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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A modular and extensible CHARMM-compatible model for all-atom simulation of polypeptoids

Peptoids (N-substituted glycines) are a class of sequence-defined synthetic peptidomimetic polymers with applications including drug delivery, catalysis, and biomimicry. Classical molecular simulations have been used to predict and understand the conformational dynamics of single chains and their self-assembly into morphologies including sheets, tubes, spheres, and fibrils. The CGenFF-NTOID model based on the CHARMM General Force Field has demonstrated success in accurate all-atom molecular modeling of peptoid structure and thermodynamics. Extension of this force field to new peptoid side chains has historically required reparameterization of side chain bonded interactions against ab initio data. This fitting protocol improves the accuracy of the force field but is also burdensome and precludes modular extensibility of the model to arbitrary peptoid sequences. In this work, we develop and demonstrate a Modular Side Chain CGenFF-NTOID (MoSiC-CGenFF-NTOID) as an extension of CGenFF-NTOID employing a modular decomposition of the peptoid backbone and side chain parameterizations, wherein arbitrary side chains within the large family of substituted methyl groups (i.e., –CH 3 , –CH 2 R, –CHRR', and –CRR'R") are directly ported from CGenFF. We validate this approach against ab initio calculations and experimental data to develop a MoSiC-CGenFF-NTOID model for all 20 natural amino acid side chains along with 13 commonly used synthetic side chains and present an extensible paradigm to efficiently determine whether a novel side chain can be directly incorporated into the model or whether refitting of the CGenFF parameters is warranted. We make the model freely available to the community along with a tool to perform automated initial structure generation.

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

Interpretable, Extensible Linear Regression Model for Electron Density Prediction

This code allows us to generate a model to predict ground state electron densities directly from atomic structure thereby bypassing the need to perform direct DFT calculations. It is based on many body correlation descriptors defined in https://arxiv.org/abs/2004.14442 (IM release numbers: LLNL-JRNL- 808791 and LLNL-JRNL-840698). The code reads in electron densities, in CHGCAR format used by the DFT code VASP, calculates many-body correlation descriptors based on atoms surrounding a grid point and then uses linear regression to obtain the fitting parameters.

Kumar, Shashikant↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach

Deep learning (DL) models are extensively used as surrogate models for high-fidelity simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts of the spatial–temporal evolution of three-dimensional (3D) state variables in geological carbon storage (GCS). However, training these models in high-dimensional space remains computationally demanding and prone to overfitting because of limited training data. This paper presents a novel workflow to address these challenges by integrating dimension reduction (DR) methods. Here, the proposed workflow employed pre-trained DR models to extract the latent variables of geological models and state variables and utilized the multi-layer perceptron (MLP) for constructing mapping functions between the input and output variables in latent spaces. Subsequently, the pre-trained reconstruction models converted the MLP-predicted latent state variables to their original high-dimensional form. Furthermore, we proposed a novel strategy for the DR and reconstruction of 3D saturation fields to account for the unique data characteristics of sparsity, nonuniformity, and discontinuity. The proposed strategy applied PCA and inverse PCA for 2D average saturation fields and developed a DL-based 3D reconstruction model, leveraging three 2D average saturation fields as input to produce a 3D saturation field as output. The pre-training of DR and reconstruction models and training of MLP models were conducted on 84 Gulf of Mexico (GoM) simulations and evaluated on 12 testing simulations. Each simulation contained 720 monthly time steps, with the first 360 months as the injection period and the rest as the post-injection period. The proposed workflow, incorporating DR and DL models, accurately predicts the normalized 3D pressure fields, achieving mean square error (MSE) of 2.92 × 10 -7 compared to the ground truth obtained from a full-physics simulator. Furthermore, the proposed strategy outperformed PCA and convolutional autoencoder (CAE) models on 3D saturation fields, resulting in minor workflow prediction errors with an MSE of 2.93 × 10 -5 . The results suggest the proposed workflow provides sufficient predictive fidelity across temporal and spatial scales, and enables a speedup of 160 times compared to the full-physics simulator, facilitating improved decision-making and risk assessment for large-scale GCS management in real-time scenarios.

