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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Measuring the Muon Anomalous Magnetic Moment to High Precision [PowerPoint]

The anomalous magnetic moment of the muon, characterized as the anomaly $a_{\mu} \equiv (g_{\mu}-2)/2$, remains a compelling hint of physics beyond the Standard Model (BSM) in light of the Brookhaven E821 measurement from the early 2000s, which differs with theoretical predictions by $>$3\,$\sigma$. To address this discrepancy, the Muon $g-2$ Experiment at Fermilab (E989) has been designed to determine $a_{\mu}$ to a precision of 140 parts per billion, a four-fold improvement over the E821 measurement. This improved precision presents an excellent opportunity to uncover BSM physics, and at minimum constrain BSM prescriptions. To date, the E989 experiment has completed its first two physics runs, recording more than 3 times the statistics recorded in the E821 experiment. The collaboration is preparing for a third physics run starting in October, with the goal of accumulating a factor of three more data than the previous runs combined, resulting in an order of magnitude more statistics than the Brookhaven measurement. An overview of the experimental technique will be discussed, along with a summary of Run 1 and 2. An update of the analysis of the Run 1 data will be presented; additionally, Run 3 progress will be summarized.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Multiscale studies of the three-dimensional dayside X-line

We review recent experience from the Cluster, Double Star, and THEMIS missions for lessons that apply to the upcoming Magnetospheric Multiscale Mission (MMS) being developed for launch in 2014. On global scales, simulation and statistical studies lead to mean configurations of dayside reconnection, implying specific relative alignments of the inflow magnetic fields and X-line, with implications for MMS operations designed to maximize the number of close encounters with the diffusion region. At intermediate MHD-to-ion scales, reconstruction of features created by one or two X-lines have developed to the point where data from a cluster of spacecraft can determine their temporal trends and the approximate three-dimensional X-line structure. Recent petascale particle-in-cell (PIC) simulations of reconnection encompass three spatial dimensions with excellent resolution, and make striking predictions of electron scale physics that creates complex interacting flux ropes under component reconnection. High time resolution measurements from MMS will determine the detailed electron scale kinetics embedded within the global and MHD–ion scale contexts. These developments will lead to the refinement of our three-dimensional multiscale picture of reconnection, yielding improved understanding of the global, MHD, and local physics controlling the onset or quenching, variability, and mean rate of reconnection. This in turn will enable improved predictability of the structural features created by transient reconnection, and their space weather consequences.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Distributed Acoustic Sensing to Estimate the Permeability

Optical fiber in a borehole can be interrogated with distributed acoustic sensors (DAS) to capture fracture displacements with the potential to map surrounding fracture networks. We designed a laboratory experiment to test the capability of DAS to determine borehole flow characteristics, and we show that for the first time DAS can be used to remotely estimate permeability. Optical fiber was wrapped around a bead filled pipe and the pressure drop and flow velocity were measured to directly calculate permeability. A machine learning model using statistical features from continuous DAS estimated the bulk permeability. Fluid interactions with the permeable material demonstrate insufficient resolution using DAS amplitude-based measurements for estimating pressure drop to infer permeability. Variations in the spectral domain relate DAS measurements to the pressure drop and provide consistent permeability estimates. Resolution with DAS is sufficient to estimate permeability and provides a reliable method to monitor at depth in borehole conditions.

58 GEOSCIENCES↗
DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

From Nucleons to Nuclei: Enabling Discovery for Neutrinos, Dark Matter and More

Neutrino interaction uncertainties are a limiting factor in current and next generation experiments probing the fundamental physics of neutrinos, a unique window on physics beyond the Standard Model. Our understanding of the elementary neutrino-nucleon amplitudes at the heart of the neutrino interaction program relies on experiments from an earlier age whose statistical and systematic precision are insufficient for current needs. The workshop brings together physicists with a diverse range of expertise to discuss the status of neutrino-nucleus scattering and to explore (i) the quantitative impact of better constraints on the elementary amplitudes; (ii) the scientific impact of a new hydrogen or deuterium target experiment; (iii) the optimal design and technical feasibility of a new hydrogen or deuterium target experiment; and (iv) complementary constraints on the elementary amplitudes from other methods. The tools and techniques required for understanding neutrino-nucleus interactions are common to many problems at the interface of particle and nuclear physics. The workshop will discuss new developments and prospects for related topics and new physics searches, including dark matter interactions with the Standard Model and neutrinoless double beta decay.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Prediction of shock initiation thresholds and ignition probability of polymer-bonded explosives using mesoscale simulations

