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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

In situ and operando investigation of the dynamic morphological and phase changes of a selenium-doped germanium electrode during (de)lithiation processes

To understand the effect of selenium doping on the good cycling performance and rate capability of a Ge 0.9 Se 0.1 electrode, the dynamic morphological and phase changes of the Ge 0.9 Se 0.1 electrode were investigated by synchrotron-based operando transmission X-ray microscopy (TXM) imaging, X-ray diffraction (XRD), and X-ray absorption spectroscopy (XAS). The TXM results show that the Ge 0.9 Se 0.1 particle retains its original shape after a large volume change induced by (de)lithiation and undergoes a more sudden morphological and optical density change than pure Ge. The difference between Ge 0.9 Se 0.1 and Ge is attributed to a super-ionically conductive Li–Se–Ge network formed inside Ge 0.9 Se 0.1 particles, which contributes to fast Li-ion pathways into the particle and nano-structuring of Ge as well as buffering the volume change of Ge. The XRD and XAS results confirm the formation of a Li–Se–Ge network and reveal that the Li–Se–Ge phase forms during the early stages of lithiation and is an inactive phase. The Li–Se–Ge network also can suppress the formation of the crystalline Li 15 Ge 4 phase. Finally, these in situ and operando results reveal the effect of the in situ formed, super-ionically conductive, and inactive network on the cycling performance of Li-ion batteries and shed light on the design of high capacity electrode materials.

25 ENERGY STORAGE↗
DOE OSTI2022

Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning

Graph convolution incorporates topological information of a graph into learning. Message passing corresponds to traversal of a local neighborhood in classical graph algorithms. We show that incorporating additional global structures, such as shortest paths, through distance preserving embedding can improve performance. Our approach, Gavotte, significantly improves the performance of a range of popular graph neu-ral networks such as GCN, GA T,Graph SAGE, and GCNII for transductive learning. Gavotte also improves the performance of graph neural networks for full-supervised tasks, albeit to a smaller degree. As high-quality embeddings are generated by Gavotte as a by-product, we leverage clustering algorithms on these embed dings to augment the training set and introduce Gavotte+. Our results of Gavotte+ on datasets with very few labels demonstrate the advantage of augmenting graph convolution with distance preserving embedding.

Cong, Guojing↗
DOE OSTI2024

Muon Neutrino Reconstruction at ICARUS with Machine Learning

The ICARUS T600 LArTPC detector successfully ran for three years at the underground LNGS laboratories, providing a first sensitive search for LSND-like anomalous electron neutrino appearance in the CNGS beam. After a significant overhauling at CERN, the T600 detector has been placed in its experimental hall at Fermilab, fully commissioned, and the first events observed with full detector readout. Regular data-taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and neutrinos six degrees off-axis from the Neutrinos at the Main Injector (NuMI). Modern developments in machine learning have allowed for the development of an end-to-end machine learning-based event reconstruction for ICARUS data. This reconstruction folds in 3D voxel-level feature extraction using sparse convolutional neural networks and particle clustering using graph neural networks to produce outputs suitable for physics analyses. This poster will summarize the performance of a high-purity and high-efficiency end-to-end machine learning-based selection of muon neutrinos from the BNB and highlight studies of electromagnetic shower reconstruction from a neutral pion selection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits

Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compound which can reduce the inflammation caused by SD-LPS. To understand the gene regulatory networks of these processes, we have generated scRNA-seq data from mouse monocytes treated with these compounds and identified 11 novel cell clusters. We have developed a machine learning method to integrate scRNA-seq, ATAC-seq, and binding motifs to characterize gene regulatory networks underlying these cell clusters. Using guided regularized random forest and feature selection, our method achieved high performance and outperformed a traditional enrichment-based method in selecting candidate regulatory genes. Our method is particularly efficient in selecting a few candidate genes to explain observed expression pattern. In particular, among 531 candidate TFs, our method achieves an auROC of 0.961 with only 10 motifs. Finally, we found two novel subpopulations of monocyte cells in response to SD-LPS and we confirmed our analysis using independent flow cytometry experiments. Our results suggest that our new machine learning method can select candidate regulatory genes as potential targets for developing new therapeutics against low grade inflammation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Graphics Processing Unit (GPU) Approach to Large Eddy Simulation (LES) for Transport and Contaminant Dispersion

