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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Searching for Axion-like Particles at DarkQuest

Axion-like particles (ALPs) interacting with photons can be abundantly produced in proton xed target experiments. Once produced, they can travel a macroscopic distance before decaying into photons. We study the sensitivity of the proposed DarkQuest experiment at Fermilab to this signature. We show that the proposed upgrade to install an electromagnetic calorimeter will enable DarkQuest to explore untested parameter space by searching for the displaced decays of ALPs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Low Cost, High Volume, Carbon Fiber Precursor for Plasma Oxidation

Light weighting with carbon fiber is critical to improving the energy efficiency of wind turbines, airplanes, and automobiles, but the high cost of carbon fiber continues to limit market growth and applications. Half of this cost is in the raw materials. For the carbon fiber market to reach the next level of adoption, the cost of both the raw materials and conversion must be reduced. This project considered the combination of low-cost, textile-grade precursors combined with the increased efficiency of plasma oxidation to greatly reduce the cost of carbon fiber. The primary objective of this project was to make industrial-grade carbon fiber (550 ksi tensile strength, 35 Msi tensile modulus) from textile-grade PAN precursors, something that cannot be achieved with conventional oxidation and to date has not been achieved with any technology. To accomplish this, the project team iteratively optimized the process conditions in the plasma oxidation and carbonization stages to maximize the tensile properties of the resultant carbon fibers. This involved implementing experimental designs for both stages, whereby process conditions (e.g. temperature) were varied and correlated to fiber properties. Samples of oxidized PAN fibers (OPFs) were prepared using a small pilot-scale plasma oxidation oven at 4XTechnologies (4XT). The thermal, chemical, and physical properties of OPF samples were analyzed by the University of Tennessee (UTK) and Oak Ridge National Laboratory (ORNL). The OPF samples were then converted to carbon fiber by subsequent low- and high-temperature carbonization using a lab-scale furnace at ORNL. The resultant carbon fibers were tested for tensile strength also at ORNL, and the results were fed back into the experimental design. Of the hundreds of carbon fiber samples produced over the course of the project, the best sample had an average break strength of 479 ksi and modulus of 33 Msi. While the best tensile properties achieved fell short of the final project targets of 550 ksi for break strength and 35 Msi for modulus, the team demonstrated the potential to produce industrial-grade carbon fiber using textile-grade precursors. The best performing fiber samples had tensile properties that are 87% of the goal for tensile strength and 94% of the goal for tensile modulus. With additional optimization work, it is likely that the targets could be achieved, since only a fraction of the process parameter space was explored during this project. 4XT and its affiliate (4M) have already secured funding through private equity, and there is a CRADA (Cooperative Research and Development Agreement) project currently underway with the Carbon Fiber Technology Center (CFTF) at ORNL to continue this research. Additionally, while not an original goal of the project, the carbon fibers for most samples had diameters >8.5μm. This is considerably larger than commercially available carbon fiber products, which can impart unique properties to the composites (e.g. increased compressive strength) and decrease manufacturing costs even further. Moreover, these fibers were oxidized with plasma oxidation using residence times ranging <60mins, which would be considered fast for normal diameter fibers, but it is as much as 5x faster when factoring in the diameter of the fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis of Hf 0.75 Ta 0.25 B 2 for self-coating TPS

Ultra-high temperature ceramic materials (UHTCs) are important for designing high-performance aerospace vehicles that can withstand repeated exposures to high temperatures. UHTCs mixed with silicides often have well-controlled oxidation due to the formation of protective silicates, which create a regenerative outer protective layer. Borides are known to have high melting points, high hardness and reasonably good oxidation resistance. In this study, Hf 0.75 Ta 0.25 B 2 (HTB) is proposed as a potential alternative to YSZ protective coatings and ZrB 2- SiC composites via the formation of Hf 6 Ta 2 O 17 (HTO) passivation layer. The objective of this paper is to explore the parameter space of HTB synthesis via borocarbothermal (BCTR). Effects on particle size, phase purity, and residual oxygen content were analyzed with parameters of atmosphere composition, reactant grain sizes, and differing reaction pathways. The BCTR of HfO 2 and Ta 2 O 5 were analyzed to predict HTB behavior. It was shown that both one-step and two-step reaction routes can yield HTB, but two-step yields a purer product. Nano B 4 C produced finer HTB and facilitated reaction completion. Using a reducing atmosphere also enhanced reaction completion

