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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Structure of amorphous materials in the NASICON system Na 1+x Ti 2 Si x P 3−x O 12

Abstract The structure of glasses in the sodium (Na) super-ionic conductor (NASICON) system Na 1 + x Ti 2 Si x P 3 − x O 12 with x = 0.8 and x = 1.0 was explored by combining neutron and high-energy x-ray diffraction with 29 Si, 31 P and 23 Na solid-state nuclear magnetic resonance (NMR) spectroscopy. The 29 Si magic angle spinning (MAS) NMR spectra reveal that the silica component remains fully polymerized in the form of Si 4 units, i.e. the silicon atoms are bound to four bridging oxygen atoms. The 31 P{ 23 Na} rotational echo adiabatic passage double resonance (REAPDOR) NMR data suggest that the 31 P MAS NMR line shape originates from four-coordinated P n units, where n = 1, 2 or 3 is the number of bridging oxygen atoms per phosphorus atom. These sites differ in their 31 P- 23 Na dipolar coupling strengths. The results support an intermediate range order scenario of a phosphosilicate mixed network-former glass in which the phosphate groups selectively attract the Na + modifier ions. Titanium takes a sub-octahedral coordination environment with a mean Ti–O coordination number of 5.17(4) for x = 0.8 and 4.86(4) for x = 1.0. A mismatch between the P–O and Si–O bond lengths of 8% is likely to inhibit the incorporation of silicon into the phosphorus sites of the NASICON crystal structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Canted standing spin-wave modes of permalloy thin films observed by ferromagnetic resonance

Non-collinear spin structures in materials that combine perpendicular and in-plane magnetic anisotropies are of great technological interest for microwave and spin wave-assisted magnetization switching. [Co/Pt] multilayers are well-known perpendicular anisotropy materials that have the potential to pin the magnetization of a soft magnetic layer, such as permalloy (Py), that has in-plane anisotropy, thereby forming a magnetic exchange spring. Here we report on multilayered [Co/Pt]/Pt/Py films, where an additional ultrathin Pt spacer has been included to control the coupling between the sub-units with in-plane and perpendicular magnetic anisotropy. Vector network analyser (VNA)-ferromagnetic resonance (FMR) measurements were made to obtain a complete picture of the resonant conditions, while the dynamical response of the sub-units was probed by synchrotron-based element- and phase selective x-ray detected FMR (XFMR). For all samples, only slight pinning of the dynamic magnetization of the Py by the [Co/Pt] was noted, and the FMR results were dominated by the 50 nm thick Py layer. Out-of-plane VNA-FMR maps reveal the presence of additional modes, e.g. a perpendicular standing spin-wave (PSSW) state. However, as the magnetic field is reduced below the saturation field, the PSSW state morphs continuously through a series of canted standing spin-wave (CSSW) states into a horizontal standing spin-wave (HSSW) state. The PSSW, CSSW and HSSW states are well described using a multilayer model of the Py film. The observation of CSSW modes is of particular relevance to microwave assisted magnetic recording, where microwave excitation stimulates precession of a soft layer canted out of plane by a pulsed magnetic field.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Adaptive pruning-based optimization of parameterized quantum circuits

Abstract Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the Dynamic Sparse Reparameterization scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k -UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Physics↗
DOE OSTI2023

Search for Nonresonant Pair Production of Highly Energetic Higgs Bosons Decaying to Bottom Quarks

A search for nonresonant Higgs boson ($H$) pair production via gluon and vector boson ($V$) fusion is performed in the four-bottom-quark final state, using proton-proton collision data at 13 TeV corresponding to 138 fb$^{−1}$ collected by the CMS experiment at the LHC. The analysis targets Lorentz-boosted $H$ pairs identified using a graph neural network. It constrains the strengths relative to the standard model of the $H$ self-coupling and the quartic VVHH couplings, $κ_{2V}$, excluding $κ_{2V} = 0$ for the first time, with a significance of 6.3 standard deviations when other H couplings are fixed to their standard model values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Flow Assisted Evaporative Cooling for Electric Motor

