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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Atomic understanding of structural deformations upon ablation of graphene

We investigate the atomic rearrangement in graphene under femtosecond pulse illumination with reactive molecular dynamics simulations and compare with ultra-fast laser ablation experiments. To model the impact of the laser pulse irradiation, heat is locally applied to a selected area of the graphene layer and the resulting structural deformation is simulated as a function of time, providing a detailed understanding of the bond breaking process under laser illumination and subsequent re-equilibration afterAQ3 the pulse is turned off. Analysis of the atomic dynamics indicates that the types of defects formed depend on the pulse energy and exposure duration. By varying the exposed area, we determine that the shape of the ablated area is not only a function of the pulse energy, but also of the beam spot size and pulse repetition. Furthermore, we apply a machine learning approach to extrapolate our simulated data to experimental length scales and reproduce the trends in ablated area as a function of temperature. Furthermore, our study provides a first step towards understanding the design parameters for graphene nano-patterning.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Phase Contrast X‐Ray Imaging of the Collapse of an Engineered Void in Single‐Crystal HMX

Abstract Imaging the collapse of a single void that creates a hot spot initiation site in an otherwise defect‐free explosive is challenging given the spatial and temporal scales involved in explosive systems. This work presents our attempt to examine a single hot spot mode (void collapse) in single‐crystal octahydro‐l,3,5,7‐tetranitro‐l,3,5,7‐tetrazocine (HMX) embedded in Sylgard. The hot spot heating mechanisms involved with pore collapse include adiabatic heating, jetting, and viscoplastic dissipation. Quantifying the dynamics of a pore collapse is a crucial step to understanding which mechanisms dominate during ignition events. Our experiments were conducted with a single‐stage, light‐gas gun at Argonne National Laboratory's Advanced Photon Source, applying the phase contrast imaging technique while collecting high‐speed video. The details of HMX single crystal production, defect (pore) engineering, and sample construction, along with experimental results are presented here. These results demonstrate that detailed collapse dynamics can be obtained from homogeneous, single‐crystal explosives with this approach. Qualitative comparisons are made with simulation data which show good agreement in the transition between a quasi‐symmetric pore collapse and an asymmetric collapse with jetting across the pore as measured with normalized pore area and pore circularity.

42 ENGINEERING↗
DOE OSTI2022

A two-fluid single-column model of turbulent shallow convection, Part III: Results and parameter sensitivity

Two-fluid modelling has recently emerged as a promising approach to representing cumulus convection in weather and climate models. This study applies the two-fluid model described in Part II Thuburn et al., 2021b to a shallow cumulus convection case study over land (ARM). Large eddy simulation data is used to tune the majority of the closures that determine the properties of entrained and detrained air. The two-fluid model is generally able to reproduce the profiles of the mean and turbulent quantities over all stages of the diurnal cycle. As such, the initiation of shallow convection and the evolution of the cloud layer are well captured. The robustness of the two-fluid model is further verified using a steady-state test case (BOMEX), in which the cloud properties are also well modelled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Sensitivity of the NEXT experiment to Xe-124 double electron capture

Double electron capture by proton-rich nuclei is a second-order nuclear process analogous to double beta decay. Despite their similarities, the decay signature is quite different, potentially providing a new channel to measure the hypothesized neutrinoless mode of these decays. The Standard-Model-allowed two-neutrino double electron capture (2$\nu$EC EC) has been predicted for a number of isotopes, but only observed in 78 Kr, 130 Ba and, recently, 124 Xe. The sensitivity to this decay establishes a benchmark for the ultimate experimental goal, namely the potential to discover also the lepton-number-violating neutrinoless version of this process, 0$\nu$EC EC. Here we report on the current sensitivity of the NEXT-White detector to 124 Xe 2$\nu$EC EC and on the extrapolation to NEXT-100. Using simulated data for the 2$\nu$EC EC signal and real data from NEXT-White operated with 124 Xe-depleted gas as background, we define an optimal event selection that maximizes the NEXT-White sensitivity. We estimate that, for NEXT-100 operated with xenon gas isotopically enriched with 1 kg of 124 Xe and for a 5-year run, a sensitivity to the 2$\nu$EC EC half-life of 6 × 10 22 y (at 90% confidence level) or better can be reached.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

