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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels

While deep neural networks (DNNs) and Gaussian Processes (GPs) are both popularly utilized to solve problems in reinforcement learning, both approaches feature undesirable drawbacks for challenging problems. DNNs learn complex non-linear embeddings, but do not naturally quantify uncertainty and are often data-inefficient to train. GPs infer posterior distributions over functions, but popular kernels exhibit limited expressivity on complex and high-dimensional data. Fortunately, recently discovered conjugate and neural tangent kernel functions encode the behavior of overparameterized neural networks in the kernel domain. We demonstrate that these kernels can be efficiently applied to regression and reinforcement learning problems by analyzing a baseline case study.We apply GPs with neural network dual kernels to solve reinforcement learning tasks for the first time. We demonstrate, using the well understood mountain-car problem, that GPs empowered with dual kernels perform at least as well as those using the conventional radial basis function kernel. Finally, we conjecture that by inheriting the probabilistic rigor of GPs and the powerful embedding properties of DNNs, GPs using NN dual kernels will empower future reinforcement learning models on difficult domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CYDRES: CYber Defense and REsilient System for securing grid-interactive efficient buildings

Smart buildings, especially Grid-interactive Efficient Buildings (GEBs), suffer from cyber-attacks and physical faults due to the integration of a large number of sensors and controls, connected devices, and associated communication networks. This study demonstrated a real-time advanced building resilient platform, called CYber Defense and REsilient System (CYDRES), which is deployable for existing and emerging Building Automation Systems (BASs). CYDRES aims to empower GEBs with cyber-attack-immune capabilities through multi-layer prevention and adaptation mechanisms to monitor, detect, and respond to cyber-attacks and physical operational faults. CYDRES is demonstrated through real-time experiments in a Hardware-in-the-Loop (HIL) testbed.

Building automation system, Cyber-attacks, Physica↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2023

Performance Evaluation of Gray-box and Machine Learning Models of a Thermal Energy Storage System with Active Insulation

An interior partition wall integrated with active thermal storage and a dynamic insulation system was built and then installed in an office building in Oak Ridge, Tennessee, TN. This smart wall, termed the Empower Wall, was equipped with embedded pipes in the building envelope core component and an additional pipe network enclosing rigid insulation to switch on and off the active insulation dynamically. The performance of the wall's contribution to cooling load reduction under different parameters has been investigated in previous publications. Aiming to be deployed into model predictive control and other optimization methods, simplified and reliable models for the developed wall and the room accommodating it are required. They are needed to characterize the properties and thermal response of both Empower Wall and building envelope, which form an essential component for accurate indoor temperature or cooling/heating demand prediction. In this study, simplified gray-box and regression models as well as machine learning model were developed and the performance of them were compared and analyzed.

Cui, Borui↗
DOE OSTI2023

2023 SAGE-Camp Report

The 7 th Summer Physics Camp for Young Women was successfully held in person in 2023 from June 5 th to 16 th at the New Mexico School for the Arts in Santa Fe, NM at Hilo Intermediate School in Hawaii. This year’s camp was dedicated to the topic of Energy Security and was made possible thanks to the strong collaboration of Los Alamos, Sandia and Hawaii teams and the logistical and financial support of Los Alamos and Sandia National Laboratories, New Mexico Consortium, SAGE- Moore Foundation, LANL Foundation, ACS, IEE, APS four corners, N3B, Hawaii Museum of Science and Technology, New Mexico School for the Arts (NMSA) and Tech Source. The camp mobilized more than 120 volunteers who made the camp a success. The camp is free of charge to the students and included free lunch and snacks for the busy brains to have plenty of energy, also included all materials needed for the hands-on activities (like drone building, crystal structure, solar panels, wind turbine fabrication, soldering, coding etc) and also a stipend for students who attended for the full two weeks and for two educators and two student mentors in NM. The camp offered 32 high school students from New Mexico and 8 from Hawaii a unique opportunity to explore science topics and meet a broad range of role model professionals across STEM fields including astrophysics, cybersecurity, Energy fields, space science, engineering, biophysics, environmental science, robotics, computer science, nuclear engineering, radiological science, physics and chemistry. With nearly 120 volunteers who came mainly from Los Alamos National Laboratory (66%) and Sandia national laboratories (18%), two funded educators from NM, Dr. Weldon Beauchamp and Dr. Ellee Cook, and two educators from Hilo, Dr. Pascale Creek Pinner and LeAnn Ragasa, the camp was educationally sound and extremely varied. The collaboration with school educators is critical for the goal of this camp to not only impact students' lives but also improve STEM education in NM and Hawaii. The ultimate goal of the camp is to increase higher education aspirations of students, empower them to consider careers in STEM and learn more about the opportunities available to them in our local colleges and DOE National Laboratories. In addition, the camp also hired 2 past students as student mentors, Megan Odom and Elisea Jackson, who currently attend NMSA and were students at the camp in 2022 when it was virtual. The in-person camp which aims at empowering under-represented minorities in STEM in our community received more than 52 applications this year from all over NM and 8 applications from Hawaii. Our selection criteria are based on diversity, equity and inclusion, and students for whom the camp can be a life-changing opportunity are given a chance to attend the camp. During COVID, the camp was held virtually and gave the opportunity to students from remote areas in NM and Hawaii to attend from their homes. This year, fantastic families supported students everyday even when home was in remote areas in NM like Lea county, Sandoval county, Bernalillo or Mora county. The organizers hope next year they can offer a residential option for students from remote areas.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations Version 2.0