The design of new materials requires establishment of macroscopic measures of material performance as functions of microstructure. Traditionally, this process has been an empirical endeavor. In this paper, an approach to computationally predict the probabilistic ignition thresholds of polymer-bonded explosives (PBXs) using mesoscale simulations is developed. The simulations explicitly account for microstructure, constituent properties, and interfacial responses and capture processes responsible for the development of hotspots and damage. The specific mechanisms tracked include viscoelasticity, viscoplasticity, fracture, post-fracture contact, frictional heating, and heat conduction. The probabilistic analysis uses sets of statistically similar microstructure samples to directly mimic relevant experiments for quantification of statistical variations of material behavior due to inherent material heterogeneities. The particular thresholds and ignition probabilities predicted are expressed in James type and Walker–Wasley type relations, leading to the establishment of explicit analytical expressions for the ignition probability as function of loading. Specifically, the ignition thresholds corresponding to any given level of ignition probability and ignition probability maps are predicted for PBX 9404 for the loading regime of U p = 200–1200 m/s where U p is the particle speed. The predicted results are in good agreement with available experimental measurements. A parametric study also shows that binder properties can significantly affect the macroscopic ignition behavior of PBXs. Finally, the capability to computationally predict the macroscopic engineering material response relations out of material microstructures and basic constituent and interfacial properties lends itself to the design of new materials as well as the analysis of existing materials.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Quantum-optical influences in optoelectronics—An introduction

This focused review discusses the increasing importance of quantum optics in the physics and engineering of optoelectronic components. Two influences relating to cavity quantum electrodynamics are presented. One involves the development of low threshold lasers, when the channeling of spontaneous emission into the lasing mode becomes so efficient that the concept of lasing needs revisiting. The second involves the quieting of photon statistics to produce single-photon sources for applications such as quantum information processing. An experimental platform, consisting of quantum-dot gain media inside micro- and nanocavities, is used to illustrate these influences of the quantum mechanical aspect of radiation. An overview is also given on cavity quantum electrodynamics models that may be applied to analyze experiments or design devices.

Chow, Weng W.↗
DOE OSTI2022

Analyzing Stochastic Computer Models: A Review with Opportunities

In modern science, computer models are often used to understand complex phenomena and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models-providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.

agent based model↗
DOE OSTI2018

The P2 experiment: A future high-precision measurement of the weak mixing angle at low momentum transfer

Here we describe the research and development work for the P 2 experiment which aims for a high precision determination of the weak mixing angle sin 2 θ W to a precision of 0.15% at a four-momentum transfer of 4.5×10 –3 GeV 2 . This accuracy, comparable to existing measurements at the Z pole, allows for a sensitive test of the Standard Model up to a mass scale of 50 TeV, extendable to 60 TeV. The weak mixing angle is connected to the weak charge of the proton which will be extracted from a measurement of the parity violating cross section asymmetry –39.94×10 –9 in elastic electron-proton scattering. A total accuracy of 0.57×10 –9 is achievable in a measurement time of 11000 h using a 150μA polarized electron beam impinging on a 60 cm liquid hydrogen target. The P 2 asymmetry is smaller than any asymmetry measured so far in electron scattering with an unprecedented goal for the accuracy. The use of a solenoid spectrometer with 100% Φ-acceptance as well as an atomic hydrogen trap polarimeter are new features, which have never before been used in parity-violation experiments. In order to collect the enormous statistics required for this measurement, the new Mainz Energy-Recovering Superconducting Accelerator (MESA) is under construction. Plans for the associated beam control system and the polarimetry are described in this article as well. A liquid hydrogen high-power target with an extremely low noise level of 10 ppm needs to be designed and constructed. We report in addition on the conceptual design of the P 2 spectrometer, its Cherenkov detectors, the integrating read-out electronics as well as the ultra-thin, fast tracking detectors. The physics program of the MESA facility comprises indirect, high precision search for physics beyond the Standard Model, measurement of the neutron distribution in nuclear physics, single-spin asymmetries, and a possible future extension to the measurement of hadronic parity violation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Inverse prediction of PuO2 processing conditions using Bayesian seemingly unrelated regression with functional data

