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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 OSTI2018

Developing a diagnostic for energetic laser-Compton produced photon beams

This paper describes the design and simulation of a radiation detector system for diagnostic measurements of photon beams produced by Thomson or Compton Scattering. The photon beam is Compton scattered in a thin passive converter, and the resulting electrons are analyzed using a charge-coupled device-based tracker and classification algorithms. The flux of the scattered electrons is much lower than that of the photon beam and is additionally dispersed. This dispersal enables measurements while avoiding pileup, which is important in order to provide diagnostic information from intense 'shot’ based pulsed systems, such as those being built to leverage laser wakefield accelerators. Simulations indicate that the designed system is capable of resolving beam parameters from a single shot. The fidelity to which various beam parameters can be resolved is presented as are methods that could result in further improvement to diagnostic resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Strongly Anharmonic Phonons and Their Role in Superionic Diffusion and Ultralow Thermal Conductivity of Cu 7 PSe 6

We report the quest for advanced superionic materials requires understanding their complex atomic dynamics, but detailed studies of the interplay between lattice vibrations and ionic diffusion remain scarce. Here inelastic and quasielastic neutron scattering measurements in the superionic argyrodite Cu 7 PSe 6 are reported, combined with molecular dynamics (MD) based on ab initio and machine-learned potentials, providing critical insights into the atomistic mechanisms underlying fast ion conduction. The results reveal how long-range Cu diffusion is limited by intercluster hopping, controlled by selective anharmonic phonons of the crystalline framework. Further, the Green–Kubo simulations reproduce the ultralow lattice thermal conductivity and identify contributions from mobile ions, phonons, and their cross-correlations. The mode resolved analysis shows that the thermal conductivity is dominated by low-energy acoustic phonon modes of the overall crystal framework. The analysis of mode-resolved spectral functions further show that vibrational modes with significant Cu contributions are strongly damped, corresponding to the breakdown of associated phonon quasiparticles. These results highlight the importance of strongly anharmonic effects in superionic systems, in which the traditional quasiharmonic phonon picture is insufficient, and pave the way toward combining machine-learning accelerated simulations with neutron scattering experiments to rationalize the complex atomic dynamics underlying ionic and thermal transport.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Electronic structure at coarse-grained resolutions from supervised machine learning

Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Parallel-in-Time Solution of Allen-Cahn Equations by Integrating Operator Learning into the Parareal Method

While recent advances in deep learning have shown promising efficiency gains in solving time-dependent partial differential equations (PDEs), matching the accuracy of conventional numerical solvers still remains a challenge. One strategy to improve the accuracy of deep learning-based solutions for time-dependent PDEs is to use the learned model as the coarse propagator in the Parareal method and a traditional numerical method as the fine solver. However, successful integration of deep learning into the Parareal method requires consistency between the coarse and fine solvers, particularly for PDEs exhibiting rapid changes such as sharp transitions. Here, to ensure this consistency, we propose using convolutional neural networks (CNNs) to learn the fully discrete time-stepping operator defined by the same numerical scheme employed as the fine solver. We demonstrate the effectiveness of the proposed method in solving the classical and mass-conservative Allen–Cahn (AC) equations. Through iterative updates in the Parareal algorithm, our approach achieves a significant computational speedup compared to traditional fine solvers while converging to high-accuracy solutions. Our results highlight that the proposed hybrid Parareal algorithm effectively accelerates simulations, particularly when implemented on multiple GPUs, and converges to the desired accuracy in only a few iterations. Another advantage of our method is that the CNN model is trained on trajectory-based data generated from random initial conditions, such that the trained model can be used to solve the AC equations with various initial conditions without retraining. This work demonstrates the potential of integrating neural network methods into parallel-in-time frameworks for efficient and accurate simulations of time-dependent PDEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Community Project for Accelerator Science and Simulation-3

The UCLA Plasma Simulation Group is a major partner of the “Community Petascale Project for Accelerator Science and Simulation”. This is the technical progress report for the UCLA effort. The UCLA effort is organized into HEP and ASCR activities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

MODELING THE INITIATION OF THE 2006 DECEMBER 13 CORONAL MASS EJECTION IN AR 10930: THE STRUCTURE AND DYNAMICS OF THE ERUPTING FLUX ROPE

