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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 847 records · Page 47

Interfacial defect reduction enhances universal power law response in Mo–SiNx granular metals

Granular metals (GMs), consisting of metal nanoparticles separated by an insulating matrix, frequently serve as a platform for fundamental electron transport studies. However, few technologically mature devices incorporating GMs have been realized, in large part because intrinsic defects (e.g., electron trapping sites and metal/insulator interfacial defects) frequently impede electron transport, particularly in GMs that do not contain noble metals. Here, we demonstrate that such defects can be minimized in molybdenum–silicon nitride (Mo–SiN x ) GMs via optimization of the sputter deposition atmosphere. For Mo–SiN x GMs deposited in a mixed Ar/N 2 environment, x-ray photoemission spectroscopy shows a 40%–60% reduction of interfacial Mo-silicide defects compared to Mo–SiN x GMs sputtered in a pure Ar environment. Electron transport measurements confirm the reduced defect density; the dc conductivity improved (decreased) by 10 4 –10 5 and the activation energy for variable-range hopping increased 10×. Since GMs are disordered materials, the GM nanostructure should, theoretically, support a universal power law (UPL) response; in practice, that response is generally overwhelmed by resistive (defective) transport. Here, the defect-minimized Mo–SiN x GMs display a superlinear UPL response, which we quantify as the ratio of the conductivity at 1 MHz to that at dc, Δσ ω ⁠. Remarkably, these GMs display a Δσ ω up to 10 7 , a three-orders-of-magnitude improved response than previously reported for GMs. By enabling high-performance electric transport with a non-noble metal GM, this work represents an important step toward both new fundamental UPL research and scalable, mature GM device applications.

36 MATERIALS SCIENCE↗

A Radiation-Hard 8-Channel 15-Bit 40-MSPS ADC for the ATLAS Liquid Argon Calorimeter Readout

The custom design of a radiation-hardened, 8-channel, 40-MSPS, 15-bit resolution, 14.2-bit dynamic range, 11.4-ENOB ADC data acquisition ASIC fabricated in a commercial 65-nm triple-well CMOS technology is presented. The ADC is developed for and integrates seamlessly into the readout system for the ATLAS liquid argon (LAr) calorimeter in the high-luminosity large hadron collider (HLLHC) upgrade at CERN, which will require a total of 364 936 ADC channels. A three-stage MDAC+SAR pipelined ADC architecture was designed to meet the physics requirements and scientific goals of the ATLAS experiment. The ADC is a fully self-contained data acquisition system that includes foreground calibration, digital data processing, digital control, and supporting circuitry. The measured performance shows the ADC achieves a competitive dynamic range and SNDR, and it meets or exceeds the ATLAS analog requirements. Radiation tolerance and scalability design considerations were implemented at the device-, circuit-, and system-level. Radiation-hardening-by-design techniques used include redundancy for digital circuits, the use of MiM capacitors, and a hybrid RC-DAC for the ADC core. The ADC ASIC was demonstrated to be robust against the effects of the intense radiation expected in the HL-LHC experimental environment.

DAQ↗

FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking

In this work, we present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open sourced under the MIT license: https://github.com/hguo/ftk.

97 MATHEMATICS AND COMPUTING↗

Toward designing effective exascale scientific computing workflows: experiences and best practices

Many fields within scientific computing have embraced advances in big-data analysis and machine learning, which often requires the deployment of large, distributed and complicated workflows that may combine training neural networks, performing simulations, running inference, and performing database queries and data analysis in asynchronous, parallel and pipelined execution frameworks. Such a shift has brought into focus the need for scalable, efficient workflow management solutions with reproducibility, error and provenance handling, traceability, and checkpoint-restart capabilities, among other needs. Here, we discuss challenges and best-practices for deploying exascale-generation computational science workflows on resources at the Oak Ridge Leadership Computing Facility (OLCF). We present our experiences with large-scale deployment of distributed workflows on the Summit supercomputer, including for bioinformatics and computational biophysics, materials science, and deep learning model optimization. We also present problems and solutions created by working within a Python-centric software base on traditional HPC systems, and discuss steps that will be required before the convergence of HPC, AI, and data science can be fully realized. Our results point to a wealth of exciting new possibilities for harnessing this convergence to tackle new scientific challenges.

Coletti, Mark↗

Synthesis of Microscopic 3D Graphene for High-Performance Supercapacitors with Ultra-High Areal Capacitance

This presentation was given at the 2024 AIChE Annual Meeting, San Diego, CA, October 27-31, 2024. This presentation describes a low-cost and scalable method to convert coal tar pitch into high-quality 3D graphene with high surface area, hierarchical pore size distribution, and high electrical conductivity which allows for the creation of supercapacitor electrodes with ultra-high mass loading (30 mg/cm2), leading to ultra-high areal capacitance.

