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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Hybrid Chemo-Mechanical Plastics Recycling: Solvent-free, High-speed Reactive Extrusion of Low-Density Polyethylene

Low-Density Polyethylene (LDPE) is ubiquitous in the packaging industry due to its flexibility, toughness, and low cost. However, it is typically contaminated with other materials, seriously limiting options for mechanical recycling. Interest in chemical recycling techniques such as pyrolysis and hydrothermal liquefaction is growing, but most of these processes face technoeconomic challenges that have limited commercial deployment. We present a hybrid chemo-mechanical approach using reactive twin-screw extrusion (TSE) for tailoring the molecular weight and chain structure of reclaimed LDPE. Two types of zeolite catalysts at several loading levels were evaluated over a range of processing conditions. Structural, thermal, and rheological properties of the extruded samples were investigated and compared to virgin LDPE and LDPE extruded without the catalyst. A nuclear magnetic resonance spectroscopic technique was used to investigate changes in the structure of the polymer. LDPE extruded with microporous y-zeolite showed lower degradation temperature and increased short chain branching. Mesoporous MCM-41 also induced increased branching but had no effect on degradation temperature. The theoretical mechanical energy input for the chemical modification was calculated using process modeling. The demonstrated hybrid reactive extrusion process provides a potential low-cost, simple approach for repurposing LDPE-based flexible packaging as coatings and adhesives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exciton fine structure in twisted transition metal dichalcogenide heterostructures

Abstract Moiré superlattices of transition metal dichalcogenide (TMD) heterostructures give rise to rich excitonic phenomena associated with the interlayer twist angle. Theoretical calculations of excitons in such systems are typically based on model moiré potentials that mitigate the computational cost. However, predictive understanding of the electron-hole coupling dominating the excitations is crucial to realize the twist-induced modifications of the optical selection rules. In this work, we use many-body perturbation theory to evaluate the relation between twist angle and exciton properties in TMD heterostructures. We present an approach for unfolding excitonic states from the moiré Brillouin zone onto the separate-layer ones. Applying this method to a large-angle twisted MoS 2 /MoSe 2 bilayer, we find that the optical spectrum is dominated by mixed electron–hole transitions with different momenta in the separate monolayers, leading to unexpected hybridization between interlayer and intralayer excitons. Our findings offer a design pathway for exciton layer-localization in TMD heterostructures.

Chemistry↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗
DOE OSTI2020

DFT modelling of explicit solid–solid interfaces in batteries: methods and challenges

Density Functional Theory (DFT) calculations of electrode material properties in high energy density storage devices like lithium batteries have been standard practice for decades. By contrast, DFT modelling of explicit interfaces in batteries arguably lacks universally adopted methodology and needs further conceptual development. In this paper, we focus on solid–solid interfaces, which are ubiquitous not just in all-solid state batteries; liquid-electrolyte-based batteries often rely on thin, solid passivating films on electrode surfaces to function. We use metal anode calculations to illustrate that explicit interface models are critical for elucidating contact potentials, electric fields at interfaces, and kinetic stability with respect to parasitic reactions. The examples emphasize three key challenges: (1) the “dirty” nature of most battery electrode surfaces; (2) voltage calibration and control; and (3) the fact that interfacial structures are governed by kinetics, not thermodynamics. To meet these challenges, developing new computational techniques and importing insights from other electrochemical disciplines will be beneficial.

25 ENERGY STORAGE↗
DOE OSTI2025

Applying Machine Learning and Bayesian Inference to Identify and Locate Moving Anthropogenic Sources Using Distributed Acoustic Sensing Data

Distributed acoustic sensing (DAS) systems, which use existing telecommunication fibers, offer high‐resolution capabilities ideal for recording anthropogenic sources. However, the complexity of urban environments and the large amount of data recorded by DAS require automated methods to efficiently detect and categorize anthropogenic sources. Here, we evaluate how well three machine learning models (k‐nearest neighbor [k‐NN], convolutional neural networks, and recurrent‐convolutional neural networks) can identify various anthropogenic sources recorded by DAS. Our findings reveal that both k‐NN and neural network methods perform well in high signal‐to‐noise ratio (SNR) settings. However, their accuracy decreases at SNRs <4. We also use Kalman filtering, a form of Bayesian inference, on backprojected locations of these sources to recover locations that generally fall within standard smartphone Global Positioning System errors. By combining machine learning and Kalman filter results, we calculate a multidimensional model of moving anthropogenic sources. These results demonstrate the potential of DAS data in urban seismology for accurately identifying and locating such sources. Depending on the research objectives, these sources can be further studied or filtered out to improve the quality of seismic data for earthquake studies. Such methods provide a valuable tool for urban seismology and seismic hazard analysis.