This paper examines a novel concept of flow assisted latent heat driven two-phase evaporative cooling (EC) confined in-between slot liner and active-winding of electric motor. Wicking micro-structure enhanced PDMS liner axially sucks coolant in the form of thin film between the PDMS liner and active-winding and eventually enables thin film evaporation on the outer surface of the active-winding. Therefore, EC based thermal management eliminates contact resistance between the winding and slot-liner and, enhances the heat extraction from the winding without compromising the electro-magnetic performance. Two-way coupled electro-magnetic (EM) – computational fluid dynamics/heat transfer (CFD/HT) and EM - lumped parameter thermal network (LPTN) models have been developed to assess the electro-thermal performance of the EC under steady and transient conditions. Taking a case study of a 125 kW jacket cooled BMW i3 motor and dielectric coolant FC-84, EC is shown to be capable of handling a maximum steady state rms current density of 26 A/mm2 at a evaporative heat transfer coefficient of 5,000 W/m2.K, which is about 78.7% higher compared to the traditional jacket cooling (JC). In case of EC, a maximum steady state and transient rms current density of 30 A/mm2 (106.2% higher compared to the JC), and 40.8 A/mm2 have been realized by using high thermal conductivity epoxy (1.9 W/m.K) impregnation material. Thermal performance of the EC is also assessed and compared with JC over a dynamic drive cycle. Lastly, a motorette testing has been performed to demonstrate the applicability of the proposed EC method and to validate the developed modeling framework.

42 ENGINEERING↗
DOE OSTI2024

Environmental Molecular Network (ENVnet) v1

Here, we present an approach that integrates mass difference based deconvolution with molecular networking to build a static reference network from all publicly available organic matter metabolomics datasets. This is accomplished using MS/MS deconvolution coupled with both recently reported (BUDDY) and novel machine learning algorithms to determine chemical formulas and perform MS/MS alignments (REM-BLINK).

Bowen, Benjamin↗
DOE OSTI2023

Single-Photon Detector & Counter for Transduction

Over the recent years, quantum computers have procured the attention of the public, largely due to their fast factoring capabilities and potential for efficient simulation of quantum dynamics \cite{RevModPhys.79.135}. This interest, coupled with advances in quantum transduction, could lead to the realization of a quantum distributed network, effectively linking dilution fridges across vast distances. Presently, there are several qubit systems developed for encoding quantum information. They primarily fall into two categories: those exploiting optical frequencies for excitation, and those operating at microwave frequencies. Optical frequency qubit systems include trapped ions, neutral atoms, quantum dots, and solid-state defects. Microwave frequency qubit system encompass superconducting qubits (transmon) and spins in crystals. Among these, transmons are particularly intriguing due to their low-loss of photons at microwave frequencies. The Josephson effect within a superconducting circuit enables high-fidelity quantum operations \cite{Microwave-optical_quantum_frequency_conversion}. Thus, it is non-trivial to develop an apparatus for efficiently bridging microwave and optical frequency regimes.

Bautista-Cortezano, Pablo↗
DOE OSTI2025

Framework for Assessing Impact of Wave-Powered Desalination on Resilience of Coastal Communities

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Routing flits in a network-on-chip based on operating states of routers

A system is described that includes an integrated circuit chip having a network-on-chip. The network-on-chip includes multiple routers arranged in a topology and a separate communication link coupled between each router and each of one or more neighboring routers of that router among the multiple routers in the topology. The integrated circuit chip also includes multiple nodes, each node coupled to a router of the multiple routers. When operating, a given router of the multiple routers keeps a record of operating states of some or all of the multiple routers and corresponding communication links. The given router then routes flits to destination nodes via one or more other routers of the multiple routers based at least in part on the operating states of the some or all of the multiple routers and the corresponding communication links.

Bharadwaj, Srikant↗
DOE OSTI2022

Modeling Integrated Electricity and Natural Gas Systems Using Co-Simulation

One approach for modeling natural gas and electricity networks as integrated systems is co-simulation, which involves separating a single model into subproblems that are solved iteratively, with coupling information passed between then. Although a fully integrated problem might be more direct, co-simulation offers advantages in that it can sometimes be more computationally tractable, and can also leverage existing simulation tools to represent the subproblems. Here we present a framework for co-simulation of natural gas and electricity networks using the HELICS tool. We provide validation of the co-simulation approach by comparing results with a fully integrated approach that uses the same simulation tool, finding that the two approaches provide identical results for a range of test cases. We then present results from natural gas and electricity network co-simulation case study of the Colorado Front Range, with an evaluation of the present-day system and also a future system with higher shares of renewable power. The presentation will close with a discussion on future avenues of work for co-simulation of gas and power networks.

co-simulation↗
DOE OSTI2025

Normalizing flows for high-dimensional detector simulations

Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

Ernst, Florian↗
DOE OSTI2025

Animal movement estimation and network-based epidemic modeling: Illustration for the swine industry in Iowa (US)

