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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Finding idle machines in a workstation-based distributed system

The authors describe the design and performance of scheduling facilities for finding idle hosts in a workstation-based distributed system. They focus on the tradeoffs between centralized and decentralized architectures with respect to scalability, fault tolerance, and simplicity of design, as well as several implementation issues of interest when multicast communication is used. They conclude that the principal tradeoff between the two approaches is that a centralized architecture can be scaled to a significantly greater degree and can more easily monitor global system statistics, whereas a decentralized architecture is simpler to implement.

Theimer, Marvin M.↗
DOE OSTI2026

Electrode Modification by Laser Ablation to Overcome Mechanical and Scalability Limitations of Solid-State Prealkylation of Electrode Architectures

In this work, laser ablation of Si electrode surfaces is utilized to provide localized strain relief during the Solid State Prealkylation of Electrode Architectures (SPEAR) process, in addition to volumetric expansion relief in the form of free volume channels across the electrode. A hexagonal mesh patterning was employed via laser ablation, where the hexagonal diameter (pitch) was varied from 250 to 1000 µm. It was demonstrated that these channels serve as expansion voids, accommodating the increase in electrode thickness observed during SPEAR and preventing cracking at the electrode surface. Optical microscopy and Raman mapping confirm that Li diffusion is the rate-limiting solid-state process, requiring a 24 h rest period to achieve homogeneous LixSi alloying through the electrode bulk. Electrochemical evaluations in full cells show that a 1000 μm pitch pattern yields a high initial discharge capacity of 2485 mA h g–1 and 86.4% capacity retention after 80 cycles, whereas smaller pitches (<500 μm) result in lithium plating at 4.1 V in full cells paired with NMC 811 due to excessive current collector exposure from the ablation process. XPS analysis reveals that the laser treatment modifies the electrode surface by increasing the Si–O and C–O species, changing the SEI composition observed via an increase in fluorophosphates upon cycling.

Musgrove, Amanda [ORNL] (ORCID:0009000220910389)↗
NASA NTRS1993

Volume server: A scalable high speed and high capacity magnetic tape archive architecture with concurrent multi-host access

A major challenge facing data processing centers today is data management. This includes the storage of large volumes of data and access to it. Current media storage for large data volumes is typically off line and frequently off site in warehouses. Access to data archived in this fashion can be subject to long delays, errors in media selection and retrieval, and even loss of data through misplacement or damage to the media. Similarly, designers responsible for architecting systems capable of continuous high-speed recording of large volumes of digital data are faced with the challenge of identifying technologies and configurations that meet their requirements. Past approaches have tended to evaluate the combination of the fastest tape recorders with the highest capacity tape media and then to compromise technology selection as a consequence of cost. This paper discusses an architecture that addresses both of these challenges and proposes a cost effective solution based on robots, high speed helical scan tape drives, and large-capacity media.

Rybczynski, Fred↗
NASA NTRSDate not supplied

Use NASA Earthdata in the Cloud

With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.

Nicholas Doty↗
DOE OSTI2024

Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors

Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.

Physics↗
DOE OSTI2026

Transformer-based operator learning framework for self-energy in strongly correlated systems

We introduce Σ-Attention, a transformer-based operator-learning framework for approximating the self-energy operator of strongly correlated electronic systems. By creating a batched dataset that combines results from three complementary approaches, i.e., many-body perturbation theory, strong-coupling expansion, and exact diagonalization, each effective in specific parameter regimes, Σ-Attention is applied to learn an accurate approximation for the self-energy operator that is valid across a wide range of parameter regimes. This hybrid strategy leverages the strengths of existing methods while relying on the transformer's ability to generalize beyond individual limitations. More importantly, the scalability of the transformer architecture allows the learned self-energy to be extended to systems with larger sizes, leading to much improved computational scaling. Using the one-dimensional Hubbard model, we demonstrate that Σ-Attention can accurately predict the Matsubara Green's function of large systems with a wide range of coupling strength. Our framework offers a promising and scalable pathway for studying strongly correlated systems with many possible generalizations.

