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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

Trifan, Anda↗
DOE OSTI2023

Engineering of Self‐Aggregation‐Resistant MnO 2 Heterostructure with A Built‐in Field for Enhanced High‐Mass‐Loading Energy Storage

Abstract Although MnO 2 has been intensively investigated for energy storage, further applications are limited by van der Waals force‐triggered self‐aggregation that always leads to poorly exposed active sites and compromised reaction dynamics, especially under high‐mass‐loading conditions. Herein, by synergistically coupling interfacial modulation with the Kirkendall effect, this work achieves in situ topological structure reorganization of MnOOH toward the high‐aspect‐ratio MnO 2 heterostructure (Heter‐MnO 2 ) with fully exposed active sites, which is ready to assemble into self‐supporting high‐mass‐loading film (30 mg cm −2 ) with restrained self‐aggregation. Theoretical calculation and dynamics analysis results demonstrate the generation of the built‐in field within the heterostructure, thus enhancing the electronic‐transfer and ionic‐adsorption/transport rates. As such, the 30 mg cm −2 Heter‐MnO 2 electrode achieves a superior areal capacitance of 4762 mF cm −2 at 1 mA cm −2 and a sound rate performance (79% at 100 mA cm −2 ) comparable to those of low‐mass‐loading/thin‐film electrodes. As a proof of concept, the fabricated planar interdigital quasi‐solid‐state symmetric micro‐supercapacitor (MSC) based on the Heter‐MnO 2 electrode can deliver a remarkable areal capacitance of 181 mF cm −2 and a considerable volumetric energy density of 10.3 mWh cm −3 . This methodology highlights the promise of surface/interface chemistry modulation for the configuration of easy‐to‐integrate hierarchical nanostructures to better meet practical energy applications.

Chemistry↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
DOE OSTI2025

Cooperative On-Ramp Merging with Time-Varying Vehicle-to-Vehicle Communication Delay Compensation via a Model-Free Approach

Cooperative merging strategies enabled by vehicle-to-vehicle (V2V) communication have shown promise in addressing congestion, fuel inefficiency, and collision risks. However, their performance can be severely degraded by time-varying and uncertain communication delays-an issue often overlooked in existing research, which primarily focuses on merging sequence determination and trajectory planning. Furthermore, practical considerations such as heterogeneous vehicle dynamics, varying road conditions, and real-time implementation complexities are frequently neglected. This paper presents a model-free, online planning framework for cooperative on-ramp merging of connected and automated vehicles (CAVs), explicitly accounting for time-varying V2V communication delays. Without relying on detailed vehicle dynamics, the proposed method introduces a data-driven delay compensation scheme. A co-simulation platform integrating high-fidelity vehicle dynamics, traffic simulation (SUMO), and V2V communication within MATLAB/Simulink is developed to evaluate the proposed method. Simulation results demonstrate that unaddressed V2V communication delays significantly impair merging performance. In contrast, the proposed framework enhances intervehicle distance tracking and maintains low CO2 emissions and fuel consumption, under communication delay across different communication frequencies. In conclusion, its lightweight design also facilitates real-time implementation, making it well-suited for deployment in practical CAV systems.

Accounting↗
DOE OSTI2024

Enhancing the dark-state bistability and thermal stability of zinc dynamic windows with nitrile electrolytes

Reversible metal electrodeposition (RME) for dynamic windows is a promising alternative to traditional electrochromics. We explore the reasons behind the bistability of practical RME dynamic windows under a wide temperature range, i.e., how well they maintain their dark state without external power. While Zn dynamic windows with dimethyl sulfoxide electrolytes exhibit adequate bistability at room temperature, their performance degrades at elevated temperatures due to the presence of small amounts of water. Here, to overcome this problem, we developed a set of Zn nitrile electrolytes. The integrated devices can maintain dark-state bistability over at least 1 month at 85°C with 0.1% transmission using a propionitrile (PrCN) electrolyte. Additionally, the PrCN-based Zn dynamic windows possess high optical contrast, color-neutral switching, and promising cyclability, making Zn RME in nitrile electrolytes a promising solution for achieving robust dynamic windows.

