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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Predicting displacement damage for ion irradiation: Origin of the overestimation of vacancy production in SRIM full-cascade calculations

Ion irradiation and implantation have wide applications that demand accurate determination of displacement damage profile and distribution of implanted ion concentration. The prediction of vacancies is especially important to determine displacements per atom (dpa), the standard parameter of primary radiation damage in materials. However, significant discrepancies exist in estimations of vacancies between full-cascade (F-C) and quick calculation (Q-C) options in the popular computer code SRIM. This study inspected the SRIM code and a relatively new code called Iradina, which uses a similar methodology, to develop an understanding of the origin of vacancy overestimation in the F-C options for SRIM and Iradina. Here, we found that the default values of thresholds (namely final energy in SRIM and replacement energy in Iradina) in displacement production calculations results in excessively large number of calculated vacancies and very few replacements. After conducting multiple calculations using SRIM, Iradina, and MARLOWE (all based on the binary collision approximation), a comparison of the results indicates that there is a shortcoming in the SRIM and Iradina F-C methodology for treating near-threshold collisions. This issue is responsible for the deficiency of replacements and excess of calculated vacancies in the SRIM and Iradina F-C results. Drawing on the principles of collision physics, we propose recommendations for modifying the source codes to address these issues.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling the impact of high thermal conductivity paper on the performance and life of power transformers

Degradation of insulation paper is a key contributor to the failure of power transformers. Insulation degradation accelerates at elevated temperatures, which highlights the potential for better thermal management to prolong life. While several studies have analyzed the benefits of high thermal conductivity oil for reducing temperatures inside a transformer, this study is an initial assessment of the benefits of high thermal conductivity paper on transformer life. Blending particulates with cellulosic fibers offers a pathway for high thermal conductivity paper (with good dielectric properties), which can reduce internal temperatures. Presently, life extensions that can be achieved by the use of such thermally conducting papers were estimated, with the thermal conductivity of the paper being the key parameter under study. The analytical-numerical thermal model used in this study was validated against experimental measurements in a distribution transformer, adding confidence to the utility of the model. This model was then used to provide estimates of hot-spot temperature reduction resulting from the use of papers with higher thermal conductivity than baseline. Transformer life was predicted conventionally by tracking the degree of polymerization of paper over time, based on an Arrhenius model. Results indicate that increasing the thermal conductivity of paper from 0.2 W/mK (baseline) to 1W/mK reduces the hot spot temperature by 10°C. While degradation significantly depends on the moisture and oxygen content, the model shows that such a temperature reduction can increase life for all conditions, by as much as a factor of three.

42 ENGINEERING↗
DOE OSTI2023

Reversal of Catalytic Material Substrate Selectivity through Partitioning of Polymers in Hierarchically Ordered Virus-like Particle Frameworks

Control over the selectivity of catalytic materials is a topic of growing interest. Virus-like particle (VLP) based materials such as protein macromolecular frameworks (PMFs) are promising for catalytic applications due to their ease of assembly, modular ability to encapsulate a variety of enzymes, and ease of separation from a reaction mixture. Here we demonstrate the reversal of the initially negative material charge through the titration of a positively charged polymer into the material, causing the reversal of guest molecule uptake and enzymatic activity of PMFs. The charge-inverse material partitions a charged enzyme substrate to concentrate the substrate near an enzyme incorporated within the material, generating up to 5.9-fold increases in enzyme activity toward the partitioned substrate over the excluded substrate. Here we also show that the polymer distributes heterogeneously through the material up to a point of saturation and the effects of guest macromolecules on the lattice parameters of PMFs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Crosslinking and Gelation of Polymer Brushes and Free Polymer Chains in a Confined Space during Controlled Radical Polymerization-A Computer Simulation Study

The confinement effect on crosslinking and gelation during controlled/living copolymerization of a monovinyl and a divinyl monomer was studied by Monte Carlo computer simulation using the dynamic lattice liquid method. The simulated reactions took place in plane-parallel slits of various widths for various grafting densities (GDs) and various initial initiator/crosslinker ratios ([X] 0 /[Ini] 0 ). Monomers and crosslinkers were initially randomly distributed in the slit, but initiators were either anchored to one solid plane (polymer brushes) or randomly distributed (free chains). For brushes, the gel point (GP) depended on GD and [X] 0 /[Ini] 0 . The higher these parameters, the lower was the gel point. The gel point was also affected by the slit width. For free, nongrafted chains, the dependence of GP on slit width was negligible. For brushes, a nonuniform density of crosslinks was found, the highest density was close to the wall. This asymmetry was high at the gel point and decreased at higher conversions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-Resolution X-ray Spectromicroscopy Reveals Process-Structure Correlations in sub-5-μm Diameter Carbon Nanotube-Polymer Composite Dry-Spun Yarns

