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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Lamination Model for Pressure-Assisted Sintering of Multilayered Porous Structures

This work describes a lamination model for pressure-assisted sintering of thin, multilayered, and porous structures based on the linear viscous constitutive theory of sintering and the classical laminated plate theory of continuum mechanics. A constant out-of-plane normal stress is assumed in the constitutive relation. The lamination relations between the force/moment resultants and the strain/curvature rates are presented. Numerical simulations were performed for a symmetric tri-layer laminate consisting of a 10% gadolinia doped ceria (Ce 0.9 Gd 0.1 O 1.95-δ ) composite structure, where porous layers were adhered to the top and bottom of a denser layer under uniaxially-applied pressures and the sinter forging conditions. The numerical results show that, compared with free sintering, the applied pressure can significantly reduce the sintering time required to achieve given layer thicknesses and porosities. Unlike free sintering, which results in a monotonic decrease of the laminate in-plane dimension, pressure-assisted sintering may produce an in-plane dimension increase or decrease, depending on the applied pressure and sintering time. Finally, the individual layers in the laminate exhibit different stress characteristics under pressure-assisted sintering.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach

Multiphase Computational Fluid Dynamics (MCFD) based on the two-fluid model is considered a promising tool to model complex two-phase flow systems. MCFD simulation can predict local flow features without resolving interfacial information. As a result, the MCFD solver relies on closure relations to describe the interaction between the two phases. Those empirical or semi-mechanistic closure relations constitute a major source of uncertainty for MCFD predictions. In this paper, we leverage a physics-informed uncertainty quantification (UQ) approach to inversely quantify the closure relations’ model form uncertainty in a physically consistent manner. This proposed approach considers the model form uncertainty terms as stochastic fields that are additive to the closure relation outputs. Combining dimensionality reduction and Gaussian processes, the posterior distribution of the stochastic fields can be effectively quantified within the Bayesian framework with the support of experimental measurements. As this UQ approach is fully integrated into the MCFD solving process, the physical constraints of the system can be naturally preserved in the UQ results. Here, in a case study of adiabatic bubbly flow, we demonstrate that this UQ approach can quantify the model form uncertainty of the MCFD interfacial force closure relations, thus effectively improving the simulation results with relatively sparse data support.

42 ENGINEERING↗
DOE OSTI2023

High salt-induced PSI-supercomplex is associated with high CEF and attenuation of state transitions

Abstract While PSI-driven cyclic electron flow (CEF) and assembly of thylakoid supercomplexes have been described in model organisms like Chlamydomonas reinhardtii , open questions remain regarding their contributions to survival under long-term stress. The Antarctic halophyte, C. priscuii UWO241 (UWO241), possesses constitutive high CEF rates and a stable PSI-supercomplex as a consequence of adaptation to permanent low temperatures and high salinity. To understand whether CEF represents a broader acclimation strategy to short- and long-term stress, we compared high salt acclimation between the halotolerant UWO241, the salt-sensitive model, C. reinhardtii , and a moderately halotolerant Antarctic green alga, C. sp. ICE-MDV (ICE-MDV). CEF was activated under high salt and associated with increased non-photochemical quenching in all three Chlamydomonas species. Furthermore, high salt-acclimated cells of either strain formed a PSI-supercomplex, while state transition capacity was attenuated. How the CEF-associated PSI-supercomplex interferes with state transition response is not yet known. We present a model for interaction between PSI-supercomplex formation, state transitions, and the important role of CEF for survival during long-term exposure to high salt.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Model-independent discovery prospects for primordial black holes at LIGO

ABSTRACT Primordial black holes may encode the conditions of the early Universe, and may even constitute a significant fraction of cosmological dark matter. Their existence has yet to be established. However, black holes with masses below ${\sim}{1}{\, \mathrm{M}_\odot }$ cannot form as an endpoint of stellar evolution, so the detection of even one such object would be a smoking gun for new physics, and would constitute evidence that at least a fraction of the dark matter consists of primordial black holes. Gravitational wave detectors are capable of making a definitive discovery of this kind by detecting mergers of light black holes. But since the merger rate depends strongly on the shape of the black hole mass function, it is difficult to determine the potential for discovery or constraint as a function of the overall abundance of black holes. Here, we directly maximize and minimize the merger rate to connect observational results to the actual abundance of observable objects. We show that LIGO can discover mergers of light primordial black holes within the next decade even if such black holes constitute only a very small fraction of dark matter. A single merger event involving such an object would (i) provide conclusive evidence of new physics, (ii) establish the nature of some fraction of dark matter, and (iii) probe cosmological history at scales far beyond those observable today.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Leptonic Model for Neutrino Emission From Active Galactic Nuclei