The phase space distance between collider events

How can one fully harness the power of physics encoded in relativistic N-body phase space? Topologically, phase space is isomorphic to the product space of a simplex and a hypersphere and can be equipped with explicit coordinates and a Riemannian metric. This natural structure that scaffolds the space on which all collider physics events live opens up new directions for machine learning applications and implementation. Here we present a detailed construction of the phase space manifold and its differential line element, identifying particle ordering prescriptions that ensure that the metric satisfies necessary properties. We apply the phase space metric to several binary classification tasks, including discrimination of high-multiplicity resonance decays or boosted hadronic decays of electroweak bosons from QCD processes, and demonstrate powerful performance on simulated data. Our work demonstrates the many benefits of promoting phase space from merely a background on which calculations take place to being geometrically entwined with a theory’s dynamics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Princeton Field-Reversed Configuration for Compact Nuclear Fusion Power Plants

The Princeton Field-Reversed Configuration (PFRC) nuclear fusion reactor concept is an innovative approach to fusion power generation prioritizing low neutron production and small size. Here, a combination of analytical modeling and numerical simulation shows that the novel heating approach generates an FRC with closed field lines. Simulation data from a single-particle Hamiltonian code predicts ms-scale plasma heating in reactor-scale conditions while PIC codes predict formation of warm FRC plasmas from initial mirror fields. The PFRC-1 and PFRC-2 experiments have heated electrons to energies well in excess of 100 eV and plasma durations to 300 ms, more than 10 4 times longer than the predicted tilt instability growth time. From these data, we have created a development plan and anticipated performance metrics for a fusion reactor based on the PFRC concept. The resulting 1–10 MW PFRC reactors would be suitable for diverse applications, from submarines to urban environments to space propulsion. PFRC is a steady-state, driven magnetic confinement device. Plasma, inside a cylindrical array of coils, is confined and heated by external RF antennae. PFRC would be ultra-low radiation due to both its fuel and small size. The choice of advanced fuels, deuterium and helium-3 (D– 3 He), may be enabled by the high-β FRC configuration. The small size of the reactor would enable rapid exhaust of the dangerous tritium ash. Low radiation would make the reactor safer to operate and, in combination with simple geometry and small size, dramatically lowers development and maintenance costs. This review paper gives an introduction to the physics of the PFRC and a summary of the PFRC-2 experiment results to date. It then discusses the future program plan and how PFRC reactors would be commercialized.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

The Impact of Wettability on the Co-moving Velocity of Two-Fluid Flow in Porous Media

Abstract The impact of wettability on the co-moving velocity of two-fluid flow in porous media is analyzed herein. The co-moving velocity, developed by Roy et al. (Front Phys 8:4, 2022), is a novel representation of the flow behavior of two fluids through porous media. Our study aims to better understand the behavior of the co-moving velocity by analyzing simulation data under various wetting conditions. We analyzed 46 relative permeability curves based on the Lattice–Boltzmann color fluid model and two experimentally determined relative permeability curves. The analysis of the relative permeability data followed the methodology proposed by Roy et al. (Front Phys 8:4, 2022) to reconstruct a constitutive equation for the co-moving velocity. Surprisingly, the coefficients of the constitutive equation were found to be nearly the same for all wetting conditions. On the basis of these results, a simple approach was proposed to reconstruct the relative permeability of the oil phase using only the co-moving velocity relationship and the relative permeability of the water phase. This proposed method provides new information on the interdependence of the relative permeability curves, which has implications for the history matching of production data and the solution of the associated inverse problem. The research findings contribute to a better understanding of the impact of wettability on fluid flow in porous media and provide a practical approach for estimating relative permeability based on the co-moving velocity relationship, which has never been shown before.

Engineering↗
DOE OSTI2021

Shortened Duration of Global Warming Slowdowns with Elevated Greenhouse Gas Emissions

Continuous emissions of anthropogenic greenhouse gases (GHGs) and aerosols in the last 160 years have resulted in an increasing trend of global mean surface temperatures (GMSTs). Due to interactions with natural variability, rates of the combined anthropogenically and naturally induced warming trends are characterized by significant slowdowns and speedups on decadal timescales. In this work, by analyzing observed and model-simulated data, we investigate how the duration of these episodes will change with different strengths of GHG and aerosol forcing. We found that the duration of warming slowdowns can be more than 30 yr with a slower rate of anthropogenic emissions but would shorten to about 5 yr with a higher one. This duration reduction depends on both the magnitude of the climate response to anthropogenic forcing and the strength of the internal variability. Moreover, the warming slowdowns can still occur even towards the end of this century under high emissions scenarios but with significantly shortened duration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Effect of Grain Boundary Facet Junctions on Segregation and Embrittlement

