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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Energy Use in Quantum Data Centers: Scaling the Impact of Computer Architecture, Qubit Performance, Size, and Thermal Parameters

As quantum computers increase in size, the total energy used by a quantum data center, including the cooling, will become a greater concern. The cooling requirements of quantum computers, which operate at temperatures near absolute zero, are determined by computing system parameters, including the number and type of physical qubits, the packaging efficiency of the system, and the split between circuits operating at cryogenic temperatures and those operating at room temperature. When combined with thermal system parameters such as cooling efficiency and cryostat heat transfer, the total energy use can be determined using a first-principles energy model. These models show that cooling of quantum computers differs in two fundamental ways from conventional data centers: (1) the energy required for cooling is much greater than the energy required for computation, and (2) the cooling loads are sensitive to the computational architecture. The temperature requirements for different qubit types can change energy requirements by orders of magnitude. Power use and computational power, as quantified by quantum volume, are analytically correlated. Approaches are identified for minimizing energy use in integrated quantum systems relative to computational power. Furthermore, designing a sustainable quantum computer will require both efficient cooling and system design that minimizes cooling requirements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

High-Throughput, Adaptive FFT Architecture for FPGA-Based Spaceborne Data Processors

Exponential growth in microelectronics technology such as field-programmable gate arrays (FPGAs) has enabled high-performance spaceborne instruments with increasing onboard data processing capabilities. As a commonly used digital signal processing (DSP) building block, fast Fourier transform (FFT) has been of great interest in onboard data processing applications, which needs to strike a reasonable balance between high-performance (throughput, block size, etc.) and low resource usage (power, silicon footprint, etc.). It is also desirable to be designed so that a single design can be reused and adapted into instruments with different requirements. The Multi-Pass Wide Kernel FFT (MPWK-FFT) architecture was developed, in which the high-throughput benefits of the parallel FFT structure and the low resource usage of Singleton s single butterfly method is exploited. The result is a wide-kernel, multipass, adaptive FFT architecture. The 32K-point MPWK-FFT architecture includes 32 radix-2 butterflies, 64 FIFOs to store the real inputs, 64 FIFOs to store the imaginary inputs, complex twiddle factor storage, and FIFO logic to route the outputs to the correct FIFO. The inputs are stored in sequential fashion into the FIFOs, and the outputs of each butterfly are sequentially written first into the even FIFO, then the odd FIFO. Because of the order of the outputs written into the FIFOs, the depth of the even FIFOs, which are 768 each, are 1.5 times larger than the odd FIFOs, which are 512 each. The total memory needed for data storage, assuming that each sample is 36 bits, is 2.95 Mbits. The twiddle factors are stored in internal ROM inside the FPGA for fast access time. The total memory size to store the twiddle factors is 589.9Kbits. This FFT structure combines the benefits of high throughput from the parallel FFT kernels and low resource usage from the multi-pass FFT kernels with desired adaptability. Space instrument missions that need onboard FFT capabilities such as the proposed DESDynl, SWOT (Surface Water Ocean Topography), and Europa sounding radar missions would greatly benefit from this technology with significant reductions in non-recurring cost and risk.

NguyenKobayashi, Kayla↗
NASA NTRS2025

NASA Open Science Data Repository: Open Science for Life in Space

Space biology and health data are critical for the success of deep space missions and sustainable human presence off-world. At the core of effectively managing biomedical risks is the commitment to open science principles, which ensure that data are findable, accessible, interoperable, reusable, reproducible and maximally open. The 2021 integration of the Ames Life Sciences Data Archive with GeneLab to establish the NASA Open Science Data Repository significantly enhanced access to a wide range of life sciences, biomedical-clinical, and mission telemetry data alongside existing ‘omics data from GeneLab. This paper describes the new database, its architecture, and new data streams supporting diverse data types and enhancing data submission, retrieval, and analysis. Features include the Biological Data Management Environment for improved data submission, a new user interface, controlled data access, an enhanced API, and comprehensive public visualization tools for environmental telemetry, radiation dosimetry data, and ‘omics analyses. By fostering global collaboration through its Analysis Working Groups and training programs, the Open Science Data Repository promotes widespread engagement in space biology, ensuring transparency and inclusivity in research. It supports the global scientific community in advancing our understanding of spaceflight's impact on biological systems, ensuring humans will thrive in future deep space missions.

