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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Scalable Track Detection in SAR CCD Images

Existing methods to detect vehicle tracks in coherent change detection images, a product of combining two synthetic aperture radar images ta ken at different times of the same scene, rely on simple, fast models to label track pixels. These models, however, are often too simple to capture natural track features such as continuity and parallelism. We present a simple convolutional network architecture consisting of a series of 3-by-3 convolutions to detect tracks. The network is trained end-to-end to learn natural track features entirely from data. The network is computationally efficient and improves the F-score on a standard dataset to 0.988, up fr om 0.907 obtained by the current state-of-the-art method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Augmenting LLM-Based Agents for Improved Performance in Pentesting and Commissioning Operational Technology in Critical Infrastructure

Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Mantis Shrimp Dataset

The Mantis Shrimp Dataset (MSD) is a combination of matched cutouts of galaxies with known redshift in the ultra-violet, optical, and infrared wavelengths. MSD redshifts come from ''high-quality'' (as reported by the spectroscopic experiment) redshift measurements from a variety of spectroscopic surveys, including: SDSS, DESI early release, DEEP2, GAMA, VVDS, VIPERS, 6dF, and WiggleZ. Each redshift is accompanies with astrometry (ra,dec) pairs that were used to query cutout image services from PanSTARRs, UnWISE, and GALEX servers. The main data products are image vectors of these cutouts accompanied by the redshift value to be used to train computer vision networks on the supervised photometric redshift task. We also provide extinction values pre-queried at each galaxies' astrometry from the PLANCK and SFD dutstmaps, the SDSS MGS photometric redshift from Beck 2014 (if available) and the WISE x PS1 STRM photometric redshift for the galaxy (if available).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Vind: A Blockchain-Enabled Supply Chain Provenance Framework for Energy Delivery Systems

Enterprise-level energy delivery systems (EDSs) depend on different software or hardware vendors to achieve operational efficiency. Critical components of these systems are typically manufactured and integrated by overseas suppliers, which expands the attack surface to adversaries with additional opportunities to infiltrate into EDSs. Due to this reason, the risk management of the EDS supply chain is crucial to ensure that we are knowledgeable about the vulnerabilities in software and hardware components that comprise any critical part, quantifiable risk metrics to assess the severity and exploitability of the attack, and provide remediation solutions that can influence a prioritized mitigation plan. There is a need to realize cyber supply chain risk management for industrial control systems’ hardware, software, and computing and networking services associated with bulk electric system (BES) operations. This article proposes a blockchain-based cyber supply chain provenance platform (“Vind”) for EDSs to realize data provenance in a cyber supply chain ecosystem.

Bandara, Eranga↗
DOE OSTI2020

LDM-148: Rubin Observatory Data Management System Design

The Rubin Observatory LSST Data Management System (DMS) is a set of services employing a variety of software components running on computational and networking infrastructure that combine to deliver science data products to the observatory's users and support observatory operations. This document describes the components, their service instances, and their deployment environments as well as the interfaces among them, the rest of the LSST system, and the outside world.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Vehicle Platooning: An Energy Consumption Perspective

Urban traffic congestion is a chronic problem faced by many cities in the US and worldwide. It results in inefficient infrastructure use as well as increased vehicle fuel consumption and emission levels. Excessive fuel consumptions add extra costs to commuters as well as transportation businesses. Consuming less fuel and thus reducing costs by a single percentage digit can have a significant impact on the balance sheet as well as the protection of the environment. Researchers have developed, and continue to develop, tools and systems to optimize the operations of fleets as well as engines in order to burn less fuel and therefore generate less CO2 emissions. Platooning is one such tool that attempts to maintain relatively small distances (i.e. pre-determined time gap) between consecutive vehicles. It has the potential to increase the capacity of the road as well as reduce the consumed fuel. In this paper, we use a fuel consumption model for internal combustion light-duty vehicles, electric vehicles, hybrid electric vehicles, buses and trucks in order to determine and quantify the effects of platooning on a fleet fuel consumption. The results suggest that a reduction of up to 3%, 3.5%, 4.5 %, 10%, and 15% in fuel consumption can be achieved for internal combustion engine vehicles, hybrid electric vehicles, electric vehicles, buses and trucks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

GPU remote communication with triggered operations

Methods, devices, and systems for transmitting data over a computer communications network are disclosed. A queue of communications commands can be pre-generated using a central processing unit (CPU) and stored in a device memory of a network interface controller (NIC). Thereafter, if a graphics processing unit (GPU) has data to communicate to a remote GPU, it can store the data in a send buffer, where the location in the buffer is pointed to by a pre-generated command. The GPU can then signal to the interface device that the data is ready, triggering execution of the pre-generated command to send the data.