Junctions are discontinuities in flat grain boundaries that arise in all polycrystalline materials and are thought to play important roles in the response of a grain boundary network to thermal and mechanical loads. A key open question concerns the mechanisms by which solute segregation to junctions impacts properties of the grain boundary. Here, in this work, we investigate the influence of grain boundary facet junctions on solute embrittlement, and we present an analytical model that uses the hydrostatic stress field contributed by dislocations at multiple junctions to describe these effects. Specifically, we study junctions between {112} facets of various lengths in Au $\langle111\rangle$ Σ3 tilt grain boundaries. Copper and silver solutes are employed to determine if the effect of junctions on solute segregation and embrittlement is dependent on size relative to the host. Combined, atomistic simulation data and the analytical model show that Cu and Ag have opposite segregation responses to junctions due to the sign of the hydrostatic stress field induced by junctions. However, a positive shift in the embrittling potency is computed near junctions regardless of solute type or the stress state of the segregation site. Hence, for the conditions studied, junctions consistently shift the energetic landscape towards embrittlement.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Infrared triggered dwell and active cooling thermal control effects on microstructural uniformity in DED

Directed Energy Deposition (DED) offers rapid large scale fabrication, but difficulty in delivering consistent microstructures and properties hinders the use of DED fabricated components in safety or performance critical applications. Variability stems from the complex thermal cycles generated by the toolpath used to print the required geometry. Several practical methods have become established in DED to regulate overheating, such as active cooling of the baseplate structure or the use of an infrared camera to inject interlayer pauses to ensure the top layer of the component cools to a set temperature, which have been shown to affect microstructure. However, no critical assessment has been performed as to how effective these controls are in promoting microstructural uniformity in the context of complex layer timing commonly generated by non-prismatic geometries. Here we show how controls influence the thermal field, phase transformations, and dynamic annealing of a low-temperature transformation steel using infrared imaging and operando neutron diffraction. Counterintuitively, common thermal homogenization process controls can reduce microstructural uniformity because these approaches stabilize peak temperature while overlooking temperatures near the solid-state phase transformation fronts. Instead, the cyclic reheating induces spatially-variant dynamically annealed regions which can be modulated via control parameters. We show that these controls have spatially linked effects centimeters away from the active weld, which implies that microstructure control must co-optimize thermal input across many subsequent layers. In conclusion, our results demonstrate the pressing need for higher order controls that integrate predictive elements of simulation data to stabilize printed properties for future qualification of DED components.

Directed energy deposition↗
DOE OSTI2018

Validation of Statistical Uncertainties in Subcritical Benchmark Measurements: Part I - Theory and Simulations

Subcritical neutron noise measurements are often used to determine multiplication of a system with Special Nuclear Material. This work presents an uncertainty approach which incorporates the singles and doubles counting rates determined by the Hage-Cifarelli formalism. This is a moments-based approach which utilizes time correlations between prompt neutrons in order to assess the system multiplication. After the method is described, a validation is presented which utilizes simulated data generated using a 0-D point-kinetics Monte Carlo code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Adaptive cold-load pickup considerations in 2-stage microgrid unit commitment for enhancing microgrid resilience

In an extended main grid outage spanning multiple days, load shedding serves as a critical mechanism for islanded microgrids to maintain essential power and energy reserves that are indispensable for fulfilling reliability and resiliency mandates. However, using load shedding for such purposes leads to increasing occurrence of cold load pickup (CLPU) events. Here, this study presents an innovative adaptive CLPU model that introduces a method for determining and incorporating parameters related to CLPU power and energy requirements into a two-stage microgrid unit commitment (MGUC) algorithm. In contrast to the traditional fixed-CLPU-curve approach, this model calculates CLPU duration, power, and energy demands by considering outage durations and ambient temperature variations within the MGUC process. By integrating the adaptive CLPU model into the MGUC problem formulation, it allows for the optimal allocation of energy resources throughout the entire scheduling horizon to fulfill the CLPU requirements when scheduling multiple CLPU events. The performance of the enhanced MGUC algorithm considering CLPU needs is assessed using actual load and photovoltaic (PV) data. Simulation results demonstrate significant improvements in dispatch optimality evaluated by the amount of load served, customer comfort, energy storage operation, and adherence to energy schedules. These enhancements collectively contribute to reliable and resilient microgrid operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Interactive multiscale modeling to bridge atomic properties and electrochemical performance in Li-CO 2 battery design