It is often stated that the observation of high-energy neutrinos from an astrophysical source would constitute a smoking gun for the acceleration of hadronic cosmic rays. Here, we point out that there exists a purely leptonic mechanism to produce TeV-scale neutrinos in astrophysical environments. In particular, very high-energy synchrotron photons can scatter with X-rays, exceeding the threshold for muon-antimuon pair production. When these muons decay, they produce neutrinos without any cosmic-ray protons or nuclei being involved. In order for this mechanism to be efficient, the source in question must produce very high-energy photons which interact in an environment that is dominated by keV-scale radiation. As an example, we consider the active galaxy NGC 1068, which IceCube has recently detected as a source of TeV-scale neutrinos. We find that the neutrino emission observed from this source could potentially be generated through muon pair production for reasonable choices of physical parameters.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Investigating the kinetic effect of prenol on iso -octane auto-ignition by means of an experimental and modeling study

A detailed experimental and kinetic modeling study was dedicated to understand the reported octane hyperboosting effect of prenol, by means of the measurement of the ignition delay times of its blends with iso -octane, and measurement of the mole fraction profiles of the fuels and intermediates inside the ULille rapid compression machine. Here, these results show that prenol addition leads to a reduction of the first-stage ignition phenomena and negative temperature coefficient behavior, which is only qualitatively captured by the model and is consistent with knock resistance improvement. It is suggested that this behavior is caused by two different factors. The first originates from gas-phase reactivity of prenol, and spans from the formation of unreactive unsaturated species through resonance-stabilized radicals, thereby constituting a competitive pathway for the radical pool generated by iso -octane. The second is of catalytic nature and cannot be captured by means of gas-phase kinetic modeling, but could also play an important role in the behavior of prenol in internal combustion engines.

42 ENGINEERING↗
DOE OSTI2020

EBS Task Force: Task 9/FEBEX Modeling Final Report: Thermo-Hydrological Modeling with PFLOTRAN

This report outlines Sandia National Laboratories modeling studies applied to Stage 1 and Stage 2 of the Full-scale Engineered Barriers Experiment in Crystalline Host Rock (FEBEX) in situ test for the SKB EBS Task Force Task 9. The FEBEX test was a full-scale test conducted over ~18 years at the Grimsel, Switzerland Underground Research Laboratory (URL) managed by NAGRA. It involved emplacing simulated waste packages, in the form of welded cylindrical heaters, inside a tunnel in crystalline granitic rock and surrounded by a bentonite barrier and cement plug. Sensors emplaced within the bentonite monitored the wetting-up, heating, and drying out of the bentonite barrier, and the large resulting data set provides an excellent opportunity for validation of multiphysics Thermal-Hydrological (TH), Thermal-Hydrologic-Chemical (THC), and Thermal-Hydrological-Mechanical (THM) modeling approaches for underground nuclear waste storage and the performance of engineered bentonite barriers. The present status of the EBS Task Force is finalizing Task 9, which follows years of modeling studies of the FEBEX test, by many notable modeling teams (Gens et al., 2009; Sanchez et al. 2010; 2012; Samper et al., 2018). These modeling studies generally use two-dimensional axisymmetric meshes, ignoring threedimensional effects, gravity and asymmetric wetting and dry out of the bentonite engineered barrier. This study investigates these effects with use of the PFLOTRAN THC code with massively parallel computational methods in modeling FEBEX Stage 1 and Stage 2 results. The PFLOTRAN numerical code is an open source, state-of-the-art, massively parallel subsurface flow and reactive transport code operating in a high-performance computing environment (Hammond et al., 2014). Section 2 describes the applied partial differential equations describing mass, momentum and energy balance used in this study, considerations derived by assuming phase equilibrium between gas and liquid phases, constitutive equations for granite, cement plug, and bentonite domains, and specific approaches for use inthe PFLOTRAN code. Section 3 describes the geometry, meshing, and model set-up. Section 4 describes modeling results, Section 5 compares modeling results to field testing data, and Section 6 gives conclusions. The Appendix provides detailed information required by the EBSTask Force for final reporting.

