OSTI2014

Implementation of Software Tools for Hybrid Control Rooms in the Human Systems Simulation Laboratory

The Institute for Energy Technology (IFE) and Idaho National Laboratory have designed, implemented, tested and installed a functioning prototype of a set of large screen overview and procedure support displays for the Generic Pressurized Water Reactor (GPWR) simulator in the U.S. Department of Energy’s Human Systems Simulation Laboratory. The overview display is based on IFE’s extensive experiences with large screen overview displays in the Halden Man-Machine Laboratory (HAMMLAB), and presents the main control room indicators on a combined three-screen display. The procedure support displays are designed and implemented to provide a compact but still comprehensive overview of the relevant process measurements and indicators to support operators' good situational awareness during the performance of various types of procedures and plant conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Nucleon and Nuclear Structure Studies in Electroproduction with the CLAS Detector at Jefferson Lab

A diverse program for studying the structure of nucleons and nuclei is underway using the CEBAF Large Acceptance Spectrometer (CLAS) at Jefferson Lab. A wealth of data has been obtained in inclusive, semi-inclusive, and exclusive electroproduction reactions using a variety of unpolarized and polarized beams and targets. The results include studies of excited nucleon states, measurements of nucleon, proton and neutron Form Factors (FFs), Parton Distribution Functions (PDFs), Transverse Momentum Distributions (TMDs), and Generalized Parton Distributions (GPDs), as well as Short Range Correlations (SRCs) in nuclei and Color Transparency phenomena. Jefferson Lab's electron accelerator, CEBAF, doubled its energy to 12~GeV and the upgrade of the experimental equipment is near completion in the experimental halls. The upgraded CLAS detector, CLAS12, enables execution of a broader physics program, with the main focus on uncovering the TMDs and GPDs using semi-inclusive and exclusive reactions in a new kinematic domain. In this talk, an overview of the CLAS electroproduction measurements with 6~GeV CEBAF and prospects of nucleon and nuclear structure studies with the 12~GeV machine and the CLAS12 detector will be discussed.

Stepanyan, Stepan↗
DOE OSTI2015

Powerloads on the front end components and the duct of the heating and diagnostic neutral beam lines at ITER

The heating and current drive beam lines (HNB) at ITER are expected to deliver ∼16.7 MW power per beam line for H beams at 870 keV and D beams at 1 MeV during the H-He and the DD/DT phases of ITER operation respectively. On the other hand the diagnostic neutral beam (DNB) line shall deliver ∼2 MW power for H beams at 100 keV during both the phases. The path lengths over which the beams from the HNB and DNB beam lines need to be transported are 25.6 m and 20.7 m respectively. The transport of the beams over these path lengths results in beam losses, mainly by the direct interception of the beam with the beam line components and reionisation. The lost power is deposited on the surfaces of the various components of the beam line. In order to ensure the survival of these components over the operational life time of ITER, it is important to determine to the best possible extent the operational power loads and power densities on the various surfaces which are impacted by the beam in one way or the other during its transport. The main factors contributing to these are the divergence of the beamlets and the halo fraction in the beam, the beam aiming, the horizontal and vertical misalignment of the beam, and the gas profile along the beam path, which determines the re-ionisation loss, and the re-ionisation cross sections. The estimations have been made using a combination of the modified version of the Monte Carlo Gas Flow code (MCGF) and the BTR code. The MCGF is used to determine the gas profile in the beam line and takes into account the active gas feed into the ion source and neutraliser, the HNB-DNB cross over, the gas entering the beamline from the ITER machine, the additional gas atoms generated in the beam line due to impacting ions and the pumping speed of the cryopumps. The BTR code has been used to obtain the power loads and the power densities on the various surfaces of the front end components and the duct modules for different scenarios of ITER operation. The gas profile and the magnetic field distribution for each scenario has been considered in these evaluations. The worst case power loads and power densities for each surface have been used to study their thermo-mechanical behaviour and manufacturing feasibility. The details of these calculations and results obtained are presented and discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Enhanced Surface Properties and Microstructure Evolution of Cr12MoV Using Ultrasonic Surface Rolling Process Combined with Deep Cryogenic Treatment