Li-CO 2 batteries are promising energy storage systems due to their high theoretical energy density and CO 2 fixation capability, relying on reversible Li 2 CO 3 /C formation during discharge/charge cycles. Here, we present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO 2 battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb 0.67 Bi 1.33 Te 3 catalyst. DFT and AIMD determined the electrical conductivities of Sb 0.67 Bi 1.33 Te 3 and Li 2 CO 3 using the Kubo–Greenwood formalism and studied the CO 2 reduction mechanism on the cathode catalyst. MD simulations calculated the CO 2 diffusion coefficient, Li + transference number, ionic conductivity, and Li + solvation structure. The FEA model, parameterized with atomistic simulation data, reproduced the available experimental voltage–capacity profile at 1 mA/cm 2 and revealed spatio-temporal variations in Li 2 CO 3 /C deposition, porosity, and CO 2 concentration dependence on discharge rates in the cathode. Accordingly, Li 2 CO 3 can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm 2 and 1 mA/cm 2 , respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm 2 to 6200 mAh/g at 1 mA/cm 2 , due to pore clogging from excessive discharge product deposition that limits CO 2 transport to the cathode interior. Therefore, the performance of Li-CO 2 batteries can be improved by enhancing CO 2 transport, regulating Li 2 CO 3 deposition, and optimizing cathode architecture.

Battery performance↗
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Modelling dislocation-graphene interactions in a BCC Fe matrix by molecular dynamics simulations and gradient plasticity theory

We report graphene nanosheets (GNS) can enhance the strength and ductility of metal-based composites as they can obstruct the propagation of dislocations. The present article employs Molecular Dynamics (MD) simulations to investigate dislocation-GNS interaction mechanisms and possible influencing factors, including the number of GNS layers, the thickness of the metallic amorphous layer and the C - C bond strength. The results indicated that the shear strength of the metal/GNS interface and the bending stiffness of GNS determined the ability of GNS to block dislocation transmission. A physically based phenomenological parameter that can capture such dislocation-GNS interactions is the mechanical interface energy that has been put forth within gradient plasticity. By fitting the theoretical expressions to the simulation data, it was possible to obtain estimates for the mechanical interface energy for the GNS. It was found that increasing the GNS layers and adding an amorphous layer resulted in a strengthening in the stress–strain response and increased the value of this interfacial parameter. This indicates that the mechanical interfacial energy can be a unified measure for capturing and tuning the strength of various interfaces such as grain boundaries, GNS, amorphous-crystalline interface and bimetal interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2019

DeepCMB: Lensing reconstruction of the cosmic microwave background with deep neural networks

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Exploring the potential and impact of single-crystal active materials on dry-processed electrodes for high-performance lithium-ion batteries

Roll-to-roll powder-to-film dry processing (DP) and single-crystal (SC) active materials (AMs) with many advantages are two hot topics of lithium-ion batteries (LIBs). However, DP of SC AMs for LIBs is rarely reported. Consequently, the impact of SC AMs on dry-processed LIBs is not well understood. Herein, for the first time, via a set of experimental and theoretical studies of the conventional polycrystalline-AM- and SC-AM-based DPed electrodes (DPEs), this work not only reports a high-performance dry SC-AM cathode for LIB manufacturing, but also establishes some fundamental understanding of SC-based dry-processed electrodes, including their morphology, structure, mechanical strength, electronic conductivity and LIB electrochemical behavior. Further, the results suggest that DP of SC AMs is promising, which can dramatically improve the electrochemical kinetics at electrode level and particle level. Specifically, for the rate capability and long-term cyclability in full cells, SC DPEs exhibit a discharge specific capacity of 152.1 mAh g -1 at 1C and a capacity retention rate of 79.9 % at C/3 over 500 cycles, which are superior to those of PC DPEs (135.6 mAh g -1 and 68.3 %) at the same conditions and are further confirmed by the simulation data from the theoretical modelling study. Therefore, this comprehensive work marks a significant milestone for DP strategy and SC AMs, enlightening future research and development of LIB manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2019

An artificial neural network framework for reduced order modeling of transient flows

Here, this paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN framework allows for a nonlinear time evolution of the modal coefficients without performing a Galerkin projection. Our POD-ANN framework can thus be considered an equation-free approach for latent space dynamics evolution of nonlinear transient systems and can be applied to a wide range of physical and engineering applications. Within this framework we introduce two architectures, namely sequential network (SN) and residual network (RN), to train the trajectory of modal coefficients. We perform a systematic analysis of the performance of the proposed reduced order modeling approaches on prediction of a nonlinear wave-propagation problem governed by the viscous Burgers equation, a simplified prototype setting for transient flows. We find that the POD-ANN-RN yields stable and accurate results for test problems assessed both within inside and outside of the database range and performs significantly better than the standard intrusive Galerkin projection model. Finally, our results show that the proposed framework provides a non-intrusive alternative to the evolution of transient physics in a POD basis spanned space, and can be used as a robust predictive model order reduction tool for nonlinear dynamical systems.

42 ENGINEERING↗