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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 sources NASA NTRS DOE OSTI

DOE OSTI2017

Single Atomic Iron Catalysts for Oxygen Reduction in Acidic Media: Particle Size Control and Thermal Activation

It remains a grand challenge to replace platinum group metal (PGM) catalysts with earth-abundant materials for the oxygen reduction reaction (ORR) in acidic media, which is crucial for large-scale deployment of proton exchange membrane fuel cells (PEMFCs). Here, we report a high-performance atomic Fe catalyst derived from chemically Fe-doped zeolitic imidazolate frameworks (ZIFs) by directly bonding Fe ions to imidazolate ligands within 3D frameworks. Although the ZIF was identified as a promising precursor, the new synthetic chemistry enables the creation of well-dispersed atomic Fe sites embedded into porous carbon without the formation of aggregates. The size of catalyst particles is tunable through synthesizing Fe-doped ZIF nanocrystal precursors in a wide range from 20 to 1000 nm followed by one-step thermal activation. Similar to Pt nanoparticles, the unique size control without altering chemical properties afforded by this approach is able to increase the number of PGM-free active sites. The best ORR activity is measured with the catalyst at a size of 50 nm. Further size reduction to 20 nm leads to significant particle agglomeration, thus decreasing the activity. Using the homogeneous atomic Fe model catalysts, we elucidated the active site formation process through correlating measured ORR activity with the change of chemical bonds in precursors during thermal activation up to 1100 °C. The critical temperature to form active sites is 800 °C, which is associated with a new Fe species with a reduced oxidation number (from Fe3+ to Fe2+) likely bonded with pyridinic N (FeN4) embedded into the carbon planes. Further increasing the temperature leads to continuously enhanced activity, linked to the rise of graphitic N and Fe–N species. The new atomic Fe catalyst has achieved respectable ORR activity in challenging acidic media (0.5 M H2SO4), showing a half-wave potential of 0.85 V vs RHE and leaving only a 30 mV gap with Pt/C (60 μgPt/cm2). Enhanced stability is attained with the same catalyst, which loses only 20 mV after 10 000 potential cycles (0.6–1.0 V) in O2 saturated acid. The high-performance atomic Fe PGM-free catalyst holds great promise as a replacement for Pt in future PEMFCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Epoxy-based vitrimeric semi-interpenetrating network/MXene nanocomposites for hydrogen gas barrier applications

Herein, we report MXene-filled epoxy-based vitrimeric nanocomposites featuring a semi-interpenetrating network (S-IPN) to develop a hydrogen gas (H 2 ) barrier coating with self-healing characteristics for compressed H 2 storage applications. The reversible epoxy network was formed by synthesizing linear epoxy chains with pendent bis-hydroxyl groups using amino diol, which were then crosslinked with 1,4-benzenediboronic acid to generate dynamic boronic ester linkages. To achieve the S-IPN-type molecular arrangement, the epoxy chains were in situ crosslinked in the presence of poly(ethylene-co-vinyl alcohol) (EVOH), giving rise to a self-healing network (EEP) with a healing efficiency of 87%. Into the S-IPN vitrimer (EEP), a 2D platelet-type nanofiller MXene was incorporated to introduce a tortuous path for H 2 gas diffusion along with improved mechanical properties. The nanocomposite coating was applied to nylon 6 liner material, which is conventionally used in all-composite H 2 storage vessels. Further, the application of a 2 wt% MXene/EEP nanocomposite coating showed a permeability coefficient of 0.062 cm 3 mm m -2 d -1 atm -1 exhibiting ~96% reduction in gas permeability compared to uncoated nylon 6. The same nanocomposite exhibited a healing efficiency of 79%. Increasing the MXene loading to 10 wt% further reduced the permeability coefficient to 0.002 cm 3 mm m -2 d -1 atm -1 ; however, the healing efficiency decreased due to restricted chain mobility. In essence, the current work highlights the potential of vitrimeric S-IPN nanocomposite coatings for H 2 gas-barrier applications, enhancing safety and performance.