In this study, the enhanced surface properties and microstructure evolution of quenched and tempered cold work die steel Cr12MoV, which were induced by ultrasonic surface rolling process combined with deep cryogenic treatment (UDCT), were systematically investigated. The results indicated that UDCT had an advantage over conventional ultrasonic surface rolling process (USRP) in improving surface mechanical properties, including smaller surface roughness and smoother machined surface with less cracks and defects; higher surface microindentation hardness with the value of ~ 890 HV (increased by ~ 6.2% compared with USRP); and lower friction coefficient. Such enhancements in surface properties are mainly attributed to the martensitic transformation of retained austenite, the dispersion strengthening of small secondary carbides and the homogenized carbides distribution during UDCT.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗
DOE OSTI2015

SU-E-T-93: Activation of Psoralen at Depth Using Kilovoltage X-Rays: Physics Considerations in Implementing a New Teletherapy Paradigm

Purpose: Psoralen is a UV-light activated anti-cancer biotherapeutic used for treating skin lesions (PUVA) and advanced cutaneous T-cell lymphoma (ECP). To date psoralen has not been used to treat deep seated tumors due to difficulty in generating UV-light at depth. We recently demonstrated psoralen activation at depth by introducing energy converting particles that absorb kV x-ray radiation and re-emit UV-light. Our in-vitro work found that 0.2–1Gy using 40–100kVp x-rays combined with psoralen and particles can induce a substantial apoptotic response beyond that expected from the sum of individual components. In preparation for a phase I clinical trial of canine companion animals, we address the physics and dosimetry considerations for applying this new teletherapy paradigm to an in-vivo setting. Methods: The kV on-board imaging (OBI) system mounted on a medical linear accelerator (Varian) was commissioned to deliver the prescribed dose (0.6Gy) using 80 and 100kVp. Dosimetric measurements included kVp, HVL, depth dose, backscatter factors, collimator and phantom scatter factors, field size factors, and blade leakage. Absolute dosimetry was performed following AAPM TG61 recommendations and verified with an independent kV dose meter. We also investigated collimated rotational delivery to minimize skin dose using simple dose calculations on homogeneous cylindrical phantoms. Results: Single beam delivery is feasible for shallow targets (<5cm) without exceeding skin tolerance, while a rotational delivery may be utilized for deeper targets; skin dose is ∼75% of target dose for 80kVp collimated rotational delivery to a 3cm target within a 20cm phantom. Heat loading was tolerable; 0.6Gy to 5cm can be delivered before the anode reaches 75% capacity. Conclusion: KV teletherapy for Psoralen activation in deep seated tissue was successfully commissioned for a Varian OBI machine for use in a phase I clinical trial in canines. Future work will use Monte Carlo dosimetry to investigate dose in presence of bone. Research funded by Immunolight LLC. H. Walder, Z. Fathi, & W. Beyer are employees of Immunolight LLC which holds a patent on the technology. Drs. Adamson and Oldham are consultants to Immunolight LLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a "melt-quench" ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pressure-induced structural and dielectric changes in liquid water at room temperature

Understanding the pressure-dependent dielectric properties of water is crucial for a wide range of scientific and practical applications. In this study, we employ a deep neural network trained on density functional theory data to investigate the dielectric properties of liquid water at room temperature across a pressure range of 0.1–1000 MPa. We observe a nonlinear increase in the static dielectric constant ɛ 0 with increasing pressure, a trend that is qualitatively consistent with experimental observations. This increase in ɛ 0 is primarily attributed to the increase in water density under compression, which enhances collective dipole fluctuations within the hydrogen-bonding network as well as the dielectric response. Despite the increase in ɛ 0 , our results reveal a decrease in the Kirkwood correlation factor G K with increasing pressure. Furthermore, this decrease in G K is attributed to pressure-induced structural distortions in the hydrogen-bonding network, which weaken dipolar correlations by disrupting the ideal tetrahedral arrangement of water molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characterizing the Spread of COVID-19 from Human Mobility Patterns and SocioDemographic Indicators

