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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying the electrochemical active site density of precious metal-free catalysts in situ in fuel cells

We report advances in the development of precious-group metal-free (PGM-free) catalysts for the oxygen reduction reaction (ORR) in fuel cell cathodes have produced active catalysts that reduce the performance gap to the incumbent Pt-based materials. However, utilization of state-of-the-art PGM-free catalysts for commercial applications is currently impeded by their relatively low durability. Methods designed to study catalyst degradation in the operation of fuel cells are therefore critical for understanding durability issues and, ultimately, their solutions. Here we report the use of Fourier-transform alternating current voltammetry as an electrochemical method for accurate quantification of the electrochemically active site density of PGM-free cathode catalysts, and to follow their degradation in situ during the operation of polymer electrolyte fuel cells. Using this method, we were able to quantify the electrochemical active site density, which will enable the elucidation of degradation mechanisms of PGM-free ORR catalysts in situ in fuel cells.

25 ENERGY STORAGE↗
DOE OSTI2023

DIII-D's role as a national user facility in enabling the commercialization of fusion energy

The path to fusion in the United States requires partnership between public and private sector. While the private sector provides the vigor to take some of the major steps necessary, there is a depth of expertise and capability in the public sector that is vital to resolving feasible approaches. As an open national user facility, DIII-D provides a crucial testbed to develop the required new technologies and approaches in relevant conditions. It has unparalleled potential to meet this challenge, thanks to its extreme flexibility and world leading diagnostics. This provides a basis to rapidly develop solutions that project to future reactors with confidence. The program has thus been redeveloped to enable public and private sector engagement and testing of new concepts. A new technology program has been launched to resolve plasma interacting technologies. With modest heating upgrades, the facility can confront the crucial “Integrated Tokamak Exhaust and Performance” gap, to resolve core, exhaust and technology solutions together. The device is also being redeveloped as a training facility, with dedicated student run time, a mentorship program, and open access to all opportunity roles, part of wider efforts to diversify and open pathways through inclusion, access, and equity. This exciting agenda is enabling scientists and technology researchers to pioneer the solutions needed for a Fusion Pilot Plant (FPP) and ITER this decade. As a national user facility, DIII D has singular potential to provide the tools, teams, and insight necessary, to do its part in moving the United States rapidly toward the commercialization of fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

On efficient Hessian computation using the edge pushing algorithm in Julia

Evaluating the Hessian matrix of second-order derivatives at a sequence of points can be costly when applying second-order methods for nonlinear optimization. In this work, we discuss our experiences implementing the recently proposed Edge Pushing (EP) method in Julia as an experimental replacement for the current colouring-based methods used by JuMP, an open-source algebraic modelling language. We propose an alternative data structure for sparse Hessians to avoid the use of hash tables and analyse the space and time complexity of EP method. In our benchmarks, we find that EP is very competitive in terms of both preprocessing time and Hessian evaluation time. As a result, we identify cases where EP closes the performance gap between JuMP's previous implementation and the implementation in AMPL, a commercial software package with similar functionality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Semihard Iron-Based Permanent-Magnet Materials

Permanent magnets generally require a favorable, but difficult-to-achieve combination of high magnetization, Curie point, and magnetic anisotropy. Thus there have been few, if any, viable permanent magnets developed since the 1982 discovery of Nd 2 Fe 14 B. In this work we point out, both by direct first-principles calculations on the iron carbides and silicides Fe 5 C 2 , Fe 5 SiC, and Fe 7 C 3 as well as a discussion of recent experimental findings, that there are numerous rare-earth-free iron-rich potential permanent-magnet materials with sufficient intrinsic magnetic properties to reasonably achieve room-temperature energy products of 20–25 MG Oe. This is substantially better than the performance of the best available rare-earth-free magnets based on ferrite, as well as shape-anisotropy-employing alnico. These magnets could plausibly fill, at low cost, the present performance “gap” between the best rare-earth-free magnets and rare-earth magnets such as Nd 2 Fe 14 B and Sm-Co.

