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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recent Progress in the Electroweak Structure of Light Nuclei Using Quantum Monte Carlo Methods

Nuclei will play a prominent role in searches for physics beyond the Standard Model as the active material in experiments. In order to reliably interpret new physics signals, one needs an accurate model of the underlying nuclear dynamics. In this review, we discuss recent progress made with quantum Monte Carlo approaches for calculating the electroweak structure of light nuclei. We place particular emphasis on recent β decay, muon capture, neutrinoless double β decay, and electron scattering results.

Physics↗
DOE OSTI2024

Magnetism in the two-dimensional dipolar XY model

Here, motivated by a recent experiment on a square-lattice Rydberg atom array realizing a long-range dipolar XY model [C. Chen et al., Nature (London) 616, 691 (2023)], we numerically study the model's equilibrium properties. We obtain the phase diagram, critical properties, entropies, variance of the magnetization, and site-resolved correlation functions. We consider both ferromagnetic and antiferromagnetic interactions and apply quantum Monte Carlo and pseudo-Majorana functional renormalization group techniques, generalizing the latter to a U⁡(1) symmetric setting. Our simulations perform extensive thermometry in dipolar Rydberg atom arrays and establish conditions for adiabaticity and thermodynamic equilibrium. On the ferromagnetic side of the experiment, we determine the entropy per particle S/N≈0.5, close to the one at the critical temperature, S c /N=0.585⁢(15). The simulations suggest the presence of an out-of-equilibrium plateau at large distances in the correlation function, thus motivating future studies on the nonequilibrium dynamics of the system.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Intrinsic Thermal Hall Effect in Mott Insulators

Here, in light of recent experimental data indicating a substantial thermal Hall effect in square lattice antiferromagnetic Mott insulators, we investigate whether a simple Mott insulator can sustain a finite thermal Hall effect. We verify that the answer is “no” if one performs calculations within a spin-only low-energy effective spin model with noninteracting magnons. However, by performing determinant quantum Monte Carlo simulations, we show the single-band 𝑡−𝑡′−𝑈 Hubbard model coupled to an orbital magnetic field does support a finite thermal Hall effect when 𝑡′ ≠0 and 𝐵 ≠0 in the Mott insulating phase. We argue that the (carrier agnostic) necessary conditions for observing a finite thermal Hall effect are time-reversal and particle-hole symmetry breaking. By considering magnon-magnon scattering using a semiclassical Boltzmann analysis, we illustrate a physical mechanism by which finite transverse thermal conductivity may arise, consistent with our symmetry argument and numerical results. Our results indicate that square and triangular lattices with SU(2) symmetry can support a finite thermal Hall effect and call for a critical reexamination of thermal Hall effect data in insulating magnets, as the magnon contribution should not be excluded a priori .

Mott insulators↗
DOE OSTI2025

Investigating the crust of neutron stars with neural-network quantum states

An accurate description of low-density nuclear matter is crucial for explaining the physics of neutron star crusts. In the density range between approximately 0.01 fm −3 and 0.1 fm −3 , matter transitions from neutron-rich nuclei to various higher-density pasta shapes, before ultimately reaching a uniform liquid. In this work, we introduce a variational Monte Carlo method based on a neural Pfaffian-Jastrow quantum state, which allows us to model the transition from the liquid phase to neutron-rich nuclei microscopically. At low densities, nuclear clusters dynamically emerge from the microscopic interactions among protons and neutrons, which we model based on pionless effective field theory. Our variational Monte Carlo approach represents a significant improvement over the state-of-the-art auxiliary-field diffusion Monte Carlo method, which is severely hindered by the fermion-sign problem in this low-density regime and cannot capture the onset of clusters. In addition to computing the energy per particle of symmetric nuclear matter and pure neutron matter, we analyze an intermediate isospin-asymmetry configuration to elucidate the formation of nuclear clusters. We also provide evidence that the presence of such nuclear clusters influences the amount of protons in the crust compared to protons in beta-equilibrated, neutrino-transparent matter.