This report explores the potential of distributed ledger technology (DLT) as a transformative tool to enhance fault-tolerant operations in electrical distribution systems. Leveraging DLT's core attributes, including an immutable decentralized ledger, distributed consensus mechanisms, and state replication capabilities, this study focuses on three critical use cases. A central aspect of this research centers on the utilization of a consensus-driven ledger, providing actors within the system, such as distributed resources, with access to a reliable data repository. This empowers these actors to collaborate effectively and make informed decisions, all securely recorded on the blockchain. The first use case concentrates on data configuration, utilizing mathematical criteria---particularly, the chi-squared test for gross error detection---to identify trustworthy sensors for advanced decision-making. Building upon this foundation of trust, the second use case, topology identification, accurately determines circuit breaker states, unveiling the distribution network's topology. Ultimately, the third use case leverages this trust to execute switching actions, reconfiguring feeders and restoring power to disconnected customers after fault events. The concept of trust serves as a cornerstone in this approach, marking a departure from traditional fault location, isolation, and service restoration (FLISR) methods. Additionally, the blockchain-based architecture introduces decentralization, empowering disconnected areas to make autonomous decisions, even when communication with a central control center is disrupted. The primary contributions of this report are twofold: (1) a novel approach for evaluating distribution system voltage areas while preserving data ownership and (2) the implementation of interactions between distribution network areas using the actor model. Unlike the previous sequential approach for evaluating the area connection voltages, which required a radial network topology, this study's area model reduction enables a more versatile approach. The area model reduction addresses issues of prolonged data waiting times and multiple points of failure within the previous approach. Notably, the presented evaluation for the reduced network model area connection reveals a significant increase in the differences in voltage magnitudes. Simulation and evaluation of area agents across four distinct cases elucidate the area-level interaction behavior during a fault event. Simulations demonstrate that the proposed distributed FLISR (DFLISR) approach can successfully restore service to an affected area. Varying message delays and message loss probabilities in each simulation case underscore their impacts on restoration times, ranging from 3 min and 32 s to 6 min and 19 s. In contrast, power is not restored in an area in one of our simulation cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Higher education institutions can do more to lead society toward resilience and sustainability

Higher education institutions (HEIs) are beginning to recognize their role as leading societal institutions in the movement toward resilience and sustainability by empowering their students as sustainability stakeholders and leaders and recognizing the value of faculty and staff as facilitators in this mobilization toward a more sustainable future. This chapter identifies seven goals derived from academic research that can be used to foster student leadership in sustainability and support faculty and staff in facilitatory roles. To date, however, most research has focused on sustainability in the classroom environment or the establishment of living labs. To illuminate how these seven goals can be achieved, the chapter adopts stakeholder theory and conducts an integrated case study assessment. Overall, the chapter helps to (i) develop a stronger understanding of sustainability and resilience, (ii) identify best practices for empowering student leaders and to better understand the facilitatory role played by faculty and staff in achieving the seven goals, and (ii) offer further insights through a reflective case study.

