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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tuning nonequilibrium phase transitions with inertia

In striking contrast to equilibrium systems, inertia can profoundly alter the structure of active systems. Here, we demonstrate that driven systems can exhibit effective equilibrium-like states with increasing particle inertia, despite rigorously violating the fluctuation–dissipation theorem. Increasing inertia progressively eliminates motility-induced phase separation and restores equilibrium crystallization for active Brownian spheres. This effect appears to be general for a wide class of active systems, including those driven by deterministic time-dependent external fields, whose nonequilibrium patterns ultimately disappear with increasing inertia. The path to this effective equilibrium limit can be complex, with finite inertia sometimes acting to accentuate nonequilibrium transitions. The restoration of near equilibrium statistics can be understood through the conversion of active momentum sources to passive-like stresses. Unlike truly equilibrium systems, the effective temperature is now density dependent, the only remnant of the nonequilibrium dynamics. Further, this density-dependent temperature can in principle introduce departures from equilibrium expectations, particularly in response to strong gradients. Our results provide additional insight into the effective temperature ansatz while revealing a mechanism to tune nonequilibrium phase transitions.

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

Statistics of base polytopes in F-theory

We propose a new statistical ensemble of toric bases for elliptic Calabi-Yaus used in F-theory models, by focusing on only the convex hull of the base, i.e., the base polytope. This physically motivated coarse-graining greatly simplifies the combinatorial complexity of the part of the 4d F-theory landscape with toric bases. We develop a Monte Carlo approach that randomly samples the base polytopes within fixed boxes, with proper statistical weights. We first apply the algorithm to the set of 2d base polytopes, generating an enlarged set of toric 2d bases that include certain types of codimension-two (4,6) points, and we validate our approach against exact numbers. We then explore the set of 3d base polytopes which fit in a set of “maximal” 3d boxes, and estimate the total number of inequivalent 3d base polytopes to be 10 85 –10 90 . We provide statistical data such as the distribution of non-Higgsable gauge groups on these bases. Amusingly, a similar method can also be applied to generate reflexive polytopes in various dimensions. In both the reflexive and base polytope cases, the number of relevant polytopes obeys a Gaussian distribution as a function of the number of vertices, which can be understood in terms of other results on random polytopes in the math literature.

Differential and algebraic geometry↗
DOE OSTI2024

Discovering neutrino tridents at the Large Hadron Collider

Neutrino trident production of dilepton pairs is well recognized as a sensitive probe of both electroweak physics and physics beyond the Standard Model. Although a rare process, it could be significantly boosted by such new physics, and it also allows the electroweak theory to be tested in a new regime. We demonstrate that the forward neutrino physics program at the Large Hadron Collider offers a promising opportunity to measure for the first time, dimuon neutrino tridents with a statistical significance exceeding 5 σ , improving on the previous claims at the ∼ 3 σ level by the CHARM-II and CCFR collaborations while accounting for additional backgrounds later identified by the NuTeV collaboration. We present predictions for various proposed experiments and outline a specific experimental strategy to identify the signal and mitigate backgrounds, based on “reverse tracking” dimuon pairs in the FASER ν 2 detector. We also discuss prospects for constraining beyond Standard Model contributions to neutrino trident rates at high energies. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗
DOE OSTI2021

Magnetic imaging and statistical analysis of the metamagnetic phase transition of FeRh with electron spins in diamond

Magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has emerged as a powerful tool for probing magnetic phenomena in fields ranging from biology to physics. A key strength of NV sensing is its local-probe nature, enabling high-resolution spatial images of magnetic stray fields emanating from a sample. However, this local character can also form a drawback for analyzing the global properties of a system, such as a phase transition temperature. Here, we address this challenge by using statistical analyses of magnetic-field maps to characterize the first-order temperature-driven metamagnetic phase transition from the antiferromagnetic to the ferromagnetic state in FeRh. After imaging the phase transition and identifying the regimes of nucleation, growth, and coalescence of ferromagnetic domains, we statistically characterize the spatial magnetic-field maps to extract the transition temperature and thermal hysteresis width. By analyzing the spatial correlations of the maps in relation to the magnetocrystalline anisotropy and external magnetic field, we detect a reorientation of domain walls across the phase transition. The employed statistical approach can be extended to the study of other magnetic phenomena with NV magnetometry or other sensing techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Input to the European strategy for particle physics: strong-field quantum electrodynamics

