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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 1,729 records · Page 96

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NASA NTRS1975

Flood hazard studies in Central Texas using orbital and suborbital remote sensing machinery

Central Texas is subject to infrequent, unusually intense rainstorms which cause extremely rapid runoff from drainage basins developed on the deeply dissected limestone and marl bedrock of the Edwards Plateau. One approach to flood hazard evaluation in this area is a parametric model relating flood hydrograph characteristics to quantitative geomorphic properties of the drainage basins. The preliminary model uses multiple regression techniques to predict potential peak flood discharge from basin magnitude, drainage density, and ruggedness number. After mapping small catchment networks from remote sensing imagery, input data for the model are generated by network digitization and analysis by a computer assisted routine of watershed analysis. The study evaluated the network resolution capabilities of the following data formats: (1) large-scale (1:24,000) topographic maps, employing Strahler's "method of v's," (2) standard low altitude black and white aerial photography (1:13,000 and 1:20,000 scales), (3) NASA - generated aerial infrared photography at scales ranging from 1:48,000 to 1:123,000, and (4) Skylab Earth Resources Experiment Package S-190A and S-190B sensors (1:750,000 and 1:500,000 respectively).

Baker, V. R.↗

NASA NTRS1975

Satellite system considerations for computer data transfer

Communications satellites will play a key role in the transmission of computer generated data through nationwide networks. This paper examines critical aspects of satellite system design as they relate to the computer data transfer task. In addition, it discusses the factors influencing the choice of error control technique, modulation scheme, multiple-access mode, and satellite beam configuration based on an evaluation of system requirements for a broad range of application areas including telemetry, terminal dialog, and bulk data transmission.

Cook, W. L.↗

NASA NTRS2002

Enabling Earth Science: The Facilities and People of the NCCS

The NCCS's mass data storage system allows scientists to store and manage the vast amounts of data generated by these computations, and its high-speed network connections allow the data to be accessed quickly from the NCCS archives. Some NCCS users perform studies that are directly related to their ability to run computationally expensive and data-intensive simulations. Because the number and type of questions scientists research often are limited by computing power, the NCCS continually pursues the latest technologies in computing, mass storage, and networking technologies. Just as important as the processors, tapes, and routers of the NCCS are the personnel who administer this hardware, create and manage accounts, maintain security, and assist the scientists, often working one on one with them.

Source record↗

DOE OSTI2015

dfnWorks: A discrete fracture network framework for modeling subsurface flow and transport

DFNWORKS is a parallelized computational suite to generate three-dimensional discrete fracture networks (DFN) and simulate flow and transport. Developed at Los Alamos National Laboratory over the past five years, it has been used to study flow and transport in fractured media at scales ranging from millimeters to kilometers. The networks are created and meshed using DFNGEN, which combines FRAM (the feature rejection algorithm for meshing) methodology to stochastically generate three-dimensional DFNs with the LaGriT meshing toolbox to create a high-quality computational mesh representation. The representation produces a conforming Delaunay triangulation suitable for high performance computing finite volume solvers in an intrinsically parallel fashion. Flow through the network is simulated in dfnFlow, which utilizes the massively parallel subsurface flow and reactive transport finite volume code PFLOTRAN. A Lagrangian approach to simulating transport through the DFN is adopted within DFNTRANS to determine pathlines and solute transport through the DFN. Example applications of this suite in the areas of nuclear waste repository science, hydraulic fracturing and CO 2 sequestration are also included.

42 ENGINEERING↗

DOE OSTI2025

Optimizing the design and operation of water networks: Two decomposition approaches

We consider the design and operation of water networks simultaneously. Water network problems can be divided into two categories: the design problem and the operation problem. The design problem involves determining the appropriate pipe sizing and placements of pump stations, while the operation problem involves scheduling pump stations over multiple time periods to account for changes in supply and demand. Our focus is on networks that involve water co-produced with oil and gas. While solving the optimization formulation for such networks, we found that obtaining a primal (feasible) solution is more challenging than obtaining dual bounds using off-the-shelf mixed-integer nonlinear programming solvers. Therefore, we propose two methods to obtain good primal solutions. One method involves a decomposition framework that utilizes a convex reformulation, while the other is based on time decomposition. To test our proposed methods, we conduct computational experiments on a network derived from the PARETO case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Training Spiking Neural Networks Using Combined Learning Approaches

Spiking neural networks (SNNs), the class of neural networks used in neuromorphic computing, are difficult to train using traditional back-propagation techniques. Spike timingdependent plasticity (STDP) is a biologically inspired learning mechanism that can be used to train SNNs. Evolutionary algorithms have also been demonstrated as a method for training SNNs. In this work, we explore the relationship between these two training methodologies. We evaluate STDP and evolutionary optimization as standalone methods for training networks, and also evaluate a combined approach where STDP weight updates are applied within an evolutionary algorithm. We also apply Bayesian hyperparameter optimization as a meta learner for each of the algorithms. We find that STDP by itself is not an ideal learning rule for randomly connected networks, while the inclusion of STDP within an evolutionary algorithm leads to similar performance, with a few interesting differences. This study suggests future work in understanding the relationship between network topology and learning rules.