A persistent lack of detailed and quantitative structural analysis of these hierarchical carbon nanotube (CNT) ensembles precludes establishing processing-structure-property relationships that are essential to enhance macroscale performance (e.g., in mechanical, electrical, thermal applications). Here, in this work, we use scanning transmission X-ray microscopy (STXM) to analyze the hierarchical, twisted morphology of dry-spun CNT yarns and their composites, quantifying key structural characteristics such as density, porosity, alignment, and polymer loading. As the yarn twist density increases (15,000 to 150,000 turns per meter), the yarn diameter decreased (4.4–1.4 μm) and density increased (0.55–1.26 g·cm –3 ), as intuitively expected. Yarn density, ρ, ubiquitously scaled with diameter d according to ρ ~ d –2 for all parameters studied here. Spectromicroscopy probes with 30 nm resolution and elemental specificity were employed to analyze the radial and longitudinal distribution of the oxygen-containing polymer content (~30% weight fraction), demonstrating nearly perfect filling of the voids between CNTs with a vapor-phase polymer coating and cross-linking process. These quantitative correlations highlight the intimate connections between processing conditions and yarn structure with important implications for translating the nanoscale properties of CNTs to the macroscale.

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

Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators

Abstract Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations . These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

Hossain, Raisa↗
DOE OSTI2025

Observing differential spin currents by resonant inelastic X-ray scattering

Controlling spin currents, that is, the flow of spin angular momentum, in small magnetic devices, is the principal objective of spin electronics, a main contender for future energy-efficient information technologies. A pure spin current has never been measured directly because the associated electric stray fields and/or shifts in the non-equilibrium spin-dependent distribution functions are too small for conventional experimental detection methods optimized for charge transport. Here we report that resonant inelastic X-ray scattering (RIXS) can bridge this gap by measuring the spin current carried by magnons—the quanta of the spin wave excitations of the magnetic order—in the presence of temperature gradients across a magnetic insulator. This is possible due to the sensitivity of the momentum- and energy-resolved RIXS intensity to minute changes in the magnon distribution under non-equilibrium conditions. Furthermore, we use the Boltzmann equation in the relaxation time approximation to extract transport parameters, such as the magnon lifetime at finite momentum, essential for the realization of magnon spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Macroscopic trends of linear tearing stability in cylindrical current profiles

Abstract The likelihood of realising tokamak power-plants will be greatly improved by the discovery of high-gain equilibria that resist the formation of small islands and hence avoid the disruptive neoclassical tearing mode. We propose a series of studies to understand how simple tokamak design can leverage aspects of tearing onset physics to maximise passive resistance to island formation. Here we investigate the variation that current profiles can bring about in preventing tearing onset through the cylindrical linear tearing stability parameter Δ ′ . A database of 159148 realistic pilot-plant current profiles was generated with Monte Carlo sampling, and the distribution of Δ ′ values was linked with interpretable profile characteristics. In agreement with prior theoretical and experimental studies, Δ ′ was found to be strongly correlated with the existence and steepness of a local toroidal current well or hill, with the former destabilising and the latter stabilising. In the absence of these two cases, the remaining Δ ′ values were linearly bounded by the toroidal current gradient at the rational surface.

Physics↗
DOE OSTI2025

Clustering and runaway merging in a primordial black hole dominated universe

If primordial black holes (PBHs) are present in the early Universe, their contribution to the energy budget grows relative to that of radiation and generically becomes dominant unless the initial abundance is exponentially small. This black hole domination scenario is largely unconstrained for PBHs with masses ≲ 10 9 g, which evaporate prior to big bang nucleosynthesis. However, if the era of PBH domination is sufficiently long, the PBHs form clusters and can merge appreciably within these objects. We calculate the population statistics of these clusters within the Press-Schechter formalism and find that, for a wide range of PBH masses and Hubble rates at the onset of PBH domination, the mergers within PBH clusters can exhibit runaway behavior, where the majority of the cluster will eventually form a single black hole with a mass much greater than the original PBH mass. Finally, these mergers can dramatically alter the PBH mass distribution and leave behind merged relic black holes that evaporate after big bang nucleosynthesis and yield various observational signatures, excluding parameter choices previously thought to be viable.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Attention to quantum complexity

