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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Quantitative measurements of electromechanical response with a combined optical beam and interferometric atomic force microscope

An ongoing challenge in atomic force microscope (AFM) experiments is the quantitative measurement of cantilever motion. The vast majority of AFMs use the optical beam deflection (OBD) method to infer the deflection of the cantilever. The OBD method is easy to implement, has impressive noise performance, and tends to be mechanically robust. However, it represents an indirect measurement of the cantilever displacement, since it is fundamentally an angular rather than a displacement measurement. Here, we demonstrate a metrological AFM that combines an OBD sensor with a laser Doppler vibrometer (LDV) to enable accurate measurements of the cantilever velocity and displacement. The OBD/LDV AFM allows a host of quantitative measurements to be performed, including in-situ measurements of cantilever oscillation modes in piezoresponse force microscopy. As an example application, we demonstrate how this instrument can be used for accurate quantification of piezoelectric sensitivity—a longstanding goal in the electromechanical community.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Improving assessments of tidal power potential using grid refinement in the Coupled Ocean-Atmosphere-Wave-Sediment Transport model

Tidal currents are a promising source for future power generation given their periodicity and predictability. Therefore, numerical hydrodynamic models are frequently utilized for resource assessments. However, the relevant scales of the simulations and hence modeling techniques depend on the problem at hand. This paper shows the potential of the grid refinement technique for the assessment of tidal current energy for particular sites given its relatively low computational expense and high accuracy for the regions with the refined resolution. Example applications are described for mapping the tidal resources near two facilities (Portsmouth Naval Shipyard in Maine and Key West Naval Station in Florida) for possible future deployments of Marine Hydro-Kinetic technologies. The grid refinement capability in the coupled ocean-atmosphere-wave-sediment transport modeling system is used to improve the spatial resolution in the regions of interest, and has shown significant advantage over the original simulation results for the tidal power assessment. The numerical simulation results from both the original coarse grids and the refined grids are validated with measured tidal constituents at available locations. This study demonstrates how the enhancement of a model system for a regional tidal energy with grid refinement can assist with the performance of site specific resource assessments with modest computational expense.

Yang, Xiufeng↗
DOE OSTI2016

Integrated fusion simulation with self-consistent core-pedestal coupling

In this study, accurate prediction of fusion performance in present and future tokamaks requires taking into account the strong interplay between core transport, pedestal structure, current profile and plasma equilibrium. An integrated modeling workflow capable of calculating the steady-state self- consistent solution to this strongly-coupled problem has been developed. The workflow leverages state-of-the-art components for collisional and turbulent core transport, equilibrium and pedestal stability. Validation against DIII-D discharges shows that the workflow is capable of robustly pre- dicting the kinetic profiles (electron and ion temperature and electron density) from the axis to the separatrix in good agreement with the experiments. An example application is presented, showing self-consistent optimization for the fusion performance of the 15 MA D-T ITER baseline scenario as functions of the pedestal density and ion effective charge Z eff .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Wide range local resistance imaging on fragile materials by conducting probe atomic force microscopy in intermittent contact mode

An imaging technique associating a slowly intermittent contact mode of atomic force microscopy (AFM) with a home-made multi-purpose resistance sensing device is presented. It aims at extending the widespread resistance measurements classically operated in contact mode AFM to broaden their application fields to soft materials (molecular electronics, biology) and fragile or weakly anchored nano-objects, for which nanoscale electrical characterization is highly demanded and often proves to be a challenging task in contact mode. Compared with the state of the art concerning less aggressive solutions for AFM electrical imaging, our technique brings a significantly wider range of resistance measurement (over 10 decades) without any manual switching, which is a major advantage for the characterization of materials with large on-sample resistance variations. After describing the basics of the set-up, we report on preliminary investigations focused on academic samples of self-assembled monolayers with various thicknesses as a demonstrator of the imaging capabilities of our instrument, from qualitative and semi-quantitative viewpoints. Then two application examples are presented, regarding an organic photovoltaic thin film and an array of individual vertical carbon nanotubes. Both attest the relevance of the technique for the control and optimization of technological processes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Optimization of a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications in nucleosynthesis experiments