42 ENGINEERING↗
DOE OSTI2020

A novel framework for elastoplastic behaviour of anisotropic solids

A general framework for developing nonlinear hyperelastic/plastic constitutive laws for anisotropic solids experiencing large strains and strain rates has been developed. The proposed framework does not rely on the “a priori” known strain energy function, but instead introduces a physical decomposition of the material element into seven physically independent stress bearing mechanisms, each of which has a constitutive law in terms of internal moments described by a scalar function of a single variable. Furthermore, the model has been encoded into a combined finite-discrete element method and tested against static geomechanical test data. The numerical validation experiments show the model can reproduce plastic anisotropic behavior in both biaxial and uniaxial loading of a geomaterial.

58 GEOSCIENCES↗
DOE OSTI2020

Superconductivity in dilute SrTiO 3 : A review

Doped SrTiO 3 , one of the most dilute bulk systems to display superconductivity, is perhaps the first example of an unconventional superconductor, as it does not fit into the standard BCS paradigm. More than five decades of research has revealed a rich temperature-carrier concentration phase diagram that showcases a superconducting dome, proximity to a putative quantum critical point, Lifshitz transitions, a multi-gap pairing state and unusual normal-state transport properties. Research has also extended beyond bulk SrTiO 3 , ushering the new field of SrTiO 3 -based heterostructures. Because many of these themes are also featured in other quantum materials of contemporary interest, recent years have seen renewed interest in SrTiO 3 . Here, we review the challenges and recent progress in elucidating the superconducting state of this model system. At the same time that its extreme dilution requires to revisit several of the approximations that constitute the successful Migdal–Eliashberg description of electron–phonon superconductivity, including the suppression of the Coulomb repulsion via the Tolmachev–Anderson–Morel mechanism, it opens interesting routes for alternative pairing mechanisms whose applicability remains under debate. For instance, pairing mechanisms involving longitudinal optical phonons have to overcome the hurdles created by the anti-adiabatic nature of the pairing interaction, whereas mechanisms that rely on the soft transverse optical phonons associated with incipient ferroelectricity face challenges related to the nature of the electron–phonon coupling. Proposals in which pairing is mediated by plasmons or promoted locally by defects are also discussed. In conclusion, we finish by surveying the existing evidence for multi-band superconductivity and outlining promising directions that can potentially shed new light on the rich problem of superconductivity in SrTiO 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Genome-scale modelling of the primary-specialized metabolism interface

Environmental challenges and development require plants to reallocate resources between primary and specialized metabolites to survive. Genome-scale metabolic models, which map carbon flux through metabolic pathways, are a valuable tool in the study of tradeoffs that arise at this interface. Due to annotation gaps, models that characterize all the enzymatic steps in individual specialized pathways and their linkages to each other and to central carbon metabolism are difficult to construct. Recent studies have successfully curated subsystems of specialized metabolism and characterized the interfaces where flux is diverted to the precursors of glucosinolates, terpenes, and anthocyanins. Although advances in metabolite profiling can help to constrain models at this interface, quantitative analysis remains challenging because of the different timescales on which specialized metabolites from constitutive and reactive pathways accumulate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Constraint on Primordial B-modes from the First Flight of the Spider Balloon-borne Telescope