Nuclear astrophysics↗
DOE OSTI2024

A generalized analytical energy balance model for evaluating agglomeration from a binary collision of wet particles

Agglomeration of wet particles, i.e., particles coated with a thin liquid layer, is a common phenomenon in many processes like fluidized bed combustion of low rank fuels. The availability of an agglomeration model that can evaluate the outcome of a binary collision between wet particles differing in solid particle properties, liquid layer thicknesses, and initial collision (impact) speeds is essential for obtaining a comprehensive understanding on the existing processes experiencing wet particle agglomeration or for a successful development of new processes with high chances of wet particle agglomeration. This study presents a generalized agglomeration model on the basis of energy conservation before and after collision when colliding wet particles may differ in solid particle properties, liquid layer thicknesses, and impact speeds. The model was established based on the approximate values of energy losses that may happen during the collision. It incorporates body forces, solid-solid contacting, liquid capillary, and viscous contributions, as well as the liquid bridge volume effect. Predictions of the new model for collision outcomes of identical wet particles were like those from an analytical energy balance model developed recently by the group for identical wet particles. We also validated the new model by experimental data from literature. The results of a collision direction analysis indicated that the direction often has a minimal effect on the collision outcome in many practical scenarios. The results of Monte Carlo uncertainty analyses with the new model revealed that proper estimations of impact speed, under capillary limiting conditions, and thickness of coating layers and asperity heights, under viscous limiting conditions, are critical for the realistic prediction of collision outcomes at impact speeds close to critical impact speed, i.e., the minimum particle speed required for the particles to rebound.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Diffusion Monte Carlo Study of the Structure and Spectroscopy of H 3 O –

A potential energy surface for H 3 O – has been developed based on the NN+(MOB-ML) approach we developed for studies of complexes of OH – with two or three water molecules. Unlike those systems, H 3 O – has two low-energy isomers, H – ·H 2 O and OH – ·H 2 , which differ in energy by less than 2.5 kcal mol –1 , and which are separated by a barrier of roughly 4.5 kcal mol –1 . We find that by training the NN+(MOB-ML) model using structures based on diffusion Monte Carlo (DMC) simulations initiated in the two potential minima, we are able to obtain a potential surface that describes both isomers. Using these potentials, the structure and spectra of H 3 O – and its deuterated analogues are investigated using DMC. These calculations show that the ground state wave function for H 3 O – is mainly localized in the H – ·H 2 O minimum in the potential, with a small amount of the probability amplitude (<5%) in the region of the OH – ·H 2 minimum. The delocalization of the wave function into the secondary minimum is lowered by deuteration of the water molecule, while replacing H – with D – increases the isomerization due to the shortening of the average distance between the hydride ion and the hydrogen atom in water to which it is bound. Introducing one quantum of excitation in the H – ···H 2 O stretching vibration increases the amount of isomerization, while the isomerization decreases with additional excitation of this mode. Excitation of the free OH stretch in the water molecule also increases the amount of isomerization, while excitation of the out-of-plane bending vibration suppresses the isomerization. Furthermore, the effects of partial deuteration on the frequencies for these vibrations are also explored.

Chemical structure↗
DOE OSTI2023

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

In this work, we present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably. In order to circumvent this problem, we use Gaussian process regression to model the energy and use analytical derivatives to produce the specific heat capacity. From there, we also calculate the entropy by numerical integration. We compare our results to cubic splines and finite differences in a variety of molecules in which Hamiltonians can be diagonalized exactly with full configuration interaction. We finally apply this method to look at larger molecules where exact diagonalization is not possible and make comparisons with more approximate ways to calculate the specific heat capacity and entropy.

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

Joint estimation of biogeochemical model parameters from multiple experiments: A bayesian approach applied to mercury methylation

Here, to characterize complex biogeochemical systems, results from multiple experiments, where each targets a specific subprocess, are commonly combined. The resulting datasets are interpreted through the calibration of biogeochemical models for process inference and predictions. Commonly used calibration approaches of fitting datasets from individual experiments to subprocess models one at a time is prone to missing information shared between datasets and incomplete uncertainty propagation. We propose a Bayesian joint-fitting scheme addressing the above-mentioned concerns by jointly fitting all the available datasets, thus calibrating the entire biogeochemical model in one go using Markov Chain Monte Carlo (MCMC). The identification of null spaces in the parameter distributions from MCMC guided the simplification of certain subprocess models. For example, fast kinetic sorption was replaced by equilibrium sorption, and Monod demethylation was replaced by first-order demethylation. Joint fitting of datasets resulted in complete uncertainty propagation with parameter estimates informed by all available data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. Here, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Effects of non-equilibrium ionization and excitation on radiation absorption in plasma plumes induced by ablation of metal targets with nanosecond laser pulses