42 ENGINEERING↗

DOE OSTI2023

AutoReP: Automatic ReLU Replacement for Fast Private Network Inference

The proliferation of the Machine-Learning-As-A-Service (MLaaS) market has brought to light a number of clients’ data privacy and security concerns. One promising solution is private inference (PI) techniques using cryptographic primitives. These techniques often come with high computation and communication overhead associated with the non-linear operator such as ReLU. Several approaches have been developed in reducing the number of ReLU operations, however, they either require a heuristic threshold selection or introduce significant accuracy drop. This work presents AutoReP, a gradient-based framework for non-linear operators reduction that aims to mitigate these concerns from a systematic perspective. AutoReP automates the process of discrete selection of ReLU and polynomial functions on neurons to accelerate PI applications. We also introduce distribution-aware polynomial approximation (DaPa) to accurately approximate ReLUs under given distribution, preserving model expressivity. Our experimental results demonstrate significant accuracy improvements of 6.12% (94.31%, 12.9K ReLU budget, CIFAR-10), 8.39% (74.92%, 12.9K ReLU budget, CIFAR-100), and 9.45% (63.69%, 55K ReLU budget, Tiny-ImageNet) over current state-of-the-art methods, e.g., SNL. Morever, AutoReP is applied to EfficientNet-B2 on ImageNet dataset, and achieved 75.55% accuracy with 176.1 × ReLU budget reduction.

Peng, Hongwu↗

DOE OSTI2021

Flow visualisation in real-size optical injectors of conventional, additised, and renewable gasoline blends

Research on renewable and alternative fuels is crucial for improving the energy and environmental efficiency of modern gasoline internal combustion engines. To highlight the influence of fuel rheological and thermodynamic properties on phase change and atomisation processes, three types of gasoline blends were tested. More specifically, the campaign comprised a reference gasoline, an ethanol/gasoline blend (10% v/v) representative of renewable fuels, and an additised gasoline sample treated with viscoelasticity-inducing agents. High-speed imaging of the transient two-phase flow field arising in the internal geometry and the near-nozzle spray region of gasoline injectors was performed employing Diffuse Backlight Illumination. The metallic body of a commercial injector was modified to fit transparent tips realising two nozzle layouts, namely a two-hole real size model resembling the Engine Combustion Network spray G injector and an enraged replica with an offset hole. Experiments were conducted at realistic operating conditions comprising an injection pressure of 100 bar and ambient pressures in the range of 0.1–6.0 bar to cover the entire range of chamber pressures prevailing in Gasoline Direct Injection engines. The action of viscoelastic additives was verified to have a suppressive effect on in-nozzle cavitation (6% reduction in cavitation extent) , while also enhancing spray atomisation at flash-boing conditions, in a manner resembling the more volatile gasoline/ethanol blends. Finally, persisting liquid ligaments were found to form after the end of injection for the additised sample, owing to the surfactant nature of the additives.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2014

EnergyFit Nevada (formerly known as the Nevada Retrofit Initiative) final report and technical evaluation

The EnergyFit Nevada (EFN) Better Buildings Neighborhood Program (BBNP, and referred to in this document as the EFN program) currently encourages Nevada residents to make whole-house energy-efficient improvements by providing rebates, financing, and access to a network of qualified home improvement contractors. The BBNP funding, consisting of 34 Energy Efficiency Conservation Block Grants (EECBG) and seven State Energy Program (SEP) grants, was awarded for a three-year period to the State of Nevada in 2010 and used for initial program design and implementation. By the end of first quarter in 2014, the program had achieved upgrades in 553 homes, with an average energy reduction of 32% per home. Other achievements included: Completed 893 residential energy audits and installed upgrades in 0.05% of all Nevada single-family homes1 Achieved an overall conversation rate of 38.1%2 7,089,089 kWh of modeled energy savings3 Total annual homeowner energy savings of approximately $525,7523 Efficiency upgrades completed on 1,100,484 square feet of homes3 $139,992 granted in loans to homeowners for energy-efficiency upgrades 29,285 hours of labor and $3,864,272 worth of work conducted by Nevada auditors and contractors4 40 contractors trained in Nevada 37 contractors with Building Performance Institute (BPI) certification in Nevada 19 contractors actively participating in the EFN program in Nevada 1 Calculated using 2012 U.S. Census data reporting 1,182,870 homes in Nevada. 2 Conversion rate through March 31, 2014, for all Nevada Retrofit Initiative (NRI)-funded projects, calculated using the EFN tracking database. 3 OptiMiser energy modeling, based on current utility rates. 4 This is the sum of $3,596,561 in retrofit invoice value and $247,711 in audit invoice value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1982

Satellite-tracking and Earth dynamics research programs

The activities carried out by the Smithsonian Astrophysical Observatory (SAO) are described. The SAO network continued to track LAGEOS at highest priority for polar motion and Earth rotation studies, and for other geophysical investigations, including crustal dynamics, Earth and ocean tides, and the general development of precision orbit determination. The network performed regular tracking of several other retroreflector satellites including GEOS-1, GEOS-3, BE-C, and Starlette for refined determinations of station coordinates and the Earth's gravity field and for studies of solid Earth dynamics. A major program in laser upgrading continued to improve ranging accuracy and data yield. This program includes an increase in pulse repetition rate from 8 ppm to 30 ppm, a reduction in laser pulse width from 6 nsec to 2 to 3 nsec, improvements in the photoreceiver and the electronics to improve daylight ranging, and an analog pulse detection system to improve range noise and accuracy. Data processing hardware and software are discussed.