This document sets out the intention of the strong-field QED community to carry out, both experimentally and numerically, high-statistics parametric studies of quantum electrodynamics in the non-perturbative regime, at fields approaching and exceeding the critical or ‘Schwinger’ field of QED (F qed ≈ m 2 c 3 / eh ≈ 1.3 x 10 18 V/m) in the rest frame of a charged particle. In this regime, several exotic and fascinating phenomena are predicted to occur that have never been directly observed in the laboratory. These include Breit–Wheeler pair production, vacuum birefringence, and quantum radiation reaction. This experimental programme will also serve as a stepping stone towards studies of elusive phenomena such as elastic scattering of real photons and the conjectured perturbative breakdown of QED at extreme fields. State-of-the-art high-power laser facilities in Europe and beyond are starting to offer unique opportunities to study this uncharted regime at the intensity frontier, which is highly relevant also for the design of future multi-TeV lepton colliders. A transition from qualitative observational experiments to quantitative and high-statistics measurements can only be performed with large-scale collaborations and with systematic experimental programmes devoted to the optimisation of several aspects of these complex experiments, including detector developments, stability and tolerances studies, and laser technology.

Sarri, G. [Queen’s University Belfast (United King↗
DOE OSTI2022

FlameNEST: explicit profile likelihoods with the Noble Element Simulation Technique

We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaboration analyses which will be fundamental for the future of experimental dark matter physics.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Using Dense Array Waveform Correlations to Build a Velocity Model with Stochastic Variability

In an ongoing effort to improve 3D seismic-wave propagation modeling for frequencies up to 10 Hz, we used cross correlations between vertical-component waveforms from an underground chemical explosion to estimate the statistical properties of small-scale velocity heterogeneities. The waveforms were recorded by a dense 2D seismic array deployed during the Source Physics Experiments for event number 5 (SPE-5) in a series of six underground chemical explosions, conducted at the Nevada National Security Site. The array consisted of 996 geophones with a 50–100 m grid spacing, deployed at the SPE site at the north end of the Yucca Flat basin. The SPE were conducted to investigate the generation and propagation of seismic and acoustic waves from underground explosions. Comparisons of decay rates of waveform cross correlations as function of interstation distance, computed for observed and synthetic seismograms from the SPE-5 chemical explosion, were used to constrain statistical properties of correlated stochastic velocity perturbations representing small-scale heterogeneities added to a geology-based velocity model of the Yucca Flat basin. Using comparisons between recorded and simulated waveform cross correlations, we were able to recover sets of statistical properties of small-scale velocity perturbations in the velocity model that produce the best-fit between the recorded and simulated ground motion. The stochastic velocity fluctuations in the velocity model that produced the smallest misfits have a horizontal correlation distance of between 400 and 800 m, a vertical correlation distance between 100 and 200 m, and a standard deviation of 10% from the nominal model velocity in the alluvium basin layers. They also have a horizontal correlation distance of 1000 m, a vertical correlation distance of 250 m, and a standard deviation of 6% in the underlying and consolidated sedimentary layers, up to a depth of 4 km. Comparisons between observed and simulated wavefields were used to assess the proposed small-scale heterogeneity enhancements to the Yucca Flat basin model. We found that adding a depth-resolved stochastic variability to the geology-based velocity model improves the overall performance of ground-motion simulations of an SPE-5 explosion in the modeled frequency range up to 10 Hz. The results may be applicable to other similar basins.

58 GEOSCIENCES↗
DOE OSTI2022

Dynamic Data-Driven Multiscale Modeling for Predicting Microstructure and Mechanical Properties of 316L Stainless Steel

We have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M 23 C 6 , γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Earth mover’s distance as a measure of CP violation

We introduce a new unbinned two sample test statistic sensitive to CP violation utilizing the optimal transport plan associated with the Wasserstein (earth mover’s) distance. The efficacy of the test statistic is shown via two examples of CP asymmetric distributions with varying sample sizes: the Dalitz distributions of B 0 → K + π – π 0 and of D 0 → π + π – π 0 decays. The windowed version of the Wasserstein distance test statistic is shown to have comparable sensitivity to CP violation as the commonly used energy test statistic, but also retains information about the localized distributions of CP asymmetry over the Dalitz plot. For large statistic datasets we introduce two modified Wasserstein distance based test statistics — the binned and the sliced Wasserstein distance statistics, which show comparable sensitivity to CP violation, but improved computing time and memory scalings. Finally, general extensions and applications of the introduced statistics are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Novel approaches in hadron spectroscopy