RME↗
DOE OSTI2022

Universal Kardar-Parisi-Zhang Dynamics in Integrable Quantum Systems

Although the Bethe ansatz solution of the spin-1/2 Heisenberg model dates back nearly a century, the anomalous nature of its high-temperature transport dynamics has only recently been uncovered. Indeed, numerical and experimental observations have demonstrated that spin transport in this paradigmatic model falls into the Kardar-Parisi-Zhang (KPZ) universality class. This has inspired the significantly stronger conjecture that KPZ dynamics, in fact, occur in all integrable spin chains with non-Abelian symmetry. Here, in this letter, we provide extensive numerical evidence affirming this conjecture. Moreover, we observe that KPZ transport is even more generic, arising in both supersymmetric and periodically driven models. Motivated by recent advances in the realization of SU(N)-symmetric spin models in alkaline-earth-based optical lattice experiments, we propose and analyze a protocol to directly investigate the KPZ scaling function in such systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Multi-scale Dynamics of Kinetic Turbulence in Weakly Collisional, High-Beta Plasmas (Final Report for DOE Grant DE-SC0019046)

This grant was a collaborative grant between Prof Matt Kunz (Princeton) and Prof. Eliot Quataert. Quataert was initially a faculty member at UC Berkeley when the grant was funded but moved to Princeton during the timeframe of this grant. Over the course of this grant we made major progress on understanding turbulence and multi-scale dynamics in weakly collisional high-beta plasmas. The most important contributions included: We developed one of the most compelling theoretical explanations for a decades-old puzzle at the heart of our understanding of the origin of the solar wind. The puzzle is that many of the observations of ion temperatures and distribution functions in the solar wind are consistent with cyclotron resonant heating. However, theoretical models of MHD turbulence in the solar wind show that most of the turbulent energy remains at low frequencies below the cyclotron frequency. In Squire et al. (2022), we showed, however, that in imbalanced turbulence (in which there is an asymmetry in the Alfven-wave flux in opposite directions along the magnetic field), which is the norm in the fast solar wind, the turbulent energy reaches a ‘bottleneck’ near the ion Larmor radius, and the amplitude and characteristic frequency of the fluctuations grow until the cyclotron frequency is reached.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Investigating the electronic structure of high explosives with X-ray Raman spectroscopy

Abstract We investigate the sensitivity and potential of a synergistic experiment-theory X-ray Raman spectroscopy (XRS) methodology on revealing and following the static and dynamic electronic structure of high explosive molecular materials. We show that advanced ab-initio theoretical calculations accounting for the core-hole effect based on the Bethe-Salpeter Equation (BSE) approximation are critical for accurately predicting the shape and the energy position of the spectral features of C and N core-level spectra. Moreover, the incident X-ray dose typical XRS experiments require can induce, in certain unstable structures, a prominent radiation damage at room temperature. Upon developing a compatible cryostat module for enabling cryogenic temperatures ( $$\approx$$ ≈ 10 K) we suppress the radiation damage and enable the acquisition of reliable experimental spectra in excellent agreement with the theory. Overall, we demonstrate the high sensitivity of the recently available state-of-the-art X-ray Raman spectroscopy capabilities in characterizing the electronic structure of high explosives. At the same time, the high accuracy of the theoretical approach may enable reliable identification of intermediate structures upon rapid chemical decomposition during detonation. Considering the increasing availability of X-ray free-electron lasers, such a combined experiment-theory approach paves the way for time-resolved dynamic studies of high explosives under detonation conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

End-of-Century Changes in Orographic Precipitation with the Intermediate Complexity Atmospheric Research Model over the Western United States