Source record↗

NASA NTRS2011

Observations of Leonids 2009 by the Tajikistan Fireball Network

The fireball network in Tajikistan has operated since 2009. Five stations of the network covering the territory of near eleven thousands square kilometers are equipped with all-sky cameras with the Zeiss Distagon "fish-eye" objectives and by digital SLR cameras Nikon with the Nikkor "fish-eye" objectives. Observations of the Leonid activity in 2009 were carried out during November 13-21. In this period, 16 Leonid fireballs have been photographed. As a result of astrometric and photometric reductions, the precise data including atmospheric trajectories, velocities, orbits, light curves, photometric masses and densities were determined for 10 fireballs. The radiant positions during the maximum night suggest that the majority of the fireball activity was caused by the annual stream component with only minor contribution from the 1466 trail. According to the PE criterion, the majority of Leonid fireballs belonged to the most fragile and weak fireball group IIIB. However, one detected Leonid belonged to the fireball group I. This is the first detection of an anomalously strong Leonid individual.

Borovicka, J.↗

DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Integrating risk assessment methods for carbon storage: A case study for the quest carbon capture and storage facility

This paper documents how tools developed by the National Risk Assessment Partnership (NRAP) complement the Bowtie risk assessment approach through the demonstration of a risk-based Area of Review (AoR) determination for the Quest Carbon Capture and Storage (CCS) facility near Edmonton, Alberta with the NRAP Open-Source Integrated Assessment Model (NRAP Open-IAM). The Bowtie risk assessment for the Quest CCS site was developed by Shell Canada Ltd., who operates the asset on behalf of the Athabasca Oil Sand Project — a joint venture, owned by Canadian Natural Resources Ltd, Chevron Canada and Shell Canada Ltd. The Quest Bowtie treats site risk comprehensively – considering a large set of technical and project risks (e.g., operational risks, above ground infrastructure, public perception, etc.). NRAP tools, methods, and analyses are focused on quantitative assessment of subsurface technical risks. NRAP-Open-IAM was used to evaluate potential groundwater impacts at the Quest site, i.e., a sufficient change above background groundwater quality levels to be measurable, should CO 2 and/or brine leak from the reservoir via a faulty and/or compromised wellbore. Our results show there is a low potential for groundwater impact and support a further reduction (from ~460 to 100 km 2 ) to the established AoR for the Quest site. Integrating both approaches early in the site characterization period could significantly benefit commercial-scale projects by drastically reducing the region over which leakage risks (e.g., legacy wells) need to be evaluated, i.e., the AoR. This is especially true for brownfield sites, which could contain hundreds to thousands of legacy wells. Further, basing monitoring network design on those regions where impacts are most likely to occur has the potential to facilitate more efficient deployment of monitoring technologies and improve the likelihood of detecting a leak should one occur.

58 GEOSCIENCES↗

DOE OSTI2023

Group-equivariant autoencoder for identifying spontaneously broken symmetries

We introduce the group-equivariant autoencoder (GE autoencoder), a deep neural network (DNN) method that locates phase boundaries by determining which symmetries of the Hamiltonian have spontaneously broken at each temperature. We use group theory to deduce which symmetries of the system remain intact in all phases, and then use this information to constrain the parameters of the GE autoencoder such that the encoder learns an order parameter invariant to these “never-broken” symmetries. This procedure produces a dramatic reduction in the number of free parameters such that the GE-autoencoder size is independent of the system size. We include symmetry regularization terms in the loss function of the GE autoencoder so that the learned order parameter is also equivariant to the remaining symmetries of the system. By examining the group representation by which the learned order parameter transforms, we are then able to extract information about the associated spontaneous symmetry breaking. We test the GE autoencoder on the 2D classical ferromagnetic and antiferromagnetic Ising models, finding that the GE autoencoder (1) accurately determines which symmetries have spontaneously broken at each temperature; (2) estimates the critical temperature in the thermodynamic limit with greater accuracy, robustness, and time efficiency than a symmetry-agnostic baseline autoencoder; and (3) detects the presence of an external symmetry-breaking magnetic field with greater sensitivity than the baseline method. Lastly, we describe various key implementation details, including a quadratic-programming-based method for extracting the critical temperature estimate from trained autoencoders and calculations of the DNN initialization and learning rate settings required for fair model comparisons.