The last two decades have witnessed the discovery of a myriad of new and unexpected hadrons. The future holds more surprises for us, thanks to new-generation experiments. Understanding the signals and determining the properties of the states requires a parallel theoretical effort. To make full use of available and forthcoming data, a careful amplitude modeling is required, together with a sound treatment of the statistical uncertainties, and a systematic survey of the model dependencies. We review the contributions made by the Joint Physics Analysis Center to the field of hadron spectroscopy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Machine learning approaches for structural and thermodynamic properties of a Lennard-Jones fluid

Predicting the functional properties of many molecular systems relies on understanding how atomistic interactions give rise to macroscale observables. However, current attempts to develop predictive models for the structural and thermodynamic properties of condensed-phase systems often rely on extensive parameter fitting to empirically selected functional forms whose effectiveness is limited to a narrow range of physical conditions. Here, we illustrate how these traditional fitting paradigms can be superseded using machine learning. Specifically, we use the results of molecular dynamics simulations to train machine learning protocols that are able to produce the radial distribution function, pressure, and internal energy of a Lennard-Jones fluid with increased accuracy in comparison to previous theoretical methods. The radial distribution function is determined using a variant of the segmented linear regression with the multivariate function decomposition approach developed by Craven et al. [J. Phys. Chem. Lett. 11, 4372 (2020)]. The pressure and internal energy are determined using expressions containing the learned radial distribution function and also a kernel ridge regression process that is trained directly on thermodynamic properties measured in simulation. The presented results suggest that the structural and thermodynamic properties of fluids may be determined more accurately through machine learning than through human-guided functional forms.

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

Reversible to irreversible transitions for cyclically driven particles on periodic obstacle arrays

Here, we examine the collective dynamics of disks moving through a square array of obstacles under cyclic square wave driving. Below a critical density, we find that the system organizes into a reversible state in which the disks return to the same positions at the end of every drive cycle. Above this density, the dynamics are irreversible and the disks do not return to the same positions after each cycle. The critical density depends strongly on the angle θ between the driving direction and a symmetry axis of the obstacle array, with the highest critical densities appearing at commensurate angles such as θ = 0° and θ = 45° and the lowest critical densities falling at θ = arctan (0.618), the inverse of the golden ratio, where the flow is the most degenerate. As the density increases, the number of cycles required to reach a reversible state grows as a power law with an exponent near ν = 1.36, similar to what is found in periodically driven colloidal and superconducting vortex systems.

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

Calculating adsorption isotherms using the two-phase thermodynamic method and molecular dynamics simulations

We describe the calculation of adsorption isotherms from molecular dynamics simulations based on the two-phase thermodynamic (2PT) model. The 2PT model developed for bulk fluid phases treats the gas-like components as hard spheres (HSs), which correctly recovers the limiting behaviors of unconfined fluids. We showed that this treatment, however, does not always lead to the correct zero-loading behavior in strongly confining systems. For methane adsorption into zeolite MFI, the HS reference state underestimates entropy by up to 20% at low loadings and leads to an order-of-magnitude increase in the adsorption onset pressure. To fix these issues, we propose the use of ideal adsorbed gas (IAG) as the gas reference model, the properties of which can be computed using the Widom insertion method on an empty adsorbent. We further describe three routes to compute adsorption isotherms from the Helmholtz free energy at different loadings. Comparing against established Monte Carlo (MC) methods, we found that the adsorption isotherms obtained using the IAG reference state agrees to within 40%, which corresponds to deviations of <5% in adsorption free energy. The isotherms calculated using the HS reference state underestimate the adsorption uptake at low to medium loadings in strongly confining systems, but its accuracy improves at higher loadings and as the pore size increases relative to the sorbate diameter. The methods described here provide an alternative approach for computing adsorption isotherms when MC simulations in an open ensemble are undesirable and enable a direct comparison of computed adsorption thermodynamics with experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Supervised machine learning-based multivariate regression of parallel closures for a high-collisionality deuterium-carbon plasma

Many plasmas of interest in laboratory experiments and space consist of multiple ion species. In tokamak edge plasmas, for instance, ionized impurities expelled from the vessel wall influence plasma transport. When describing multi-species plasmas using fluid equations, we need accurate closure relations to close the set of fluid equations. In this study, we introduce the development of fitting formulas for parallel closures using supervised machine learning, in conjunction with the recent closure theory, considering multi-ion collisions and arbitrary ion temperatures. We apply this approach to a high-collisionality deuterium-carbon plasma and demonstrate its effectiveness. As a result, the machine learning-based method for developing practical and accurate closures can be extended to a wider range of plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