Mobility is an indicator of human movement through space and time. With the increasing availability of geolocated data (from GPS, accelerometers, etc.), it is now possible to examine individual as well as group human mobility patterns. Human mobility is influenced by both intrinsic (i.e. personal motivations) and extrinsic (i.e., events like natural hazards or a pandemic like the COVID-19) factors. However, the intricate relationships between human mobility patterns and sociodemographic characteristics in the context of a pandemic are yet to be fully explored. Our goal is to overcome this gap by using human mobility data at the census block group level from mobile phones and combining those with social vulnerability indicators to examine the overall spread of COVID-19 at local spatial scales. We used 585,878 weekly visits to 37,871 points of interests (POIs) from Safegraph to quantify mobility indices and social distancing metrics in 2,820 census block groups in the city of Los Angeles (LA) - before and during lockdown as well as during the phase1 and phase 2 reopening. Finally, using supervised machine learning algorithms, we classified the census block groups in LA into High, Medium and Low categories that represented the vulnerability of these block groups based on the cumulative number of occurrences of COVID-19 cases till July 24, 2020. Our results indicate that the tree-based classifiers performed well in comparison to the Support Vector Machines and Multinomial Logit models. Gradient Boosting had the highest classification accuracy of 97.4% COVID-19 with an AUC score of 0.987. The block groups with high COVID-19 cases also had a high concentration of socially vulnerable populations, high human mobility index and a low social distancing index.

Roy, Avipsa↗
DOE OSTI2019

Test-Based Modeling of Photovoltaic Inverter Impact on Distribution Systems

Distributed PV has important effects on overcurrent, overvoltage and islanding protection. These effects are more difficult to analyze because the faulted behavior of PV inverters is less well understood than for rotating machines. Full transient models of the inverter can be accurate, but that approach requires inverter design details and simulation software that doesn’t easily interoperate with utility protection system software. Further, anomalous model behaviors often emerge at different grid conditions, and these are difficult to fix in a proprietary model. Testing provides another approach to characterize inverters, validate models or answer questions of application. This earlier work found major impacts from the inverter design, firmware, operating point and balance-of-system components. Our objective is to characterize PV inverters from black-box testing, leading to standard test reports that could provide parameter sets for widely-implemented models in software tools. The tool vendors can then develop standard inverter models, as they did for rotating machines and exciters. Besides fully detailed transient models, inverters can be represented with steady-state current sources, dynamically controlled current sources or non-linear frequency-dependent black boxes. Type-test data could support the last three kinds of model. Short circuit and sag tests would encompass the basic fault types, reporting the maximum transient and rms currents over all power levels and points-on-wave within four time ranges : 1) 0 to 1 cycles, for close and latch consideration 2) 1 to 4 cycles, for equipment withstand 3) 4 to 30 cycles, for interrupt ratings 4) 30 cycles and beyond, for backup protection analysis Open circuit tests would vary output power and power factor, and report the maximum rms voltage from 1 to 10 cycles after the event. Without models, the type-test data could also provide upper limits on the expected overvoltages and overcurrents.

Wieserman, Laura M.↗
DOE OSTI2016

Parameterization of photon beam dosimetry for a linear accelerator

Purpose: In radiation therapy, accurate data acquisition of photon beam dosimetric quantities is important for (1) beam modeling data input into a treatment planning system (TPS), (2) comparing measured and TPS modeled data, (3) the quality assurance process of a linear accelerator’s (Linac) beam characteristics, (4) the establishment of a standard data set for comparison with other data, etcetera. Parameterization of the photon beam dosimetry creates a data set that is portable and easy to implement for different applications such as those previously mentioned. The aim of this study is to develop methods to parameterize photon beam dosimetric quantities, including percentage depth doses (PDDs), profiles, and total scatter output factors (S{sub cp}). Methods: S{sub cp}, PDDs, and profiles for different field sizes, depths, and energies were measured for a Linac using a cylindrical 3D water scanning system. All data were smoothed for the analysis and profile data were also centered, symmetrized, and geometrically scaled. The S{sub cp} data were analyzed using an exponential function. The inverse square factor was removed from the PDD data before modeling and the data were subsequently analyzed using exponential functions. For profile modeling, one halfside of the profile was divided into three regions described by exponential, sigmoid, and Gaussian equations. All of the analytical functions are field size, energy, depth, and, in the case of profiles, scan direction specific. The model’s parameters were determined using the minimal amount of measured data necessary. The model’s accuracy was evaluated via the calculation of absolute differences between the measured (processed) and calculated data in low gradient regions and distance-to-agreement analysis in high gradient regions. Finally, the results of dosimetric quantities obtained by the fitted models for a different machine were also assessed. Results: All of the differences in the PDDs’ buildup and the profiles’ penumbra regions were less than 2 and 0.5 mm, respectively. The differences in the low gradient regions were 0.20% ± 0.20% (<1% for all) and 0.50% ± 0.35% (<1% for all) for PDDs and profiles, respectively. For S{sub cp} data, all of the absolute differences were less than 0.5%. Conclusions: This novel analytical model with minimum measurement requirements was proved to accurately calculate PDDs, profiles, and S{sub cp} for different field sizes, depths, and energies.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