42 ENGINEERING↗

DOE OSTI2013

Characterizing acetogenic metabolism using a genome-scale metabolic reconstruction of Clostridium ljungdahlii

Background: The metabolic capabilities of acetogens to ferment a wide range of sugars, to grow autotrophically on H-2/CO2, and more importantly on synthesis gas (H-2/CO/CO2) make them very attractive candidates as production hosts for biofuels and biocommodities. Acetogenic metabolism is considered one of the earliest modes of bacterial metabolism. A thorough understanding of various factors governing the metabolism, in particular energy conservation mechanisms, is critical for metabolic engineering of acetogens for targeted production of desired chemicals. Results: Here, we present the genome-scale metabolic network of Clostridium ljungdahlii, the first such model for an acetogen. This genome-scale model (iHN637) consisting of 637 genes, 785 reactions, and 698 metabolites captures all the major central metabolic and biosynthetic pathways, in particular pathways involved in carbon fixation and energy conservation. A combination of metabolic modeling, with physiological and transcriptomic data provided insights into autotrophic metabolism as well as aided the characterization of a nitrate reduction pathway in C. ljungdahlii. Analysis of the iHN637 metabolic model revealed that flavin based electron bifurcation played a key role in energy conservation during autotrophic growth and helped identify genes for some of the critical steps in this mechanism. Conclusions: iHN637 represents a predictive model that recapitulates experimental data, and provides valuable insights into the metabolic response of C. ljungdahlii to genetic perturbations under various growth conditions. Thus, the model will be instrumental in guiding metabolic engineering of C. ljungdahlii for the industrial production of biocommodities and biofuels.

Nagarajan, H↗

NASA NTRS1974

Respirable particulate monitoring with remote sensors. (Public health ecology: Air pollution)

The feasibility of monitoring atmospheric aerosols in the respirable range from air or space platforms was studied. Secondary reflectance targets were located in the industrial area and near Galveston Bay. Multichannel remote sensor data were utilized to calculate the aerosol extinction coefficient and thus determine the aerosol size distribution. Houston Texas air sampling network high volume data were utilized to generate computer isopleth maps of suspended particulates and to establish the mass loading of the atmosphere. In addition, a five channel nephelometer and a multistage particulate air sampler were used to collect data. The extinction coefficient determined from remote sensor data proved more representative of wide areal phenomena than that calculated from on site measurements. It was also demonstrated that a significant reduction in the standard deviation of the extinction coefficient could be achieved by reducing the bandwidths used in remote sensor.

Severs, R. K.↗

NASA NTRS2012

Long-Lasting Science Returns from the Apollo Heat Flow Experiments

The Apollo astronauts deployed geothermal heat flow instruments at landing sites 15 and 17 as part of the Apollo Lunar Surface Experiments Packages (ALSEP) in July 1971 and December 1972, respectively. These instruments continuously transmitted data to the Earth until September 1977. Four decades later, the data from the two Apollo sites remain the only set of in-situ heat flow measurements obtained on an extra-terrestrial body. Researchers continue to extract additional knowledge from this dataset by utilizing new analytical techniques and by synthesizing it with data from more recent lunar orbital missions such as the Lunar Reconnaissance Orbiter. In addition, lessons learned from the Apollo experiments help contemporary researchers in designing heat flow instruments for future missions to the Moon and other planetary bodies. For example, the data from both Apollo sites showed gradual warming trends in the subsurface from 1971 to 1977. The cause of this warming has been debated in recent years. It may have resulted from fluctuation in insolation associated with the 18.6-year-cycle precession of the Moon, or sudden changes in surface thermal environment/properties resulting from the installation of the instruments and the astronauts' activities. These types of reanalyses of the Apollo data have lead a panel of scientists to recommend that a heat flow probe carried on a future lunar mission reach 3 m into the subsurface, approx 0.6 m deeper than the depths reached by the Apollo 17 experiment. This presentation describes the authors current efforts for (1) restoring a part of the Apollo heat flow data that were left unprocessed by the original investigators and (2) designing a compact heat flow instrument for future robotic missions to the Moon. First, at the conclusion of the ALSEP program in 1977, heat flow data obtained at the two Apollo sites after December 1974 were left unprocessed and not properly archived through NASA. In the following decades, heat flow data from January 1975 through February 1976, as well as the metadata necessary for processing the data (the data reduction algorithm, instrument calibration data, etc.), were somehow lost. In 2010, we located 450 original master archival tapes of unprocessed data from all the ALSEP instruments for a period of April through June 1975 at the Washington National Records Center. We are currently extracting the heat flow data packets from these tapes and processing them. Second, on future lunar missions, heat flow probes will likely be deployed by a network of small robotic landers, as recommended by the latest Decadal Survey of the National Academy of Science. In such a scenario, the heat flow probe must be a compact system, and that precludes use of heavy excavation equipment such as a rotary drill for reaching the 3-m target depth. The new heat flow system under development uses a pneumatically driven penetrator. It utilizes a stem that winds out of a reel and pushes its conical tip into the regolith. Simultaneously, gas jets, emitted from the cone tip, loosen and blow away the soil. Lab experiments have demonstrated its effectiveness in lunar vacuum.