OSDR↗
DOE OSTI2021

Correlator convolutional neural networks as an interpretable architecture for image-like quantum matter data

Image-like data from quantum systems promises to offer greater insight into the physics of correlated quantum matter. However, the traditional framework of condensed matter physics lacks principled approaches for analyzing such data. Machine learning models are a powerful theoretical tool for analyzing image-like data including many-body snapshots from quantum simulators. Recently, they have successfully distinguished between simulated snapshots that are indistinguishable from one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from such approaches. Here, we develop a set of nonlinearities for use in a neural network architecture that discovers features in the data which are directly interpretable in terms of physical observables. Applied to simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, we uncover that the key distinguishing features are fourth-order spin-charge correlators. Our approach lends itself well to the construction of simple, versatile, end-to-end interpretable architectures, thus paving the way for new physical insights from machine learning studies of experimental and numerical data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2004

Selection of a Data Acquisition and Controls System Communications and Software Architecture for Johnson Space Center's Space Environment Simulation Laboratory Thermal and Vacuum Test Facilities

Upgrade of data acquisition and controls systems software at Johnson Space Center's Space Environment Simulation Laboratory (SESL) involved the definition, evaluation and selection of a system communication architecture and software components. A brief discussion of the background of the SESL and its data acquisition and controls systems provides a context for discussion of the requirements for each selection. Further framework is provided as upgrades to these systems accomplished in the 1990s and in 2003 are compared to demonstrate the role that technological advances have had in their improvement. Both of the selections were similar in their three phases; 1) definition of requirements, 2) identification of candidate products and their evaluation and testing and 3) selection by comparison of requirement fulfillment. The candidates for the communication architecture selection embraced several different methodologies which are explained and contrasted. Requirements for this selection are presented and the selection process is described. Several candidates for the software component of the data acquisition and controls system are identified, requirements for evaluation and selection are presented, and the evaluation process is described.

Jordan, Eric A.↗
NASA NTRS1997

Implementation of Helioseismic Data Reduction and Diagnostic Techniques on Massively Parallel Architectures

Under the direction of Dr. Rhodes, and the technical supervision of Dr. Korzennik, the data assimilation of high spatial resolution solar dopplergrams has been carried out throughout the program on the Intel Delta Touchstone supercomputer. With the help of a research assistant, partially supported by this grant, and under the supervision of Dr. Korzennik, code development was carried out at SAO, using various available resources. To ensure cross-platform portability, PVM was selected as the message passing library. A parallel implementation of power spectra computation for helioseismology data reduction, using PVM was successfully completed. It was successfully ported to SMP architectures (i.e. SUN), and to some MPP architectures (i.e. the CM5). Due to limitation of the implementation of PVM on the Cray T3D, the port to that architecture was not completed at the time.

Korzennik, Sylvain↗
NASA NTRS2010

CCSDS - Advancing Spaceflight Technology for International Collaboration

The Consultative Committee for Space Data Systems (CCSDS) has been developing data and communications standards since 1982, with the objective of providing interoperability for enabling international collaboration for spaceflight missions. As data and communications technology has advanced, CCSDS has progressed to capitalize on existing products when available and suitable for spaceflight, and to develop innovative new approaches when available products fail. The current scope of the CCSDS architecture spans the end-to-end data architecture of a spaceflight mission, with ongoing efforts to develop and standardize cutting-edge technology. This manuscript describes the overall architecture, the position of CCSDS in the standards and international mission community, and some CCSDS processes. It then highlights in detail several of the most interesting and critical technical areas in work right now, and how they support collaborative missions. Special topics include: Delay/Disruption Tolerant Networking (DTN), Asynchronous Message Service (AMS), Multispectral/Hyperspectral Data Compression (MHDC), Coding and Synchronization, Onboard Wireless, Spacecraft Monitor and Control, Navigation, Security, and Time Synchronization/Correlation. Broad international participation in development of CCSDS standards is encouraged.

