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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 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fully Conjugated Poly(phthalocyanine) Scaffolds Derived from a Mechanochemical Approach Towards Enhanced Energy Storage

Phthalocyanines (Pc)-derived materials represent an attractive category of porous organic scaffolds featured by extensive π-conjugated networks, but their construction is still limited to the solution-based pathways, producing materials with inferior conductivity and porosity. Herein, a mechanochemistry-driven approach was developed leveraging the on-surface polymerization of aromatic nitrile monomers with ortho-positioned dicyano groups in the presence of metal catalysts (magnesium, zinc, or aluminum) under neat and ambient conditions. Diverse Pc-functionalized conjugated porous networks (Pc-CPNs) were obtained featured by extensively and fully π-conjugated skeletons, high surface areas, and hierarchical porosities. The monomers in this mechanochemical approach could be extended to those difficult to be handled in solution-based procedures. In this work, the Pc-CPNs displayed attractive electrochemical performance as supercapacitor and anodes in batteries, together with superb long-term stability.

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

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

quantitative structure–property relationship (QSPR↗
DOE OSTI2018

Sea Level Rise Impacts on Wastewater Treatment Systems Along the U.S. Coasts

As sea levels rise, coastal communities will experience more frequent and persistent nuisance flooding, and some low-lying areas may be permanently inundated. Critical components of lifeline infrastructure networks in these areas are also at risk of flooding, which could cause significant service disruptions that extend beyond the flooded zone. Thus, identifying critical infrastructure components that are exposed to sea level rise is an important first step in developing targeted investment in protective actions and enhancing the overall resilience of coastal communities. Wastewater treatment plants are typically located at low elevations near the coastline to minimize the cost of collecting consumed water and discharging treated effluent, which makes them particularly susceptible to coastal flooding. For this analysis, we used geographic information systems to assess the exposure of wastewater infrastructure to various sea level rise projections at the national level. We then estimated the number of people who would lose wastewater services, which could be more than five times as high as previous predictions of the number of people at risk of direct flooding due to sea level rise. We also performed a regional comparison of wastewater exposure to marine and groundwater flooding in the San Francisco Bay Area. Overall, this analysis highlights the widespread exposure of wastewater infrastructure in the United States and demonstrates that local disruptions to infrastructure networks may have far-ranging impacts on areas that do not experience direct flooding.Plain Language Summary Wastewater treatment plants are susceptible to flooding resulting from sea level rise. Previous estimates of wastewater exposure have only considered the impacts of marine flooding at the local or regional scale. In this analysis, we quantify the exposure to marine flooding across the coastal United States and then consider the relative impacts of marine and groundwater flooding at the regional scale in the San Francisco Bay Area. We also estimate the number of people who may lose access to wastewater services if no actions are taken to prevent flooding at wastewater treatment plants. We find that the number of people impacted by sea level rise due to loss of wastewater services could be five times as high as previous predictions of the number of people who experience direct flooding of their homes or property. We also find that groundwater flooding poses a significant threat to wastewater plants in the San Francisco Bay region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Data Federation Challenges in Remote Near-Real-Time Fusion Experiment Data Processing

Fusion energy experiments and simulations provide critical information needed to plan future fusion reactors. As next-generation devices like ITER move toward long-pulse experiments, analyses, including AI and ML, should be performed in a wide range of time and computing constraints, from near-real-time constraints, between-shot analysis, and to campaign-wide long-term analysis. However, the data volume, velocity, and variety make it extremely challenging for analyses using only local computational resources. Researchers need the ability to compose and execute workflows spanning edge resources to large-scale high-performance computing facilities.We present Delta, a system to address data analysis challenges, including AI/ML, in fusion science, by leveraging the ADIOS I/O library and middleware, to support executing science workflows over the wide area network for near-real-time streaming. We discuss the data federation challenges in performing remote workflows, focusing on on-going research work in (1) managing, reducing, and streaming data to minimize I/O and data movement overheads, (2) decompressing and reorganizing data for analysis, and (3) executing workflows for automated data analysis. We introduce examples for deep-learning based data analysis for the fusion domain and demonstrate how we use Delta to construct end-to-end workflows for a fusion device in Korea, connecting a remote DOE facility in the USA. The capability demonstrated by this project is the basis for improving the state of the art for near-real-time data federation amongst remote facilities.

