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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

S-QGPU: Shared quantum gate processing unit for distributed quantum computing

We propose a distributed quantum computing (DQC) architecture in which individual small-sized quantum computers are connected to a shared quantum gate processing unit (S-QGPU). The S-QGPU comprises a collection of hybrid two-qubit gate modules for remote gate operations. In contrast to conventional DQC systems, where each quantum computer is equipped with dedicated communication qubits, S-QGPU effectively pools the resources (e.g., the communication qubits) together for remote gate operations, and, thus, significantly reduces the cost of not only the local quantum computers but also the overall distributed system. Our preliminary analysis and simulation show that S-QGPU's shared resources for remote gate operations enable efficient resource utilization. When not all computing qubits (also called data qubits) in the system require simultaneous remote gate operations, S-QGPU-based DQC architecture demands fewer communication qubits, further decreasing the overall cost. Alternatively, with the same number of communication qubits, it can support a larger number of simultaneous remote gate operations more efficiently, especially when these operations occur in a burst mode.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

CTGAN-TVAE

SAND2026-18914O CTGAN-TVAE (Conditional Tabular Generative Adversarial Networks-Tabular Variational Autoencoders) generates extensive sets of variable generation data through a hybrid framework. It enhances latent space representation by combining TVAE's robust feature-embedding with CTGAN's ability to condition categorical variables such as time. CTGAN-TVAE employs a fully connected neural network within a conditional generative adversarial network framework to manage continuous and categorical data effectively, capturing complex feature interactions without needing sequential modeling. This was developed as part of NNSA-MSIPP: Minority Serving Institution Partnership Program, Grant Number DE-NA0004016. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Newlun, Cody [Sandia National Lab. (SNL-CA), Liver↗
NASA NTRS1977

Coupled dynamics analysis of wind energy systems

A qualitative description of all key elements of a complete wind energy system computer analysis code is presented. The analysis system addresses the coupled dynamics characteristics of wind energy systems, including the interactions of the rotor, tower, nacelle, power train, control system, and electrical network. The coupled dynamics are analyzed in both the frequency and time domain to provide the basic motions and loads data required for design, performance verification and operations analysis activities. Elements of the coupled analysis code were used to design and analyze candidate rotor articulation concepts. Fundamental results and conclusions derived from these studies are presented.

Hoffman, J. A.↗
NASA NTRS1991

Determination of the Critical Parameters for Remote Microscope Control

As part of a program to determine the capabilities of Telescience as applied to Microgravity Materials Science the need for a remotely controlled microscope was recognized. For this purpose we equipped a microscope with an X-Y-Z positioning device and motors on the zoom and focus controls. Computer control of these devices allowed remote operation. A standard TV camera was mounted to the computer controlled video board which could compress the image in resolution and grey scale. The operator control console was programmed to display three still video pictures as well as provide command access. A standard data transfer network was used to transmit the video data files and the command interaction was via a high speed phone modem. This system, with the microscope in the Microgravity Materials Science Laboratory (MMSL) at LeRC and the control at RPI, was used to determine the accuracy of setting, time required to achieve setting and the operator ease factor. It was found that the focus setting could be established well within the resolution limit of the TV system and that each motion took about 50 seconds and approximately 12 minutes was required to reach ?best? focus. These times could be reduced significantly with operator experience. The operators were provided with ancillary equipment which provided assistance in making the necessary decisions and they reported satisfaction with the control.

Hahn, R. C.↗
NASA NTRS1995

Envision: An interactive system for the management and visualization of large geophysical data sets

Envision is a software project at the University of Illinois and Texas A&M, funded by NASA's Applied Information Systems Research Project. It provides researchers in the geophysical sciences convenient ways to manage, browse, and visualize large observed or model data sets. Envision integrates data management, analysis, and visualization of geophysical data in an interactive environment. It employs commonly used standards in data formats, operating systems, networking, and graphics. It also attempts, wherever possible, to integrate with existing scientific visualization and analysis software. Envision has an easy-to-use graphical interface, distributed process components, and an extensible design. It is a public domain package, freely available to the scientific community.