Sustainability and Resilience, Sustainability Syst↗
DOE OSTI2024

Developing a Mapping Inquiry Tool and Geodatabase to Support Characterization of Candidate Geologic Carbon Storage Sites

Poster to showcase the Class VI Mapping Inquiry Tool (C6 MapIT) and support geodatabase available on EDX. C6 MapIT and the underpinning Class VI Data Support Tool Geodatabase serve as comprehensive resources to aid with understanding data availability and supporting the initial stages of Class VI siting and permit applications. In addition to enabling users to explore available spatial data, additional resources are provided to empower users to find more information, such as links to documentation to support project goals, a query tool for USGS publications, and links to relevant sections of the EPA Class VI documentation. Safe and appropriate selection of UIC Class VI well sites is key as the U.S. makes the energy transition. The Class VI Mapping Inquiry Tool and Class VI Data Tool Geodatabase are valuable, intuitive resources that empower commercial and industry stakeholders to explore, analyze, and acquire Class VI related data.

Pantaleone, Scott↗
DOE OSTI2023

A Fair and Equitable Clean Energy Transition in Alachua County

Alachua County, Florida is committed to transitioning to renewable energy in order to address the climate crisis and secure energy justice for low-income and underserved communities. Alachua County's community-led Project EMPOWER (Energy Modernization for People, Opportunity, Work, Equity, and Renewables) is focused on making the clean energy transition fair and equitable for everyone, and is partnering with the U.S. Department of Energy's Communities LEAP (Local Energy Action Program) Pilot.

Alachua County↗
DOE OSTI2023

Endowing Polythioester Vitrimer with Intrinsic Crystallinity and Chemical Recyclability

Abstract Technologically important thermosets face a long‐standing end‐of‐life (EoL) problem of non‐reprocessability, a more sustainable solution of which has resolved to nascent vitrimers that can merge the robust material properties of thermosets and the reprocessability of thermoplastics. However, the lifecycle of vitrimers is still finite, as they often suffer from significant deterioration of mechanical performance following multiple reprocessing cycles, analogous to mechanical recycling, and they often show undesired creep under working conditions. To address these two key limitations, we have developed a cross‐linked semi‐crystalline polythioester with both dynamic covalent bonds and intrinsic crystallinity and chemical recyclability, affording a vitrimeric system that exhibits not only reprocessability and crystallinity‐restricted creep but also complete chemical recyclability to initial monomer by catalyzed depolymerization in solution or bulk. Therefore, reported herein is an “infinite” vitrimer system that is empowered with a facile closed‐loop EoL option once serial reprocessing deteriorates performance and the material can no longer meet the application requirements. Specifically, the polythioester vitrimer was constructed by copolymerization of a bicyclic thioester with a bis‐dithiolane, producing dynamically cross‐linked polythioesters with excellent property tunability, from amorphous to semi‐crystalline states and melting transition temperatures from 91 to 178 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Abstract Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Talebpour, Mahdad↗
DOE OSTI2022

Graph identification of proteins in tomograms ( GRIP‐Tomo )

Abstract In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distinctive for the purpose of protein identification from distorted data presented in volume densities. Three‐dimensional densities of a protein or a complex from simulated tomographic volumes were transformed into mathematical graphs as observables. We systematically introduced data distortion or defects such as missing fullness of data, the tumbling effect, and the missing wedge effect into the simulated volumes, and varied the distance cutoffs in pixels to capture the varying connectivity between the density cluster centroids in the presence of defects. A similarity score between the graphs from the simulated volumes and the graphs transformed from the physical protein structures in point data was calculated by comparing their network theory order parameters including node degrees, betweenness centrality, and graph densities. By capturing the essential topological features defining the heterogeneous morphologies of a network, we were able to accurately identify proteins and homo‐multimeric complexes from 10 topologically distinctive samples without realistic noise added. Our approach empowers future developments of tomogram processing by providing pattern mining with interpretability, to enable the classification of single‐domain protein native topologies as well as distinct single‐domain proteins from multimeric complexes within noisy volumes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Graph Identification of Proteins in Tomograms (GRIP-Tomo) 2.0: Topologically aware classification for proteins