Luckie, Thomas William [Sandia National Laboratori↗
DOE OSTI2024

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

DelloStritto, Mark (ORCID:0000000206785860)↗
DOE OSTI2023

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. Here, the quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6–31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6–31+G(2df,p) and ωB97XD/6–311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Frank–Kasper Phases of Diblock Copolymer Melts: Self-Consistent Field Results of Two Commonly Used Models

We constructed phase diagrams of conformationally asymmetric diblock copolymer A-B melts using the polymer self-consistent field (SCF) calculations of both the dissipative particle dynamics chain (DPDC) model (i.e., compressible melts of discrete Gaussian chains with the DPD non-bonded potential) and the “standard” model (i.e., incompressible melts of continuous Gaussian chains with the Dirac δ-function non-bonded potential) in the χN-ε plane, where χN and ε characterize, respectively, the repulsion and conformational asymmetry between the A and B blocks, at the A-block volume fraction f = 0.2 and 0.3. Consistent with previous SCF calculations of the “standard” model, σ and A15 are the only stable Frank–Kasper (FK) phases among the five FK (i.e., σ, A15, C14, C15 and Z) phases considered. The stability of σ and A15 is due to their delicate balance between the energetic and entropic contributions to the Helmholtz free energy per chain of the system, which, within our parameter range, increases in the order of σ/A15, Z, and C14/C15. While in general the SCF phase diagrams of these two models are qualitatively consistent, A15 is not stable for the DPDC model at the copolymer chain length N = 10 and f = 0.3; any differences in the SCF phase diagrams are solely due to the differences between these two models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A guide on modeling electrostatics of semiconductor detectors in COMSOL Multiphysics ® for DRiFT

Adding a new semiconductor detector into the detector response function toolkit (DRiFT) requires a model of the electric potential and the electric field. To get an accurate electrostatics model, the geometry of the detector should be modeled as closely to the real geometry as feasible, including the semiconductor materials, doping layers and concentrations, and contacts. Since the electric field and potential are used to calculate the induced signal, the results of the detector response functions are largely influenced by the electrostatics models. Initial electrostatics models were completed using Silvaco, however, this document provides guidance on using COMSOL Mulitphysics ® (COMSOL) to model the detectors to give users additional flexibility. Guidance on using COMSOL and its user interface are mostly left out of this document, but best practices for geometry, modeling methods, and data format/exporting are included to help users generate models that are compatible with DRiFT, prevents bugs, issues, and inaccuracies during charge collection calculations. The following summary provides an overview of the document.

42 ENGINEERING↗
DOE OSTI2020

High-Performance Simulation of Dynamic Hydrologic Exchange and Implications for Surrogate Flow and Reactive Transport Modeling in a Large River Corridor

Hydrologic exchange flows (HEFs) have environmental significance in riverine ecosystems. Key river channel factors that influence the spatial and temporal variations of HEFs include river stage, riverbed morphology, and riverbed hydraulic conductivity. However, their impacts on HEFs were often evaluated independently or on small scales. In this study, we numerically evaluated the combined interactions of these factors on HEFs using a high-performance simulator, PFLOTRAN, for subsurface flow and transport. The model covers 51 square kilometers of a selected river corridor with large sinuosity along the Hanford Reach of the Columbia River in Washington, US. Three years of spatially distributed hourly river stages were applied to the riverbed. Compared to the simulation when riverbed heterogeneity is not ignored, the simulation using homogeneous riverbed conductivity underestimated HEFs, especially upwelling from lateral features, and overestimated the mean residence times derived from particle tracking. To derive a surrogate model for the river corridor, we amended the widely used transient storage model (TSM) for riverine solute study at reach scale with reactions. By treating the whole river corridor as a batch reactor, the temporal changes in the exchange rate coefficient for the TSM were derived from the dynamic residence time estimated from the hourly PFLOTRAN results. The TSM results were evaluated against the effective concentrations in the hyporheic zone calculated from the PFLOTRAN simulations. Our results show that there is potential to parameterize surrogate models such as TSM amended with biogeochemical reactions while incorporating small-scale process understandings and the signature of time-varying streamflow to advance the mechanistic understanding of river corridor processes at reach to watershed scales. However, the assumption of a well-mixed storage zone for TSM should be revisited when redox-sensitive reactions in the storage zones play important roles in river corridor functioning.