Searight, K. R.↗
NASA NTRS2022

A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation

Electron heat flux is an important value for ionospheric space weather modeling networks. Utilizing the 2D array of Time History of Events and Macroscale Interactions during Substorms all-sky-imager (ASI) observations, Gabrielse et al. (2021, https://doi.org/10.3389/fphy.2021.744298) described a new method that estimates the auroral scale sizes of intense precipitating electron energy fluxes and their mean energies during two substorms on 16 February 2010. These parameters in combination with SuperThermal Electron Transport code were used to develop a new methodology to calculate electron thermal fluxes from data inputs in 2D during one of the substorms at 09:40:00 UT across Canada and Alaska. To test the effect of various precipitation lifetimes on electron heat flux values, boxcar averages ranging from 0 to 900 s were applied to the ASI data. These data are then combined with the newly developed kinetic simulation to determine the thermal fluxes associated with the observed diffuse and discrete precipitation.

Electron precipitation in aurora↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
NASA NTRS2001

Improving the Capture and Re-Use of Data with Wearable Computers

At the Goddard Space Flight Center, members of the Real-Time Software Engineering Branch are developing a wearable, wireless, voice-activated computer for use in a wide range of crosscutting space applications that would benefit from having instant Internet, network, and computer access with complete mobility and hands-free operations. These applications can be applied across many fields and disciplines including spacecraft fabrication, integration and testing (including environmental testing), and astronaut on-orbit control and monitoring of experiments with ground based experimenters. To satisfy the needs of NASA customers, this wearable computer needs to be connected to a wireless network, to transmit and receive real-time video over the network, and to receive updated documents via the Internet or NASA servers. The voice-activated computer, with a unique vocabulary, will allow the users to access documentation in a hands free environment and interact in real-time with remote users. We will discuss wearable computer development, hardware and software issues, wireless network limitations, video/audio solutions and difficulties in language development.

Pfarr, Barbara↗
NASA NTRS1994

Scaling effects of defects in fiber-reinforced composites

Material defects may be introduced willingly or unwillingly during material manufacturing and structural component fabrication stages. Their presence in the material plays a dominant role in determining the material's strength and the associate failure mechanisms. In the sense that the size and the number of defects may increase with the volume of the material, the effect of dimensional scaling may manifest itself in the dependence of material strength on volume. Or, alternatively, there may exist a scaling effect of material defects. In fiber-reinforced composites, manufacturing or fabrication defects may come in several forms: matrix voids, matrix microcracks, fiber misalignment, broken fibers, or interface disbonds, just to mention a few. These are interacting and competing defects in the sense that one type of defect may become dominant under one stress condition and another type of defect may become dominant under a different stress condition. This happens because the fiber reinforcement network, together with the distribution of defects, constitutes the prime microstructure of the composite, and there exist continued interactions between the evolving microstructure and the distribution of defects. In the process, the scaling effects of defects are complicated by this interaction. In this presentation, the scaling effects of defects in fiber-reinforced composites will be briefly discussed with the introduction of the concept of effective defects. It is then shown with the aid of some actual experimental and analysis results that the scaling effects are very much present, but they are regulated by the characteristic dimension of the composite microstructure due to the aforementioned microstructure-defect interaction effect.

Wang, A. S. D.↗
DOE OSTI2024

Structural studies of intrinsically disordered MLL -fusion protein AF9 in complex with peptidomimetic inhibitors

AF9 (MLLT3) and its paralog ENL(MLLT1) are members of the YEATS family of proteins with important role in transcriptional and epigenetic regulatory complexes. These proteins are two common MLL fusion partners in MLL -rearranged leukemias. The oncofusion proteins MLL-AF9/ENL recruit multiple binding partners, including the histone methyltransferase DOT1L, leading to aberrant transcriptional activation and enhancing the expression of a characteristic set of genes that drive leukemogenesis. The interaction between AF9 and DOT1L is mediated by an intrinsically disordered C-terminal ANC1 homology domain (AHD) in AF9, which undergoes folding upon binding of DOT1L and other partner proteins. We have recently reported peptidomimetics that disrupt the recruitment of DOT1L by AF9 and ENL, providing a proof-of-concept for targeting AHD and assessing its druggability. Intrinsically disordered proteins, such as AF9 AHD, are difficult to study and characterize experimentally on a structural level. In this study, we present a successful protein engineering strategy to facilitate structural investigation of the intrinsically disordered AF9 AHD domain in complex with peptidomimetic inhibitors by using maltose binding protein (MBP) as a crystallization chaperone connected with linkers of varying flexibility and length. The strategic incorporation of disulfide bonds provided diffraction-quality crystals of the two disulfide-bridged MBP–AF9 AHD fusion proteins in complex with the peptidomimetics. These successfully determined first series of 2.1–2.6 Å crystal complex structures provide high-resolution insights into the interactions between AHD and its inhibitors, shedding light on the role of AHD in recruiting various binding partner proteins. We show that the overall complex structures closely resemble the reported NMR structure of AF9 AHD/DOT1L with notable difference in the conformation of the β-hairpin region, stabilized through conserved hydrogen bonds network. These first series of AF9 AHD/peptidomimetics complex structures are providing insights of the protein–inhibitor interactions and will facilitate further development of novel inhibitors targeting the AF9/ENL AHD domain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