Polar-direct-drive exploding pushers are used as a high-yield, low-areal-density fusion product source at the National Ignition Facility with applications including diagnostic calibration, nuclear security, backlighting, electron-ion equilibration, and nucleosynthesis-relevant experiments. In this paper, two different paths to improving the performance of this platform are explored: (i) optimizing the laser drive, and (ii) optimizing the target. While the present study is specifically geared towards nucleosynthesis experiments, the results are generally applicable. Example data from T 2 / 3 He-gas-filled implosions with trace deuterium are used to show that yield and ion temperature (Tion) from 1.6 mm-outer-diameter thin-glass-shell capsule implosions are improved at a set laser energy by switching from a ramped to a square laser pulse shape, and that increased laser energy further improves yield and Tion, although by factors lower than predicted by 1 D simulations. Using data from D2-3He-gas-filled implosions, yield at a set Tion is experimentally verified to increase with capsule size. Uniform D-3He-proton spectra from 3 mm-outer-diameter CH shell implosions demonstrate the utility of this platform for studying charged-particle-producing reactions relevant to stellar nucleosynthesis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Guidelines for creating artificial neural network empirical interatomic potential from first-principles molecular dynamics data under specific conditions and its application to $α$-Ag 2 Se

First-principles molecular dynamics (FPMD) simulations are highly accurate, but due to their high calculation cost, the computational scale is often limited to hundreds of atoms and few picoseconds under specific temperature and pressure conditions. As such, we present here the guidelines for creating artificial neural network empirical interatomic potential (ANN potential) trained with such a limited FPMD data, which can perform long time scale MD simulations at least under the same conditions. The FPMD data for training are prepared on the basis of the convergence of radial distribution function [$g(r)$]. While training the ANN using total energy and atomic forces of the FPMD data, the error of pressure is also monitored and minimized. To create further robust potential, we add a small amount of FPMD data to reproduce the interaction between two atoms that are close to each other. ANN potentials for $α$-Ag 2 Se were created as an application example, and it has been confirmed that not only $g(r)$ and mean square displacements but also the specific heat requiring a long time scale simulation matched the FPMD and the experimental values. In addition, the MD simulation using the ANN potential achieved over 10 4 acceleration over the FPMD one. The guidelines proposed here mitigate the creation difficulty of the ANN potential, and a lot of FPMD data sleeping on the hard disk after the research may be put on the front stage again.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Continuum electromagnetic gyrokinetic simulations of turbulence in the tokamak scrape-off layer and laboratory devices

Here, we present algorithms and results from Gkeyll, a full-f continuum, electromagnetic gyrokinetic code, designed to study turbulence in the edge region of fusion devices. The edge is computationally very challenging, requiring robust algorithms that can handle large-amplitude fluctuations and stable interactions with plasma sheaths. We present an energy-conserving high-order discontinuous Galerkin scheme that solves gyrokinetic equations in Hamiltonian form. Efficiency is improved by a careful choice of basis functions and automatically generated computation kernels. Previous verification tests were performed in the straight-field-line large plasma device [Shi et al., J. Plasma Phys. 83, 905830304 (2017)] and the Texas Helimak, a simple magnetized torus [Bernard et al., Phys. Plasmas 26, 042301 (2019)], including the effect of end-plate biasing on turbulence. Results for the scrape-off layer for NSTX parameters with a model helical magnetic geometry with bad curvature have been obtained [Shi et al., Phys. Plasmas 26, 012307 (2019)]. In this paper, we present algorithms for the two formulations of electromagnetic gyrokinetics: the Hamiltonian and the symplectic. We describe each formulation and show results of benchmark tests. Although our scheme works for the Hamiltonian formulation, the presence of spurious numerical modes for high-β and large $k^2_⊥ρ^2_s$ regimes shows that the symplectic formulation is more robust. We then review our recent algorithm for the symplectic formulation [Mandell et al., J. Plasma Phys. 86, 905860109 (2020)], along with example application of this new capability. Maintaining positivity of the distribution function can be challenging, and we describe a new and novel exponential recovery based algorithm to address this.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Shockwave dissipation by interface-dominated porous structures

The advent of additive manufacturing (AM) has enabled topological control of structures at the micrometer scale, transforming the properties of polymers for a variety of applications. Examples include tailored mechanical responses, acoustic properties, and thermal properties. Porous polymer materials are a class of materials used for shock and blast mitigation, yet they frequently possess a lack of structural order and are largely developed and evaluated via trial-and-error. Here, we demonstrate control of shockwave dissipation through interface-dominated structures prepared by AM using 2-photon polymerization. A fractal structure with voids, or free surfaces, arranged less than 100 μm apart, allows for rarefaction interactions on the timescale of the shockwave loading. Simulations and dynamic x-ray phase contrast imaging experiments show that fractal structures with interfaces assembled within a “critical” volume reduce shockwave stress and wave velocity by over an order of magnitude within the first unit cell.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reduced scaling formulation of CASPT2 analytical gradients using the supporting subspace method