Ionization and radiation absorption in nanosecond laser-induced plasma plumes are routinely modeled using the Saha–Boltzmann equilibrium ionization model (EQM). However, the equilibrium assumption can be inaccurate during the laser pulse when non-equilibrium effects significantly impact radiation absorption. In the present work, the EQM and non-equilibrium collisional-radiative model (CRM) are compared to reveal the effect of plasma non-equilibrium on radiation absorption in non-homogeneous plumes and degree of plasma shielding. Simulations of plume expansion induced by irradiation of a copper target in 1 atm argon background gas with a 10 ns Gaussian pulse at a fluence from 8 Jcm −2 to 14 Jcm −2 are performed with a hybrid computational model that couples a lumped particle direct simulation Monte Carlo method with either CRM or EQM. The simulations show that the EQM strongly underestimates the effects of ionization and radiation absorption compared to CRM and, contrary to the CRM, predict strong ionization of the background gas. The differences between the models are explained by the qualitatively different coupling between plume expansion and dynamics of ionization and exitation processes in the CRM and EQM under conditions when the characteristic times for most radiation- and electron-induced processes are longer than the pulse duration. The CRM-based predictions are also found to agree much better with available experimental data. In conclusion, these results indicate that the model of Saha–Boltzmann equilibrium cannot be used for reliable prediction of the degree of plasma shielding in plumes induced by nanosecond laser pulses or for processing results of spectroscopic measurements at early stages of expansion of such plumes.

Physics - Plasma physics↗
DOE OSTI2023

Modeling of a spatially resolved ion temperature diagnostic for inertial confinement fusion

Here, the performance of modern laser-driven inertial confinement fusion (ICF) experiments is degraded by contamination of the deuterium–tritium (DT) fuel with high-Z material during compression. Simulations suggest that this mix can be described by the ion temperature distribution of the implosion, given that such contaminants deviate in temperature from the surrounding DT plasma. However, existing neutron time-of-flight (nTOF) diagnostics only measure the spatially integrated ion temperature. This paper describes the techniques and forward modeling used to develop a novel diagnostic imaging system to measure the spatially resolved ion temperature of an ICF implosion for the first time. The technique combines methods in neutron imaging and nTOF diagnostics to measure the ion temperature along one spatial dimension at yields currently achievable on the OMEGA laser. A detailed forward model of the source and imaging system was developed to guide instrument design. The model leverages neutron imaging reconstruction algorithms, radiation hydrodynamics and Monte Carlo simulations, optical ray tracing, and more. The results of the forward model agree with the data collected on OMEGA using the completed diagnostic. The analysis of the experimental data is still ongoing and will be discussed in a separate publication.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Doping-driven resistive collapse of the Mott insulator in a minimal model for VO 2

Here we study the resistive collapse of the Mott insulator state in the dimer Hubbard model. This minimal model has been used to describe the physics of VO 2 , and should be relevant to other strongly correlated materials. It incorporates the physics of correlated dimers and the explicit competition between on-site Coulomb repulsion and magnetic exchange interactions. Our results unveil that between the Mott insulator at half filling and the Fermi liquid metal at high doping there is an intermediate bad metallic phase with exotic features such as a pseudogap, orbital selectivity, and a first-order metal-metal transition. The model is solved within dynamical mean field theory by means of quantum Monte Carlo, which provides the numerically exact solution of the model in the limit of large lattice dimensionality. This model can be considered as a minimal one that captures exotic phenomena associated to the physics of a doped Mott insulator, shading light on their basic physical mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncertainty Aware Deep Learning for Fault Prediction Using Multivariate Time Series Signals

The superconducting radio-frequency cavities are a crucial component of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. When a cavity faults, beam delivery to experimental end users is disrupted. Prediction of cavity faults prior to onset is essential to reduce operation and maintenance costs. In this work, a parallel long short-term memory (LSTM)-convolution neural network (CNN)-based deep learning (DL) model is proposed to predict impending faults using pre-fault signals. Further, we introduce an uncertainty quantification approach using Monte Carlo dropout with the LSTM-CNN model to ascertain confidence in the prediction. The model was tested using multivariate time series signals from stable cavity operations and before faults. Initial results show that on the test dataset, the model can identify impending faults before their onset with an average 10-fold cross validation accuracy of 97.39% and a standard deviation of 0.12% using a 100-ms time window. It is also observed that the model performs better as the prediction time moves closer to the fault onset. For additional context, we compare the performance of the model with three machine-learning-based (ML) fault prediction models. Our proposed parallel LSTM-CNN-based DL method shows better performance than the ML-based methods.