Pham, Viet Hung↗

Development of an Advanced Hydrogen Energy Storage System using Aerogel in a Cryogenic Flux Capacitor

The Cryogenic Flux Capacitor (CFC) is a cold, dense fluid storage core with integrated design features that afford the designer flexibility and provide new possibilities for the storage and discharge of energy. The stored energy, in this case, is represented by hydrogen physically bonded within the nanoscale pores within the aerogel composite blanket material, and the process of bonding or debonding is governed by the principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (~1,000 m 2 /g) allows for storage densities close to, or in some cases exceeding, that of normal boiling point liquids. Its performance easily exceeds what can be achieved via ambient temperature and high-pressure gas storage for an equivalent volume. CFC storage is predicted to be easily scalable, constructed from readily available commercial materials, and lends itself to a range of pressure applications. The project team designed and manufactured the CFC, integrating it into a set of temperature-controlled flow loops. The CFC test setup was validated against existing data from previous NASA work. Tests used bottled hydrogen gas for test validation. A cryogenic fluid, nitrogen, flowed through a metered control system. Hydrogen temperature, pressure, and flow rates, as well as the temperature control targets, provided all necessary control targets. The team analyzed the recorded data and updated the Technology Maturation Plan based on the results of the test, making recommendations for a follow-up test, scale-up, and maturation pathway. For the engineering development and maturation of CFC hydrogen storage technology, a Techno-Economic Assessment (TEA) and Commercialization Plan for the integration of this technology with various power generation assets were produced. A companion analytical model was developed, experimentally validated, compared against performance metric, and used to tune the model and scale up the technology.

08 HYDROGEN↗

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

Multi defect detection and analysis of electron microscopy images with deep learning

Electron microscopy is widely used to explore defects in crystal structures, but human detecting of defects is often time-consuming, error-prone, and unreliable, and is not scalable to large numbers of images or real-time analysis. In this work, we discuss the application of machine learning approaches to find the location and geometry of different defect clusters in irradiated steels. We show that a deep learning based Faster R-CNN analysis system has a performance comparable to human analysis with relatively small training data sets. Furthermore, this study proves the promising ability to apply deep learning to assist the development of automated microscopy data analysis even when multiple features are present and paves the way for fast, scalable, and reliable analysis systems for massive amounts of modern electron microscopy data.

36 MATERIALS SCIENCE↗

Open Source Platform for Live Processing of High Data Rate Electron Microscopy (SBIR Phase I Award Topic 15, Subtopic B, Final Scientific / Technical Report)

4D scanning transmission electron microscopy (STEM) is experiencing a revolution both in terms of data size, and data veracity, as are many other areas of experimental data acquisition. This presents enormous opportunities to make new scientific discoveries, but is also hamstrung by aging software that is no longer fit for purpose. Experimental data acquisition platforms must make the same investments in software infrastructure that the simulation community made decades ago to take advantage of high-performance computing (HPC) if they hope to realize the full potential of the latest advances made in detector technology. This requires rethinking the design and deployment of hardware, moving away from small, fixed commodity computing capabilities with closed interfaces to open, modular, scalable systems designed for parallelism and throughput. The technical approach will leverage best-in-class open source technologies, and develop new approaches strategically to build an open source platform for experimental data acquisition targeting high data rate electron microscopy. The project will develop a software platform that uses the power and performance of modern HPC resources to ingest the large volume of data produced by electron microscopes using direct electron detectors. A set of configurable pipelines will be used to enable the processing to be tailored to the problem being investigated with builtin visualization of data at interactive rates. This project will leverage investments made by the DOE and other agencies to provide a powerful software platform for the experimental data community working with most powerful electron microscopes, such as 4D STEM detectors. It will create a platform that serves as a reference for the community. Permissive open source licensing will make the platform available to all, with a simple deployment strategy offering the ability to keep data close to where it is generated. This advanced software platform will require customization for each deployment, creating opportunities for software services that are typically offered to extend open source software platforms.

97 MATHEMATICS AND COMPUTING↗

JSONize: A Scalable Machine Learning Pipeline to Model Medical Notes as Semi-structured Documents

The Department of Veteran's Affairs (VA) archives the largest corpora of clinical notes in their corporate data warehouse (CDW) as unstructured text data. Unstructured text easily supports keyword searches and regular expressions. Often these simple searches do not adequately support the complex searches that need to be performed on notes. For example, a researcher may want all notes with a Duke Treadmill Score less than 5 or people that smoke more than 1 pack per day. Range queries like this and more can be supported by modelling text as semi-structured documents. In this paper, we implement a scalable machine learning pipeline that models plain medical text as useful semi-structured documents. We improve on existing models and achieve a F1-score of 0.912 and scale our methods to the entire VA corpus.

