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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing the D-region ionosphere globally with Earth Networks Total Lightning Network data

An existing technique to use broadband lightning waveforms to probe the D-region ionosphere (60–90 km altitude) is shown to be extendable to a global scale using the Earth Networks Total Lightning Network (ENTLN). This paper demonstrates the technique in detail on a region of the Southeastern United States. This demonstration shows that diurnal D-region height variation and smaller time-scale variations on the order of tens of minutes to hours are evident in the measurement. The technique is then extended to three additional global regions on this same day: Northeastern U.S., India, and Japan. The diurnal behavior between these different regions is compared to a D-region model from the International Reference Ionosphere.

D-region ionosphere↗
DOE OSTI2021

Enhanced Data Efficiency Using Deep Neural Networks and Gaussian Processes for Aerodynamic Design Optimization

Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then be used in a gradient-based optimizer. This makes them very well suited for high-fidelity simulation based aerodynamic shape optimization of highly parametrized geometries such as aircraft wings. However, the development of adjoint-based solvers involve careful mathematical treatment and their implementation require detailed software development. Furthermore, they can become prohibitively expensive when multiple optimization problems are being solved, each requiring multiple restarts to circumvent local optima. In this work, we propose a machine learning enabled, surrogate-based framework that replaces the expensive adjoint solver, without compromising on predicting predictive accuracy. Specifically, we first train a deep neural network (DNN) from training data generated from evaluating the high-fidelity simulation model on a model-agnostic design of experiments on the geometry shape parameters. The optimum shape may then be computed by using a gradient-based optimizer coupled with the trained DNN. Subsequently, we also perform a gradient-free Bayesian optimization, where the trained DNN is used as the prior mean. We observe that the latter framework (DNN-BO) improves upon the DNN-only based optimization strategy for the same computational cost. Overall, this framework predicts the true optimum with very high accuracy, while requiring far fewer high-fidelity function calls compared to the adjoint-based method. Furthermore, we show that multiple optimization problems can be solved with the same machine learning model with high accuracy, to amortize the offline costs associated with constructing our models. Our methodology finds applications in the early stages of aerospace design. (C) 2021 Published by Elsevier Masson SAS.

Renganathan, S. Ashwin↗
DOE OSTI2022

Tensor Network Path Integral Study of Dynamics in B850 LH2 Ring with Atomistically Derived Vibrations

The recently introduced multisite tensor network path integral (MS-TNPI) allows simulation of extended quantum systems coupled to dissipative media. We use MS-TNPI to simulate the exciton transport and the absorption spectrum of a B850 bacteriochlorophyll (BChl) ring. The MS-TNPI network is extended to account for the ring topology of the B850 system. Accurate molecular-dynamics-based description of the molecular vibrations and the protein scaffold is incorporated through the framework of Feynman–Vernon influence functional. To relate the present work with the excitonic picture, an exploration of the absorption spectrum is done by simulating it using approximate and topologically consistent transition dipole moment vectors. Comparison of these numerically exact MS-TNPI absorption spectra are shown with second-order cumulant approximations. Finally, the effect of temperature on both the exact and the approximate spectra is also explored.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Neutron Spin–Echo Studies of the Structural Relaxation of Network Liquid ZnCl 2 at the Structure Factor Primary Peak and Prepeak

Using neutron spin-echo spectroscopy, we studied the microscopic structural relaxation of a prototypical network ionic liquid ZnCl 2 at the structure factor primary peak and pre-peak. The results show that the relaxation at the primary peak is faster than the pre-peak and the activation energy is ≈33% higher. Stretched exponential relaxation is observed even at temperatures well above the melting point T m . Surprisingly, the stretching exponent shows a rapid increase upon cooling, especially at the primary peak, where it changes from stretched exponential to simple exponential on approaching T m . Furthermore, these results suggest that the appearance of glassy dynamics typical of the supercooled state even in the equilibrium liquid state of ZnCl 2 as well as the difference of activation energy at the two investigated length scales are related to the formation of network structure on cooling.