We carry out a 3D magnetohydrodynamic simulation to model the initiation of the coronal mass ejection (CME) on 2006 December 13 in the emerging δ -sunspot active region NOAA 10930. The setup of the simulation is similar to a previous simulation by Fan, but with a significantly widened simulation domain to accommodate the wide CME. The simulation shows that the CME can result from the emergence of a east–west oriented twisted flux rope whose positive, following emerging pole corresponds to the observed positive rotating sunspot emerging against the southern edge of the dominant pre-existing negative sunspot. The erupting flux rope in the simulation accelerates to a terminal speed that exceeds 1500 km s{sup −1} and undergoes a counter-clockwise rotation of nearly 180° such that its front and flanks all exhibit southward directed magnetic fields, explaining the observed southward magnetic field in the magnetic cloud impacting the Earth. With continued driving of flux emergence, the source region coronal magnetic field also shows the reformation of a coronal flux rope underlying the flare current sheet of the erupting flux rope, ready for a second eruption. This may explain the build up for another X-class eruptive flare that occurred the following day from the same region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Limited‐memory adaptive snapshot selection for proper orthogonal decomposition

Summary Reduced order models are useful for accelerating simulations in many‐query contexts, such as optimization, uncertainty quantification, and sensitivity analysis. However, offline training of reduced order models (ROMs) can have prohibitively expensive memory and floating‐point operation costs in high‐performance computing applications, where memory per core is limited. To overcome this limitation for proper orthogonal decomposition, we propose a novel adaptive selection method for snapshots in time that limits offline training costs by selecting snapshots according an error control mechanism similar to that found in adaptive time‐stepping ordinary differential equation solvers. The error estimator used in this work is related to theory bounding the approximation error in time of proper orthogonal decomposition‐based ROMs, and memory usage is minimized by computing the singular value decomposition using a single‐pass incremental algorithm. Results for a viscous Burgers' test problem demonstrate convergence in the limit as the algorithm error tolerances go to zero; in this limit, the full‐order model is recovered to within discretization error. A parallel version of the resulting method can be used on supercomputers to generate proper orthogonal decomposition‐based ROMs, or as a subroutine within hyperreduction algorithms that require taking snapshots in time, or within greedy algorithms for sampling parameter space. Copyright © 2016 John Wiley & Sons, Ltd.

Oxberry, Geoffrey M.↗
DOE OSTI2016

Passive SCR for lean gasoline NO X control: Engine-based strategies to minimize fuel penalty associated with catalytic NH 3 generation

Lean gasoline engines offer greater fuel economy than common stoichiometric gasoline engines. However, excess oxygen prevents the use of the current three-way catalyst (TWC) to control nitrogen oxide (NO X ) emissions in lean exhaust. A passive SCR concept, introduced by General Motors Global R&D, makes use of a TWC that is already onboard to generate NH 3 under slightly rich conditions, which is stored on the downstream SCR. The stored NH 3 is then used to reduce NO X emissions when the engine switches to lean operation. In this work, the effect of engine parameters, such as air-fuel equivalence ratio and spark timing, on NH 3 generation over a commercial Pd-only TWC with no dedicated oxygen storage component was evaluated on a 2.0-liter BMW lean burn gasoline direct injection engine. NO X reduction, NH 3 formation, and reductant utilization processes were evaluated, and fuel efficiency was assessed and compared to the stoichiometric engine operation case. We found air-fuel equivalence ratio to be one of the most important parameters in controlling the NH 3 production; however, the rich operation necessary for NH 3 production results in a fuel consumption penalty. The fuel penalty can be minimized by adjusting spark timing to increase rich-phase engine out NO X emissions and, thereby, NH 3 levels. Additionally, higher engine out NO X during engine load increase to simulate acceleration resulted in additional fuel savings. Ultimately, a 10% fuel consumption benefit was achieved with the passive SCR approach by optimizing rich air-fuel equivalence ratio and spark timing while also utilizing acceleration load conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run four orders of magnitude faster than MD. In conclusion, the transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

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

Dynamic Metal–Support Interaction Dictates Cu Nanoparticle Sintering on Al 2 O 3 Surfaces