Rush III, Everett↗

Scalable Gas Diffusion Electrode Fabrication for Electrochemical CO 2 Reduction Using Physical Vapor Deposition Methods

Electrochemical CO 2 reduction (ECR) promises the replacement of fossil fuels as the source of feedstock chemicals and seasonal storage of renewable energy. While much progress has been made in catalyst development and electrochemical reactor design, few studies have addressed the effect of catalyst integration on device performance. Using a microfluidic gas diffusion electrolyzer, we systematically studied the effect of thickness and the morphology of electron beam (EB) and magnetron-sputtered (MS) Cu catalyst coatings on ECR performance. We observed that EB-Cu outperforms MS-Cu in current density, selectivity, and energy efficiency, with 400 nm thick catalyst coatings performing the best. Additionally, the superior performance of EB-Cu catalysts is assigned to their faceted surface morphology and sharper Cu/gas diffusion layer interface, which increases their hydrophobicity. Tests in a large-scale zero-gap electrolyzer yielded similar product selectivity distributions with an ethylene Faradaic efficiency of 39% at 200 mA/cm 2 , demonstrating the scalability for industrial ECR applications.

36 MATERIALS SCIENCE↗

Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency

Synchronous SGD with data parallelism, the most popular parallelization strategy for CNN training, suffers from the expensive communication cost of averaging gradients among all workers. The iterative parameter updates of SGD cause frequent communications and it becomes the performance bottleneck. In this paper, we propose a lazy parameter update algorithm that adaptively adjusts the parameter update frequency to address the expensive communication cost issue. Our algorithm accumulates the gradients if the difference of the accumulated gradients and the latest gradients is sufficiently small. Here, the less frequent parameter updates reduce the per-iteration communication cost while maintaining the model accuracy. Our experimental results demonstrate that the lazy update method remarkably improves the scalability while maintaining the model accuracy. For ResNet50 training on ImageNet, the proposed algorithm achieves a significantly higher speedup (739.6 on 2048 Cori KNL nodes) as compared to the vanilla synchronous SGD (276.6) while the model accuracy is almost not affected (<0.2% difference).

97 MATHEMATICS AND COMPUTING↗

Extreme Water Uptake of Hygroscopic Hydrogels through Maximized Swelling‐Induced Salt Loading

Abstract Hygroscopic hydrogels are emerging as scalable and low‐cost sorbents for atmospheric water harvesting, dehumidification, passive cooling, and thermal energy storage. However, devices using these materials still exhibit insufficient performance, partly due to the limited water vapor uptake of the hydrogels. Here, the swelling dynamics of hydrogels in aqueous lithiumchloride solutions, the implications on hydrogel salt loading, and the resulting vapor uptake of the synthesized hydrogel–salt composites are characterized. By tuning the salt concentration of the swelling solutions and the cross‐linking properties of the gels, hygroscopic hydrogels with extremely high salt loadings are synthesized, which enable unprecedented water uptakes of 1.79 and 3.86 gg −1 at relative humidity (RH) of 30% and 70%, respectively. At 30% RH, this exceeds previously reported water uptakes of metal–organic frameworks by over 100% and of hydrogels by 15%, bringing the uptake within 93% of the fundamental limit of hygroscopic salts while avoiding leakage problems common in salt solutions. By modeling the salt‐vapor equilibria, the maximum leakage‐free RH is elucidated as a function of hydrogel uptake and swelling ratio. These insights guide the design of hydrogels with exceptional hygroscopicity that enable sorption‐based devices to tackle water scarcity and the global energy crisis.

Graeber, Gustav↗

Multifunctional Coatings on Sulfide‐Based Solid Electrolyte Powders with Enhanced Processability, Stability, and Performance for Solid‐State Batteries

Abstract Sulfide‐based solid‐state electrolytes (SSEs) exhibit many tantalizing properties including high ionic conductivity and favorable mechanical properties for next‐generation solid‐state batteries. Widespread adoption of these materials is hindered by their intrinsic instability under ambient conditions, which makes them difficult to process at scale, and instability at the Li||SSE and cathode||SSE interfaces, which limits cell performance and lifetime. Atomic layer deposition is leveraged to grow thin Al 2 O 3 coatings on Li 6 PS 5 Cl powders to address both issues simultaneously. These coatings can be directly grown onto Li 6 PS 5 Cl particles with negligible chemical modification of the underlying material and enable exposure of powders to pure and H 2 O‐saturated oxygen environments for ≥4 h with minimal reactivity, compared with significant degradation of the uncoated powder. Pellets fabricated from coated powders exhibit ionic conductivities up to 2× higher than those made from uncoated material, with a simultaneous decrease in electronic conductivity and significant suppression of chemical reactivity at the Li‐SSE interface. These benefits result in significantly improved room temperature cycle life at high capacity and current density. It is hypothesized that this enhanced performance derives from improved intergranular properties and improved Li metal adhesion. This work points to a completely new framework for designing active, stable, and scalable materials for next‐generation solid‐state batteries.

