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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Preliminary Assessment of Qiskit Quantum Simulator Capabilities for Development of Quantum Hopfield Neural Network for Anomaly Detection Applications (Q4 Report)

Quantum information processing offers potentially faster performance compared to the classical counterpart. The goal of this work is to investigate performance of Quantum Hopfield Neural Network for applications to anomaly detection. Preliminary study consist of assessment of computational capabilities of Qiskit quantum simulator. Eventual objective is to implement Quantum Hopfield Network algorithm for detection of weak nuisance and anomaly signal in the presence of strong and highly varying background in gamma radiation data measured during environmental screening.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Mixed-conducting interlayer boosting the electrochemical performance of Ni-rich layered oxide cathode materials for lithium ion batteries

In this work, a unique artificial interface combing characteristics of both high ionic and electronic conductivities has been successfully constructed at the surface of Ni-rich LiNi 0·8 Co 0·1 Mn 0·1 O 2 (NCM811). The ionic conductor layer is fabricated through reacting H 3 PO 4 with the lithium residuals on the surface of NCM811 to form Li 3 PO 4 . The interface with high electronic conductivity is constructed by attaching graphene fragments to the NCM811 spherical particles. Due to the synergistic effect of the Li3PO4 coating layer and the graphene network, the modified sample (GN-LPO-NCM811) exhibits high capacity retention of 94.3% after 150 cycles at 0.5C between 3.0 and 4.3 V, while the pristine material shows a much lower retention of only 88.1%. In addition, the GN-LPO-NCM811 also presents improved cycling stability at elevated temperature of 55 °C. Even at an extremely high rate of 10C, the GN-LPO-NCM811 still remains 70% of its original capacity, while the pristine NCM811 only delivers 50% of the capacity. The stable cycling performance of GN-LPO-NCM811 is demonstrated in a full cell with graphite anode at ambient temperatures. Importantly, the thermal stability of the modified samples is also greatly enhanced. This study provides an effective method to improve the electrochemical performance of LiNi 0·8 Co 0·1 Mn 0·1 O 2 .

25 ENERGY STORAGE↗
DOE OSTI2022

Effect of different manufacturing methods on polyamide reverse-osmosis membranes for desalination: Insights from molecular dynamics simulations

Membranes are a key technology platform for a broad application of energy-efficient separations. To best serve the separation demands of industry, the manufacturing processes for these membranes are garnering increasing attention. In particular, for water desalination, the industry leading polyamide (PA) reverse osmosis (RO) membranes can be manufactured via molecular layer-by-layer (mLBL) deposition, interfacial polymerization (IP), and 3D-printing technique. However, the influence of different manufacturing methods on PA membrane’s properties is far from understood. Here, in this study, we present the high-pressure transport behavior of water and salt ions for PA membranes formed with IP, mLBL, and 3D-printing through non-equilibrium molecular dynamics simulations. Studies show that membranes fabricated with 3D-printing have similar performances to those manufactured using mLBL, quantified by water permeability, rejection of salt ions, structural integrity, and porosity features. However, the membranes formed with IP exhibit faster water transport, lower rejection, worse structural integrity, and more inhomogeneous network pores than those constructed using mLBL and 3D-printing. The unconnected water-accessible space governs water transport for PA membranes formed with mLBL and 3D-printing, which offers the impermanent open-closed pores that enable water to jump through PA membranes. In contrast, the permeated water-enterable space plays a prominent role in water movement across the PA membrane formed with IP, providing a continuous transport channel at high pressure. Importantly, we observe the more significant compaction features at high pressure for PA membranes formed with IP than mLBL and 3D printing. In short, these findings provide a comprehensive understanding of existing membrane preparation technologies. It also provides a guide for developing the new membrane preparation process at the molecular level.