Recent advances in the development of large eddy simulation (LES) atmospheric models with corresponding atmospheric transport and dispersion (AT&D) modeling capabilities have made it possible to simulate short, time-averaged, single realizations of pollutant dispersion at the spatial and temporal resolution necessary for common atmospheric dispersion needs, such as designing air sampling networks, assessing pollutant sensor system performance, and characterizing the impact of airborne materials on human health. The high computational burden required to form an ensemble of single-realization dispersion solutions using an LES and coupled AT&D model has, until recently, limited its use to a few proof-of-concept studies. An example of an LES model that can meet the temporal and spatial resolution and computational requirements of these applications is the joint outdoor-indoor urban large eddy simulation (JOULES). A key enabling element within JOULES is the computationally efficient graphics processing unit (GPU)-based LES, which is on the order of 150 times faster than if the LES contaminant dispersion simulations were executed on a central processing unit (CPU) computing platform. JOULES is capable of resolving the turbulence components at a suitable scale for both open terrain and urban landscapes, e.g., owing to varying environmental conditions and a diverse building topology. In this paper, we describe the JOULES modeling system, prior efforts to validate the accuracy of its meteorological simulations, and current results from an evaluation that uses ensembles of dispersion solutions for unstable, neutral, and stable static stability conditions in an open terrain environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Real Time-Optimal Power Flow-Based Distributed Energy Resource Management System (DERMS)

This project aims to promote lab-proven clean energy technology to commercially scalable versions of the technology, integrate the technology with broader systems, provide extended performance data, and validate the manufacturability and reliability of the technology. The lab-proven technology, RT-OPF DERMS, was developed and validated through previous U.S. Department of Energy-funded efforts, including Advanced Research Projects Agency-Energy funding under the Network Optimized Distributed Energy Systems program and Holy-Cross Energy High Impact Project. In the Advanced Research Projects Agency-Energy Network Optimized Distributed Energy Systems project, the RT-OPF DERMS was developed and implemented in multiple hardware platforms, demonstrating its performance and capabilities in the lab and field environments. The technology was also evaluated and matured via a participation in the U.S. Department of Energy I-Corps program, whose goal is to pair teams of researchers with industry mentors for an intensive 2-month training in which the researchers define technology value propositions, conduct customer discovery interviews, and develop viable market pathways for their technologies. These activities indicate the high technology maturity and Technology Readiness Level of the RT-OPF DERMS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

AN INTERNET RACK MONITOR-CONTROLLER FOR APS LINAC RF ELECTRONICS UPGRADE

To support the research and development in APS LINAC area, the existing LINAC rf control performance needs to be much improved, and thus an upgrade of the legacy LINAC rf electronics becomes necessary. The proposed upgrade plan centers on the concept of using a modern, network-attached, rackmount digital electronics platform –Internet Rack Monitor-Controller (or IRMC) to achieve the goal of modernizing the rf electronics at a lower cost. The system model of the envisioned IRMC is basically a 3-tier stack with a high-performance DSP in the mid-layer to perform the core tasks of real-time rf data processing and controls. The Digital Front-End (DFE) attachment layer at bottom bridges the applicationspecific rf front-ends to the DSP. A network communication gateway, together with an embedded event receiver (EVR) in the top layer merges the Internet Rack MonitorController node into the networks of the accelerator controls infrastructure. Although the concept is very much in trend with today’s Internet-of-Things (IoT), this implementation has actually been used in the accelerators for over two decades.

Ma, Hengjie↗
DOE OSTI2018

Experimental and Theoretical Insights into the Structure of Tellurium Chloride Glasses

Here, the structure of the binary chalcohalide glasses Te 1-x CL x (0.35 <= x <= 0.65) is considered by combining experimental and theoretical results. The structural network properties are influenced by a competition between ionic and covalent bonding in such glasses. At first, a focus is placed on the detailed information available by using the complementary high-energy X-ray and the neutron diffractions in both the reciprocal and real spaces. The main characteristic suggested by the structure factors S(Q) concerns the presence of three length scales in the intermediate range order. The total correlation function T(r) lets us also suppose that the structure of these glasses is more complicated than Te-chain fragments with terminal Cl as demonstrated in crystalline Te 3 Cl 2 . Molecular dynamics simulations were subsequently performed on Te 3 Cl 2 and Te 2 Cl 3 , and coupled with the experimental data, a highly reticulated network of chalcogen atoms, with a fair amount of chlorine atoms bonded in a bridging mode, is proposed. The simulations clearly lead to a glass description that differs markedly from the simple structural model based on only Te atom chains and terminal Cl atoms. Solidstate NMR experiments and NMR parameters calculations allowed validation of the presence of Te highly coordinated with chlorine in these glasses.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A New Design Approach to Mitigating the Effect of Parasitics in Capacitive Wireless Power Transfer Systems for Electric Vehicle Charging