3D reconstruction model↗
DOE OSTI2024

Responses of summer mesoscale convective systems to irrigation over the North China Plain based on convection-permitting model simulations

Extensive irrigation activities in the North China Plain (NCP) significantly influence regional weather and climate. However, previous studies focusing on the NCP were primarily based on coarse-resolution models, which are unable to explicitly resolve convection systems, causing large uncertainty in precipitation simulations. In this study, a convection-permitting model coupled with a dynamic irrigation scheme is utilized to investigate the impacts of irrigation on summertime mesoscale convective systems (MCSs) over the NCP. Sensitivity experiments with irrigation off and on are conducted for 5 summers and an MCS identification and tracking algorithm is applied to both satellite observations and model simulations. We find that incorporating irrigation in the model increases MCS precipitation, which agrees more with observations. The probability distributions of MCS lifetime, area, propagation speed, and intensity are all better simulated with irrigation. Irrigation increases the occurrence frequency of MCSs throughout the entire day. The nighttime increase is partly because of more frequent local initiation of MCS developed from isolated deep convection, while the daytime increase is mainly attributed to the changes in MCSs initiating elsewhere and then propagating to the NCP. On average, irrigation induces additional moisture that is more thermodynamically favorable for precipitation, but this effect is partially offset by the weakened ascending air motion primarily caused by irrigation surface cooling. Compared to weak MCS precipitation events, strong MCS precipitation events experience greater enhancement in precipitation intensity when including irrigation because the offset effect from the change in large-scale ascending air motion is insignificant. In addition, irrigation makes the variation of MCS precipitation intensity more correlated with the variation in ascending motion but less correlated with that in atmospheric moisture. Our results suggest the pronounced impacts of irrigation on MCSs over the NCP which should be included in numerical models to improve regional precipitation simulation and prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Core-corona hadronization model and muon puzzle in extensive air showers

The discrepancy between models and measurements concerning the muon content in air showers produced by ultra-high energy cosmic rays, the so-called muon puzzle, remains unsolved. String fragmentation models currently used in hadronic interactions fail to reproduce the observations, while recent measurements at the LHC hint towards the existence of production mechanisms, such as collective statistical hadronization, which lead to an increase in the muon production after a hadronic interaction. The core-corona model of heavy ions implements both production mechanisms, where the large-density region of interaction (core) hadronizes statistically, while the low-density (corona) hadronizes following string fragmentation. In this contribution, we present a modification of the CONEX framework to implement the core-corona model in air shower simulations. We demonstrate a significant impact of the core effect, as observed at the LHC, on the muon content in air showers produced by ultra-high energy cosmic rays.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Unveiling the mechanism of surface corrugation formation on a quasi free-standing bi-layer graphene via experimental and modeling investigations

Extensive studies have been conducted to accomplish the controls over the formation of surface corrugations on chemical vapor deposited (CVD) graphene. However, the underlying mechanism of the surface corrugation formation on graphene remains elusive, due to the difficulty delineating the growth-induced surface corrugations from wrinkles formed during graphene transfer with the use of a supporting layer casted on top of the sample. Here, in this study, “quasi free-standing” graphene was realized using the weakly-interacting h-BN layer during a wet transfer process to study the surface corrugation formation on graphene. The weak interaction of graphene with the supporting h-BN layer allows for the growth-induced surface corrugations to be released, enabling the investigation of the primary mechanism for the graphene corrugation behavior during the transfer process. The experimental measurements of surface corrugations were conducted using atomic force microscopy (AFM) to reveal that surface corrugations are formed following the graphene’s crystallographic directions. Density functional theory calculations using the projector augmented-wave (PAW) method and Perdew-Burke-Ernzerhof (PBE) exchange correlation functional were also conducted for two models of “atomic-scale ripples” and “nanoscale wrinkles” and showed that all of them are formed along the crystallographic axis of graphene, which is in good agreement with the experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Development and assessment of models for turbulent Rayleigh-Taylor mixing using the macroscopic forcing method