The imminent era of error-corrected quantum computing demands robust methods to characterize quantum state complexity from limited, noisy measurements. We introduce the Quantum Attention Network (QuAN), a classical artificial intelligence (AI) framework leveraging attention mechanisms tailored for learning quantum complexity. Inspired by large language models, QuAN treats measurement snapshots as tokens while respecting permutation invariance. Combined with our parameter-efficient miniset self-attention block, this enables QuAN to access high-order moments of bit-string distributions and preferentially attend to less noisy snapshots. We test QuAN across three quantum simulation settings: driven hard-core Bose-Hubbard model, random quantum circuits, and toric code under coherent and incoherent noise. QuAN directly learns entanglement and state complexity growth from experimental computational basis measurements, including complexity growth in random circuits from noisy data. In regimes inaccessible to existing theory, QuAN unveils the complete phase diagram for noisy toric code data as a function of both noise types, highlighting AI’s transformative potential for assisting quantum hardware.

Kim, Hyejin [Cornell Univ., Ithaca, NY (United Sta↗
DOE OSTI2023

Search for low mass dark matter in DarkSide-50: the bayesian network approach

Abstract We present a novel approach for the search of dark matter in the DarkSide-50 experiment, relying on Bayesian Networks. This method incorporates the detector response model into the likelihood function, explicitly maintaining the connection with the quantity of interest. No assumptions about the linearity of the problem or the shape of the probability distribution functions are required, and there is no need to morph signal and background spectra as a function of nuisance parameters. By expressing the problem in terms of Bayesian Networks, we have developed an inference algorithm based on a Markov Chain Monte Carlo to calculate the posterior probability. A clever description of the detector response model in terms of parametric matrices allows us to study the impact of systematic variations of any parameter on the final results. Our approach not only provides the desired information on the parameter of interest, but also potential constraints on the response model. Our results are consistent with recent published analyses and further refine the parameters of the detector response model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Software-Hardware Co-design of Heterogeneous SmartNIC System for Recommendation Models Inference and Training

Deep Learning Recommendation Models (DLRMs) are critical applications in various domains and have evolved as one of the single largest machine learning applications. Trillions of DLRM parameters exceed the on-chip memory capacity of GPUs. Large-scale multi-node systems are required for distributed DLRM inference and training, which suffer from the all-to-all communication bottleneck, mainly limiting the scalability of ever-growing DLRMs. In recent years, SmartNICs have evolved with coupled computation and communication capabilities providing opportunities for a powerful heterogeneous device in the system. However, there isn't such a distributed system that fully leverages the abundant smartNIC resources that resolve the scalability issue of DLRMs. In this work, we proposed a software-hardware co-design of a heterogeneous smartNIC system that resolves the communication bottleneck of distributed DLRMs, mitigates the memory bandwidth pressure, and improves computation efficiency. We provide a set of smartNIC designs of cache systems (including local cache and remote cache) and smartNIC computation kernels which reduce data movement, relieve memory lookup intensity, and improve the GPU's computation efficiency. In addition, we propose a graph algorithm that improves the data locality of queries within batches which optimizes the overall system performance with higher data reuse. Our evaluation shows that our system achieves 2.1x latency speedup for inference and 1.6x throughput speedup for training.

Guo, Anqi↗
DOE OSTI2022

HPS-RL: Hyperparameter tuning for deep RL applications (HPS-RL) v1

Genetic Algorithms meets Deep RL for Hyperparameters Hyperparameter optimization and architecture search can easily become cumbersome and finding the right hyperparameters can seriously impact the robustness of the deep RL application being developed. We use genetic algorithms to evolve optimum deep RL architectures in a scalable manner. HPS-RL is designed to work with multiple gym enviornments, allow users to test their own optimization functions and tune multi-objective parameters in multiple deep RL algorithms. HPS-RL uses multi-threading and is being extended with mpipy for distributed processing on HPC. https://arxiv.org/abs/2201.11182