Over the past decade, a variety of innovative methodologies have been developed to better characterize the relationships between processing conditions and the physical, morphological, and chemical features of special nuclear material (SNM). Different processing conditions generate SNM products with different features, which are known as “signatures” because they are indicative of the processing conditions used to produce the material. These signatures can potentially allow a forensic analyst to determine which processes were used to produce the SNM and make inferences about where the material originated. This article investigates a statistical technique for relating processing conditions to the morphological features of PuO 2 particles. We develop a Bayesian implementation of seemingly unrelated regression (SUR) to inverse-predict unknown PuO 2 processing conditions from known PuO 2 features. Model results from simulated data demonstrate the usefulness of the technique. Applied to empirical data from a bench-scale experiment specifically designed with inverse prediction in mind, our model successfully predicts nitric acid concentration, while results for Pu concentration and precipitation temperature were equivalent to a simple mean model. Our technique compliments other recent methodologies developed for forensic analysis of nuclear material and can be generalized across the field of chemometrics for application to other materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

This report presents the project Idaho National Laboratory conducted for Nuclear Regulatory Commission to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on the operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. Finally, the report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers. The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations was published by the NRC as in FRN NRC-2021-0048 in April 2021. A summary of the survey including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey is provided in the report. Finally, the report investigates potential applications of using AI/ML in operating NPPs and advanced reactors (both advanced LWRs and advanced NLWRs) to improve nuclear plant safety and efficiency. Three main application fields are defined and discussed: (1) plant safety and security assessments; (2) plant degradation modeling, fault and accident diagnosis and prognosis; and (3) plant operation and maintenance efficiency improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Experimental Design for Hanford Low-Activity Waste Glasses with High Waste Loading

This report discusses the development of an experimental design for the initial phase of the Hanford low-activity waste (LAW) enhanced glass study. This report is based on a manuscript written for an applied statistics journal. Appendices A, B, and E include additional information relevant to the LAW enhanced glass experimental design that is not included in the journal manuscript. The glass composition experimental region is defined by single-component constraints (SCCs), linear multiple-component constraints (MCCs), and a nonlinear MCC involving 15 LAW glass components. Traditional methods and software for designing constrained mixture experiments with SCCs and linear MCCs are not directly applicable because of the nonlinear MCC. A modification of existing methodology to account for the nonlinear MCC was developed and is described in this report. One of the glass components, SO 3 , has a solubility limit in glass that depends on the composition of the balance of the glass. A goal was to design the experiment so that SO 3 would not exceed its predicted solubility limit for any of the experimental glasses. The SO 3 solubility limit had previously been modeled by a partial quadratic mixture model expressed in the relative proportions of the 14 other components. The partial quadratic mixture model was used to construct a nonlinear MCC in terms of all 15 components. In addition, there were SCCs and linear MCCs. This report describes how a layered design was generated to (i) account for the SCCs, linear MCCs, and nonlinear MCC and (ii) meet the goals of the study. A layered design consists of points on an outer layer, and inner layer, and a center point. There were 18 outer-layer glasses chosen using optimal experimental design software to augment 147 existing glass compositions that were within the LAW glass composition experimental region. Then 13 inner-layer glasses were chosen with the software to augment the existing and outer-layer glasses. The experimental design was completed by a center-point glass, a Vitreous State Laboratory glass, and replicates of the center point and Vitreous State Laboratory glasses.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

The Muon g-2 Experiment [Slides]