36 MATERIALS SCIENCE↗

Matrimid substrates with bicontinuous surface and macrovoids in the bulk: A nearly ideal substrate for composite membranes in CO 2 capture

Scalable fabrication of thin-film composite (TFC) membrane for post-combustion carbon capture is often limited by the availability of high-performance nanoporous substrate. Ideally, the substrate should allow for fast gas transport at the selective layer/substrate interface as well as in the bulk of the substrate. In this study, highly permeable Matrimid substrates were prepared via vapor- and nonsolvent-induced phase separations. The phase separation mechanism of the Matrimid®/N-methyl-2-pyrrolidone casting solution was modulated by the addition of LiCl to control the solution thermodynamic stability, and nanoporous Matrimid substrates were formed with a bicontinuous surface and macrovoids in the bulk. This hierarchically optimized structure resulted in a CO 2 permeance of 2.60 × 10 5 GPU (1 GPU = 1 × 10 –6 cm 3 (STP) cm –2 s –1 cmHg –1 = 3.349 × 10 –10 mol m –2 s –1 Pa –1 ), which was ca. 11 times more permeable than a benchmark polyethersulfone substrate with cellular pores. The benefit of using this new substrate was demonstrated by coating a 170-nm amine-containing polymer to form a TFC facilitated transport membrane. The membrane exhibited a CO 2 permeance of 932 GPU at 57°C, which was 72 GPU higher than the counterpart coated on the benchmark substrate. Meanwhile, the CO 2 /N 2 selectivity was remained at 158. This permeance improvement could be well explained by the resistance-in-series model, where the improved permeance was attributed to the reductions in substrate and lateral diffusion mass transfer resistances. The upper bound (UB) analysis indicates that the Matrimid substrate is nearly ideal for the state-of-the-art polymers for CO 2 /N 2 separation. Here, the substrate improvement could also reduce the parasitic energy associated with the membrane process due to the reduced membrane cost and flue gas compression requirement.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Translational research in the MPICH project

The MPICH project is an example of translational research in computer science before that term was well known or even coined. The project began in 1992 as an effort to develop a portable, high-performance implementation of the emerging Message-Passing Interface (MPI) Standard. It has enabled the widespread adoption of MPI as a way to write scalable parallel applications on systems of all sizes including upcoming exascale supercomputers. In this paper, we describe how the translational research process was used in MPICH, how that led to its success, the challenges encountered and lessons learned, and how the process could be applied to other similar projects.

97 MATHEMATICS AND COMPUTING↗

Quantum Time Dynamics Mediated by the Yang–Baxter Equation and Artificial Neural Networks

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang–Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANNs for noise mitigation combined with the YBE to generate noisy data. This approach effectively reduces noise in quantum simulations, enhancing the accuracy of the results. The YBE rigorously preserves quantum correlations and symmetries in spin chain simulations in certain classes of integrable lattice models, enabling effective compression of quantum circuits while retaining linear scalability with the number of qubits. This compression facilitates both full and partial implementations, allowing the generation of noisy quantum data on hardware alongside noiseless simulations using classical platforms. By introducing controlled noise through the YBE, we enhance the data set for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in mitigating errors in time-evolving quantum states, providing a scalable framework to enhance quantum computation fidelity, particularly in noisy intermediate-scale quantum (NISQ) systems. We demonstrate the efficacy of this approach by performing quantum time dynamics simulations using the Heisenberg XY Hamiltonian on real quantum devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Sparse Linear Solvers for Large-scale Electromagnetic Transient Simulations

Linear solvers form the basis for electromagnetic transient (EMT) simulations. There is a need to speed up EMT simulations as larger regions are analyzed using EMT simulations. For the same, the performance of linear solvers plays an important role. Exploiting the sparsity of the matrices generated in EMT simulations could assist with speed-up. Scalability is also crucial as power grids expand, demanding solutions capable of accommodating the increasing system size. Recent studies from the North American Electric Reliability Corporation (NERC) increasingly emphasize that EMT simulation models of the power grid will grow larger with the inclusion of power electronics components. Parallelisms in sparsity patterns exploit modern central processing units (CPUs), multi-core CPUs, and graphics processing units (GPUs) architectures in sparse solver designs. Therefore, this paper explores publicly available existing linear solvers and investigates their efficiency in large-scale power grid simulations. A large-scale power grid is developed by increasing the size of the IEEE 39 bus test system to up to 39000 bus systems.

Hsu, Kuan-Chieh↗