Fang, Yilin↗
DOE OSTI2022

Atomistic and cluster dynamics modeling of fission gas (Xe) diffusivity in TRISO fuel kernels

TRISO fuel particles are candidates for use in next generation reactors including gas reactors, fluoride salt-cooled high temperature reactors, and micro-reactors. The UCO fuel kernel consists of a uranium dioxide (UO) and uranium carbide mixture. The addition of UC helps suppress the formation of carbon monoxide gas, which led to failures during initial TRISO development. The addition of uranium carbide alters the chemistry of the UO kernel, which is known to influence performance parameters such as fission gas diffusivity, although the impact has not been quantified and no models exist that take the change in chemistry into account. Therefore, better understanding and more accurate models of the impact of chemistry on fuel performance are of high priority. In this paper, a first-principles density functional theory (DFT) and empirical potential based multi-scale study has been carried out to model the diffusivity of fission gas xenon (Xe) in UCO TRISO fuel kernels. The focus is on the UO component in the UCO fuel kernels, as that represents the largest volume fraction of the fuel kernels. The study relies on DFT and empirical potential calculations to determine Xe and point defect properties, which are then used in thermodynamic and kinetic models to predict diffusion for intrinsic conditions. In addition, the information is utilized in cluster dynamics simulations using the Centipede code to estimate the impact of irradiation on defect transport. Additionally, the presence of UC or UC in the UCO fuel kernels is shown to have a substantial impact on the UO non-stoichiometry by inducing oxygen vacancies and driving UO sub-stoichiometric, which causes much slower Xe diffusion in UCO compared to light water reactor UO fuel. The application of this model in fuel performance simulations using the Bison code is also demonstrated.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Stable isotope equilibria in the dihydrogen-water-methane-ethane-propane system. Part 1: Path-integral calculations with CCSD(T) quality potentials

Isotopic compositions of alkanes are typically assumed to be kinetically controlled, but recently is has been proposed that alkanes can isotopically equilibrate for both C and H isotopes during natural gas generation. Evaluation of this requires knowledge of the isotopic equilibrium between alkanes and other common hydrogen and carbon bearing species. Here, in this study, we calculate isotopic equilibria within and between gaseous dihydrogen (H 2 ), water (H 2 O), methane (CH 4 ), ethane (C 2 H 6 ) and propane (C 3 H 8 ), including isotope fractionation among molecules, clumped isotope effects, as well as among sites of propane (i.e., the site-specific isotope effects) from 0°C to 500°C using a path-integral method paired with high-level descriptions of molecular potentials and the diagonal correction to the Born Oppenheimer approximation. While path-integral calculations with high- level CCSD(T) potentials are available for the isotopic equilibria involving methane, the path-integral calculations for ethane and propane have only been performed based on lower-level descriptions of the molecular potentials. We analyze the relative importance of various approximations that are commonly employed when isotopic equilibria are evaluated. We find that clumped isotope effects can be calculated to the same accuracy using computationally inexpensive combination of the Bigeleisen-Mayer-Urey model with the molecular potential from density functional theory. In contrast, fractionation and site preferences of both deuterium and carbon-13 benefit from the use of the higher level CCSD(T) potentials and accounting for anharmonic effects. Additionally, for fractionation and site preference of deuterium corrections to Born-Oppenheimer approximation can also be important.

03 NATURAL GAS↗
DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

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

Data used in manuscript "Quantifying Subsurface Flow and Solute Transport in a Snowmelt-recharged Hillslope with Multiyear Water Balance", submitted to Water Resources Research on 5-26-2022

Data in this package were used to calculate subsurface flow and solute (specifically dissolved nitrogen) transport in the Pumphouse Lower Montane (PLM) hillslope. These data are included in separate CSV files associated with each figure presented in the manuscript. The files describe site locations and elevations, weather data (air temperatures and precipitation), modeled evapotranspiration, time dependent groundwater elevations, calculated annual subsurface flow, hydraulic conductivity values, time trends in subsurface flow within different zones, pore water concentrations of total dissolve nitrogen (TDN), rates of TDN transport, soil water matric potentials following snowmelt, soil water contents during drainage, and evapotranspiration calculated by soil water mass balance versus by CLM4 model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ROSE: A reduced-order scattering emulator for optical models