Cryo-electron tomography (cryo-ET) enables structural characterization of biomolecules under near-native conditions. Existing approaches for interpreting the resulting three-dimensional volumes are computationally expensive and have difficulty interpreting density associated with small proteins/complexes. To explore alternate approaches for identifying proteins in cryo-ET data we pursued a Graph Network and topologically invariant approach. Here, we report on a fast algorithm that classifies particles by searching for nuances of evolutionarily conversed motifs and the geometrical characteristics of protein structure. GRIP-Tomo 2.0 is a machine-learning pipeline that extracts interpretable topological features of protein structures within noisy experimental backgrounds. Compared to version 1.0, the new pipeline includes three upgrades that significantly improve performance including synthetic tomogram generation simulating realistic noise, graph-based persistent feature extraction as protein fingerprints, and high-performance computing acceleration. GRIP-Tomo 2.0 achieves over 90% accuracy in classifying between proteins and noise using both real and synthetic datasets which represents a foundational step toward advancing cryo-ET workflows and empowering automated visual proteomics.

Li, Chengxuan↗
DOE OSTI2021

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Abstract Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Decoding α-MoC 1− x Nanoparticle Formation in Continuous Flow via Machine Learning

Molybdenum carbide nanoparticles (α-MoC 1−x NPs) are promising catalysts that offer noble-metal-like performance at lower cost. We report a mild continuous-flow synthesis of α-MoC 1−x NPs from Mo(CO) 6 , coupled with in-line spectroscopic monitoring and machine learning (ML)-based analysis to quantify precursor conversion and product formation in real time. A multilayer perceptron ML model was found to accurately deconvolute complex, nonlinear spectral patterns, enabling identification of a two-step reaction pathway, involving precursor conversion to an amorphous intermediate followed by intraparticle crystallization to α-MoC 1−x NPs, with the first step being rate limiting. Ex situ small angle X-ray scattering (SAXS) and X-ray diffraction (XRD) validation confirm the predicted concentration profiles and crystallization behavior. This integrated approach showcases how ML can empower insights into NP nucleation and growth, paving the way for self-driving, flow-based platforms for NP synthesis.

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

Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos, and cosmic rays cover a wide range of wavelengths and timescales. Combining and processing these datasets that vary in volume, speed, and dimensionality requires new modes of instrument coordination, funding, and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal-processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets has powered the rise of artificial intelligence. This digital revolution now powers a multibillion dollar industry, with far-reaching implications in technology and society. In this chapter, we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in multi-messenger astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017 to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data.

Artificial Intelligence↗
DOE OSTI2023

Non-standard neutrino interactions in light mediator models at reactor experiments

Compared to other neutrino sources, the huge anti-neutrino fluxes at nuclear reactor based experiments empower us to derive stronger bounds on non-standard interactions of neutrinos with electrons mediated by light scalar/vector mediators. At neutrino energy around 200 keV reactor anti-neutrino flux is at least an order of magnitude larger compared to the solar flux. The atomic and crystal form factors of the detector materials related to the details of the atomic structure becomes relevant at this energy scale as the momentum transfers would be small. Non-standard neutrino-electron interaction mediated by light scalar/vector mediator arises naturally in many low-scale models. We also propose one such new model with a light scalar mediator. Here, we investigate the parameter space of such low-scale models in reactor based neutrino experiments with low threshold Ge and Si detectors, and find the prospect of probing/ruling out the relevant parameter space by finding the projected sensitivity at 90% confidence level by performing a $χ$ 2 -analysis. We find that a detector capable of discriminating between electron recoil and nuclear recoil signal down to a very low threshold such as 5 eV placed in reactor based experiment would be able to probe a larger region in parameter space compared to the previously explored region. A Ge (Si) detector with 10 kg-yr exposure and 1 MW reactor anti-neutrino flux would be able to probe the scalar and vector mediators with masses below 1 keV for coupling products $\sqrt{g_vg_e}$ ~ 1 × 10 -6 (9.5 × 10 -7 ) and 1 × 10 -7 (8 × 10 -8 ), respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