36 MATERIALS SCIENCE↗
DOE OSTI2018

ARCHIE: Data Analysis Acceleration with Array Caching in Hierarchical Storage

© 2018 IEEE. Scientific data analysis typically involves reading massive amounts of data that was generated by simulations, experiments, and observations. Performance of reading such large volumes of data from disk-based file systems is often poor because of the slow and mechanical components in the disks. Recent supercomputing systems are adding non-volatile storage layers in a hierarchy to handle the performance gap between fast main memory and slow disk-based storage. Software libraries for managing this hierarchy not only need efficient reading of data but also reduce user-involvement for cross-layer data movement. Furthermore, these libraries need to support array data access patterns into hierarchical storage management as scientific data is often organized in array-based data structures. Existing software typically manage individual storage layers requiring significant manual process in moving data among them. In this paper, we introduce a new array caching in hierarchical storage (ARCHIE) to accelerate array data analysis in a seamless fashion. ARCHIE evaluates array access patterns and prefetches data with array semantics between storage layers. Our evaluation shows that ARCHIE outperforms state-of-the-art file systems, i.e., Lustre and DataWarp, on a production supercomputing system by up to 5.8× in accessing data by scientific analysis applications.

Dong, Bin↗
DOE OSTI2026

Predictive Model for Starlink Maritime Performance Using Multi-Horizon RandomForest

Low Earth orbit (LEO) satellite systems have become a crucial enabler of broadband access for maritime industries, where traditional networks are unavailable. However, the high mobility of LEO constellations and constantly changing weather conditions result in unpredictable link fluctuations, limiting the ability of maritime platforms to plan bandwidth usage proactively. To the best of our knowledge, no prior work has developed a short-term predictive model for maritime LEO connectivity using real experimental field measurements. This paper proposes a data-driven forecasting model that predicts future downlink throughput using multi-horizon RandomForest regression. The model is trained using real experimental coastal measurement data incorporating recent throughput history, network-layer indicators, and environmental variables. The proposed approach reduces mean absolute error by approximately 31% compared to a persistence baseline for 15-minute horizons. It maintains a measurable improvement at 30 minutes, despite increased stochasticity. These findings confirm that proactive bandwidth awareness is feasible on maritime platforms and can effectively support operational decisions such as adaptive streaming, routing, and resource scheduling. The performance gap between forecasting horizons also highlights the need for expanded offshore datasets to improve prediction robustness under harsher maritime environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Versatile feature learning with graph convolutions and graph structures

Graphs represent real world relationships, and graph embedding projects nodes in a graph to a latent space that can help simplify downstream tasks. Recent development of graph convolutions in deep learning significantly improves the performance of many learning tasks on graphs. Unfortunately, prior embedding methods either do not embed graphs with node features, or fail to produce high-quality embeddings for downstream learning tasks that result in large performance gap in comparison to direct learning on graphs.We present a versatile and effective embedding method, Conv2Vec, for embedding graphs with or without node features. It is based on graph convolutions with objective functions motivated by concepts and structures from classical graph algorithms. Conv2Vec produce high-quality embedding for both plain graphs and graphs with node features for downstream tasks.We evaluate the embeddings generated by Conv2Vec with a transductive node classification task. With the generated embeddings and very simple machine learning approaches, we are able to achieve accuracies similar to those achieved by direct learning with graph convolutions. Interestingly, if we strip the node features from the graph and thus learning an embedding has to rely entirely on the graph topology, node classification with our embedding significantly outperforms direct learning with various graph convolutions. This suggests that structures from classical graph algorithms may play an important role in learning on graphs.

Cong, Guojing↗
DOE OSTI2023

Understanding SYCL Portability for Pseudorandom Number Generation: a Case Study with Gene-Expression Connectivity Mapping

Towards the goal of improving functional and performance portability of SYCL, we study a bioinformatics application that has been accelerated with CUDA and fast pseudorandom number generation on a GPU. We describe the experience of migrating pseudorandom number generation from CUDA to SYCL, evaluate the performance of pseudorandom number generators using the CUDA random number generation library, suggest the support of the XORWOW pseudorandom number generator in the oneAPI math kernel library (oneMKL) interface for performance portability, and identify the performance gap using the MKL interface in SYCL that supports pseudorandom number generation with third-party libraries. We hope that the results are valuable for the development of the SYCL ecosystem.

