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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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A nanowire energy storage device such as a nanowire battery or a capacitor having a cathode comprising a plurality of nanowires and an anode comprising a plurality of nanowires interlaced with the plurality of nanowires of the cathode, and embedded in a PMMA gel electrolyte.
Systems and methods for reducing data movement in a computer system. The systems and methods use information or knowledge about the structure of an algorithm, operations to be executed at a receiving processing unit, variables or subsets or groups of variables in a distributed algorithm, or other forms of contextual information, for reducing the number of bits transmitted from at least one transmitting processing unit to at least one receiving processing unit or storage device.
Systems and methods for energy storage and energy recovery are provided. An electrical-to-electrical energy storage system includes a thermochemical energy storage device, a blower, a compressor, a turbine, and an electrical generator. The TCES device includes a vessel, a porous bed, and a heater. The porous bed is disposed within an interior volume of the vessel. The porous bed comprises a reactive material. The reactive material is configured to release oxygen upon being heated to a reduction temperature, and generate heat when exposed to oxygen. The heater is in thermal contact with the reactive material. The blower is configured to remove oxygen from the interior volume. The compressor is configured to flow oxygen into the interior volume. The turbine is configured to receive a heated, oxygen-depleted gas from the interior volume. The generator is configured to be powered by the turbine to generate electricity.
A gel electrolyte composite containing a nonvolatile electrolyte and a zwitterionic polymer scaffold in which the non-volatile electrolyte is a non-lithium-containing ionic liquid, a sodium-containing ionic liquid, or a lithium-containing ionic liquid and the zwitterionic polymer scaffold is formed from one or more zwitterionic monomers only or both one or more zwitterionic monomers and one or more non-zwitterionic monomers. The zwitterionic polymer scaffold contains 8 mol % or higher of zwitterions relative to the total content of the gel electrolyte composite when the nonvolatile electrolyte is a non-lithium-containing ionic liquid, and contains 1 mol % or higher of zwitterions relative to the total content of the gel electrolyte composite when the nonvolatile electrolyte is a sodium-containing ionic liquid or a lithium-containing ionic liquid. Also disclosed are a method of preparing the above-described gel electrolyte composite and an electrochemical energy storage device containing same.
Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.
Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.
Large-scale anodes containing high weight percentages of silicon suitable for use in lithium-ion energy storage devices and batteries, and methods of manufacturing the same, are described. The anode material described herein can include a film cast on a current collector substrate, with the film including a plurality of active material particles and a conductive polymer membrane coated over the active material particles. In some embodiments, the conductive polymer membrane comprises polyacrylonitrile (PAN). The method of manufacturing the anode material can include preparation of a slurry including the active material particles and the conductive polymer material, casting the slurry on a current collector substrate, and subjecting the composite material to drying and heat treatments.
Systems and methods for energy storage and energy recovery are provided. An electrical-to-electrical energy storage system includes a thermochemical energy storage device, a blower, a compressor, a turbine, and an electrical generator. The TCES device includes a vessel, a porous bed, and a heater. The porous bed is disposed within an interior volume of the vessel. The porous bed comprises a reactive material. The reactive material is configured to release oxygen upon being heated to a reduction temperature, and generate heat when exposed to oxygen. The heater is in thermal contact with the reactive material. The blower is configured to remove oxygen from the interior volume. The compressor is configured to flow oxygen into the interior volume. The turbine is configured to receive a heated, oxygen-depleted gas from the interior volume. The generator is configured to be powered by the turbine to generate electricity.
A quinone-containing poly(arylene) includes repeating units of formula (I), (II), (III), (IV), (V), or (VI) as defined herein. The quinone-containing poly(arylene) can be useful in composites, electrode assemblies, electrochemical cells, gas separation systems, energy storage devices, and electrochromic devices.
This project focuses on the digitization and public accessibility to the Fusion Safety Archives at the Idaho National Laboratory. The first phase involves a thorough review of each document in the physical archives to determine its online availability. For documents that are available online, PDF copies and unique identifiers are collected for database integration. Documents not available online are delivered to Red Inc. for digitization. Additionally, defunct storage devices such as diskettes are sent to INL’s archival department for data retrieval where possible. The second phase of the project involves the creation of a comprehensive database to house the digital copies of the archives. The database will facilitate easy access and management of the digitized documents. Following the database creation, we plan to train a Retrieval-Augmented Generation (RAG) based AI on publicly available documents. The trained AI will be integrated into a front-facing application, allowing the public to easily access information from the Fusion Safety Archives. This project aims to preserve valuable historical data, improve accessibility, and promote transparency in fusion safety research.