We present a reduced scaling and exact reformulation of state specific complete active space second-order perturbation (CASPT2) analytical gradients in terms of the MP2 and Fock derivatives using the supporting subspace method. This work follows naturally from the supporting subspace formulation of the CASPT2 energy in terms of the MP2 energy using dressed orbitals and Fock builds. For a given active space configuration, the terms corresponding to the MP2-gradient can be evaluated with O(N5) operations, while the rest of the calculations can be computed with O(N3) operations using Fock builds, Fock gradients, and linear algebra. When tensor-hyper-contraction is applied simultaneously, the computational cost can be further reduced to O(N4) for a fixed active space size. The new formulation enables efficient implementation of CASPT2 analytical gradients by leveraging the existing graphical processing unit (GPU)-based MP2 and Fock routines. We present benchmark results that demonstrate the accuracy and performance of the new method. Example applications of the new method in ab initio molecular dynamics simulation and constrained geometry optimization are given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Novel 2D velocity estimation method for large transient events in plasmas

Dynamics of fast transient events are challenging to be analyzed with high time resolution. Such events can occur in fusion plasmas such as the filaments during edge-localized modes (ELMs). Here, we present a robust method—the spatial displacement estimation—for estimating the displacements of structures with fast dynamics from high spatial and time resolution imaging diagnostics [e.g., gas-puff imaging (GPI)] with sampling time temporal resolution. First, a background suppression method is shown, which suppresses the slowly time-evolving and spatially non-uniform background in the signal. In the second step, a two-dimensional polynomial trend subtraction method is presented to tackle the remaining polynomial order trend in the signal. After performing these pre-processing steps, the spatial displacement of the propagating structure is estimated from the two-dimensional spatial cross-correlation coefficient function calculated between consecutive frames. The method is tested for its robustness and accuracy by simulated Gaussian events and spatially displaced random noise. An example application of the method is presented on propagating ELM filaments measured by the GPI system on the National Spherical Torus Experiment spherical tokamak.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

AENET–LAMMPS and AENET–TINKER : Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet–TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet–LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials.

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

Differentiable quantum chemistry with PySCF for molecules and materials at the mean-field level and beyond

We introduce an extension to the PySCF package, which makes it automatically differentiable. The implementation strategy is discussed, and example applications are presented to demonstrate the automatic differentiation framework for quantum chemistry methodology development. These include orbital optimization, properties, excited-state energies, and derivative couplings, at the mean-field level and beyond, in both molecules and solids. We also discuss some current limitations and directions for future work.

Chemistry↗
DOE OSTI2024

A nonlinear journey from structural phase transitions to quantum annealing

Motivated by an exact mapping between equilibrium properties of a one-dimensional chain of quantum Ising spins in a transverse field (the transverse field Ising (TFI) model) and a two-dimensional classical array of particles in double-well potentials (the “$\phi$ 4 model”) with weak inter-chain coupling, we explore connections between the driven variants of the two systems. Here, we argue that coupling between the fundamental topological solitary waves in the form of kinks between neighboring chains in the classical $\phi$ 4 system is the analog of the competing effect of the transverse field on spin flips in the quantum TFI model. As an example application, we mimic simplified measurement protocols in a closed quantum model system by studying the classical $\phi$ 4 model subjected to periodic perturbations. This reveals memory/loss of memory and coherence/decoherence regimes, whose quantum analogs are essential in annealing phenomena. In particular, we examine regimes where the topological excitations control the thermal equilibration following perturbations. This paves the way for further explorations of the analogy between lower-dimensional linear quantum and higher-dimensional classical nonlinear systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

A physics-constrained deep learning treatment of runaway electron dynamics

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker–Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained, the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Three-dimensional mesostructures as high-temperature growth templates, electronic cellular scaffolds, and self-propelled microrobots