Choi, Jong Youl↗
DOE OSTI2018

Rational fabrication of carbon nanotubes arrays on porous nickel matrix as advanced electrode materials of supercapacitors

Highlights: • Construct self-supported porous Ni/CNTs composite arrays. • Porous Ni/CNTs arrays show superior capacitive performance. • Ni/CNTs arrays structure boosts fast electron/ion transfer. - Abstract: Rational synthesis of advanced carbon materials is of great importance to the development of supercapacitors. In this work, we propose a facile combined electrodeposition-chemical vapor deposition for tailored construction of carbon nanotubes (CNTs) arrays on highly porous Ni matrix forming integrated 3D Ni/CNTs electrode. Interconnected CNTs with diameters of 20–30 nm are strongly anchored on the cross-linked porous Ni branches consisting of nanoparticles of 500–600 nm. Capacitive performance of the obtained porous Ni/CNTs electrode is thoroughly characterized by electrochemical means including charge/discharge tests and cyclic voltammetry. The 3D Ni/CNTs electrode shows good high-rate performance with a capacitance of 186 F g{sup −1} at 2.5 A g{sup −1}, and excellent cycling stability with a capacitance retention of 99% after 10,000 cycles. The integrated conductive network composed of 3D Ni/CNTs offers large active area, good electronic conductivity and fast transportation paths of ions/electrons, leading to superior capacitive performance.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

Abstract This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present‐day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high‐resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid‐levels for intense thunderstorm development when low‐level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human‐labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out‐of‐sample robustness with hyperparameter tuning in certain applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Collective neural network behavior in a dynamically driven disordered system of superconducting loops

Collective properties of complex systems composed of many interacting components such as neurons in our brain can be modeled by artificial networks based on disordered systems. We show that a disordered neural network of superconducting loops with Josephson junctions can exhibit computational properties like categorization and associative memory in the time evolution of its state in response to information from external excitations. Superconducting loops can trap multiples of fluxons in many discrete memory configurations defined by the local free energy minima in the configuration space of all possible states. A memory state can be updated by exciting the Josephson junctions to fire or allow the movement of fluxons through the network as the current through them surpasses their critical current thresholds. Simulations performed with a lumped element circuit model of a 4-loop network show that information written through excitations is translated into stable states of trapped flux and their time evolution. Experimental implementation on a high-Tc superconductor YBCO-based 4-loop network shows dynamically stable flux flow in each pathway characterized by the correlations between junction firing statistics. Neural network behavior is observed as energy barriers separating state categories in simulations in response to multiple excitations, and experimentally as junction responses characterizing different flux flow patterns in the network. The state categories that produce these patterns have different temporal stabilities relative to each other and the excitations. This provides strong evidence for time-dependent (short-to-long-term) memories, that are dependent on the geometrical and junction parameters of the loops, as described with a network model.

Josephson junctions↗
DOE OSTI2024

Streamlining latent spaces in machine learning using moment pooling

Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose “moment pooling,” a natural extension of deep sets networks which drastically decreases the latent space dimensionality of these networks while maintaining or even improving performance. Moment pooling generalizes the summation in deep sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a fixed learned latent space dimension. We demonstrate moment pooling on the collider physics task of quark/gluon jet classification by extending energy flow networks (EFNs) to moment EFNs. We find that moment EFNs with latent dimensions as small as 1 perform similarly to ordinary EFNs with higher latent dimension. This small latent dimension allows for the internal representation to be directly visualized and interpreted, which in turn enables the learned internal jet representation to be extracted in closed form. Published by the American Physical Society 2024

Gambhir, Rikab (ORCID:0000000251080448)↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2025

Prototyping DAQ network functions on FABRIC

This poster relates to a project that is investigating the use of the FABRIC federated testbed to scale-up research infrastructure to develop network functions that use smart (programmable) network equipment to support High Energy Physics (HEP) research. The poster describes preliminary work that involves non trivial packet parsing related to Fermilab DAQ workloads that is implemented on real hardware, and a correctness and performance evaluation.