$a_{\mu} \equiv (g-2)/2$, most recently at Brookhaven National Lab (E821) in the early 2000s, differ from theoretical calculations by nearly 3.5\,$\sigma$. To address this discrepancy, the Muon $g-2$ Experiment at Fermilab (E989) has been designed to determine $a_{\mu}$ to a precision of 140 parts per billion, a four-fold improvement over the E821 measurement. This improved precision presents an excellent opportunity to uncover new physics beyond the standard model (BSM), and at minimum constrain BSM models. The E989 experiment is currently taking physics-quality data, to date having recorded nearly 60\% more statistics than was obtained at Brookhaven. An overview of the experimental technique will be discussed, along with an update of the run progress and selected hardware performance and analysis highlights.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

A Laboratory Atom Interferometer Instrument

We have demonstrated a laboratory based, high bandwidth atom interferometer instrument and have performed an incipient gravity measurement with a fractional statistical uncertainty of ag/g = 4.4 x 10 -6 where g is the acceleration due to gravity. We have designed, constructed, and optimised numerous laser systems for this purpose, most notably a powerful Raman laser (12 W at 780 nm) which will allow large momentum transfer and large detuning interferometry to be carried out at high bandwidth for the first time. This experiment is a general purpose test bed for exploring the fundamental limitations of atom interferometer techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

A statistical analysis of the effects of urease pre-treatment on the measurement of the urinary metabolome by gas chromatography–mass spectrometry

Urease pre-treatment of urine has been utilized since the early 1960s to remove high levels of urea from samples prior to further processing and analysis by gas chromatography-mass spectrometry (GC-MS). Aside from the obvious depletion or elimination of urea, the effect, if any, of urease pre-treatment on the urinary metabolome has not been studied in detail. Here, we report the results of three separate but related experiments that were designed to assess possible indirect effects of urease pre-treatment on the urinary metabolome as measured by GC-MS. In total, 235 GC-MS analyses were performed and over 106 identified and 200 unidentified metabolites were quantified across the three experiments. The results showed that data from urease pre-treated samples 1) had the same or lower coefficients of variance among reproducibly detected metabolites, 2) more accurately reflected quantitative differences and the expected ratios among different urine volumes, and 3) increased the number of metabolite identifications. Altogether, we observed no negative consequences of urease pre-treatment. In contrast, urease pretreatment enhanced the ability to distinguish between volume-based and biological sample types compared to no treatment. Taken together, these results show that urease pretreatment of urine offers multiple beneficial effects that outweigh any artifacts that may be introduced to the data in urinary metabolomics analyses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables

Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction models and phenomenological cross-section parametrizations often exhibit significant deviations over broad regions of mass and charge. In this work, a Bayesian-inspired model-averaging framework is developed to combine abrasion-ablation (AA) calculations based on multiple nuclear mass tables into a single statistically weighted estimate. For the calibrated systems, the model weights are assigned empirically according to the relative quality of fit to measured cross sections, thereby reducing systematic model bias while preserving the underlying physics content of the AA description. The weights are constrained using proton-rich fragmentation data for the 78 Kr and 124 Xe projectiles. The resulting parameter trends are then propagated to the 92 Mo and 144 Sm systems through a controlled scaling procedure. In the present implementation, the excitation-energy prescription is fixed, while the averaging is performed across nuclear-mass inputs; the framework provides both weighted cross sections and associated uncertainty estimates. Applied to proton-rich fragmentation, the present approach provides a practical basis for interpolation and limited extrapolation in regions relevant to rare-isotope production. The resulting predictions are used to assess the production of very proton-rich nuclei, and candidate new isotopes are discussed.

Bayesian methods↗
DOE OSTI2025

Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning

Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

STUDY OF THE η'π 0 SYSTEM IN GLUEX

The η'π is an important decay channel for the potentially exotic hybrid meson π 1 (1600) that has been detected in the COMPASS, VES and E852 experiments. These previous experiments used a charged pion beam as a probe. In contrast, the GlueX experiment is designed to search for the exotic hybrid mesons using a linearly polarized photon beam. This thesis project analyzes the η'π 0 system produced in GlueX by the reaction γp → η'π 0 p. After optimizing the event selection to minimize background contributions, a mass independent partial wave analysis is performed in search of an exotic signature. Within the currently available statistical precision, no signal was found.

Dotel, Rupesh↗