Reynolds-Averaged Navier Stokes (RANS) simulations are a popular method for designing ICF experiments, and accurate mixing models are crucial for these simulations to give good predictions. To this end, the present work seeks to demonstrate the Macroscopic Forcing Method (MFM) as a tool for both improving existing RANS models as well as assessing RANS model forms. First, MFM analysis from Lavacot et al. (Phys. Rev. Fluids, 2025) is used to develop the k–L–F model, an extension of the k–L model of Dimonte and Tipton (Phys. Fluids, 2006) that incorporates nonlocality through addition of a turbulent species flux transport equation. MFM is then applied to the k–L–F model along with the k–L and BHR–4 models to assess their forms and compare the model-implied eddy diffusivity moments to those measured from high-fidelity simulations. Furthermore, the analysis reveals that models incorporating nonlocality (k–L–F and BHR–4) match the high-fidelity simulation data better than purely local models (k–L), both in terms of mean fields and eddy diffusivity moments. However, all of the considered RANS models struggle to match temporal moments at high Atwood numbers, highlighting the importance of temporal nonlocality in these regimes and the need for additional improvement even among models incorporating nonlocality.

general physics↗
DOE OSTI2023

Voltage-Based Strategies for Preventing Battery Degradation under Diverse Fast-Charging Conditions

Maintaining safe operating conditions is a key challenge for high-performance lithium-ion battery applications. The lithium-plating reaction remains a risk during charging, but limited studies consider the highly variable charging conditions possible in commercial cells. Here we combine pseudo-2D electrochemical modeling with data visualization methods to reveal important relationships between the measurable cell voltage and difficult-to-predict Li-plating onset criteria. An extensively validated model is used to compute Li plating for thousands of multistep charging conditions spanning diverse rates, temperatures, states-of-charge, and cell aging. Here we observe an empirical cell operating voltage limit below which plating does not occur across all conditions, and this limit varies with the battery state-of-charge and aging. A model sensitivity analysis also indicates that, when comparing two charging voltage profiles, the capacity difference at 4.0 V correlates well with the difference in the plating onset capacity. These results encourage simple strategies for Li-plating prevention that are complementary to existing battery controls.

25 ENERGY STORAGE↗
DOE OSTI2021

Development of five-moment two-fluid modeling for Z-pinch physics

The Z-pinch m = 0 instability as well as its stabilization by radially sheared axial flow is studied using the nonlinear ideal five-moment twofluid (5M2F) model with an extension of that model to include Braginskii heat and momentum transport. Using the ideal 5M2F model, linear growth rate results are compared with prior work using MHD and Hall MHD. At small normalized wavenumber, 1 < k a < 4 , where a is the effective pinch radius, 5M2F results agree with Hall MHD within 20% in scenarios without radially sheared axial flow. With the sheared flow and focusing on ka = 10/3, agreement with Hall MHD is excellent. In the limit of small ion inertial length, results also match with MHD. A comparison with PIC modeling of shear-free m = 0 stability focuses on a plasma scenario based on recent experimental results. In a scan of mode wavenumber, ideal 5M2F results are qualitatively similar to PIC: the growth rate rises to a peak at a moderate wavenumber and declines at a large wavenumber in contrast to MHD results, which show the saturation of the growth rate with the increasing wavenumber rather than a decline. The peak normalized 5M2F growth rate is &#x3B3; &#x3C4; A = 1.5 , where sA is the Alfven transit time across the pinch. The peak occurs at normalized wavenumber ka = 10. For comparison, PIC results have a peak growth of &#x3B3; &#x3C4; A = 0.77 at ka = 5. Including Braginskiibased closure of the 5M2F model does not qualitatively change the ideal results in this particular case. Nonlinear saturation is studied using the 5M2F model with the dissipative Braginskii-based closure in cases with pinch-edge sheared-flow speed equal to half the Alfven speed. Nonlinear mixing due to the sheared flow yields a quasi-steady state after modest losses of pinch ion inventory and pinch thermal energy, approximately 30% and 10%, respectively. 5M2F modeling captures the essential physics of m = 0 instability and offers a computationally tractable route to high-fidelity modeling of 3D Z-pinch behavior, including m = 1 instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