Rahman, Md Monibor↗
DOE OSTI2022

Abundance matching analysis of the emission-line galaxy sample in the extended Baryon Oscillation Spectroscopic Survey

We present the measurements of the small-scale clustering for the emission-line galaxy (ELG) sample from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) in the Sloan Digital Sky Survey IV (SDSS-IV). We use conditional abundance matching method to interpret the clustering measurements from 0.34 to 70 h -1 Mpc. In order to account for the correlation between properties of ELGs and their environment, we add a secondary connection between star formation rate of ELGs and halo accretion rate. Three parameters are introduced to model the ELG [O II ] luminosity and to mimic the target selection of eBOSS ELGs. The parameters in our models are optimized using Markov Chain Monte Carlo (MCMC) method. We find that by conditionally matching star formation rate of galaxies and the halo accretion rate, we are able to reproduce the eBOSS ELG small-scale clustering within 1σ error level. Our best-fitting model shows that the eBOSS ELG sample only consists of ~ 12 per cent of all star-forming galaxies, and the satellite fraction of eBOSS ELG sample is 19.3 per cent. We show that the effect of assembly bias is ~ 20 per cent on the two-point correlation function and ~ 5 per cent on the void probability function at scale of r ~ 20 h -1 Mpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Reliability-Informed Life-Cycle Warranty Cost Analysis: A Case Study on a Transmission in Agricultural Equipment

In agricultural and industrial equipment, both new and remanufactured systems are often available for warranty coverage. In such cases, it may be challenging for equipment manufacturers to properly trade-off between the system reliability and the cost associated with a replacement option (e.g., replace with a new or remanufactured system). To address this problem, we present a reliability-informed life-cycle warranty cost (LCWC) analysis framework that enables equipment manufacturers to evaluate different warranty policies. These warranty policies differ in whether a new or remanufactured system is used for replacement in the case of product failure. The novelty of this LCWC analysis framework lies in its ability to incorporate real-world field reliability data into warranty policy assessment using probabilistic warranty cost models that consider multiple life cycles. First, the reliability functions for the new and remanufactured systems are built as the time-to-failure distributions that provide the best-fit to the field reliability data. Then, these reliability functions and their corresponding warranty policies are used to build the LCWC models according to the specific warranty terms. Finally, Monte Carlo simulation is used to propagate the time-to-failure uncertainty of each system, modeled by its reliability function, through each LCWC model to produce a probability distribution of the LCWC. The effectiveness of the proposed reliability-informed LCWC analysis framework is demonstrated with a real-world case study on a transmission used in some agricultural equipment.

agricultural equipment↗
DOE OSTI2021

Comparison of Radiation Models for a Turbulent Piloted Methane/Air Jet Flame: A Frozen-Field Study

Numerical modeling of radiative transfer in nongray reacting media is a challenging problem in computational science and engineering. The choice of radiation models is important for accurate and efficient high-fidelity combustion simulations. Different applications usually involve different degrees of complexity, so there is yet no consensus in the community. In this paper, the performance of different radiative transfer equation (RTE) solvers and spectral models for a turbulent piloted methane/air jet flame are studied. The flame is scaled from the Sandia Flame D with a Reynolds number of 22,400. Three classes of RTE solvers, namely the discrete ordinates method, spherical harmonics method, and Monte Carlo method, are examined. The spectral models include the Planck-mean model, the full-spectrum k-distribution (FSK) method, and the line-by-line (LBL) calculation. The performances of different radiation models in terms of accuracy and computational cost are benchmarked. The results have shown that both RTE solvers and spectral models are critical in the prediction of radiative heat source terms for this jet flame. The trade-offs between the accuracy, the computational cost, and the implementation difficulty are discussed in detail. The results can be used as a reference for radiation model selection in combustor simulations.

David, Chloe↗
DOE OSTI2024

Fluctuating charge-density-wave correlations in the three-band Hubbard model

The high-temperature superconducting cuprates host unidirectional spin- and charge-density-wave orders that can intertwine with superconductivity in nontrivial ways. While the charge components of these stripes have now been observed in nearly all cuprate families, their detailed evolution with doping varies across different materials and at high and low temperatures. We address this problem using nonperturbative determinant quantum Monte Carlo calculations for the three-band Hubbard model. Using an efficient implementation, we can resolve the model’s fluctuating spin and charge modulations and map their evolution as a function of the charge transfer energy and doping. We find that the incommensurability of the charge modulations is decoupled from the spin modulations and decreases with hole doping, consistent with experimental measurements at high temperatures. These findings support the proposal that the high-temperature charge correlations are distinct from the intertwined stripe order observed at low-temperature and in the single-band Hubbard model.

42 ENGINEERING↗