This paper introduces a new design approach to mitigate the effect of parasitic capacitances and achieve high performance in large air-gap capacitive wireless power transfer (WPT) systems for electric vehicle (EV) charging. In a capacitive WPT system for EVs, the vehicle chassis and roadway introduce multiple parasitic capacitances that can overwhelm the coupling capacitance and severely degrade power transfer and efficiency. The proposed approach addresses this challenge by employing split-inductor matching networks, which allow the complex network of parasitic capacitances to be simplified into an equivalent four-capacitance model. The shunt capacitances of this model are directly utilized as the matching network capacitors, hence, absorbing the parasitic capacitances and eliminating the need for discrete high-voltage capacitors. A systematic procedure is developed to accurately measure the equivalent capacitances of the model, enabling the system's performance to be reliably predicted. The proposed approach is used to design two 6.78-MHz 12-cm air-gap prototype capacitive WPT systems with capacitor-free matching networks. The first system transfers up to 590 W using 150-cm 2 square coupling plates and achieves an efficiency of 88.4%. The second prototype system transfers up to 1217 W using 118-cm 2 circular coupling plates, achieving a power transfer density of 51.6 kW/m 2 . The measured output power profiles of the two systems match well with their predicted counterparts, validating the proposed design approach.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

High-speed quantitative X-ray multi-contrast imaging with deep learning based modulated pattern analysis

The advent of X-ray multi-contrast imaging methods, providing absorption, phase, and dark-field images, holds tremendous promise for complementary and non-destructive visualization of inner structures within materials and bio-samples. However, the low efficiency in measuring and analyzing X-ray modulated patterns has hindered their application in high-resolution in situ imaging. In this work, the Enhanced Scanning Pattern-based Imaging Neural Network (ESPINNet) is introduced as a powerful tool for achieving high-speed, high-resolution quantitative imaging. ESPINNet is faster than correlation-based speckle tracking methods such as XSVT and UMPA, and provides a balanced performance in terms of resolution and speed for data collection by using fewer scanning images. In comparison with our previously developed neural network, ESPINNet introduces the capability to generate dark-field images, further enhancing its versatility. By leveraging scanning patterns, ESPINNet significantly improves resolution and measurement precision. Furthermore, its adaptability to various modulation patterns, including those produced by sandpaper, coded masks, or gratings, ensures broad applicability. These features enable real-time 2D and 3D multi-contrast imaging, positioning ESPINNet as a transformative solution for applications in materials science and biomedical research, particularly for high-speed and in situ measurements.

X-ray at-wavelength metrology↗
DOE OSTI2022

Physics-driven learning of Wasserstein GAN for density reconstruction in dynamic tomography

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learning models could prove beneficial for accurate density reconstruction, particularly in dynamic imaging, where the time evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. Here, we use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time series) from simulations. A supervised loss is also included in the training, which leads to an improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during the network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks

Abstract This work develops a novel ice detection framework specifically suitable for rotorcraft using computational aeroacoustics and Bayesian neural networks. In an offline phase of the work, the acoustic signature of glaze and rime ice shapes on an oscillating wing are computed. In addition, the aerodynamic performance indicators corresponding to the ice shapes are also monitored. These performance indicators include the lift, drag, and moment coefficients. A Bayesian neural network is subsequently trained using projected Stein variational gradient descent to create a mapping from the acoustic signature generated by the iced wings to predict their performance indicators along with quantified uncertainty that is highly important for time- and safety-critical decision-making scenarios. While the training is carried out fully offline, usage of the Bayesian neural network to make predictions can be conducted rapidly online allowing for an ice detection system that can be used in real time and in-flight.