Nagihara, S.↗

NASA NTRS2016

Generalized Fluid System Simulation Program, Version 6.0

The Generalized Fluid System Simulation Program (GFSSP) is a general purpose computer program for analyzing steady state and time-dependent flow rates, pressures, temperatures, and concentrations in a complex flow network. The program is capable of modeling real fluids with phase changes, compressibility, mixture thermodynamics, conjugate heat transfer between solid and fluid, fluid transients, pumps, compressors, and external body forces such as gravity and centrifugal. The thermofluid system to be analyzed is discretized into nodes, branches, and conductors. The scalar properties such as pressure, temperature, and concentrations are calculated at nodes. Mass flow rates and heat transfer rates are computed in branches and conductors. The graphical user interface allows users to build their models using the 'point, drag, and click' method; the users can also run their models and post-process the results in the same environment. Two thermodynamic property programs (GASP/WASP and GASPAK) provide required thermodynamic and thermophysical properties for 36 fluids: helium, methane, neon, nitrogen, carbon monoxide, oxygen, argon, carbon dioxide, fluorine, hydrogen, parahydrogen, water, kerosene (RP-1), isobutene, butane, deuterium, ethane, ethylene, hydrogen sulfide, krypton, propane, xenon, R-11, R-12, R-22, R-32, R-123, R-124, R-125, R-134A, R-152A, nitrogen trifluoride, ammonia, hydrogen peroxide, and air. The program also provides the options of using any incompressible fluid with constant density and viscosity or ideal gas. The users can also supply property tables for fluids that are not in the library. Twenty-four different resistance/source options are provided for modeling momentum sources or sinks in the branches. These options include pipe flow, flow through a restriction, noncircular duct, pipe flow with entrance and/or exit losses, thin sharp orifice, thick orifice, square edge reduction, square edge expansion, rotating annular duct, rotating radial duct, labyrinth seal, parallel plates, common fittings and valves, pump characteristics, pump power, valve with a given loss coefficient, Joule-Thompson device, control valve, heat exchanger core, parallel tube, and compressible orifice. The program has the provision of including additional resistance options through User Subroutines. GFSSP employs a finite volume formulation of mass, momentum, and energy conservation equations in conjunction with the thermodynamic equations of state for real fluids as well as energy conservation equations for the solid. The system of equations describing the fluid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods. The application and verification of the code has been demonstrated through 30 example problems.

Majumdar, A. K.↗

DOE OSTI2013

Silver vanadium diphosphate Ag{sub 2}VP{sub 2}O{sub 8}: Electrochemistry and characterization of reduced material providing mechanistic insights