3D modeling of n = 1 RMP driven heat fluxes on the SPARC tokamak PFCs using HEAT

3D heat flux calculations at the lower outer divertor plate of SPARC using the HEAT code show that 3D fields generated from error field correction coils can lead to enhanced peak heat fluxes up to 15 times larger compared to the axisymmetric case. Previously employed to simulate axisymmetric heat flux on 3D plasma facing components, the HEAT code can now predict 3D heat flux generated by non-axisymmetric plasmas. This is achieved via a new HEAT module which leverages the 3D field line tracing capabilities of MAFOT starting from an M3D-C1 (MHD resistive code) perturbed equilibria. The resulting heat flux is assigned using the magnetic footprint and the heat flux layer model, an extension of the 2D heat flux model also known as the Eich, to 3D non-axisymmetric plasmas. For SPARC, the new capabilities of HEAT are used to calculate the 3D heat loads resulting from n = 1 perturbation fields (with n indicating the toroidal periodicity) applied through a toroidal array of six picture frame coils with different amplitude. The comparison with the unperturbed case shows significant changes in shape and intensity of the heat flux profile. The results show that the application of n = 1 3D field leads to a localized enhancement of the heat flux peak, influenced by the wetted area impacted by the magnetic footprint, and the appearance of a secondary heat flux peak, whose intensity depends on amplitude of the applied 3D field and toroidal location.

3D heat flux↗
DOE OSTI2021

Dynamic flow stress of pure polycrystalline aluminum: Pressure-shear plate impact experiments and extension of dislocation-based modeling to large strains

The dynamic thermo-mechanical behavior of pure aluminum has attracted renewed interest lately due to experimental observations of an anomalous increase in Hugoniot Elastic Limit (HEL) at incipient plasticity and elevated temperatures in polycrystalline pure metals. In context of current dislocation-mediated plasticity models for metals, this increase in dynamic strength is indicative of a transition in the rate controlling mechanism for dislocation glide from being thermally assisted to phonon-drag restricted due to increase in phonon viscosity at elevated temperatures. Though these studies have helped to shed light on these important mechanisms operative in FCC metals at incipient plasticity, the extent to which they contribute to flow stress, particularly at larger strains, remains unclear. Here, we address these questions through a combined experimental and modeling effort focused on investigating the evolution of dynamic flow stress in polycrystalline aluminum using experimental data gathered from a series of combined pressure-and-shear plate impact (PSPI) experiments designed to reveal the flow stress of pure aluminum at strain rates ~ 10 5 /s, plastic strains of up to 40% and temperatures ranging from room to 866 K. In all cases, the flow stress of aluminum, as inferred from the measured transverse particle velocity histories at the free surface of an elastic tungsten carbide target plate, reveals saturation with increasing plastic strains at stress levels that decrease with increasing test temperatures. Numerical simulations are performed to correlate the experimentally observed temperature and strain rate dependence of flow stress at small and large plastic strains using the Austin-McDowell dislocation-mediated plasticity model parametrized to normal plate impact experiments conducted in an earlier study by Zaretsky and Kanel (2012). Extensions to the model are made to better represent dynamic behavior of pure aluminum at larger plastic strains as observed in elevated temperature split Hopkinson Pressure bar (SPHB) experiments of Samanta (1971) and Lindholm and Yeakly (1965), and the combined pressure-and-shear plate-impact experiments conducted in the present study. The main theoretical extension to the Austin-McDowell model made in this paper is the introduction of a new rate- and temperature- dependent dynamic recovery function, which can potentially allow for an effective reduction in the rate of accumulation of dislocations at large plastic strains. The numerical predictions of the revised and re-calibrated plasticity model are brought into agreement with the experimental observations, correlating sufficiently well with dynamic yield stress at incipient plasticity and the flow stress levels at larger plastic strains, plastic strain rates in the range 10 3 – 10 6 /s, and elevated temperatures up to near melt. The model, in concert with the experimental measurements, suggests that at incipient plasticity the rate governing mechanism for plastic flow is phonon-drag restricted dislocation glide, whereas, at higher magnitudes of plastic strain, it transitions to stress-assisted thermally activated glide. The transition between these two rate governing mechanisms is controlled by the evolution of dislocations throughout the deformation process.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Combination of searches for singly produced vectorlike top quarks in 𝑝⁢𝑝 collisions at $\sqrt{𝑠}$ =13 TeV with the ATLAS detector