Jin, Zheming↗
DOE OSTI2019

On the Need for Communication for Voltage Regulation of Power Distribution Grids

We consider the problem of regulating the voltage profile of a power distribution grid by controlling the reactive power injection of distributed microgenerators. We define a very general class of purely local feedback controllers in which reactive power injection is adjusted based on the local voltage measurements. This class includes most of the solutions proposed in the literature and in the latest grid codes. We show that these strategies do not guarantee the desired regulation, as each of them can have an equilibrium that is not feasible with respect to the desired voltage and power constraints. We then propose a networked feedback law to show that, by adding short-range communication between microgenerators, it is possible to design control strategies that provably converge to the feasible set. This fundamental performance gap between local and networked strategies is finally illustrated via simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

High-Performance Design Patterns for Modern Fortran

This paper presents ideas for using coordinate-free numerics in modern Fortran to achieve code flexibility in the partial differential equation (PDE) domain. We also show how Fortran, over the last few decades, has changed to become a language well-suited for state-of-the-art software development. Fortran’s new coarray distributed data structure, the language’s class mechanism, and its side-effect-free, pure procedure capability provide the scaffolding on which we implement HPC software. These features empower compilers to organize parallel computations with efficient communication. We present some programming patterns that support asynchronous evaluation of expressions comprised of parallel operations on distributed data. We implemented these patterns using coarrays and the message passing interface (MPI). We compared the codes’ complexity and performance. The MPI code is much more complex and depends on external libraries. The MPI code on Cray hardware using the Cray compiler is 1.5–2 times faster than the coarray code on the same hardware. The Intel compiler implements coarrays atop Intel’s MPI library with the result apparently being 2–2.5 times slower than manually coded MPI despite exhibiting nearly linear scaling efficiency. As compilers mature and further improvements to coarrays comes in Fortran 2015, we expect this performance gap to narrow.

Haveraaen, Magne↗
DOE OSTI2017

Beyond 100 Gb/s: Capacity, Flexibility, and Network Optimization

Here, we discuss building blocks that enable the exploitation of optical capacities beyond 100 Gb/s. Optical networks will benefit from more flexibility and agility in their network elements, especially from coherent transceivers. To achieve capacities of 400 Gb/s and more, coherent transceivers will operate at higher symbol rates. This will be made possible with higher bandwidth components using new electro-optic technologies implemented with indium phosphide and silicon photonics. Digital signal processing will benefit from new algorithms. Multi-dimensional modulation, of which some formats are already in existence in current flexible coherent transceivers, will provide improved tolerance to noise and fiber nonlinearities. Constellation shaping will further improve these tolerances while allowing a finer granularity in the selection of capacity. Frequency-division multiplexing will also provide improved tolerance to the nonlinear characteristics of fibers. Algorithms with reduced computation complexity will allow the implementation, at speeds, of direct pre-compensation of nonlinear propagation effects. Advancement in forward error correction will shrink the performance gap with Shannon's limit. At the network control and management level, new tools are being developed to achieve a more efficient utilization of networks. This will also allow for network virtualization, orchestration, and management. Lastly, FlexEthernet and FlexOTN will be put in place to allow network operators to optimize capacity in their optical transport networks without manual changes to the client hardware.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗
DOE OSTI2014

A Compute Capable SSD Architecture for Next-Generation Non-volatile Memories

Existing storage technologies (e.g., disks and ash) are failing to cope with the processor and main memory speed and are limiting the overall perfor- mance of many large scale I/O or data-intensive applications. Emerging fast byte-addressable non-volatile memory (NVM) technologies, such as phase-change memory (PCM), spin-transfer torque memory (STTM) and memristor are very promising and are approaching DRAM-like performance with lower power con- sumption and higher density as process technology scales. These new memories are narrowing down the performance gap between the storage and the main mem- ory and are putting forward challenging problems on existing SSD architecture, I/O interface (e.g, SATA, PCIe) and software. This dissertation addresses those challenges and presents a novel SSD architecture called XSSD. XSSD o oads com- putation in storage to exploit fast NVMs and reduce the redundant data tra c across the I/O bus. XSSD o ers a exible RPC-based programming framework that developers can use for application development on SSD without dealing with the complication of the underlying architecture and communication management. We have built a prototype of XSSD on the BEE3 FPGA prototyping system. We implement various data-intensive applications and achieve speedup and energy ef- ciency of 1.5-8.9 and 1.7-10.27 respectively. This dissertation also compares XSSD with previous work on intelligent storage and intelligent memory. The existing ecosystem and these new enabling technologies make this system more viable than earlier ones.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Multiyear Plan for Validation of EnergyPlus Multi-Zone HVAC System Modeling using ORNL's Flexible Research Platform