Silver vanadium phosphorous oxides (Ag{sub w}V{sub x}P{sub y}O{sub z}) are notable battery cathode materials due to their high energy density and demonstrated ability to form in-situ Ag metal nanostructured electrically conductive networks within the cathode. While analogous silver vanadium diphosphate materials have been prepared, electrochemical evaluations of these diphosphate based materials have been limited. We report here the first electrochemical study of a silver vanadium diphosphate, Ag{sub 2}VP{sub 2}O{sub 8}, where the structural differences associated with phosphorous oxides versus diphosphates profoundly affect the associated electrochemistry. Reminiscent of Ag{sub 2}VO{sub 2}PO{sub 4} reduction, in-situ formation of silver metal nanoparticles was observed with reduction of Ag{sub 2}VP{sub 2}O{sub 8}. However, counter to Ag{sub 2}VO{sub 2}PO{sub 4} reduction, Ag{sub 2}VP{sub 2}O{sub 8} demonstrates a significant decrease in conductivity upon continued electrochemical reduction. Structural analysis contrasting the crystallography of the parent Ag{sub 2}VP{sub 2}O{sub 8} with that of the proposed Li{sub 2}VP{sub 2}O{sub 8} reduction product is employed to gain insight into the observed electrochemical reduction behavior, where the structural rigidity associated with the diphosphate anion may be associated with the observed particle fracturing upon deep electrochemical reduction. Further, the diphosphate anion structure may be associated with the high thermal stability of the partially reduced Ag{sub 2}VP{sub 2}O{sub 8} materials, which bodes well for enhanced safety of batteries incorporating this material. - Graphical abstract: Structure and galvanostatic intermittent titration-type test data for silver vanadium diphosphate, Ag{sub 2}VP{sub 2}O{sub 8}. Highlights: ► First electrochemical study of a silver vanadium diphosphate, Ag{sub 2}VP{sub 2}O{sub 8}. ► In-situ formation of Ag{sup 0} nanoparticles was observed upon electrochemical reduction. ► Structural analysis used to provide insight of the electrochemical behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multivariate prognosis of battery advanced state of health via transformers

De-risking energy storage investments necessary to meet CO 2 reduction targets requires a deep understanding of the connections between battery health, design, and use. The historical definition of the battery state of health (SOH) as the percentage of current versus initial capacity is inadequate for this purpose, motivating an expanded SOH consisting of an interrelated set of descriptors including capacity, energy, ionic and electronic impedances, open-circuit voltages, and microstructure metrics. In this work, we introduce deep transformer networks for the simultaneous prognosis of 28 battery SOH descriptors using two cycling datasets representing six lithium-ion cathode chemistries, multiple electrolyte/anode compositions, and different charge-discharge scenarios. The accuracy of these predictions for battery life (with an unprecedented mean absolute error of 19 cycles in predicting end of life for a lithium-iron-phosphate fast-charging dataset) illustrates the promise of deep learning toward providing enhanced understanding and control of battery health.

25 ENERGY STORAGE↗

DOE OSTI2016

SU-D-206-02: Evaluation of Partial Storage of the System Matrix for Cone Beam Computed Tomography Using a GPU Platform

Purpose: Iterative reconstruction algorithms in computed tomography (CT) require a fast method for computing the intersections between the photons’ trajectories and the object, also called ray-tracing or system matrix computation. This work evaluates different ways to store the system matrix, aiming to reconstruct dense image grids in reasonable time. Methods: We propose an optimized implementation of the Siddon’s algorithm using graphics processing units (GPUs) with a novel data storage scheme. The algorithm computes a part of the system matrix on demand, typically, for one projection angle. The proposed method was enhanced with accelerating options: storage of larger subsets of the system matrix, systematic reuse of data via geometric symmetries, an arithmetic-rich parallel code and code configuration via machine learning. It was tested on geometries mimicking a cone beam CT acquisition of a human head. To realistically assess the execution time, the ray-tracing routines were integrated into a regularized Poisson-based reconstruction algorithm. The proposed scheme was also compared to a different approach, where the system matrix is fully pre-computed and loaded at reconstruction time. Results: Fast ray-tracing of realistic acquisition geometries, which often lack spatial symmetry properties, was enabled via the proposed method. Ray-tracing interleaved with projection and backprojection operations required significant additional time. In most cases, ray-tracing was shown to use about 66 % of the total reconstruction time. In absolute terms, tracing times varied from 3.6 s to 7.5 min, depending on the problem size. The presence of geometrical symmetries allowed for non-negligible ray-tracing and reconstruction time reduction. Arithmetic-rich parallel code and machine learning permitted a modest reconstruction time reduction, in the order of 1 %. Conclusion: Partial system matrix storage permitted the reconstruction of higher 3D image grid sizes and larger projection datasets at the cost of additional time, when compared to the fully pre-computed approach. This work was supported in part by the Fonds de recherche du Quebec - Nature et technologies (FRQ-NT). The authors acknowledge partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council of Canada (Grant No. 432290).

60 APPLIED LIFE SCIENCES↗