Kiran, Mariam↗
DOE OSTI2022

Statistical Properties of Undulator Radiation

Two experiments were carried out to study the statistical properties of undulator radiation in the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. The first experiment studied the turn-to-turn fluctuations in the power of the radiation generated by an electron bunch. The magnitude of these fluctuations depends on the 6D phase-space distribution of the electron bunch. In IOTA, we demonstrated that this effect can be used to measure some electron bunch parameters, small transverse emittances in particular. In the second experiment, a single electron was stored in the ring, emitting a photon only once per several hundred turns. In this regime, any classical interference-related collective effects were eliminated, and the quantum fluctuations could be studied in detail to search for possible deviations from the expected Poissonian photon statistics. In addition, the photocount arrival times were used to track the longitudinal motion of a single electron and to compare it with simulations. This allowed us to determine several dynamical parameters of the storage ring such as the rf cavity phase jitter and the dependence of the synchrotron motion period on amplitude.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2024

Quantum Sensing for Energy Applications

Quantum sensing is creating potentially transformative opportunities to exploit intricate quantum mechanical phenomena in new ways to make ultrasensitive measurements of multiple parameters. A growing interest in quantum sensing has created opportunities for its deployment to improve processes pertaining to energy production, distribution, and consumption. NETL is leveraging experimental and computational quantum tools to enhance sensitivity of hybrid quantum-classical ultrasensitive sensors for the detection of hydrocarbons and rare earth elements (REEs).

Paudel, Hari P.↗
DOE OSTI2023

Constructing Regulatory Networks to Compare Axenic and Interspecies Microbial Gene Transcription

In this preliminary study, we constructed gene regulatory networks (GRNs) from transcriptional expression data of axenic and interspecies microbial cultures with the goal of predicting how cocultivation affected greenhouse gas respiration by these species. The specific strains of Methylotuvimicrobium alkaliphilum 20Z, a methylotroph, and Cyanobacterium stanieri HL-69, a phototroph, were chosen for their viability in industrial bioprocessing. We ranked directed interactions between gene pairs based on the ability of the input gene to predict the expression of a target gene relative to their transcriptomes. While we were able to identify topological differences between conditions, our initial findings require validation through experimental analysis and further modeling. We aimed to develop a systematic thresholding approach to optimize the accuracy of our networks. We filtered out trial networks separately from top gene interactions of the scored rankings. Parameters of unfiltered and filtered networks were used to test and develop thresholding approaches. Knee point detection of edge weight distributions was explored as an approach for separating significant interactions from insignificant interactions in unfiltered networks. While knee detection failed to produce analogous networks for broad cross-condition comparisons, the results informed us about the proportions of significant edges present in unfiltered networks. We also calculated the average mean degree for nodes in a selection of trial networks to find a thresholding value characteristic to all groups. While we did not reach a definitive conclusion, we gained insight into the coregulatory structures of our groups and made critical evaluations of systematic methods for filtering networks. We recommend an iterative process for the inference of GRNs, where the most significant results from preliminary explorations are used to improve the efficiency with which regulatory motifs are chosen for experimental characterization. Experimental results can then inform the framework of adjusted models to improve broad interpretations of GRNs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Galaxy Clustering in the Mira-Titan Universe. I. Emulators for the Redshift Space Galaxy Correlation Function and Galaxy–Galaxy Lensing

Abstract We construct accurate emulators for the projected and redshift space galaxy correlation functions and excess surface density as measured by galaxy–galaxy lensing, based on halo occupation distribution modeling. Using the complete Mira-Titan suite of 111 N -body simulations, our emulators vary over eight cosmological parameters and include the effects of neutrino mass and dynamical dark energy. We demonstrate that our emulators are sufficiently accurate for the analysis of the Baryon Oscillation Spectroscopic Survey DR12 CMASS galaxy sample over the range 0.5 ≤ r ≤ 50 h −1 Mpc. Furthermore, we show that our emulators are capable of recovering unbiased cosmological constraints from realistic mock catalogs over the same range. Our mock catalog tests show the efficacy of combining small-scale galaxy–galaxy lensing with redshift space clustering and that we can constrain the growth rate and σ 8 to 7% and 4.5%, respectively, for a CMASS-like sample using only the measurements covered by our emulator. With the inclusion of a cosmic microwave background prior on H 0 , this reduces to a 2% measurement of the growth rate.

79 ASTRONOMY AND ASTROPHYSICS↗