Nanoparticle sintering remains a critical challenge in heterogeneous catalysis. In this work, we present a unified deep potential (DP) model based on the Perdew–Burke–Ernzerhof approximation of density functional theory for Cu nanoparticles on three Al 2 O 3 surfaces (γ-Al 2 O 3 (100), γ-Al 2 O 3 (110), and α-Al 2 O 3 (0001)). Using DP-accelerated simulations, we reveal that the nanoparticle size-mobility relationship strongly depends on the supporting surface. The diffusion of nanoparticles on the two γ-Al 2 O 3 surfaces is almost independent of the size of the nanoparticle, while the diffusion on α-Al 2 O 3 (0001) decreases rapidly with increasing size. Interestingly, nanoparticles with fewer than 55 atoms diffuse several times faster on α-Al 2 O 3 (0001) than on γ-Al 2 O 3 (100) at 800 K while expected to be more sluggish based on their larger binding energy at 0 K. The diffusion on α-Al 2 O 3 (0001) is facilitated by dynamic metal–support interaction (MSI), where Al atoms move out of the surface plane to optimize contact with the nanoparticle and relax back to the plane as the nanoparticle moves away. In contrast, the MSI on γ-Al 2 O 3 (100) and on γ-Al 2 O 3 (110) is dominated by more stable and directional Cu–O bonds, consistent with the limited diffusion observed on these surfaces. Our extended MD simulations provide insight into the sintering processes, showing that the dispersity of the nanoparticles strongly influences the coalescence driven by nanoparticle diffusion. We observed that the coalescence of Cu 13 nanoparticles on α-Al 2 O 3 (0001) can occur in a short time (10 ns) at 800 K even with an initial internanoparticle distance increased to 3 nm, while the coalescence on the two γ-Al 2 O 3 surfaces are inhibited significantly by increasing the initial internanoparticle distance. These findings demonstrate that the dynamics of the supporting surface is crucial to understanding the sintering mechanism and offer guidance for designing sinter-resistant catalysts by engineering the support morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Seed-Mediated Colloidal Synthesis of Multimetallic and High-Entropy Alloy Nanocrystal Libraries with Enhanced Catalytic Performance

Engineering colloidally stable multimetallic nanocrystals offers many benefits in a wide range of applications and allows manipulation of physical, chemical, and electronic properties of materials at the nanoscale. Synthesis routes are challenged by the chemical complexity required to temporally and spatially coordinate the reduction and alloying of multiple metal species, which has hampered the development of tunable libraries of colloidal materials to date. Here, in this work, we demonstrate a seed-mediated synthesis method to incorporate five or more metal elements into uniform, colloidally stable nanocrystals. By integrating machine learning-accelerated simulations, the synthesis of shortlisted high-entropy alloy nanocrystals was demonstrated. Multiple seed materials can be used, leading to a library of multimetallic nanocrystals with tunable electronic, physical, and alloy structures. The advantage of this synthetic protocol is highlighted in the preparation of catalytic materials that showed 2 orders of magnitude higher reaction rates than monometallic catalysts and outstanding thermal stability, thus highlighting the promise of this approach for high-performance materials in many areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Diameter-dependent multiple proton jumps dictate hydronium and hydroxide transport in carbon nanotubes

Nanofluidic channels impose extreme confinement on water, giving rise to unusual transport phenomena of the liquid. However, how the transport of hydroxide and hydronium ions is influenced by such confinement is still not fully understood. This study employs machine learning-accelerated simulations, based on the SCAN density functional, to investigate proton transfer dynamics in CNTs of varying diameters (0.8 nm to 2.8 nm). The extreme confinement of water inside a 0.8 nm CNT not only enhances the probability of multiple consecutive proton jumps, but also reverses the relative diffusion coefficient of hydronium and hydroxide ions in bulk water. In CNTs with diameters larger than 0.8 nm, hydronium diffuses slightly faster than in bulk water, whereas hydroxide diffusion slows because of its localization near CNT walls, hindering multiple proton jumps. This work highlights the significant impact of nanoscale confinement on proton transfer dynamics, with implications for designing nanoscale systems with controlled proton transport.