Kearney, Mike↗
NASA NTRS1987

A static data flow simulation study at Ames Research Center

Demands in computational power, particularly in the area of computational fluid dynamics (CFD), led NASA Ames Research Center to study advanced computer architectures. One architecture being studied is the static data flow architecture based on research done by Jack B. Dennis at MIT. To improve understanding of this architecture, a static data flow simulator, written in Pascal, has been implemented for use on a Cray X-MP/48. A matrix multiply and a two-dimensional fast Fourier transform (FFT), two algorithms used in CFD work at Ames, have been run on the simulator. Execution times can vary by a factor of more than 2 depending on the partitioning method used to assign instructions to processing elements. Service time for matching tokens has proved to be a major bottleneck. Loop control and array address calculation overhead can double the execution time. The best sustained MFLOPS rates were less than 50% of the maximum capability of the machine.

Barszcz, Eric↗
DOE OSTI2025

End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment

X-ray crystallography reconstruction, which transforms discrete X-ray diffraction patterns into three-dimensional molecular structures, relies critically on accurate Bragg peak finding for structure determination. As X-ray free electron laser (XFEL) facilities advance toward MHz data rates (1 million images per second), traditional peak finding algorithms that require manual parameter tuning or exhaustive grid searches across multiple experiments become increasingly impractical. While deep learning approaches offer promising solutions, their deployment in high-throughput environments presents significant challenges in automated dataset labeling, model scalability, edge deployment efficiency, and distributed inference capabilities. We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. Using this integrated approach, our pipeline achieves accuracy comparable to traditional methods tuned by human experts while eliminating the need for experiment-specific parameter tuning. Although current throughput requires optimization for MHz facilities, our system's scalable architecture and demonstrated model compression capabilities provide a foundation for future high-throughput XFEL deployments.

Wang, Cong↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials

Our interdisciplinary effort successfully generated FAIR (Findable, Accessible, Interoperable, and Reusable) benchmark datasets for mechanical metamaterials while introducing a novel Artificial Intelligence (AI) framework known as Learning Refined Compositional Rules (LRCR). This framework was specifically designed to bridge the gap across varying computational length scales and extract the underlying physical mechanisms that connect a material's structural geometry to its bulk acoustic properties. Historically, the discovery of such structured materials relied heavily on human intuition or opaque, black-box optimization algorithms that were difficult to generalize. By combining interpretable machine learning techniques with rigorous experimental validation, this project established clear, generalizable design guidelines for tuning wave dispersion and controlling vibrations. Ultimately, the public availability of these structured datasets and algorithms will significantly reduce computational costs and accelerate the design of advanced multi-functional acoustic devices, offering broad societal impacts across fields like aerospace engineering, telecommunications, and biomedical implant design.

36 MATERIALS SCIENCE↗
NASA NTRS2019

Architecture and Dynamics of Kepler's Multi-Transiting Planet Systems.III. Comprehensive Investigation Using All Four Years of Kepler Mission Data

We study the orbital architectures of planetary systems orbiting within ∼1 AU of their stars by analyzing the ensemble of Kepler systems having two or more planet candidates. We use data from the entire Kepler mission, and in many cases we apply improved analysis techniques (e.g., replacing histograms by top-hat Kernel Density Estimators that avoid the loss of information resulting from choosing a particular phase for the bin boundaries) to extend and enhance the studies of Lissauer et al. (2011, ApJS 197, 8) and Fabrycky et al. (2014, ApJ 790, 146).These data show ~ 1700 transiting planet candidates in > 600 multiple-planet systems, far more than were available for our previous two studies. The increased numbers and better information about planetary radii and the properties of stellar hosts made possible by Gaia DR2 allow more statistically-robust analyses of the entire ensemble of Kepler multis as well as independent analyses of subsets of the population. We are thus able to contrast the dynamical configurations of small and large planets, short-period and longer-period planets, and planets orbiting various types of host stars. We reinforce our previous findings that most pairs of planets within the same system are neither in nor near low-order mean motion resonances and that there is a substantial excess of planets having period ratios slightly larger than those of first-order mean-motion resonances. However, neglecting three systems whose planets are locked in 3- body resonances and summing over all first-order mean motion resonances, the deficit of planet pairs with period ratios just narrow of resonance is as large as the excess of planets wide of resonance (within statistical uncertainties), suggesting that overall there is no overall excess of planet pairs in the vicinity of resonance. Other aspects of our study, including estimates of the typical relative inclinations of planetary orbits and their variations as functions of orbital period, planet sizes and stellar properties, are in progress, with results expected to be available for presentation by the time of the conference.