Animal movement plays a critical role in disease transmission between farms. However, in the United States, the lack of available animal shipment data, sometimes coupled with a lack of detailed information about farm demographics and characteristics, presents great challenges for epidemic modeling and prediction. In this study, we proposed a new method based on the maximum entropy to generate “synthetic” animal movement networks, considering available statistics about the premises operation type, operation size, and the distance between premises. We illustrated our method for the swine movement networks in Iowa and performed network analyses to gain insights into the swine industry. We then applied the generated networks to a network-based epidemic model to identify potential system vulnerabilities in terms of disease transmission. The model was parameterized for African Swine Fever (ASF) as the US swine industry is quite concerned about this disease. Results show that premises with a central role in the network are more vulnerable to disease outbreaks and play an important role in disease spread. Simulations with outbreaks starting from random farms reveal no significant large outbreaks, indicating the system’s relative robustness against arbitrary disease introductions. However, outbreaks originating from high out-degree farms can lead to large epidemic sizes. This underscores the importance for stakeholders and policymakers to continue improving animal movement records and traceability programs in the US and the value of making that data available to epidemiologists and modelers to better understand risk and inform strategies aimed to cost-effectively prevent and control disease transmission. Our approach could be easily adapted to estimate movement networks in other animal production systems and to inform disease spread models for various infectious diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Direct Probe of the Hydrogen Bond Network in Aqueous Glycerol Aerosols

The properties of aerosols are of paramount importance in atmospheric chemistry and human health. The hydrogen bond network of glycerol-water aerosols generated from an aqueous solution with different mixing ratios is probed directly with X-ray photoelectron spectroscopy. The carbon and oxygen X-ray spectra reveal contributions from gas and condensed phase components of the aerosol. It is shown that water suppresses glycerol evaporation up to a critical mixing ratio. A dielectric analysis using terahertz spectroscopy coupled with infrared spectroscopy of the bulk solutions provides a picture of the microscopic heterogeneity prevalent in the hydrogen bond network when combined with the photoelectron spectroscopy analysis. The hydrogen bond network is composed of three intertwined regions. At low concentrations, glycerol molecules are surrounded by water forming a solvated water network. Adding more glycerol leads to a confined water network, maximizing at 22 mol %, beyond which the aerosol resembles bulk glycerol. This microscopic view of hydrogen bonding networks holds promise in probing evaporation, diffusion dynamics, and reactivity in aqueous aerosols.

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

Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems

Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.

DC–AC power systems↗
DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2023

CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling

Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modelling. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate ab initio molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study technologically relevant phenomena. Here we present the Crystal Hamiltonian Graph Neural Network (CHGNet), a graph neural network-based machine-learning interatomic potential (MLIP) that models the universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory calculations of more than 1.5 million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li x MnO 2 , the finite temperature phase diagram for Li x FePO 4 and Li diffusion in garnet conductors. We highlight the significance of charge information for capturing appropriate chemistry and provide insights into ionic systems with additional electronic degrees of freedom that cannot be observed by previous MLIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons

We report on switching dynamics of individual and coupled vanadium dioxide (VO 2 ) devices subject to voltage pulses as the temperature is systematically varied from room temperature spanning the insulator–metal transition (IMT) temperature. The switching voltage of single devices has a strong relationship with both temperature and voltage pulsewidth. Two-step switching in connected VO 2 devices has been noted in current transient plots and was found to depend on temperature, pulsewidth, and pulse amplitude. Experimental switching behavior measured from VO 2 artificial neurons was implemented into a spiking neural network (SNN). During training, modulating the switching voltage via temperature affords a novel method to implement homeostasis with the coupled devices. Simulation results show the efficacy of the stochastic neuronal characteristics and the proposed homeostasis mechanism on a standard digit recognition task. As a result, these studies contribute to ongoing efforts in neuromorphic computing exploiting collective phase transitions.

42 ENGINEERING↗
DOE OSTI2024

Nonreciprocal Dissipation Engineering via Strong Coupling with a Continuum of Modes

Optical nonreciprocity plays a key role in almost every optical system, directing light flow and protecting optical components from backscattered light. Controllable forms of on-chip nonreciprocity are needed for the robust operation of increasingly sophisticated photonic integrated circuits (PICs) in the context of classical and quantum computation, networking, communications, and sensing. However, it has been challenging to achieve wideband, low-loss optical nonreciprocity on-chip. In this paper, we demonstrate strong coupling and Rabi-like energy exchange between photonic bands, possessing a continuum of modes, to unlock nonreciprocity and frequency translation over wide optical bandwidths in silicon. Using a traveling-wave phonon field to drive indirect interband photonic transitions, we demonstrate band hybridization that enables an intriguing form of nonreciprocal dissipation engineering. Using the converted mode to create a nonreciprocal dissipation channel, we demonstrate a frequency-neutral, low-loss (less than 1 dB) isolator with high nonreciprocal contrast (more than 14 dB) and broad operating bandwidth (more than 59 GHz). Additionally, through the implementation of complete interband conversion, we demonstrate a high extinction (more than 55 dB) optical frequency translation operation with near-unity efficiency.

Zhou, Yishu↗