This document describes the Oak Ridge National Laboratory (ORNL) multiyear experimental plan for validation and uncertainty characterization of whole-building energy simulation for a multi-zone research facility using a traditional rooftop unit (RTU) as a baseline heating, ventilating, and air conditioning (HVAC) system. The project’s overarching objective is to increase the accuracy of energy simulation tools by enabling empirical validation of key inputs and algorithms. Doing so is required to inform the design of increasingly integrated building systems and to enable accountability for performance gaps between design and operation of a building. The project will produce documented data sets that can be used to validate key functionality in different energy simulation tools and to identify errors and inadequate assumptions in simulation engines so that developers can correct them. ASHRAE Standard 140, Method of Test for the Evaluation of Building Energy Analysis Computer Programs (ASHRAE 2004), currently consists primarily of tests to compare different simulation programs with one another. This project will generate sets of measured data to enable empirical validation, incorporate these test data sets in an extended version of Standard 140, and apply these tests to the Department of Energy’s (DOE) EnergyPlus software (EnergyPlus 2016) to initiate the correction of any significant deficiencies. The fitness-for-purpose of the key algorithms in EnergyPlus will be established and demonstrated, and vendors of other simulation programs will be able to demonstrate the validity of their products. The data set will be equally applicable to validation of other simulation engines as well.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Progress, Performance, and Prospects of Ultra-High Resolution Microcalorimeter Spectrometers

In 2005 the LANL/NIST team used a single high-resolution microcalorimeter detector to measure the gamma-ray spectrum of a plutonium sample. After more than a decade of research and development on this topic, both the technology and our general understanding of its capabilities have advanced greatly, such that a progress review is now timely. We examine the scenario of a large-scale reprocessing plant and conclude that current non-destructive analysis (NDA) methods are inadequate to safeguard such a facility to the desired levels, leading to undesirable dependence on massspectrometry (MS) destructive analysis (DA). The development of microcalorimeter detectors is intended to close the performance gap between NDA and DA methods to address the needs of nuclear facilities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Detector needs for STS instruments

This white paper contains an analysis of the requirements for the detectors for the initial suite of neutron scattering instruments at the Second Target Station, STS. The detector technologies proposed are compared with the instrument requirements, in-house proficiencies and strengths, and where appropriate, expertise at other facilities. Where relevant, alternative detector options are proposed. The performance gaps between the current state-of-the-art and the required characteristics are identified and projects within the R&D program are proposed in order to close these gaps. Further, neutron beam monitor needs are considered in a similar light.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic

We introduce a quantum information theory-inspired method to improve the characterization of many-body Hamiltonians on near-term quantum devices. We design a new class of similarity transformations that, when applied as a preprocessing step, can substantially simplify a Hamiltonian for subsequent analysis on quantum hardware. By design, these transformations can be identified and applied efficiently using purely classical resources. In practice, these transformations allow us to shorten requisite physical circuit-depths, overcoming constraints imposed by imperfect near-term hardware. Importantly, the quality of our transformations is t u n a b l e : we define a 'ladder' of transformations that yields increasingly simple Hamiltonians at the cost of more classical computation. Using quantum chemistry as a benchmark application, we demonstrate that our protocol leads to significant performance improvements for zero and finite temperature free energy calculations on both digital and analog quantum hardware. Specifically, our energy estimates not only outperform traditional Hartree-Fock solutions, but this performance gap also consistently widens as we tune up the quality of our transformations. In short, our quantum information-based approach opens promising new pathways to realizing useful and feasible quantum chemistry algorithms on near-term hardware.

Physics↗
DOE OSTI2022

The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era

Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.

47 OTHER INSTRUMENTATION↗