Zhu, Yuanran↗
NASA NTRS2012

MAARSS: Magnet Architectures and Active Radiation Shielding Study

Protecting humans from space radiation is a major hurdle for human exploration of the solar system and beyond. Like on Earth, large magnetic fields surrounding a spaceship would deflect charged particles away from the habitat region and reduce the radiation dose to acceptable limits. The objective of this study is to determine the feasibility of current state of the art (SOA) high temperature superconducting (HTS) magnets as a means to protect crew from space radiation exposure on long duration missions beyond Low Earth Orbit (LEO). The study will look at architecture concepts to deflect high energy Galactic Cosmic Radiation (GCR) and Solar Proton Events (SPEs). Mass, power, and shielding efficiency will be considered and compared with current passive shielding capabilities. This report will walk the reader through several designs considered over the one year study and discuss the multiple parameters that should be evaluated for magnetic shielding. The study team eventually down-selects to a scalable light weight solenoid architecture that is launchable and then deployable using magnetic pressure to expand large diameter coils. Benefitting from the low temperature and high vacuum environment of deep space, existing high-temperature superconductors make such radiation shields realistic, near-term technical developments.

Westover, Shayne C.↗
NASA NTRS2016

Using JWST Heritage to Enable a Future Large Ultra-Violet Optical Infrared Telescope

To the extent it makes sense, leverage JWST knowledge, designs, architectures, GSE. Develop a scalable design reference mission (9.2 meter). Do just enough work to understand launch break points in aperture size. Demonstrate 10 pm stability is achievable on a design reference mission. Make design compatible with starshades. While segmented coronagraphs with high throughput and large bandpasses are important, make the system serviceable so you can evolve the instruments. Keep it room temperature to minimize the costs associated with cryo. Focus resources on the contrast problem. Start with the architecture and connect it to the technology needs.

JWST UV Optical Infrared↗
DOE OSTI2023

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques in downstream decision making tasks and scenarios. In recent years, ensemble-based methods have achieved significant success for the uncertainty quantification in DNNs on a number of benchmark problems. However, their performance on real-world applications remains under-explored. In this work, we present an automated approach to DNN discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification. Specifically, we propose the use of a scalable neural and hyperparameter architecture search for discovering an ensemble of DNN models for complex dynamical systems. We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly. This is achieved by using genetic algorithms and Bayesian optimization for sampling the search space of neural network architectures and hyperparameters. Subsequently, a model selection approach is used to identify candidate models for an ensemble set construction. Afterwards, a variance decomposition approach is used to estimate the uncertainty of the predictions from the ensemble. We demonstrate the feasibility of this framework for two tasks — forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature. In conclusion, we demonstrate superior performance from the ensemble in contrast with individual high-performing models and other benchmarks.

Deep ensembles↗
DOE OSTI2026

InterQnet: A Heterogeneous Full-Stack Approach to Co-Designing Scalable Quantum Networks

Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in much the same way that classical networks revolutionized classical computing. Quantum communications will also enable breakthroughs in quantum sensing, metrology, and other areas. However, scalability has emerged as a major challenge, particularly in terms of the number and heterogeneity of nodes, the distances between nodes, the diversity of applications, and the scale of user demand. This article describes InterQnet, a multidisciplinary project that advances scalable quantum communications through a comprehensive approach that improves devices, error handling, and network architecture. InterQnet has a two-pronged strategy to address scalability challenges: InterQnet-Achieve focuses on practical realizations of heterogeneous quantum networks by building and then integrating first-generation quantum repeaters with error mitigation schemes and centralized automated network control systems. The resulting system will enable quantum communications between two heterogeneous quantum platforms through a third type of platform operating as a repeater node. InterQnet-Scale focuses on a systems study of architectural choices for scalable quantum networks by developing forward-looking models of quantum network devices, advanced error correction schemes, and entanglement protocols. Here, we report our current progress toward achieving our scalability goals.

Chung, Joaquin [Argonne] (ORCID:0000000173833810)↗
NASA NTRS2017

Medical Data Architecture Capabilities and Design

Mission constraints will challenge the delivery of medical care on a long-term, deep space explorationmission. This type of mission will be restricted in the availability of medical knowledge, skills, procedures and resourcesto prevent, diagnose, and treat in-flight medical events. Challenges to providing medical care are anticipated, includingresource and resupply constraints, delayed communications and no ability for medical evacuation. The Medical DataArchitecture (MDA) project will enable medical care capability in this constrained environment.The first version of thesystem, called Test Bed 1, includes capabilities for automated data collection, data storage and data retrieval to provideinformation to the Crew Medical Officer (CMO). Test Bed 1 seeks to establish a data architecture foundation and developa scalable data management system through modular design and standardized interfaces. In addition, it will demonstrateto stakeholders the potential for an improved, automated, flow of data to and from the medical system over the currentmethods employed on the International Space Station (ISS). It integrates a set of external devices, software andprocesses, and a Subjective, Objective, Assessment, and Plan (SOAP) note commonly used by clinicians. Medical datalike electrocardiogram plots, heart rate, skin temperature, respiration rate, medications taken, and more are collectedfrom devices and stored in the Electronic Medical Records (EMR) system, and reported to crew and clinician. Devicesintegrated include the Astroskin biosensor vest and IMED CARDIAX electrocardiogram (ECG) device with INEED MDECG Glove, and the NASA-developed Medical Dose Tracker application.The system is designed to be operated as astandalone system, and can be deployed in a variety of environments, from a laptop to a data center. The system isprimarily composed of open-source software tools, and is designed to be modular, so new capabilities can be added. Thesoftware components and integration methods will be discussed.