High performance SRF accelerator structure and method

A high performance accelerator structure and method of production. The method includes precision machining the inner surfaces of a pair of half-cells that are maintained in an inert atmosphere and at a temperature of 100 K or less. The method includes removing thin layers of the inner surfaces of the half-cells after which the roughness of the inner surfaces in measured with a profilimeter. Additional thin layers are removed until the inner surfaces of the half-cell measure less than 2 nm root mean square (RMS) roughness over a 1 mm2 area on the profilimeter. The two half-cells are welded together in an inert atmosphere to form an SRF cavity. The resultant SRF cavity includes a high accelerating gradient (Eacc) and a high quality factor (Q0).

36 MATERIALS SCIENCE↗
DOE OSTI2022

PWR loading pattern optimization with reinforcement learning

The core loading pattern optimization problem belongs to the class of combinatorial optimization problem and has been studied since the dawn of commercial nuclear energy industry. It is characterized by multiple objectives and constraints, with a very high number of candidate patterns, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities and vendors to perform fuel cycle reload design. Nevertheless, hand-designed solutions continue to be the prevalent method in the industry. To improve the state-of-the-art core reload patterns, we aim to create a method as scalable as possible, that agrees with the designer's goal of performance and safety. To help in this task Deep Reinforcement Learning (DRL), in particular Proximal Policy Optimization is leveraged. DRL has recently experienced a strong impetus from its successes applied to games, sometimes even reaching 'super-human' performances. This paper lays out the foundation of this method and proposes to study the behavior of several hyper-parameters that influence the DRL algorithm. The algorithm is highly dependent on multiple factors such as an exploration/exploitation trade-off that manifests through different parameters such as the number of loading patterns seen and the number of samples collected before a policy update, but also the shape of the objective function derived for the core design. Experimental results also demonstrate the effectiveness of the method in finding high-quality solutions from scratch within a reasonable amount of time. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2024

Toward first principles-based simulations of dense hydrogen

Accurate knowledge of the properties of hydrogen at high compression is crucial for astrophysics (e.g., planetary and stellar interiors, brown dwarfs, atmosphere of compact stars) and laboratory experiments, including inertial confinement fusion. There exists experimental data for the equation of state, conductivity, and Thomson scattering spectra. However, the analysis of the measurements at extreme pressures and temperatures typically involves additional model assumptions, which makes it difficult to assess the accuracy of the experimental data rigorously. On the other hand, theory and modeling have produced extensive collections of data. They originate from a very large variety of models and simulations including path integral Monte Carlo (PIMC) simulations, density functional theory (DFT), chemical models, machine-learned models, and combinations thereof. At the same time, each of these methods has fundamental limitations (fermion sign problem in PIMC, approximate exchange–correlation functionals of DFT, inconsistent interaction energy contributions in chemical models, etc.), so for some parameter ranges accurate predictions are difficult. Recently, a number of breakthroughs in first principles PIMC as well as in DFT simulations were achieved which are discussed in this review. Here we use these results to benchmark different simulation methods. We present an update of the hydrogen phase diagram at high pressures, the expected phase transitions, and thermodynamic properties including the equation of state and momentum distribution. Furthermore, we discuss available dynamic results for warm dense hydrogen, including the conductivity, dynamic structure factor, plasmon dispersion, imaginary-time structure, and density response functions. We conclude by outlining strategies to combine different simulations to achieve accurate theoretical predictions that are based on first principles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Stimulated Emission of Signal Photons from Dark Matter Waves

The manipulation of quantum states of light has resulted in significant advancements in both dark matter searches and gravitational wave detectors. Current dark matter searches operating in the microwave frequency range use nearly quantum-limited amplifiers. Future high frequency searches will use photon counting techniques to evade the standard quantum limit. We present a signal enhancement technique that utilizes a superconducting qubit to prepare a superconducting microwave cavity in a nonclassical Fock state and stimulate the emission of a photon from a dark matter wave. By initializing the cavity in an | n = 4 ⟩ Fock state, we demonstrate a quantum enhancement technique that increases the signal photon rate and hence also the dark matter scan rate each by a factor of 2.78. Using this technique, we conduct a dark photon search in a band around 5.965 GHz ( 24.67 μ eV ), where the kinetic mixing angle ε ≥ 4.35 × 10 − 13 is excluded at the 90% confidence level. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