Lissauer, Jack↗
DOE OSTI2016

Mini-apps for high performance data analysis

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Sukumar, Sreenivas R.↗
DOE OSTI2016

On-demand data analytics in HPC environments at leadership computing facilities: Challenges and experiences

Scaling-up scientific data analysis and machine learning algorithms for data-driven discovery is a grand challenge that we face today. Despite the growing need for analysis from science domains that are generating `Big Data' from instruments and simulations, building high-performance analytical workflows of data-intensive algorithms have been daunting because: (i) the `Big Data' hardware and software architecture landscape is constantly evolving, (ii) newer architectures impose new programming models, and (iii) data-parallel kernels of analysis algorithms and their performance facets on different architectures are poorly understood. To address these problems, we have: (i) identified scalable data-parallel kernels of popular data analysis algorithms, (ii) implemented `Mini-Apps' of those kernels using different programming models (e.g. Map Reduce, MPI, etc.), (iii) benchmarked and validated the performance of the kernels in diverse architectures. In this paper, we discuss two of those Mini-Apps and show the execution of principal component analysis built as a workflow of the Mini-Apps. We show that Mini-Apps enable scientists to (i) write domain-specific data analysis code that scales on most HPC hardware and (ii) and offers the ability (most times with over a 10x speed-up) to analyze data sizes 100 times the size of what off-the-shelf desktop/workstations of today can handle.

Harney, John↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Considerations for an Earth Relay Satellite with RF and Optical Trunklines

Support for user platforms through the use of optical links to geosynchronous relay spacecraft are expected to be part of the future space communications architecture. The European Data Relay Satellite System (EDRS) has its first node, EDRS-A, in orbit. The EDRS architecture includes space-to-space optical links with a Ka-Band feeder link or trunkline. NASA's Laser Communications Relay Demonstration (LCRD) mission, originally baselined to support a space-to-space optical link relayed with an optical trunkline, has added an Radio Frequency (RF) trunkline. The use of an RF trunkline avoids the outages suffered by an optical trunkline due to clouds, but an RF trunkline will be bandwidth limited. A space relay architecture with both RF and optical trunklines could relay critical realtime data, while also providing a high data volume capacity. This paper considers the relay user scenarios that could be supported, and the implications to the space relay system and operations. System trades such as the amount of onboard processing and storage required, the use of link layer switching vs. network layer routing, and the use of Delay/Disruption Tolerant Networking (DTN) are discussed.

Space Communications↗
NASA NTRS2016

Considerations for an Earth Relay Satellite with RF and Optical Trunklines

Support for user platforms through the use of optical links to geosynchronous relay spacecraft are expected to be part of the future space communications architecture. The European Data Relay Satellite System (EDRS) has its first node, EDRS-A, in orbit. The EDRS architecture includes space-to-space optical links with a Ka-Band feeder link or trunkline. NASA's Laser Communications Relay Demonstration (LCRD) mission, originally baselined to support a space-to-space optical link relayed with an optical trunkline, has added an Radio Frequency (RF) trunkline. The use of an RF trunkline avoids the outages suffered by an optical trunkline due to clouds, but an RF trunkline will be bandwidth limited. A space relay architecture with both RF and optical trunklines could relay critical realtime data, while also providing a high data volume capacity. This paper considers the relay user scenarios that could be supported, and the implications to the space relay system and operations. System trades such as the amount of onboard processing and storage required, the use of link layer switching vs. network layer routing, and the use of Delay/Disruption Tolerant Networking (DTN) are discussed.

Space Communications↗