LeBeane, Michael W.↗
DOE OSTI2017

Real-time supervisory control implementation of SmAHTR power plant - 143

This paper discusses an implementation of a supervisory control system for a nuclear power plant model using Small modular Advanced High Temperature Reactors (SmAHTR). The simulation operates in real-time using a network of computer agents, implemented on Raspberry Pi's (RPi's), where each major task is performed by one or more RPi's running calculations in parallel. The system architecture contains local, module, master, and operator actions to automate all aspects of the plant. Local level controllers run individual control loops to ensure the desired reference signals are met. The module level controllers set the local level reference signals within each SMR unit and detect sensor and actuator failures to adjust those reference signals as needed. Master level controllers balance plant power by calculating required heat power from each reactor and electric power from each generator and sending that data to the module controllers. Finally, the operator level contains an operator interface to summarize data and faults gathered through the controller levels, with options to review plant and reactor details as desired. This control implementation allows the plant to run autonomously, load follow a desired electrical power curve, and detect a growing number of fault scenarios. Ultimately, this type of supervisory control system will reduce labor-intensive surveillance and testing and allow fewer operators to more safely monitor an entire plant. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations

In this work, we propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional neural networks have proven to be highly advantageous for compressing data arising from systems demonstrating a slow-decaying Kolmogorov n-width. However, these networks are restricted to data on structured meshes. Unstructured meshes are often required for performing analyses of real systems with complex geometry. Our custom graph convolution operators based on the available differential operators for a given spatial discretization effectively extend the application space of deep convolutional autoencoders to systems with arbitrarily complex geometry that are typically discretized using unstructured meshes. We propose sets of convolution operators based on the spatial derivative operators for the underlying spatial discretization, making the method particularly well suited to data arising from the solution of partial differential equations. We demonstrate the method using examples from heat transfer and fluid mechanics and show better than an order of magnitude improvement in accuracy over linear methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Few-view computed tomography reconstruction using deep neural network inference

A system for generating 2D slices of a 3D image of a target volume is provided. The system receives a target sinogram collected during a computed tomography scan of the target volume. The system inputs the target sinogram to a convolutional neural network (CNN) to generate predicted 2D slices of the 3D image. The CNN is trained using training 2D slices of training 3D images. The system initializes 2D slices to the predicted 2D slices. The system reconstructs 2D slices of the 3D image from the target sinogram and the initialized 2D slices.

Kim, Hyojin↗
DOE OSTI2020

Softwarized Federations of Science Instruments with Edge-Continuum Containers

Significant expansion of capabilities of DOE science complex of supercomputers, instruments and networks is expected as powerful experimental facilities, exascale computers and terabit networks are added. Combined with the advances in edge and cloud computing technologies, DOE science users now have the promise of unprecedented execution of complex, continuum workflows, namely, small and latency-sensitive computations at the edge and site, massive computations at remote HPC systems, and everything in between on the cloud. But, bringing this capability to the science user requires overcoming the overwhelming complexity of forming the federations of systems, and efficiently and effectively orchestrating the workflows while ensuring high utilization of the expensive facilities. Current manual configuration of the federated systems simply will not scale, since the coordination across sites may take weeks to months, often leading to under-utilized and hard-to-diagnose compositions. A powerful, composable software stack will be developed to (i) wrap the systems so that federations can be composed fast in software, and (ii) containerize computations to be orchestrated across the edge, site, cloud and HPC resources.

Rao, Nageswara S.↗
DOE OSTI2026

Real-time estimators for scattering observables: A full account of finite-volume errors for quantum simulation

The real-time correlators of quantum field theories can be directly probed through new approaches to simulation, such as quantum computing and tensor networks. This provides a new framework for computing scattering observables in lattice formulations of strongly interacting theories, such as lattice quantum chromodynamics. In this paper, we prove that the proposal of real-time estimators of scattering observables is universally applicable to all scattering observables of gapped quantum field theories. All finite-volume errors are exponentially suppressed, and the rate of this suppression is controlled by the regulator considered, namely, a displacement of the spectrum of the theory into the complex plane. A partial restoration of Lorentz symmetry by averaging over different boosts gives an additional suppression of finite volume errors. Our results also apply to the simulation of wave packet scattering, where a similar averaging is performed to construct the wave packets that regulate the finite volume effects. This result represents a necessary key step toward determining a broad class of scattering observables via quantum computing that are currently inaccessible via classical computing. Such observables are relevant for various applications, including hadron spectroscopy, hadron structure, and precision tests of the Standard Model. We also comment on potential applications of our results to traditional computational schemes.

Burbano, Ivan M. [University of California, Berkel↗
DOE OSTI2019

A Framework for Self-Adaptive Dispersal of Computing Services

Modern networking architectures are making it increasingly possible to disperse services not just across servers but into intermediate network devices as well. Here we introduce the Misson-oriented Adaptive Placement (MAP) architecture, which synthesizes prior work on middleware, load-balancing, constraint solving, and aggregate programming into a framework for self-adaptive management of dispersed services. We provide a first evaluation of the efficacy and resilience that can be provided through this approach: results in simulation demonstrating that MAP can autonomously change the deployment of services to adapt to changing needs and failures.

Paulos, Aaron↗