Exploration medical system↗
DOE OSTI2021

A compute-bound formulation of Galerkin model reduction for linear time-invariant dynamical systems

This work aims to advance computational methods for projection-based reduced-order models (ROMs) of linear time-invariant (LTI) dynamical systems. For such systems, current practice relies on ROM formulations expressing the state as a rank-1 tensor (i.e., a vector), leading to computational kernels that are memory bandwidth bound and, therefore, ill-suited for scalable performance on modern architectures. This weakness can be particularly limiting when tackling many-query studies, where one needs to run a large number of simulations. This work introduces a reformulation, called rank-2 Galerkin, of the Galerkin ROM for LTI dynamical systems which converts the nature of the ROM problem from memory bandwidth to compute bound. We present the details of the formulation and its implementation, and demonstrate its utility through numerical experiments using, as a test case, the simulation of elastic seismic shear waves in an axisymmetric domain. We quantify and analyze performance and scaling results for varying numbers of threads and problem sizes. In conclusion, we present an end-to-end demonstration of using the rank-2 Galerkin ROM for a Monte Carlo sampling study. We show that the rank-2 Galerkin ROM is one order of magnitude more efficient than the rank-1 Galerkin ROM (the current practice) and about 970 times more efficient than the full-order model, while maintaining accuracy in both the mean and statistics of the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An immersed interface method for microstructure-scale electrochemical battery models: numerical formulation and performance portable implementation

We present the numerical formulation, verification, and performance portable implementation of an immersed interface method for microstructure scale electrochemical modeling of batteries. The innovation in this approach is the resolution of chemical species and electrostatic potential discontinuities at active interfaces without the use of interface conforming unstructured grids. A unified formulation on Cartesian grids for all domains (electrodes and electrolyte) is used with interfacial flux conditions applied using volume fraction or “color” function gradients. We have developed one dimensional and two dimensional test cases with analytic solutions for electrochemical modeling using which we verified the consistency and accuracy of our scheme. Our solver is also validated against solutions from a macroscale model and an unstructured multi-subdomain solver for a full lithium ion cell. We then demonstrated the utility of our solver on an image-based complex battery electrode microstructure at high charging rate. Our technique also exhibits good scalability on distributed memory architectures using central processing units (CPU), with problem sizes up to 1.8 billion degrees of freedom and with 5400 ranks. Initial performance studies of our open-source performance portable solver showed about 70 times speed up using a graphics processing unit (GPU) compared to single compute core for a problem with 4 million cells.

25 ENERGY STORAGE↗
DOE OSTI2024

Control of Quantized Spontaneous Emission from Single GaAs Quantum Dots Embedded in Huygens’ Metasurfaces

Advancements in photonic quantum information systems (QIS) have driven the development of high-brightness, on-demand, and indistinguishable semiconductor epitaxial quantum dots (QDs) as single photon sources. Strain-free, monodisperse, and spatially sparse local-droplet-etched (LDE) QDs have recently been demonstrated as a superior alternative to traditional Stranski–Krastanov QDs. However, integration of LDE QDs into nanophotonic architectures with the ability to scale to many interacting QDs is yet to be demonstrated. Here, we present a potential solution by embedding isolated LDE GaAs QDs within an Al 0.4 Ga 0.6 As Huygens’ metasurface with spectrally overlapping fundamental electric and magnetic dipolar resonances. We demonstrate for the first time a position- and size-independent, 1 order of magnitude increase in the collection efficiency and emission lifetime control for single-photon emission from LDE QDs embedded within the Huygens’ metasurfaces. Our results represent a significant step toward leveraging the advantages of LDE QDs within nanophotonic architectures to meet the scalability demands of photonic QIS.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Linear thermomagnetic energy harvester for low-grade thermal energy harvesting