Abstract Downscaled precipitation projections were created using the Intermediate Complexity Atmospheric Research (ICAR) model over the western United States to increase the physical realism in orographic precipitation changes. End-of-century simulations from eight models in phase 5 of the Coupled Model Intercomparison Project (CMIP5) were downscaled with ICAR and compared to the widely utilized statistically downscaled dataset, localized constructed analogs (LOCAs), to understand where and why projections of cool-season (September–May) precipitation differed. ICAR and LOCA precipitation projections were similar, but their sign differed in hydrologically relevant regions likely due to ICAR’s simulation of microphysics and mesoscale dynamics with high-resolution topography (6 km). In the Pacific Northwest, cool-season precipitation projections from ICAR showed an increase on the windward side of the Cascades and no significant change within the lee. This difference between the windward and leeward side was attributed to reduced zonal wind speeds, allowing more time for microphysical processes within ICAR. This contrast is enhanced by rain’s faster fall speed compared to snow, limiting transport into the lee. Meanwhile, LOCA projected an increase in precipitation across the Cascades. In the Upper Colorado River basin, LOCA projected an increase in precipitation in high elevation regions (>3000 m), but ICAR projected no significant change or a decrease in precipitation. High elevation differences were most evident in the spring and fall and were also attributed to a snow-to-rain transition and dynamical processes that impacted orographic enhancement within ICAR. Idealized, controlled studies are needed to better isolate individual processes, but these results underscore the importance of including microphysics and mesoscale dynamics within regional-scale precipitation projections. Significance Statement A set of global climate model simulations was downscaled using an atmospheric model that contains key physical equations, referred to as Intermediate Complexity Atmospheric Research (ICAR). ICAR was used to examine projected changes in end-of-century cool-season precipitation over mountains in the western United States. Precipitation projections from ICAR were similar to projections that used statistical relationships to downscale climate projections. However, projections differed between ICAR and statistically downscaled datasets in whether they increased, decreased, or stayed the same in specific, hydrologically relevant regions such as the eastern Cascades and high elevation areas of the Upper Colorado River basin. These differences were attributed to the simulation of physical processes in ICAR. The results highlight the importance of kilometer-scale atmospheric processes in regional climate projections.

Currier, William Ryan [NOAA/Physical Sciences Labo↗
DOE OSTI2025

Dynamic condensation model of rolling droplets for high-performance heat transfer

Condensation is a critical process during vapor-liquid phase change in relation to heat transfer. To achieve a high heat transfer coefficient, the classical model for dropwise condensation requires a low contact angle and low contact angle hysteresis, failing to align with experimental observations on a hydrophobic and slippery quasi-liquid surface (QLS). Here, we report a dynamic condensation model that incorporates high-frequency condensate removal by emphasizing the role of timescale during droplet growth and shedding. Our model agrees well with the experimental result that a surface with high contact angle and low contact angle hysteresis promotes condensation, particularly during rolling-propelled condensate removal. Particle image velocimetry reveals that rolling droplets on a hydrophobic QLS exhibit 4-fold higher shedding speeds than the sliding droplets on a hydrophilic QLS, leading to significant heat transfer enhancement. This work deepens our theoretical understanding of condensation heat transfer and provides advanced physics-informed design rationales for water and energy systems.

42 ENGINEERING↗
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Mode-Selective Vibrational Energy Transfer Dynamics in 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX) Thin Films

The coupling of inter- and intramolecular vibrations plays a critical role in initiating chemistry during the shock-to-detonation transition in energetic materials. In this work, we report on the subpicosecond to subnanosecond vibrational energy transfer (VET) dynamics of the solid energetic material 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) by using broadband, ultrafast infrared transient absorption spectroscopy. Experiments reveal VET occurring on three distinct time scales: subpicosecond, 5 ps, and 200 ps. The ultrafast appearance of signal at all probed modes in the mid-infrared suggests strong anharmonic coupling of all vibrations in the solid, whereas the long-lived evolution demonstrates that VET is incomplete, and thus thermal equilibrium is not attained, even on the 100 ps time scale. Density functional theory and classical molecular dynamics simulations provide valuable insights into the experimental observations, revealing compression-insensitive time scales for the initial VET dynamics of high-frequency vibrations and drastically extended relaxation times for low-frequency phonon modes under lattice compression. Mode selectivity of the longest dynamics suggests coupling of the N–N and axial NO2 stretching modes with the long-lived, excited phonon bath.