3D-printing↗
DOE OSTI2025

Search for $t\bar{t}H/A \rightarrow t\bar{t}t\bar{t}$ production in proton–proton collisions at $\sqrt{s}=13$ $\text {TeV}$ with the ATLAS detector

A search is presented for a heavy scalar (H) or pseudo-scalar (A) predicted by the two-Higgs-doublet models, where the H/A is produced in association with a top-quark pair $(t\bar{t}H/A),$ and with the H/A decaying into a $t\bar{t}$ pair. The full LHC Run 2 proton–proton collision data collected by the ATLAS experiment is used, corresponding to an integrated luminosity of $139~\text {fb}^{-1}.$ Events are selected requiring exactly one or two opposite-charge electrons or muons. Data-driven corrections are applied to improve the modelling of the $t\bar{t}$ +jets background in the regime with high jet and b-jet multiplicities. These include a novel multi-dimensional kinematic reweighting based on a neural network trained using data and simulations. An H/A-mass parameterised graph neural network is trained to optimise the signal-to-background discrimination. In combination with the previous search performed by the ATLAS Collaboration in the multilepton final state, the observed upper limits on the $t\bar{t}H/A \rightarrow t\bar{t}t\bar{t}$ production cross-section at 95% confidence level range between 14 fb and 5.0 fb for an H/A with mass between 400 GeV and 1000 GeV , respectively. Assuming that both the H and A contribute to the $t\bar{t}t\bar{t}$ cross-section, tan β values below 1.7 or 0.7 are excluded for a mass of 400 GeV or 1000 GeV , respectively. The results are also used to constrain a model predicting the pair production of a colour-octet scalar, with the scalar decaying into a $t\bar{t}$ pair.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Multi-input and binary reproducible, high bandwidth floating point adder in a collective network

To add floating point numbers in a parallel computing system, a collective logic device receives the floating point numbers from computing nodes. The collective logic devices converts the floating point numbers to integer numbers. The collective logic device adds the integer numbers and generating a summation of the integer numbers. The collective logic device converts the summation to a floating point number. The collective logic device performs the receiving, the converting the floating point numbers, the adding, the generating and the converting the summation in one pass. One pass indicates that the computing nodes send inputs only once to the collective logic device and receive outputs only once from the collective logic device.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Multi-input and binary reproducible, high bandwidth floating point adder in a collective network

To add floating point numbers in a parallel computing system, a collective logic device receives the floating point numbers from computing nodes. The collective logic devices converts the floating point numbers to integer numbers. The collective logic device adds the integer numbers and generating a summation of the integer numbers. The collective logic device converts the summation to a floating point number. The collective logic device performs the receiving, the converting the floating point numbers, the adding, the generating and the converting the summation in one pass. One pass indicates that the computing nodes send inputs only once to the collective logic device and receive outputs only once from the collective logic device.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Development of Real-Time Quantitative Safety Evaluation Method for Nuclear Power Plants

Human operators involved with large process control systems such as nuclear power plants (NPPs) perform significant roles in the operation of systems. In this reason, deficiencies in human factors such as poor HSI (Human System Interface) design, procedure, and training, are significant contributing factors to NPPs incidents and accidents. In this regard, many kinds of studies including human reliability analysis (HRA), operation support system, automation, have been conducted in order to reduce human errors. Among these fields of studies, automation has its own specialty since it mainly focuses on eliminating the human error-inducing tasks to reduce human error, while other topics are primarily focusing on reducing human error by aiding human operators during operation. It is commonly believed that replacing human operators to the automated system would guarantee greater efficiency, lower workloads, and fewer human errors. However, these beliefs are not always true in large and complex systems such as NPP, since excessive automation can generate new roles for human operators, change human operators' activities in unexpected ways, and inducing 'out-of-the-loop' (OOTL) problem. Especially, although human error probability drastically increases during abnormal situations in NPP due to overload of information, high workload, and short time available for diagnosis, automation is not actively adopted since conventional machine learning techniques are neither capable nor reliable to handle overwhelmingly complex abnormal situations in NPP. Recently, newly developed 'deep learning' techniques have been actively applied to many fields, and some of these studies proved that the artificial intelligences (AI) based on deep learning techniques show better performance than that of human being. In 2015, deep Q-network (DQN) which is one of the deep learning techniques was developed and applied to train AI that automatically plays various Atari 2800 games, and this AI surpassed the human-level playing in many kind of games. Also in 2016, 'AlphaGo', which was developed by 'Google Deepmind' based on deep learning technique to play the game of Go, was defeated Se-dol Lee who is the World Go champion with score of 4:1. In the point of view that the game of Go is considered as one of the challenging area in machine learning field, it is verified that these new machine learning techniques can perform well, even better than the human being in high-complexity tasks. Previously, most of AI applications in nuclear field were focusing on supporting human operators' tasks such as diagnosis and decision making by rapid classification and prediction, based on fixed training data sets. However, it is expected that AI can be applied to solve more complicated problems (i.e. automation during abnormal states in NPP) by adopting advanced machine learning methodologies. In order to apply the automation system in NPP, firstly it is needed to evaluate the status of the plant quantitatively in real-time, since the information about which state is better (i.e. safe) or worse (i.e. unsafe) is essential for training properly working automation AI. Currently, there are several methodologies for NPP safety evaluation such as probabilistic safety assessment (PSA), safety performance indicator (SPI). However, existing methodologies are not feasible for development of automated system since they are not capable for the cases which are not included in the underlying model, not able to consider delicate changes in the NPP, and hard to evaluate in real-time. Therefore, development of new NPP safety evaluation methodology which can cover previously mentioned requirements is needed to conduct the study on NPP automation further. In this study, the concept of early warning score (EWS) in medical field was adopted to develop the methodology which can evaluate the safety level of the NPP quantitatively and in real-time. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