OMP-26 - An interactive scheduling tool for the 26 meter subnetwork

Operations Mission Planner for the 26 Meter (26M) Antenna Subnetwork (OMP-26) is an interactive scheduling tool developed by the Jet Propulsion Laboratory (JPL) to assist in allocating the Deep Space Network (DSN) 26M antennas to support Earth-orbiting Spacecraft (SC). Prior to integrating OMP-26 into the 26M scheduling arena, the text-based, labor-intensive task lacked the sufficient means to support the 1992 mission support increase. Therefore, JPL developed the OMP-26 interactive scheduling tool to automate the time consuming tasks of the existing, manual scheduling process. OMP-26 has addressed the scheduling dilemma by assisting schedulers to identify and resolve conflicts more quickly through its graphical display features and constraint-checking editing capability. Although OMP-26 has reduced the scheduler's existing workload by easing the identification and resolution of conflicts, it will not meet the needs of another mission support increase to occur by 1995. Therefore, future plans for OMP-26 involve transitioning from a solely interactive scheduling tool to one that is highly automated and more powerful.

Rosas, Jody L.↗
DOE OSTI2025

Active Fluids Form System-Spanning Filamentary Networks

Recent experimental realizations of liquid-liquid phase separation of active liquid crystals have offered an insight into the interaction between phase separation, ubiquitous in soft matter and biology, and chaotic active flows. Here, in this Letter, we use continuum theory to examine phase separation of an active liquid crystal and a passive fluid and report two new results. First, we provide an analytical derivation of the activity-induced suppression of the phase boundary of the coexistence region—a result first reported in simulations and experiments. We show that the shift in the critical point is a result of the balance between self-stirring active flows and phase-separating diffusive fluxes. Second, we show that this same balance is responsible for dramatically changing the morphology of the phase separated state, resulting in the emergence of a new mixed active phase consisting of a dynamical filamentous active network that invades the entire system area, trapping droplets of passive material. This structure exists even for very low volume fractions of active material. Our work provides an important step towards the goal of understanding how to use activity as a new handle for sculpting interfaces.

active nematics↗
DOE OSTI2023

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Neutrino interaction vertex reconstruction in DUNE with Pandora deep learning

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗
DOE OSTI2025

Conflict Detection in Open RAN with Recurrent Neural Networks Using Geometric Manifolds

Allowing third-party applications on Radio Access Network (RAN) Intelligent Controllers (RICs) within the OpenRAN (O-RAN) framework introduces conflicting interactions that are often difficult to detect in advance. These conflicts, occurring between third-party applications in the Near RealTime RIC (Near-RT RIC), known as xApps, can lead to performance degradation and instability in O-RAN if not identified early. Existing conflict detection and mitigation solutions in the literature assume that the conflicts are known beforehand, which is not always accurate due to the complex and often hidden relationships between control parameters and Key Performance Indicators (KPIs). In this paper, we propose a novel Recurrent Neural Network (RNN) to detect both known and unknown conflicts in O-RAN xApps as specified in the O-RAN standards. We model the xApps, control parameters, and KPIs with nodes and edges to create graph structures and use the hidden nonEuclidean geometric properties of the Riemannian manifold to train the RNN model. The performance of this proposed model is validated using evaluation metrics and compared with benchmarks. Results demonstrate that the proposed RNN model, leveraging Riemannian geometric properties, can achieve 100% of the F1-score provided by an optimal solution in just 20 iterations.

5G↗