A new generation of phenomenological optical potentials requires robust calibration and uncertainty quantification, motivating the use of Bayesian statistical methods. These Bayesian methods usually require calculating observables for thousands or even millions of parameter sets, making fast and accurate emulators highly desirable or even essential. Emulating scattering across different energies or with interactions such as optical potentials is challenging because of the nonaffine parameter dependence, meaning the parameters do not all factorize from individual operators. Here we introduce and demonstrate the reduced-order scattering emulator ( ROSE ) framework, a reduced basis emulator that can handle nonaffine problems. ROSE is fully extensible and works within the publicly available band framework software suite for calibration, model mixing, and experimental design. As a demonstration problem, we use ROSE to calibrate a realistic nucleon-target scattering model through the calculation of elastic cross sections. This problem shows the practical value of the ROSE framework for Bayesian uncertainty quantification with controlled trade-offs between emulator speed and accuracy as compared to high-fidelity solvers. Finally, planned extensions of ROSE are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Thermomechanical response of thickly tamped targets and diamond anvil cells under pulsed hard x-ray irradiation

In the laboratory study of extreme conditions of temperature and density, the exposure of matter to high intensity radiation sources has been of central importance. Here, we interrogate the performance of multi-layered targets in experiments involving high intensity, hard x-ray irradiation, motivated by the advent of extremely high brightness hard x-ray sources, such as free electron lasers and 4th-generation synchrotron facilities. Intense hard x-ray beams can deliver significant energy in targets having thick x-ray transparent layers (tampers) around samples of interest for the study of novel states of matter and materials’ dynamics. Heated-state lifetimes in such targets can approach the microsecond level, regardless of radiation pulse duration, enabling the exploration of conditions of local thermal and thermodynamic equilibrium at extreme temperature in solid density matter. The thermal and mechanical responses of such thick layered targets following x-ray heating, including hydrodynamic relaxation and heat flow on picosecond to millisecond timescales, are modeled using radiation hydrocode simulation, finite element analysis, and thermodynamic calculations. Assessing the potential for target survival over one or more exposures and resistance to damage arising from heating and resulting mechanical stresses, this study doubles as an investigation into the performance of diamond anvil high pressure cells under high x-ray fluences. Long used in conjunction with synchrotron x-ray radiation and high power optical lasers, the strong confinement afforded by such cells suggests novel applications at emerging high intensity x-ray facilities and new routes to studying thermodynamic equilibrium states of warm, very dense matter.

Meza-Galvez, J. (ORCID:000000032699849X)↗
DOE OSTI2025

Status of Multiple Channel Fuel Performance Capabilities Within the SAS4A/SASSYS-1 Safety Analysis Software

SAS4A/SASSYS-1 (SAS) is a fast-running simulation tool used to perform deterministic analysis of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. It is a critical element of safety analysis capabilities for the U.S. Department of Energy and is utilized within industry to perform the transient safety analyses required to support the licensing of Liquid Metal-cooled Fast Reactors (LMFRs). Although SAS is exceptionally fast for most transient scenarios, fuel performance calculations, along with the associated pre-transient characterization of the fuel pin, may be required for transient scenarios where fuel pin failure is hypothesized. Both the pre-transient characterization and the transient fuel performance calculation are necessary to properly quantify margins to potential fuel failure and assess the time spent potentially exceeding such margins during events. While safety analysis calculations with fuel performance models provide a more detailed characterization of the reactor during a transient, the pre-transient characterization can be time-consuming and computationally expensive. Often, large numbers of fuel pins have been exposed to similar pre-transient irradiation conditions. Similarly, the same pre-transient fuel characterization may be applicable to numerous transient conditions. This provides an opportunity to optimize the SAS computational framework such that pre-transient fuel characterization can be shared across multiple channels (fuel pins) and across multiple simulations, thus dramatically reducing overall computational costs. This report summarizes progress toward enhancing the SAS computational framework to support shared, multiple channel fuel performance characterizations intended to significantly reduce computational costs. Preliminary testing has shown that the computational time saved by using the pre-transient sharing capability is approximately equal to the time it takes to perform the pre-transient characterization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