A combination of searches for the single production of vectorlike top quarks (𝑇) is presented. These analyses are based on proton-proton collisions at $\sqrt{𝑠}$ = 13 TeV recorded in 2015–2018 with the ATLAS detector at the Large Hadron Collider, corresponding to an integrated luminosity of 139 fb −1 . The 𝑇 decay modes considered in this combination are into a top quark and either a Standard Model Higgs boson or a 𝑍 boson (𝑇 → 𝐻⁡𝑡 and 𝑇 → 𝑍⁢𝑡). The individual searches used in the combination are differentiated by the number of leptons (𝑒, 𝜇) in the final state. The observed data are found to be in good agreement with the Standard Model background prediction. Interpretations are provided for a range of masses and couplings of the vectorlike top quark for benchmark models and generalized representations in terms of 95% confidence level limits. For a benchmark signal prediction of a vectorlike top quark SU(2) singlet with electroweak coupling, 𝜅, of 0.5, masses below 2.1 TeV are excluded, resulting in the most restrictive limits to date.

Composite models↗
DOE OSTI2019

Phenomenology of SUGRA extensions of the Starobinsky model

We analyze BI-extended model in a complete form and compare the predictions with that of Starobinsky model. Under the parameter constraints in Planck 2018, we find that the dynamics of the whole inflation process described by BI-extended and Starobinsky models are nearly the same, even though there are some differences in the regions out of inflation. We also find the scales of parameters in BI-extended model and inflaton values at the first horizon crossing required to implement inflation. The changes of (n{sub s},r) fingerprints of BI-extended model and that of evolutions of inflaton field due to the variations of relevant parameters are also investigated.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Discussion on molecular dynamics (MD) simulations of the asphalt materials

The application of asphalt materials in pavement engineering has been increasingly widespread and sophisticated over the past several decades. Variations in the properties of asphalt binder during mixing, transportation, and paving can affect the performance of asphalt pavement. However, the asphalt material is a non-homogeneous and complex organic substance, consisting of various molecules with widely various molecular weights, elemental compositions, and structures. This complexity leads to difficulties for researchers to clearly and immediately understand the properties of asphalt materials and their variations. The multi-scale research approach combines macroscopic experimental data and microscopic simulation results from a practical engineering perspective. It helps to improve the understanding of asphalt materials. The molecular dynamics (MD) simulation proposes a corresponding molecular model of asphalt material based on experimental data, and the simulation algorithm is able to derive properties similar to those of real asphalt. Here, this paper provides a comprehensive review of the current studies on MD simulation of asphalt materials, including modeling, properties, and multi-scale analysis. As a key part of the computational simulation, this paper discusses the typical asphalt binder and asphalt-aggregate interface models constructed by different groups, and also presents their differences from real samples and their feasibility based on fundamental properties. After the introduction of molecular models, the extensive work made by researchers based on molecular models is categorically reviewed and discussed. The strengths and weaknesses of MD simulation methods in the study of asphalt materials are also summarized in order to provide the reader with a more comprehensive understanding of the relevant contents and to guide subsequent research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A solvable model of a nonlinear extension of quantum mechanics

We introduce a particular nonlinear generalization of quantum mechanics which has the property that it is exactly solvable in terms of the eigenvalues and eigenfunctions of the Hamiltonian of the usual linear quantum mechanics problem. Here, we hope that this simple example will elucidate some of the issues of interpreting nonlinear generalization of quantum mechanics that have been put forth to resolve questions about quantum measurement theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