Sagstad, Bjoern [IIT, Chicago]↗
DOE OSTI2026

VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration

This is the final technical report for the SETO-funded VRN3P project (PNNL# 76914). The goal of this project, led by Pacific Northwest National Laboratory (PNNL), in collaboration with Lawrence Livermore National Laboratory (LLNL) and Portland General Electric (PGE), was to develop and validate a deep variational recurrent neural network-based net-load prediction (VRN3P) framework for probabilistic time-series forecasting of day-ahead net-load under high solar penetration scenarios. The project team reports successful design of a novel probabilistic net-load forecasting architecture, comprising of a variational autoencoder and a recurrent neural network, which demonstrates 30% improvement in forecast performance, 60% improvement in training time, and consumes 44% less memory, when compared with conventional baseline models. The team tested the VRN3P model performance on GridLAB-D test-cases representing varying BTM solar penetration levels of 20%, 30%, and 50%, with integrated time-series net-load profiles provided by the utility partner (PGE). The VRN3P model demonstrate <2% hourly MAPE (averaged over the year) for day- ahead net-load forecast on the test scenario with 20% BTM solar. Transfer learning extension of the VRN3P model has demonstrated 8.33× speed-up in training, while still achieving acceptable forecast performance of 2.24% hourly MAPE on the 30% BTM solar penetration test-scenario. A preliminary version of the VRN3P GridAPPS-D™has been developed, along with a web-based interactive user-interface (named ‘Forte’) which has made available on GitHub for public use.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

AI-driven Neutrino Beam Diagnostics for Next-Generation Neutrino Experiments

The accelerator-driven beam uncertainty limits oscillation measurements in long-baseline neutrino experiments. Spill-resolved beam diagnostics and real-time inference are necessary to address these neutrino flux systematics. As such, we present a machine-learning-based beam monitoring framework developed and validated using data from the T2K experiment. Our approach uses downstream, spill-by-spill muon monitor observables to predict upstream parameters such as proton beam position and width. We achieve high predictive accuracy on nominal runs, demonstrating robust baseline performance whether the model is trained on stable runs or systematically varied conditions. The framework is designed to be robust against domain shifts, allowing the neural network architectures and inference strategies developed with T2K data to be retrained and validated using LBNF simulations, with the goal of eventual deployment under real LBNF/DUNE operating conditions. This scalable approach to real-time beam inference offers a pathway toward reducing flux systematics for next-generation neutrino experiments such as DUNE.

Aney, Noah [Fermilab; U. Chicago (main)]↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.

Damish, S.↗
DOE OSTI2016

A source-attractor approach to network detection of radiation sources

Radiation source detection using a network of detectors is an active field of research for homeland security and defense applications. We propose Source-attractor Radiation Detection (SRD) method to aggregate measurements from a network of detectors for radiation source detection. SRD method models a potential radiation source as a magnet -like attractor that pulls in pre-computed virtual points from the detector locations. A detection decision is made if a sufficient level of attraction, quantified by the increase in the clustering of the shifted virtual points, is observed. Compared with traditional methods, SRD has the following advantages: i) it does not require an accurate estimate of the source location from limited and noise-corrupted sensor readings, unlike the localizationbased methods, and ii) its virtual point shifting and clustering calculation involve simple arithmetic operations based on the number of detectors, avoiding the high computational complexity of grid-based likelihood estimation methods. We evaluate its detection performance using canonical datasets from Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) tests. SRD achieves both lower false alarm rate and false negative rate compared to three existing algorithms for network source detection.