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

Dynamic Adaptive Relaying for Distribution Grids with High Inverter-Based Generation

The penetration of distributed energy resources (DERs) in distribution systems is rapidly increasing. This can lead to large variations in the operating load and the fault current seen by a relay, which can impact the reliability of overcurrent protection. Adaptive overcurrent relaying (AOCR) can address this concern by enabling the relay to adjust its pickup settings based on the system operating conditions. This paper presents a novel dynamic AOCR approach that allows the relays to locally estimate the pickup settings and dynamically modify them. The approach is based on a two-stage estimation algorithm that uses local measurements and system information to estimate the worst-case fault current and the relay pickup settings. The relay pickup settings are then dynamically modified based on the estimated fault current. The performance of the proposed approach is evaluated on multiple configurations of the EPRI J1 feeder and the IEEE 13-bus system. The results show that the proposed approach can effectively improve the reliability of overcurrent protection in distribution systems with DERs.

adaptive overcurrent protection↗
DOE OSTI2024

Towards modelling AR Sco: generalized particle dynamics and strong radiation-reaction regimes

Numerical simulations of relativistic plasmas have become more feasible, popular, and crucial for various astrophysical sources with the availability of computational resources. The necessity for high-accuracy particle dynamics is especially highlighted in pulsar modelling due to the extreme associated electromagnetic fields and particle Lorentz factors. Including the radiation-reaction force in the particle dynamics adds even more complexity to the problem, but is crucial for such extreme astrophysical sources. We have also realized the need for such modelling concerning magnetic mirroring and particle injection models proposed for AR Sco, the first white dwarf pulsar. This paper demonstrates the benefits of using higher-order explicit numerical integrators with adaptive time-step methods to solve the full particle dynamics with radiation-reaction forces included. We show that for standard test scenarios, namely various combinations of uniform E- and B-fields and a static dipole B-field, the schemes we use are equivalent to and in extreme field cases outperform standard symplectic integrators in accuracy. We show that the higher-order schemes have massive computational time improvements due to the adaptive time-steps we implement, especially in non-uniform field scenarios and included radiation reaction where the particle gyro-radius rapidly changes. When balancing accuracy and computational time, we identified the adaptive Dormand–Prince eighth-order scheme to be ideal for our use cases. The schemes we use maintain accuracy and stability in describing the particle dynamics and we indicate how a charged particle enters radiation-reaction equilibrium and conforms to the analytical Aristotelian Electrodynamics expectations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Prediction of vacancy defect diffusion paths in high entropy alloys via machine learning on molecular dynamics data

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.

Reimer, C↗
DOE OSTI2025

Quantum computing for energy correlators

In recent years, energy correlators have emerged as powerful observables for probing the fragmentation dynamics of high-energy collisions. We introduce the first numerical strategy for calculating energy correlators using the Hamiltonian lattice approach, providing access to the intriguing nonperturbative dynamics of these observables. Furthermore, motivated by rapid advances in quantum computing hardware and algorithms, we propose a quantum algorithm for calculating energy correlators in quantum field theories. This algorithm includes ground state preparation, the application of source, sink, energy flux and real-time evolution operators, and the Hadamard test. We validate our approach by applying it to the SU(2) pure gauge theory in 2 + 1 dimensions on 3 × 3 and 5 × 5 honeycomb lattices with 𝑗 max = $\frac{1}{2}$ at various couplings, utilizing both classical methods and the quantum algorithm, the latter tested using the IBM emulator for specific configurations. The results are consistent with the expected behavior of the strong coupling regime and motivate a more comprehensive study to probe the confinement dynamics across the weak and strong coupling regimes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

High-Performance Electron Sources: Numerical Methods and Beam Dynamics at the Precision Frontier

Electron sources have a wide range of applications and there are many stakeholders that express continuing need for improvements and performance enhancements. Whether we consider ultra-cold, high-brightness, high-charge or high-average current source needs, there are some common themes from the point of view of the beam dynamics involved. These are the following ones: ability to model accurately the emission processes, including the presence of often complicated cathode and other boundary surfaces with a wide range of spatial scales; ability to deal accurately and efficiently with a large number of particles interacting pair-wise, including the stochastic part of these interactions with a wide range of spatial scales; and ability to propagate the particle distributions in time, including collisions with a wide range of temporal scales. These tasks require high precision and accuracy since the goal is usually generation, transport and preservation of very high-quality beams. This grant addressed one of the rem

43 PARTICLE ACCELERATORS↗