42 ENGINEERING↗
DOE OSTI2025

Dynamic disulfide bond networks enable self-healable and mechanically resilient intrinsically stretchable organic solar cells

A dynamic disulfide network introduced into donor/acceptor blends enables room-temperature self-healing and mechanical resilience in intrinsically stretchable organic solar cells, achieving performance recovery after high mechanical strain. The development of intrinsically stretchable organic solar cells (IS-OSCs) faces significant challenges in balancing mechanical durability and optoelectronic performance. Conventional π-conjugated polymer-based donor/acceptor blend films often exhibit limited stretchability and irreversible performance degradation under mechanical strain. To address these limitations, we propose a novel self-healable donor/acceptor blended film with a dual-network morphology, achieved by incorporating a dynamic disulfide bond-based crosslinked network into the bulk-heterojunction film. The resulting thin films demonstrate a power conversion efficiency (PCE) of 16.39% in rigid OSC devices and a fracture strain of 15.6%. Remarkably, the IS-OSCs retain 80% of their initial PCE under 30% strain and exhibit performance recovery after multiple stretch-release cycles at 40% strain through a room-temperature self-healing process. This work provides a proof-of-concept for highly stretchable and durable IS-OSCs, offering valuable insights for advancing the field of wearable energy systems, adaptive solar textiles, and sustainable electronics.

Yang, Wenyu↗
DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids With Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. Demand-side participation cannot be leveraged effectively without explicitly including the distribution system dynamics in the optimization-based wholesale market operations. This project grew from a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the lower voltage system (distribution or microgrid) can also sell (buy) excess (necessary) energy from the transmission system. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. An ideal microgrid is defined as an electric entity capable of operating in both interconnected (with the high-voltage grid) and islanded mode. As such, the microgrid should incorporate generating units (traditional units and intermittent) and if needed, exchange power with the high-voltage grid. The interplay between the microgrid and high-voltage grid motivated the development of the co-optimization approach to ensure efficient performance of the interconnected network. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2016

Prediction of the stability and electronic properties of carbon nanotori synthesized by a high-voltage pulsed discharge in ethanol vapor

An experimental technique for increasing the yield of carbon-nanotube nanotori using the modified arc synthesis method is proposed. New physical knowledge on the systematic features of the interrelation between the properties of nanotori and atomic-network topology are theoretically established for the first time. The experiments are performed based on new technology for synthesizing nanotori on nickel-catalyst particles by a high-voltage pulsed discharge in ethanol vapor and using atomic-force microscopy. Stability is predicted using an original procedure for calculating local atomic stresses. Simulation shows that the zigzag chirality corresponds to the most stable topology of nanotori. Using the tight binding method, it is shown that, depending on the chirality type, nanotori are divided into two classes, i.e., those with metal and semiconductor conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Carbon Nanotube Web with Carboxylated Polythiophene “Assist” for High-Performance Battery Electrodes

In this paper, a carbon nanotube (CNT) web electrode comprising magnetite spheres and few-walled carbon nanotubes (FWNTs) linked by the carboxylated conjugated polymer, poly[3-(potassium-4-butanoate) thiophene] (PPBT), was designed to demonstrate benefits derived from the rational consideration of electron/ion transport coupled with the surface chemistry of the electrode materials components. To maximize transport properties, the approach introduces monodispersed spherical Fe 3 O 4 (sFe 3 O 4 ) for uniform Li + diffusion and a FWNT web electrode frame that affords characteristics of long-ranged electronic pathways and porous networks. The sFe 3 O 4 particles were used as a model high-capacity energy active material, owing to their well-defined chemistry with surface hydroxyl (-OH) functionalities that provide for facile detection of molecular interactions. PPBT, having a π-conjugated backbone and alkyl side chains substituted with carboxylate moieties, interacted with the FWNT π-electron-rich and hydroxylated sFe 3 O 4 surfaces, which enabled the formation of effective electrical bridges between the respective components, contributing to efficient electron transport and electrode stability. To further induce interactions between PPBT and the metal hydroxide surface, polyethylene glycol was coated onto the sFe 3 O 4 particles, allowing for facile materials dispersion and connectivity. Additionally, the introduction of carbon particles into the web electrode minimized sFe 3 O 4 aggregation and afforded more porous FWNT networks. Finally, as a consequence, the design of composite electrodes with rigorous consideration of specific molecular interactions induced by the surface chemistries favorably influenced electrochemical kinetics and electrode resistance, which afforded high-performance electrodes for battery applications.

25 ENERGY STORAGE↗