36 MATERIALS SCIENCE↗
DOE OSTI2023

Deep Neural Network Informed Markov Chain Monte Carlo Methods

In subsurface flow modeling, quantifying the uncertainty of model parameters and the corresponding uncertainly on output quantities is a crucial task for groundwater management. Markov chain Monte Carlo (MCMC) methods can take advantage of observed data to estimate parameters in a Bayesian setting. However, MCMC can be slow to converge and produce highly correlated samples when the dimensions of the parameters is high. Using gradients for the posterior distribution can help samplers explore the parameter space more efficiently, but obtaining gradients can be computationally challenging.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The MAGIS-100 Experiment and a Future, Kilometer-scale Atom Interferometer

The dearth of signals unambiguously attributable to WIMP dark matter motivates exploring new parameter space, particularly the ultra-light dark matter (ULDM) regime, where the signal is not scattering events but wave phenomena. The proliferation of quantum technologies has enabled searches for ULDM at previously inaccessible sensitivity. One such technology is atom interferometry which can measure time-dependent fluctuations in the energy spacing of atoms as well as in the light-travel time across the apparatus, enabling searches for ULDM and gravitational waves, respectively. In this talk, I will review the science case for long baseline atom interferometers, such as MAGIS-100 at Fermilab, provide an update on the experiment’s status, and discuss the need for a future, kilometer-scale atom interferometer which would achieve unprecedented sensitivity to ULDM and gravitational waves in the “Mid-Band” region between LIGO and LISA.

Temples, Dylan J. [Fermilab] (ORCID:00000001601725↗
DOE OSTI2022

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware

The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Igniting Weak Interactions in Neutron Star Postmerger Accretion Disks

The merger of two neutron stars or a neutron star and a black hole typically results in the formation of a postmerger accretion disk. Outflows from disks may dominate the overall ejecta from mergers and be a major source of r-process nuclei in our universe. We explore the parameter space of such disks and their outflows and r-process yields by performing 3D general-relativistic magnetohydrodynamic simulations with weak interactions and approximate neutrino transport. We discuss the mapping between the initial binary parameters and the parameter space of the resulting disks, chiefly characterized by their initial accretion rate. We demonstrate the existence of an ignition threshold for weak interactions at around ~10 -3 M⊙ s -1 for typical parameters by means of analytic calculations and numerical simulations. We find a degenerate, self-regulated, neutrino-cooled regime above the threshold and an advection-dominated regime below the threshold. Excess heating in the absence of neutrino cooling below the threshold leads to ≳60% of the initial disk mass being ejected in outflows, with typical velocities of ~(0.1–0.2)c, compared to ≲40% at ~(0.1–0.15)c above the threshold. While disks below the threshold show suppressed production of light r-process elements, disks above the threshold can produce the entire range of r-process elements, in good agreement with the observed solar system abundances. Disks below the ignition threshold may produce an overabundance of actinides seen in actinide-boost stars. As gravitational-wave detectors start to sample the neutron star merger parameter space, different disk realizations may be observable via their associated kilonova emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Filamentary Dust Polarization and the Morphology of Neutral Hydrogen Structures

Filamentary structures in neutral hydrogen (H$\tiny{I}$) emission are well aligned with the interstellar magnetic field, so H$\tiny{I}$ emission morphology can be used to construct templates that strongly correlate with measurements of polarized thermal dust emission. We explore how the quantification of filament morphology affects this correlation. We introduce a new implementation of the Rolling Hough Transform (RHT) using spherical harmonic convolutions, which enables efficient quantification of filamentary structure on the sphere. We use this Spherical RHT algorithm along with a Hessian-based method to construct H$\tiny{I}$-based polarization templates. We discuss improvements to each algorithm relative to similar implementations in the literature and compare their outputs. By exploring the parameter space of filament morphologies with the Spherical RHT, we find that the most informative H$\tiny{I}$ structures for modeling the magnetic field structure are the thinnest resolved filaments. For this reason, we find a ~10% enhancement in the B-mode correlation with polarized dust emission with higher-resolution H$\tiny{I}$ observations. We demonstrate that certain interstellar morphologies can produce parity-violating signatures, i.e., nonzero TB and EB, even under the assumption that filaments are locally aligned with the magnetic field. Finally, we demonstrate that B modes from interstellar dust filaments are mostly affected by the topology of the filaments with respect to one another and their relative polarized intensities, whereas E modes are mostly sensitive to the shapes of individual filaments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Snowmass 2021 Rare & Precision Frontier (RF6): Dark Matter Production at Intensity-Frontier Experiments

Dark matter particles can be observably produced at intensity-frontier experiments, and opportunities in the next decade will explore important parameter space motivated by thermal DM models, the dark sector paradigm, and anomalies in data. This whitepaper describes the motivations, detection strategies, prospects and challenges for such searches, as well as synergies and complementarity both within RF6 and across HEP.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis

The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.

Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗
DOE OSTI2026

Neutrino-Antineutrino Conversion from Ultralight Vector Dark Matter

We show that Majorana neutrinos convert into antineutrinos in a background of ultralight vector dark matter coupled to lepton number, such as the gauge boson of $\text{U}(1)_{B-L}$ or $\text{U}(1)_{L_i - L_j}$ with $i, j = e , μ, τ$. This effect is suppressed by the small neutrino mass, but the enhancement by long astrophysical baselines can enable future searches for solar and supernova neutrinos to explore uncharted parameter space. For instance, for $\text{U}(1)_{B-L}$ dark matter, the observation of a supernova neutrino burst at DUNE, Hyper-Kamiokande, and JUNO could probe gauge couplings as small as $e^\prime \sim 10^{-32} - 10^{-25}$ for dark matter masses of $m_{A^\prime} \sim 10^{-22} \ \text{eV} - 10^{-14} \ \text{eV}$, beyond the capability of other future probes.

Berlin, Asher [Fermilab] (ORCID:0000000211561482)↗
DOE OSTI2020

Consistent Evaluation of the Prompt-fission Neutron Spectrum and Multiplicity for n+ 235,238 U and n+ 239 Pu

This report was written to satisfy a FY20 NCSP milestone on 235,238 U and 239 Pu. The milestone requires to “finalize a report assessing our methodology to evaluate prompt-fission neutron spectrum (PFNS) and multiplicity consistently”. More specifically, we study whether the code CGMF can reproduce ENDF/B-VIII.0 evaluated PFNS and average prompt-fission neutron multiplicities, $\bar{v}$, for 235,238 U and 239 Pu using one joint parameter set per isotope. If CGMF is shown to be able to reasonably reproduce ENDF/B-VIII.0 within its model-parameter space, this code could be used for future consistent evaluations of PFNS and $\bar{v}$. To answer this question, we explore here the parameter space of CGMF and its impact on calculated values and whether they are close to evaluated and experimental data. We also list experimental data that would enter a future evaluation and statistics method that could be used to obtain evaluated data and covariances. We conclude that values of $\bar{v}$ calculated by CGMF are reasonably close to ENDF/B-VIII.0 data, while more work on modeling the PFNS is needed (parameter optimization and model improvements) to reliably use it for evaluations.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Investigating boosted decision trees as a guide for inertial confinement fusion design

Inertial confined fusion experiments at the National Ignition Facility have recently entered a new regime approaching ignition. Improved modeling and exploration of the experimental parameter space were essential to deepening our understanding of the mechanisms that degrade and amplify the neutron yield. The growing prevalence of machine learning in fusion studies opens a new avenue for investigation. Here in this paper, we have applied the Gradient-Boosted Decision Tree machine-learning architecture to further explore the parameter space and find correlations with the neutron yield, a key performance indicator. We find reasonable agreement between the measured and predicted yield, with a mean absolute percentage error on a randomly assigned test set of 35.5%. This model finds the characteristics of the laser pulse to be the most influential in prediction, as well as the hohlraum laser entrance hole diameter and an enhanced capsule fabrication technique. We used the trained model to scan over the design space of experiments from three different campaigns to evaluate the potential of this technique to provide design changes that could improve the resulting neutron yield. While these data-driven model cannot predict ignition without examples of ignited shots in the training set, it can be used to indicate that an unseen shot design will at least be in the upper range of previously observed neutron yields.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Throughput Optimization of Molybdenum Carbide Nanoparticle Catalysts in a Continuous Flow Reactor Using Design of Experiments