Chemistry↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2016

Explosive synchronization coexists with classical synchronization in the Kuramoto model

Explosive synchronization has recently been reported in a system of adaptively coupled Kuramoto oscillators, without any conditions on the frequency or degree of the nodes. Here, we find that, in fact, the explosive phase coexists with the standard phase of the Kuramoto oscillators. We determine this by extending the mean-field theory of adaptively coupled oscillators with full coupling to the case with partial coupling of a fraction f. This analysis shows that a metastable region exists for all finite values of f > 0, and therefore explosive synchronization is expected for any perturbation of adaptively coupling added to the standard Kuramoto model. We verify this theory with GPU-accelerated simulations on very large networks (N ∼ 10{sup 6}) and find that, in fact, an explosive transition with hysteresis is observed for all finite couplings. By demonstrating that explosive transitions coexist with standard transitions in the limit of f → 0, we show that this behavior is far more likely to occur naturally than was previously believed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

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

Efficient exact exchange using Wannier functions and other related developments in planewave-pseudopotential implementation of RT-TDDFT

The plane-wave pseudopotential (PW-PP) formalism is widely used for the first-principles electronic structure calculation of extended periodic systems. The PW-PP approach has also been adapted for real-time time-dependent density functional theory (RT-TDDFT) to investigate time-dependent electronic dynamical phenomena. In this work, we detail recent advances in the PW-PP formalism for RT-TDDFT, particularly how maximally localized Wannier functions (MLWFs) are used to accelerate simulations using the exact exchange. We also discuss several related developments, including an anti-Hermitian correction for the time-dependent MLWFs (TD-MLWFs) when a time-dependent electric field is applied, the refinement procedure for TD-MLWFs, comparison of the velocity and length gauge approaches for applying an electric field, and elimination of long-range electrostatic interaction, as well as usage of a complex absorbing potential for modeling isolated systems when using the PW-PP formalism.

Chemistry↗
DOE OSTI2022

Interplay between beam-driven chirping modes and plasma confinement transitions in spherical tokamak ST40

Experiments on the high field spherical tokamak ST40 have led to the recent observation of interplay between beam-driven modes of sweeping frequency (chirping modes) and transitions to the enhanced global confinement regime (H-mode) and back to the low confinement regime (L-mode). The H-modes of plasma confinement are identified from decreased intensity of D α signal and from clear distinctions in the edge gradients of the visible plasma boundary (observed as a sharp plasma edge in camera images). The beam-driven chirping modes, identified as ideal magnetohydrodynamics beta-induced Alfvén acoustic eigenmodes modes, are observed in Mirnov coil signals, interferometry, and soft x-ray diagnostics. A moderate amplitude ‘primer’ chirping mode usually precedes an H–L transition. This is followed by a ‘dominant’ chirping mode with higher amplitude during the L-mode. The L–H transition back to the improved confinement occurs on a longer time scale of tens of ms, consistent with the slowing down time scale of fast beam ions. A dramatic decrease in toroidal plasma rotation is systematically observed associated with chirping modes sweeping down to zero frequency. Resonance maps built for the beam-driven chirping modes with the ASCOT (accelerated simulation of charged particle orbits in torodoial devices) code show that the resonant beam ions have orbits near the trapped-passing boundary. The ASCOT modelling assesses how losses of the resonant fast ions caused by the chirping modes with high enough amplitude modify the torque, potentially affecting the plasma rotation.

magnetic confinement fusion↗
DOE OSTI2021

Constraining $\mathcal{f(R)}$ gravity with a $\mathcal{k}$-cut cosmic shear analysis of the Hyper Suprime-Cam first-year data

Here, using Subaru Hyper Suprime-Cam (HSC) year 1 data, we perform the first k-cut cosmic shear analysis constraining both Λ CDM and f(R) Hu-Sawicki modified gravity. To generate the f(R) cosmic shear theory vector, we use the matter power spectrum emulator trained on COLA (COmoving Lagrangian Acceleration) simulations [Phys. Rev. D 103, 123525 (2021)]. The k-cut method is used to significantly down-weight sensitivity to small scale (k > 1h Mpc -1 ) modes in the matter power spectrum where the emulator is less accurate, while simultaneously ensuring our results are robust to baryonic feedback model uncertainty. We have also developed a test to ensure that the effects of poorly modeled small scales are nulled as intended. For Λ CDM we find S 8 =σ 8 (Ω m /0.3) 0.5 =0.789$^{+0.039}_{-0.022}$, while the constraints on the f(R) modified gravity parameters are prior dominated. In the future, the k-cut method could be used to constrain a large number of theories of gravity where computational limitations make it infeasible to model the matter power spectrum down to extremely small scales.

79 ASTRONOMY AND ASTROPHYSICS↗