We present the Galaxy Assembly and Interaction Neural Networks (Gainn), a series of artificial neural networks for predicting the redshift, stellar mass, halo mass, and mass-weighted age of simulated galaxies based on James Webb Space Telescope (JWST) photometry. Our goal is to determine the best neural network for predicting these variables at 11 < z < 15. The parameters of the optimal neural network can then be used to estimate these variables for real, observed galaxies. The inputs of the neural networks are JWST filter magnitudes of a subset of five broadband filters (F150W, F200W, F277W, F356W, and F444W) and two medium-band filters (F162M and F182M). We compare the performance of the neural networks using different combinations of these filters, as well as different activation functions and numbers of layers. The best neural network predicted redshift with a normalized rms error of $0.010^{+0.003}_{-0.001}$, stellar mass with rms = $0.089^{+0.044}_{-0.022}$, halo mass with a mean-squared error of $0.022^{+0.014}_{-0.008}$, and mass-weighted age with rms = $12.466^{+5.065}_{-2.408}$. We also test the performance of Gainn on real data from MACS0647JD, an object observed by JWST. Predictions from Gainn for the first projection of the object (JD1) have normalized bias $\langle$Δz$\rangle$ < 0.00228, which is significantly smaller than found with template-fitting methods. We find that the optimal filter combination is F277W, F356W, F162M, and F200W when considering both theoretical accuracy and observational resources from JWST.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗
DOE OSTI2018

Studying the Impact of Distributed Solar PV on Power Systems Using Integrated Transmission and Distribution Models

Rapid growth of distributed energy resources has prompted increasing interest in integrated Transmission (T) and Distribution (D) modeling. This paper presents the results of a distributed generation from solar photovoltaics (DGPV) impact assessment study that was performed using a synthetic T &D model. The primary objective of the study was to present a new approach for DGPV impact assessment, where along with detailed models of transmission and distribution networks, consumer loads were modeled using the physics of end-use equipment, and DGPV was geographically dispersed and connected to the secondary distribution networks. The study highlights (i) how a lack of DGPV forecasting can increase the Area Control Error (ACE) at the transmission level for high penetration levels; and (ii) how capturing transmission voltage changes using integrated T &D can change simulated distribution voltage profiles and voltage regulator operations between integrated T &D and distribution-only simulations.

14 SOLAR ENERGY↗
DOE OSTI2021

Deciphering the non-linear impact of Al on chemical durability of silicate glass

The role of Al in aluminosilicate glasses remains somewhat a mystery: at low concentrations, it increases the resistance to hydrolysis of the glass, whereas at high concentrations an opposite effect is observed. To understand the origin of the phenomenon on a fundamental atomistic scale, we performed 577 MD simulations and applied potential mean force (PMF) calculations to estimate the activation barriers for hydrolysis and to statistically correlate them with local structural features of the glass. Models of pure silicate and aluminosilicate glasses are constructed and investigated. PMF simulation results are further validated by the experimental measurements and revealed that Al is very easy to dissociate, but it also increases the glass chemical durability through significantly increasing both the strength of Si and network connectivity of the glass. In contrast, at high Al concentration, preferential dissolution of Al weakens the silicate network, which it supposes to strengthen, and so the glass resistance becomes poor. Through PMF calculations, we evaluated the activation barriers for dissociating bonds around Al as 0.49 eV, which is less than a half of the energy to dissociate bonds around Si in pure silicate (1.22 eV) and around Si in aluminosilicate glass (1.34 eV), all these energy differences being statistically significant. Molecular structural level investigation revealed that Si with Al as a second neighbor in the glass network has a significantly higher activation energy for dissociation than Si in pure silicate glass. Furthermore, the proposed approach opens the way to the development of quantitative predictive models of glass durability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
DOE OSTI2023