Recent work demonstrates that processes of stress release in prestrained elastomeric substrates can guide the assembly of sophisticated 3D micro/nanostructures in advanced materials. Reported application examples include soft electronic components, tunable electromagnetic and optical devices, vibrational metrology platforms, and other unusual technologies, each enabled by uniquely engineered 3D architectures. A vast disadvantage of these systems is that the elastomeric substrates, while essential to the assembly process, can impose significant engineering constraints in terms of operating temperatures and levels of dimensional stability; they also prevent the realization of 3D structures in freestanding forms. In this work, we introduce concepts in interfacial photopolymerization, nonlinear mechanics, and physical transfer that bypass these limitations. The results enable 3D mesostructures in fully or partially freestanding forms, with additional capabilities in integration onto nearly any class of substrate, from planar, hard inorganic materials to textured, soft biological tissues, all via mechanisms quantitatively described by theoretical modeling. Illustrations of these ideas include their use in 3D structures as frameworks for templated growth of organized lamellae from AgCl–KCl eutectics and of atomic layers of WSe 2 from vapor-phase precursors, as open-architecture electronic scaffolds for formation of dorsal root ganglion (DRG) neural networks, and as catalyst supports for propulsive systems in 3D microswimmers with geometrically controlled dynamics. Taken together, these methodologies establish a set of enabling options in 3D micro/nanomanufacturing that lie outside of the scope of existing alternatives.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities

As more distributed energy resources become part of the demand-side infrastructure, quantifying their energy flexibility on a community scale is crucial. CityLearn v1 provided an environment for benchmarking control algorithms. However, there is no standardized environment utilizing realistic building-stock datasets for distributed energy resource control benchmarking without co-simulation or third-party frameworks. CityLearn v2 extends CityLearn v1 by providing a stand-alone simulation environment that leverages the End-Use Load Profiles for the U.S. Building Stock dataset to create grid-interactive communities for resilient, multi-agent, and objective control of distributed energy resources with dynamic occupant feedback. While the v1 environment used pre-simulated building thermal loads, the v2 environment uses data-driven thermal dynamics and eliminates the need for co-simulation with building energy performance software. This work details the v2 environment and provides application examples that use reinforcement learning control to manage battery energy storage system, vehicle-to-grid control, and thermal comfort during heat pump power modulation.

Nweye, Kingsley↗
DOE OSTI2019

Forecasting super-sample covariance in future weak lensing surveys with SuperSCRAM

The observable universe contains density perturbations on scales larger than any finite volume survey. Perturbations on scales larger than a survey can measure degrade its power to constrain cosmological parameters. The dependence of survey observables such as the weak lensing power spectrum on these long-wavelength modes results in super-sample covariance. Accurately forecasting parameter constraints for future surveys requires accurately accounting for the super-sample effects. If super-sample covariance is in fact a major component of the survey error budget, it may be necessary to investigate mitigation strategies that constrain the specific realization of the long-wavelength modes. We present a Fisher matrix based formalism for approximating the magnitude of super-sample covariance and the effectiveness of mitigation strategies for realistic survey geometries. We implement our formalism in the public code SuperSCRAM: Super-Sample Covariance Reduction and Mitigation. We illustrate SuperSCRAM with an example application, where the modes contributing to supersample covariance in the WFIRST weak lensing survey are constrained by the low-redshift galaxy number counts in the wider LSST footprint. We find that super-sample covariance increases the volume of the error ellipsoid in 7D cosmological parameter space by a factor of 4.5 relative to Gaussian statistical errors only, but our simple mitigation strategy more than halves the contamination, to a factor of 2.0.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Quantification of spatial and seasonal trends in the atmosphere and construction of statistical models for infrasonic propagation

SUMMARY Infrasonic waves are influenced by variations in the density, pressure and temperature as well as the ambient winds. Modelling infrasonic propagation can be challenging due to the dynamic nature of the atmosphere as well as the sparseness of measurements which result in variability and notable uncertainty. A framework is presented to quantify spatial and seasonal trends in atmospheric structure via analysis of the effective sound speed profile and identification of temporal trends in the middle atmospheric waveguide produced by the circumpolar vortex winds. Seasonal definitions identifying typical atmospheric structures during the summer, winter and spring/fall transition periods are identified using atmospheric data from 2010 through 2020. Seasonal trend analysis is conducted for a number of locations across the contiguous United States to quantify spatial variations in atmospheric structure that impact infrasonic propagation. For each season and location, empirical orthogonal function analysis is used to reduce the historical archive of atmospheric data into a smaller representative set that can be analysed using numerical tools more efficiently. Infrasonic ray tracing and finite-frequency modal propagation analyses are applied to construct propagation path geometry and transmission loss statistics which are useful in localization and yield estimation for infrasonic sources, respectively. An example application is detailed in which transmission loss statistics are combined with an explosive source model and noise statistics to quantify the capability of a network to detect nearby sources.

58 GEOSCIENCES↗