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

Designing Glass and Crystalline Phases of Metal–Bis(acetamide) Networks to Promote High Optical Contrast

Owing to their high tunability and predictable structures, metal–organic materials offer a powerful platform to study glass formation and crystallization processes and to design glasses with unique properties. In this work, we report a novel series of glass-forming metal–ethylenebis(acetamide) networks that undergo reversible glass and crystallization transitions below 200 °C. The glass-transition temperatures, crystallization kinetics, and glass stability of these materials are readily tunable, either by synthetic modification or by liquid-phase blending, to form binary glasses. Pair distribution function (PDF) analysis reveals extended structural correlations in both single and binary metal–bis(acetamide) glasses and highlights the important role of metal–metal correlations during structural evolution across glass–crystal transitions. Notably, the glass and crystalline phases of a Co–ethylenebis(acetamide) binary network feature a large reflectivity contrast ratio of 4.8 that results from changes in the local coordination environment around Co centers. These results provide new insights into glass–crystal transitions in metal–organic materials and have exciting implications for optical switching, rewritable data storage, and functional glass ceramics.

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

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Chemistry↗
DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗
DOE OSTI2020

Mapping the Temperature-dependent and network site-specific onset of spectral diffusion at the surface of a water cluster cage

We explore the kinetic processes that sustain equilibrium in a microscopic, finite system. This is accomplished by monitoring the spontaneous, time-dependent frequency evolution (the frequency autocorrelation) of a single OH oscillator, embedded in a water cluster held in a temperature-controlled ion trap. The measurements are carried out by applying two-color, IR-IR photodissociation mass spectrometry to the D3O+?(HDO)(D2O)19 isotopologue of the “magic number” protonated water cluster, H+?(H2O)21. The OH group can occupy any one of the five spectroscopically distinct sites in the distorted pentagonal dodecahedron cage structure. The OH frequency is observed to evolve over tens of milliseconds in the temperature range (90-120 K). Starting at 100 K, large “jumps” are observed between two OH frequencies separated by ~300 cm-1 indicating migration of the OH group from the bound OH site at 3350 cm-1 to the free position at 3686 cm-1. Increasing the temperature to 110 K leads to partial interconversion among many sites. All sites are observed to interconvert at 120 K such that the distribution of the unique OH group among them adopts the form one would expect for a canonical ensemble. The spectral dynamics displayed by the clusters thus offer an unprecedented view into the molecular-level processes that drive spectral diffusion in an extended network of water molecules.

Yang, Nan↗
DOE OSTI2022

A Solar-assisted Voltage Optimization Method for Transmission Solar Network Power System

This paper proposes a new formulation and solution algorithm that uses transmission level solar inverters to address the security-constrained optimal power flow (SCOPF) problem. The goal is to stabilize voltage fluctuations in transmission networks in base case and contingency scenarios, by using bulk solar power plant with a minimal number of post-contingency corrections. To achieve this goal, a two-stage volt/var optimization method is proposed to first correct all voltage violations with the volt-var alternating current optimal power flow (ACOPF) algorithm for a base case. Then a linearized SCOPF volt-var control algorithm is proposed to identify the corrective actions for all potential voltage violations in all contingency scenarios. The proposed method was tested and validated on a modified IEEE 118-bus system with solar photovoltaic (PV) data.

Photovoltaic, Volt/Var Control↗
DOE OSTI2020

The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.

Ruby, Max↗
DOE OSTI2020

Neuroevolution of Spiking Neural Networks Using Compositional Pattern Producing Networks

Spiking neural networks (SNNs) offer tremendous potential for the future of AI, including the ability to be implemented efficiently on neuromorphic systems. One of the challenges in building functioning SNNs is the training process, as standard error back-propagation cannot be easily applied. In this work, we extend an evolutionary approach for training SNNs by implementing an indirect encoding of individuals. Specifically, we evolve SNNs using Compositional Pattern Producing Networks, which are able to learn the connectivity patterns between neurons defined in a coordinate space. We validate the approach on multiple control and classification tasks.

Elbrecht, Daniel↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
DOE OSTI2021

Machine-learning-aided cognitive reconfiguration for flexible-bandwidth HPC and data center networks [Invited]

This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.

Chen, Xiaoliang (ORCID:0000000278056237)↗
DOE OSTI2023

Designing noise-robust quantum networks coexisting in the classical fiber infrastructure

The scalability of quantum networking will benefit from quantum and classical communications coexisting in shared fibers, the main challenge being spontaneous Raman scattering noise. We investigate the coexistence of multi-channel O-band quantum and C-band classical communications. We characterize multiple narrowband entangled photon pair channels across 1282 nm-1318 nm co-propagating over 48 km of installed standard fiber with record C-band power (>18 dBm) and demonstrate that some quantum-classical wavelength combinations significantly outperform others. We analyze the Raman noise spectrum, optimal wavelength engineering, multi-photon pair emission in entangled photon-classical coexistence, and evaluate the implications for future quantum applications.