Low-grade thermal energy, either from waste heat or from natural resources, constitutes an enormous energy reserve that remains to be fully harvested. Harvesting low-grade heat is challenging because of the low Carnot efficiency. Among various thermal energy harvesting mechanisms available for capturing low-grade heat (temperature less than 100 degrees C), the thermomagnetic effect has been found to be quite promising. In this study, we demonstrate a scalable thermomagnetic energy harvester architecture that exhibits 140% higher power density compared to the previously published spring-mass designs. The alternating force required to oscillate the thermomagnetic mass is generated through the interaction between two magnetic forces in opposite directions. We employed numerical modeling to illustrate the behavior of a thermomagnetic device under different operating conditions and to obtain the optimal hot-side and cold-side temperatures for continuous mode operations. A miniaturized thermomagnetic harvester was fabricated and experiments were conducted to systematically evaluate the performance. The prototype was found to exhibit an oscillation frequency of 0.33 Hz, a work output of 0.6 J/kg/cycle, and a power density of 0.2 W/kg of gadolinium under the temperature difference of 60 K.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

In situ compression artifact removal in scientific data using deep transfer learning and experience replay

The massive amount of data produced during simulation on high-performance computers has grown exponentially over the past decade, exacerbating the need for streaming compression and decompression methods for efficient storage and transfer of this data---key to realizing the full potential of large-scale computational science. Lossy compression approaches such as JPEG when applied to scientific simulation data realized as a stream of images can achieve good compression rates but at the cost of introducing compression artifacts and loss of information. This paper develops a unified framework for in situ compression artifact removal in which the fully convolutional neural network architectures are combined with scalable training, transfer learning, and experience replay to achieve superior accuracy and efficiency while significantly decreasing the storage footprint as compared with the traditional optimization-based approaches. We demonstrate the proposed approach and compare it with compressed sensing postprocessing and other baseline deep learning models using climate simulations and nuclear reactor simulations, both of which are driven by hyperbolic partial differential equations. Our approach when applied to remove the compression artifacts on the JPEG-compressed nuclear reactor simulation data (using a transfer-trained model that was pretrained on the climate simulation data and updated incrementally as the nuclear reactor simulation progressed), achieved a significant improvement---mean peak signal-to-noise ratio of 42.438 as compared with 27.725 obtained with the compressed sensing approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Multi-Objective Boundary Analysis of Discrete and Integrated SiC FET Modular Non-inverting Buck and Boost Converters for Fuel Cell EVs

This paper presents a multi-objective analysis of discrete and integrated SiC FET-based non-inverting buck-boost converter modules for modular fuel cell electric vehicle (EV) systems. Two converter ratings, 60 kW and 90 kW, are evaluated for both implementations, scalable up to 420 kW and 450 kW, respectively. Performance is assessed across efficiency, volumetric and gravimetric power density, cost, thermal stress, and estimated lifetime, where lifetime is derived from SiC FET B10 power-cycling data and junction temperature variations at rated power. A normalized overall performance index combined with a Pareto-boundary framework is used to identify configurations that optimally balance competing objectives. Results show that most configurations lie on the Pareto front, providing balanced trade-offs, while certain high-power discrete (90 kW at 450 kW) and integrated (60 kW at 180−420 kW) configurations are dominated. In general, discrete modules are more favorable for lower-power modular systems due to higher power density and lower cost, whereas integrated modules become more advantageous at higher power levels due to improved thermal behavior and longer lifetime. These findings provide practical design guidance for scalable fuel cell converter architectures and highlight the importance of system-level trade-offs in modular power electronics design.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2020

ArborX: A Performance Portable Geometric Search Library

Searching for geometric objects that are close in space is a fundamental component of many applications. The performance of search algorithms comes to the forefront as the size of a problem increases both in terms of total object count as well as in the total number of search queries performed. Scientific applications requiring modern leadership-class supercomputers also pose an additional requirement of performance portability, i.e., being able to efficiently utilize a variety of hardware architectures. In this article, we introduce a new open-source C++ search library, ArborX, which we have designed for modern supercomputing architectures. Herein, we examine scalable search algorithms with a focus on performance, including a highly efficient parallel bounding volume hierarchy implementation, and propose a flexible interface making it easy to integrate with existing applications. We demonstrate the performance portability of ArborX on multi-core CPUs and GPUs and compare it to the state-of-the-art libraries such as Boost.Geometry.Index and nanoflann.

97 MATHEMATICS AND COMPUTING↗