The DUNE detector is a neutrino physics experiment that is expected to take data starting from 2028. The data acquisition (DAQ) system of the experiment is designed to sustain several TB/s of incoming data which will be temporarily buffered while being processed by a software based data selection system. In DUNE, some rare physics processes (e.g. Supernovae Burst events) require storing the full complement of data produced over 1-2 minute window. These are recognised by the data selection system which fires a specific trigger decision. Upon reception of this decision data are moved from the temporary buffers to local, high performance, persistent storage devices. In this paper we characterize the performance of novel 3DXPoint SSD devices under different workloads suitable for high-performance storage applications. We then illustrate how such devices may be applied to the DUNE use-case: to store, upon a specific signal, 100 seconds of incoming data at 1.5 TB/s distributed among 150 identical units each operating at approximately 10GB/s.
As thermal energy storage (TES) systems gain increasing recognition as next-generation energy storage solutions, evaluating their techno-economic performance is crucial. This perspective analyzes the cost-performance of latent heat-based TES systems and introduces the concept of iso-cost-performance—maintaining a constant cost per unit energy ($\$$/kWh) despite material degradation. Using an empirical degradation model, we show that after 1200 thermal cycles, the thermal conductivity and volumetric energy density of a paraffin-based phase change material (PCM) decreased by 36.0% and 26.1%, respectively, resulting in a 31.2% reduction in the figure of merit (FOM) and, consequently, the system cost-performance. However, by introducing thermally conductive additives to enhance effective thermal conductivity, the TES system can recover its initial FOM, achieving iso-cost-performance operation. This framework quantitatively demonstrates how degradation-mitigation strategies—such as improving thermal conductivity—can offset material degradations and maintain long-term cost-effectiveness. Beyond PCM-based TES, the proposed FOM-based approach provides a generalized pathway for cost-performance optimization across various TES technologies.
The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.
The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.
Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low storage efficiency. We use passive optimization and algorithmic optimization techniques to demonstrate nearly a sixfold enhancement in quantum memory efficiency. In this regime, we demonstrate coherent and single-photon-level storage with a high signal-to-noise ratio. The optimization technique presented here can be applied to most solid-state quantum memories to significantly improve the storage efficiency without compromising the memory bandwidth. Published by the American Physical Society 2024
The development of new electrolyte and electrode designs and compositions has led to advances in electrochemical energy-storage (EES) devices over the past decade. However, focusing on either the electrode or electrolyte separately is insufficient for developing safer and more efficient EES devices in various working environments, as the energy-storage ability is determined by the ion arrangement and charge and/or electron transfer at the electrode–electrolyte interface. Here, we assess the fundamental physicochemical and electrochemical properties at the electrode–electrolyte interfaces in Li-ion batteries and supercapacitors using safe and electrochemically stable ionic-liquid electrolytes. Key reactions and interactions at the electrode–electrolyte interface, as well as geometric constraints and temperature effects, are highlighted. Building on the fundamental understanding of interfacial processes, we suggest potential strategies for designing stable and efficient ionic-liquid-based EES devices with emerging electrode materials.
The current optimization-based algorithms to operate grid-tied battery energy storage systems (BESS) typically do not look much under the hood of the BESS, i.e., the device-level characteristics of the batteries. This is often due to modeling as well as optimization complexities. However, simplified models may significantly degrade the performance of BESS operation in practice. Therefore, in this article, we propose a new BESS scheduling optimization framework that accounts for features such as cell-to-cell variations in maximum capacity, charge level balance, and internal resistance. The proposed framework is in the form of tractable mixed integer linear programs. Our approach is to estimate and update the device-level battery model parameters continuously, without the need to interrupt BESS normal operation. We validate the performance compared to an offline approach, which is based on dedicated model testing and calibration. To assure accurate performance evaluation, this article also includes developing a power hardware-in-the-loop testbed that allows for flexible operation and detailed monitoring of BESS under different design scenarios.