Transition metal carbides (TMCs) have attracted significant attention because of their applications toward a wide range of catalytic transformations. However, the practicality of their synthesis is still limited because of the harsh conditions in which most TMCs are prepared. Recently, a solution-phase synthesis of phase-pure a-MoC1-x nanoparticles was presented. While this synthetic route yielded nanoparticles with exceptional catalytic performance, the reaction parameter space was not explored, and catalyst throughput was not optimized for scale-up. Continuous flow platforms coupled with statistical design of experiments (DoE) can provide a powerful method for understanding the reaction parameter space for optimizations. Here, we demonstrate the use of statistical DoE in tandem with response surface methodology for a parametric screening analysis to optimize the throughput of a MoC1-x nanoparticle synthesis utilizing a millifluidic flow reactor. A full factorial design was implemented to evaluate four input variables (reaction temperature, flow rate, solvent fraction of oleylamine, and precursor concentration) that carry statistically significant effects on three responses (throughput, residence time, and isolated yield). A Doehlert matrix was implemented to investigate each significant variable at a higher number of levels to optimize throughput. Our results give a nonintuitive set of experimental conditions that resulted in an optimized throughput of 2.2 g h-1. This translates to a 50-fold increase in throughput compared to the previously reported batch method. The catalytic performance of the MoC1-x nanoparticles produced under optimized throughput was demonstrated in the CO2 hydrogenation reaction. This DoE screening analysis and throughput optimization of MoC1-x synthesis open the door to an increased feasibility for scale-up.

design of experiments↗
DOE OSTI2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.

42 ENGINEERING↗
DOE OSTI2025

Optimizing Grain Boundary Structures with LAMMPS Using Evolutionary Algorithms

Grain boundary structure optimization is an important part of materials modeling. Current methods for grain boundary structure optimization involve inefficient, time-consuming processes that do not fully explore the interface parameter space. Evolutionary algorithms have recently been demonstrated to be effective at determining both stable and metastable grain boundary interface structures. In this work, we demonstrate the use of GBOpt, a grain boundary structure optimization software designed to use the Large-scale Atomic/Molecular Massively Parallel Simulation (LAMMPS) software to efficiently determine grain boundary structures. We demonstrate that a only a few manipulations, namely atom insertion, atom removal, and relative grain displacement, are sufficient to explore much of the grain boundary structure parameter space. The efficacy of this approach is demonstrated on an FCC Ni system, and a BCC Fe system. The computational cost is compared against the gamma-surface sampling approach to demonstrate performance improvement.

Evolutionary algorithms↗
DOE OSTI2023

gLaSDI: Parametric physics-informed greedy latent space dynamics identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. Here, an interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a -nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658× speed-up with 1 to 5% relative errors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A dual dynamic shutter system for accelerating ion irradiation sample throughput via lateral gas implantation gradients

Ion irradiation for material performance testing is limited due to its serial nature, which allows for only one value of the implantation (appm) versus dose (dpa) parameter space to be explored for each ion and experiment at a time. While ion irradiation can accelerate the process by up to three orders of magnitude compared to neutron irradiation experiments, the sample throughput for ion irradiation remains relatively low. To address these limitations, a novel capability has been developed at the Michigan Ion Beam Laboratory (MIBL), enabling for the creation of single- and two-dimensional lateral ion implantation gradients using recently installed motorized-controlled ion-beam shutters. This advancement can generate a wide scope of the two-dimensional (H+, He2+) implantation parameter space within a single sample. Integration of this new capability now allows for dual- and triple-ion beam experiments to be performed with full user control over not only the ion implantation depth, but also laterally across the sample by imposing ion implantation concentration gradients, thus providing researchers with a high-throughput means for material testing under various irradiation conditions. Furthermore, recent improvements in MIBL's microbeam ion-beam analysis (IBA) target station now allow for probing these concentration gradients in irradiated alloys with exceptional spatial resolution, down to 10 µm. These two approaches promise to significantly improve ion irradiation capabilities and increase the sample throughput by several orders of magnitude. The application of the shutter technique plus the subsequent microbeam characterization of the imposed implantation gradients are showcased by two proof-of-principle ion-irradiated experiments, one performed on single-crystal Si and the other on the fusion-candidate alloy F82H-IEA. These advancements mark a substantial leap in ion-beam technology, offering researchers a robust, high-throughput method to efficiently investigate candidate alloys with high technological readiness for both advanced fission and fusion reactor applications, in a time- and cost-effective manner.

36 - MATERIALS SCIENCE↗