Optics↗
DOE OSTI2020

Establishing a Technical Assistance Network to Build Capacity in Southwest Alaska (Southwest Alaska Energy Network - Final Report)

The Southwest Alaska Municipal Conference (SWAMC) is a non-profit regional membership economic development organization that represents the Aleutian/Pribilof Islands, Bristol Bay, and Kodiak regions of southwest Alaska. SWAMC applied for the DOE-OIE Establishment of an Inter-Tribal Technical Assistance Energy Providers Network grant FOA to work with our partners to provide energy planning and project development technical assistance. The project team was made up of SWAMC, three regional organizations, a management consulting firm, and a panel of technical consultants. SWAMC sub-contracted with the three Alaska Native regional non-profit organizations – Aleutian Pribilof Islands Association (APIA), Bristol Bay Native Association (BBNA), and Kodiak Area Native Association (KANA) – to fund full or partial Regional Energy Coordinator (REC) positions. The project period ran from September 2016 to March 2020. The project goal was to help southwest Alaska regional tribal partners and communities to develop efficient and financially sustainable structures for identifying and developing energy projects that enhance community resiliency and energy sustainability. This project established energy coordinators and management structures in the Aleutian, Bristol Bay, and Kodiak regions to expand technical assistance capacity of regional residents; demonstrate this capacity by advancing energy efficiency, heat, and power supply projects; and secure long-term funding commitments to establish a sustained technical assistance structure. The project team expanded technical assistance capacity of energy coordinators and regional stakeholders in several ways: by providing funding for the SWAMC project manager to attend three Office of Indian Energy trainings; for energy coordinators to attend numerous energy conferences; for utility clerks from several villages to receive one-on-one reporting training on Alaska’s Power Cost Equalization electric subsidy program; and for the Kodiak REC to complete the Arctic Remote Energy Networks Academy and NREL’s Executive Energy Leadership Academy. The energy coordinators demonstrated and shared their increased capacity by hosting several public events: SWAMC hosted two full-day energy workshops in February 2017 and 2018; the Kodiak REC hosted seven Energy Committee meetings for Kodiak stakeholders and gave several presentations at other events; and SWAMC and BBNA organized a Bristol Bay Regional Energy Visioning Session in May 2019. The project team created platforms to both share and request information to involve energy stakeholders in this project, including an energy website, a Facebook group, a periodic newsletter, surveys, mass emails, and paper mailers. An increase in regional capacity was demonstrated through several grant awards, including a $1.2 million USDA grant for Akhiok for an electric distribution infrastructure replacement; an AHFC Kickstarter grant for Aleknagik to audit 2 Tribal and 3 City buildings; and installation of an Air Source Heat Pump demonstration project in Atka. Two communities and one region – Ouzinkie (May 2017), Ugashik (July 2017), and the Bristol Bay region (May 2019) – utilized DOE’s technical assistance services to hold Strategic Energy Planning sessions with NREL and DOE facilitation assistance. And in early 2018, SWAMC established a parallel program, funded through a USDA Energy Audit and Renewable Energy Development grant to provide subsidized energy audits for small businesses in the region. SWAMC and partners have now completed energy audits of over 60 businesses (buildings and fishing vessels) and are currently operating a third round of the USDA program. Fifteen of those business owners have now received additional grant funding to cover 25% of the cost of the energy efficiency upgrades identified in the audit. This technical assistance structure will be sustained beyond DOE grant funding in several forms. As a sign of increased grant writing and project management capacity, the Kodiak Regional Energy Coordinator applied for and received a USDA Community Facilities Technical Assistance and Training grant to continue work begun under this program. Energy coordination tasks have been folded into existing economic development positions at SWAMC and at BBNA, ensuring long-term outreach and support in the region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1988

Local area networking: Ames centerwide network

A computer network can benefit the user by making his/her work quicker and easier. A computer network is made up of seven different layers with the lowest being the hardware, the top being the user, and the middle being the software. These layers are discussed.

Price, Edwin↗