Low power and privacy preserving sensor platform for occupancy detection

A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Velipasalar, Senem↗
DOE OSTI2022

Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. Here, the results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Facile scalable synthesis of Co{sub 3}O{sub 4}/carbon nanotube hybrids as superior anode materials for lithium-ion batteries

Graphical abstract: Co{sub 3}O{sub 4}/MWCNT hybrids were synthesized via strong ultra-sonication assisted shaking processes. The resultant samples as anode electrode display enhanced cycling performance and rate capability compared with pure Co{sub 3}O{sub 4} particle. - Highlights: • Co{sub 3}O{sub 4}/MWCNT hybrids were synthesized via ultra-sonication assisted shaking process. • The resulting Co{sub 3}O{sub 4} nanoparticles are highly dispersed onto MWCNT network backbone. • Co{sub 3}O{sub 4}/MWCNT hybrid displays highly enhanced lithium storage properties. • The present synthetic approach is facile, controllable, and scalable. - Abstract: In this report, Co{sub 3}O{sub 4}/multiple-wall carbon nanotube (MWCNT) hybrid materials were synthesized via strong ultrasonication-assisted shaking and magnetic stirring processes. The prepared samples were well characterized by utilizing powder X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy techniques. Results indicated that the resulting Co{sub 3}O{sub 4} nanoparticles were highly dispersed in the MWCNT network backbone and further form Co{sub 3}O{sub 4}/MWCNT hybrid materials. The obtained Co{sub 3}O{sub 4}/MWCNT hybrids can be employed as anode electrode in Lithium-ion batteries and deliver as high as discharge capacity of 1250 mA h g{sup −1} at a current density of 0.2 C, additionally, 81% of the discharge capacity for sample 2 with 20 wt.% MWCNT loading could be retained after 70 cycles, which could be associated with the specific hybrid structure of the electrode as well as the addition of MWCNT. Most importantly, the present synthetic approach is facile, controllable, and scalable, which allowing it more easily adapted to prepare other hybrid materials with specific architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2018

CuMn2O4/graphene nanosheets as excellent anode for lithium-ion battery

Highlights: • Spinel CuMn{sub 2}O{sub 4} and CuMn{sub 2}O{sub 4}/rGO are synthesized via a reflux route. • The electrochemical properties of CuMn{sub 2}O{sub 4}/rGO for LIBs are firstly explored. • CuMn{sub 2}O{sub 4}/rGO composites display a good battery performance. • Capacities of 1148 and 935 mA h g{sup −1} can be maintained after 20 and 150 cycles. - Abstract: Spinel copper manganese oxide (CuMn{sub 2}O{sub 4}) is synthesized via a reflux route and studied as anode for lithium-ion battery for the first time. In this work, graphene nanosheets are incorporated into the CuMn{sub 2}O{sub 4} composites to enhance the cycling performance of CuMn{sub 2}O{sub 4} material. Graphene nanosheets not only provide enough void space to buffer the volume expansion/contraction but also afford a conductive network to enhance the conductivity. The as-obtained CuMn{sub 2}O{sub 4}/graphene composites deliver a high discharge specific capacity of 935 mA h g{sup −1} after 150 cycles at a current density of 50 mA g{sup −1}. Additionally, reaction mechanism studies indicate that CuMn{sub 2}O{sub 4} transforms into nano-sized hybrid of CuO-MnO with a spongy porous structure during discharge-charge processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Indistinguishable photons from an artificial atom in silicon photonics

Silicon is the ideal material for building electronic and photonic circuits at scale. Integrated photonic quantum technologies in silicon offer a promising path to scaling by leveraging advanced semiconductor manufacturing and integration capabilities. However, the lack of deterministic quantum light sources and strong photon-photon interactions in silicon poses a challenge to scalability. In this work, we demonstrate an indistinguishable photon source in silicon photonics based on an artificial atom. We show that a G center in a silicon waveguide can generate high-purity telecom-band single photons. We perform high-resolution spectroscopy and time-delayed two-photon interference to demonstrate the indistinguishability of single photons emitted from a G center in a silicon waveguide. Our results show that artificial atoms in silicon photonics can source single photons suitable for photonic quantum networks and processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