Wu, Qishi↗
DOE OSTI2019

Final Conversion of the Spallation Neutron Source Extraction Kicker Pulse Forming Network to a High Voltage Solid-State Switch

The Spallation Neutron Source (SNS) extraction kicker 60Hz pulsed system uses 14 Blumlein pulse-forming network (PFN) modulators that require timing synchronization with stable rise times. A replacement design has been investigated and the kickers have been converted over to use a solid-state switch design, eliminating the lifetime and stability issues associated with thyratrons and subsequent maintenance costs. All kickers have been converted, preventing thyratron jitter from impacting the beam performance and allowing higher-precision target impact. This paper discusses the completion of the conversion of the high-voltage switch from a thyratron to a solid-state switch with improved stability of the extraction system and associated accelerator beam stability.

Morris, B↗
DOE OSTI2017

Ab initio molecular dynamics simulations of liquid water using high quality meta-GGA functionals

We have used ab initio molecular dynamics (AIMD) to characterize water properties using two meta-generalized gradient approximation (meta-GGA) functionals, M06-L-D3 and B97M-rV, and compared their performance against a standard GGA corrected for dispersion, revPBE-D3, at ambient conditions (298 K, and 1 g cm –3 or 1 atm). Simulations of the equilibrium density, radial distribution functions, self-diffusivity, the infrared spectrum, liquid dipole moments, and characterizations of the hydrogen bond network show that all three functionals have overcome the problem of the early AIMD simulations that erroneously found ambient water to be highly structured, but they differ substantially among themselves in agreement with experiment on this range of water properties. We show directly using water cluster data up through the pentamer that revPBE-D3 benefits from a cancellation of its intrinsic functional error by running classical trajectories, whereas the meta-GGA functionals are demonstrably more accurate and would require the simulation of nuclear quantum effects to realize better agreement with all cluster and condensed phase properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Oxidation-resistant, solution-processed plasmonic Ni nanochain-SiO{sub x} (x < 2) selective solar thermal absorbers

Metal oxidation at high temperatures has long been a challenge in cermet solar thermal absorbers, which impedes the development of atmospherically stable, high-temperature, high-performance concentrated solar power (CSP) systems. In this work, we demonstrate solution-processed Ni nanochain-SiO{sub x} (x < 2) and Ni nanochain-SiO{sub 2} selective solar thermal absorbers that exhibit a strong anti-oxidation behavior up to 600 °C in air. The thermal stability is far superior to previously reported Ni nanoparticle-Al{sub 2}O{sub 3} selective solar thermal absorbers, which readily oxidize at 450 °C. The SiO{sub x} (x < 2) and SiO{sub 2} matrices are derived from hydrogen silsesquioxane and tetraethyl orthosilicate precursors, respectively, which comprise Si-O cage-like structures and Si-O networks. Fourier transform infrared spectroscopy shows that the dissociation of Si-O cage-like structures and Si-O networks at high temperatures have enabled the formation of new bonds at the Ni/SiO{sub x} interface to passivate the surface of Ni nanoparticles and prevent oxidation. X-ray photoelectron spectroscopy and Raman spectroscopy demonstrate that the excess Si in the SiO{sub x} (x < 2) matrices reacts with Ni nanostructures to form silicides at the interfaces, which further improves the anti-oxidation properties. As a result, Ni-SiO{sub x} (x < 2) systems demonstrate better anti-oxidation performance than Ni-SiO{sub 2} systems. This oxidation-resistant Ni nanochain-SiO{sub x} (x < 2) cermet coating also exhibits excellent high-temperature optical performance, with a high solar absorptance of ∼90% and a low emittance ∼18% measured at 300 °C. These results open the door towards atmospheric stable, high temperature, high-performance solar selective absorber coatings processed by low-cost solution-chemical methods for future generations of CSP systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