We present the first linear polarization measurements from the 2015 long-duration balloon flight of Spider, which is an experiment that is designed to map the polarization of the cosmic microwave background (CMB) on degree angular scales. The results from these measurements include maps and angular power spectra from observations of 4.8% of the sky at 95 and 150 GHz, along with the results of internal consistency tests on these data. While the polarized CMB anisotropy from primordial density perturbations is the dominant signal in this region of sky, Galactic dust emission is also detected with high significance. Galactic synchrotron emission is found to be negligible in the Spider bands. We employ two independent foreground-removal techniques to explore the sensitivity of the cosmological result to the assumptions made by each. The primary method uses a dust template derived from Planck data to subtract the Galactic dust signal. A second approach, which constitutes a joint analysis of Spider and Planck data in the harmonic domain, assumes a modified-blackbody model for the spectral energy distribution of the dust with no constraint on its spatial morphology. Using a likelihood that jointly samples the template amplitude and r parameter space, we derive 95% upper limits on the primordial tensor-to-scalar ratio from Feldman–Cousins and Bayesian constructions, finding r < 0.11 and r < 0.19, respectively. Roughly half the uncertainty in r derives from noise associated with the template subtraction. New data at 280 GHz from Spider's second flight will complement the Planck polarization maps, providing powerful measurements of the polarized Galactic dust emission.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluation of Constitutive Relationship for Silicon Carbide Cladding Material

A curved representative volume element (RVE) was employed to analyze SiC/SiC tubular composites. By imposing periodicity on the tubular model through appropriate boundary conditions, the elastic constants of the composite was evaluated. It was found that as the radius of the tubular composite increases, the more the flat RVE model and curved RVE model disagreed.

Thandaga Nagaraju, Hemanth↗
DOE OSTI2023

Exploring strongly correlated quantum spin systems with quantum computers

At inception, quantum annealing leveraged quantum mechanics for classical optimization tasks. However, as machine coherence increases, the realization of quantum spin systems has emerged as an even more fruitful application of this computational model. Alternatives to the standard gate model deserve exploration, to achieve useful quantum advantage via analog quantum computing. The recent demonstration of so-called coherent quantum annealing with 1,000s of qubits, further expands the potential for this hardware to explore quantum system dynamics where the effects of quantum fluctuations can be carefully controlled and observed directly. Implementing existing celebrated spin models—or in fact new and dedicated ones—into quantum annealers will lead to observation and detection of quantum phenomena not yet observed, or not visualized directly, in experimental physics laboratories. Rather than computing or simulating quantum systems, these quantum computers allow one to simply build quantum systems, and experiment on them in an uniquely controlled way, with characterization down to the constitutive degree of freedom. This provides an unprecedented opportunity for understanding the physics of quantum spin systems. Keywords: Theoretical physics currently abounds of interesting theoretical spin models to explore frustration, strongly correlated spins, spin liquids, fractionalized excitations, and topological matter. However enticing, such models are generally only weak proxies for the properties of actual materials. In quantum annealers these models could be realized and experimented upon. Moreover, many more realistic models of such materials could be realized in quantum annealers. Employed in this way, quantum annealers provide an extraordinary versatile platform to explore quantum effects that are hard to find, detect, and characterize in natural materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Causal hydrodynamic fluctuations in non-static and inhomogeneous backgrounds

Highlights: •The fluctuation–dissipation relation is modified by inhomogeneous backgrounds. •The constitutive equations are restricted by causality and fluctuation properties. •The structure of constitutive equation gives a natural cutoff scale of the theory. •The memory function with tensor structures in inhomogeneous backgrounds is obtained. -- Abstract: To integrate hydrodynamic fluctuations, namely thermal fluctuations of hydrodynamics, into dynamical models of high-energy nuclear collisions based on relativistic hydrodynamics, the property of the hydrodynamic fluctuations given by the fluctuation–dissipation relation should be carefully investigated. The fluctuation–dissipation relation for causal dissipative hydrodynamics with the finite relaxation time is naturally given in the integral form of the constitutive equation by the linear-response theory. While, the differential form of the constitutive equation is commonly used in analytic investigations and dynamical calculations for practical reasons. We give the fluctuation–dissipation relation for the general linear-response differential form and discuss the restrictions to the structure of the differential form, which comes from the causality and the positive semi-definiteness of the noise autocorrelation, and also the relation of those restrictions to the cutoff scale of the hydrodynamic fluctuations. We also give the fluctuation–dissipation relation for the integral form in non-static and inhomogeneous background by introducing new tensors, the pathline projectors. We find new modification terms to the fluctuation–dissipation relation for the differential form in non-static and inhomogeneous background which are particularly important in dynamical models to describe rapidly expanding systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