Elbrecht, Daniel↗

DOE OSTI2019

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Lastly, overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

An Interaction Library for the FcεRI Signaling Network

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3 . We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2007

Definition and maintenance of a telemetry database dictionary

A telemetry dictionary database includes a component for receiving spreadsheet workbooks of telemetry data over a web-based interface from other computer devices. Another component routes the spreadsheet workbooks to a specified directory on the host processing device. A process then checks the received spreadsheet workbooks for errors, and if no errors are detected the spreadsheet workbooks are routed to another directory to await initiation of a remote database loading process. The loading process first converts the spreadsheet workbooks to comma separated value (CSV) files. Next, a network connection with the computer system that hosts the telemetry dictionary database is established and the CSV files are ported to the computer system that hosts the telemetry dictionary database. This is followed by a remote initiation of a database loading program. Upon completion of loading a flatfile generation program is manually initiated to generate a flatfile to be used in a mission operations environment by the core ground system.

Knopf, William P.↗

DOE OSTI2023

Frost prediction using machine learning and deep neural network models

This study describes accurate, computationally efficient models that can be implemented for practical use in predicting frost events for point-scale agricultural applications. Frost damage in agriculture is a costly burden to farmers and global food security alike. Timely prediction of frost events is important to reduce the cost of agricultural frost damage and traditional numerical weather forecasts are often inaccurate at the field-scale in complex terrain. In this paper, we developed machine learning (ML) algorithms for the prediction of such frost events near Alcalde, NM at the point-scale. ML algorithms investigated include deep neural network, convolution neural networks, and random forest models at lead-times of 6–48 h. Our results show promising accuracy (6-h prediction RMSE = 1.53–1.72°C) for use in frost and minimum temperature prediction applications. Seasonal differences in model predictions resulted in a slight negative bias during Spring and Summer months and a positive bias in Fall and Winter months. Additionally, we tested the model transferability by continuing training and testing using data from sensors at a nearby farm. We calculated the feature importance of the random forest models and were able to determine which parameters provided the models with the most useful information for predictions. We determined that soil temperature is a key parameter in longer term predictions (>24 h), while other temperature related parameters provide the majority of information for shorter term predictions. The model error compared favorable to previous ML based frost studies and outperformed the physically based High Resolution Rapid Refresh forecasting system making our ML-models attractive for deployment toward real-time monitoring of frost events and damage at commercial farming operations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Improving ideal MHD equilibrium accuracy with physics-informed neural networks

We present a novel approach to compute three-dimensional magnetohydrodynamic equilibria with isotropic pressure profiles and nested surfaces by parametrizing Fourier modes with artificial neural networks (NNs). The full nonlinear global force residual of single equilibria across the volume in real space is then minimized with first order optimizers and compared to equilibria computed by conventional solvers. Already, we observe competitive computational cost to arrive at the same minimum residuals computable with existing codes. With increased computational cost, lower minima of the residual are computable with the NNs than with any other tested solver, establishing a new lower bound for the force residual. We use minimally complex NNs, and we expect significant improvements for solving not only single equilibria with NNs, but also for creating NN models valid over continuous distributions of equilibria.

ideal magnetohydrodynamics↗

DOE OSTI2011

Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems

Background: Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades. Results: In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms. Conclusions: This is the first demonstration of a global regulatory network modeling conserved host response between in vitro and in vivo models.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1986

Scientific computing environment for the 1980s

An emerging scientific computing environment in which computers are used not only to solve large-scale models, but are also integrated into the daily activities of scientists and engineers, is discussed. The requirements of the scientific user in this environment are reviewed, and the hardware environment is described, including supercomputers, work stations, mass storage, and communications. Significant increases in memory capacity to keep pace with performance increases, the introduction of powerful graphics displays into the work station, and networking to integrate many computers are stressed. The emerging system software environment is considered, including the operating systems, communications software, and languages. New scientific user tools and utilities that will become available are described.

Bailey, F. R.↗

NASA NTRS1978

Implementation of real-time digital signal processing systems

Special purpose hardware implementation of DFT Computers and digital filters is considered in the light of newly introduced algorithms and IC devices. Recent work by Winograd on high-speed convolution techniques for computing short length DFT's, has motivated the development of more efficient algorithms, compared to the FFT, for evaluating the transform of longer sequences. Among these, prime factor algorithms appear suitable for special purpose hardware implementations. Architectural considerations in designing DFT computers based on these algorithms are discussed. With the availability of monolithic multiplier-accumulators, a direct implementation of IIR and FIR filters, using random access memories in place of shift registers, appears attractive. The memory addressing scheme involved in such implementations is discussed. A simple counter set-up to address the data memory in the realization of FIR filters is also described. The combination of a set of simple filters (weighting network) and a DFT computer is shown to realize a bank of uniform bandpass filters. The usefulness of this concept in arriving at a modular design for a million channel spectrum analyzer, based on microprocessors, is discussed.

Narasimha, M.↗

DOE OSTI2019

Data Assessment and Assimilation for Atmospheric Radiation Measurement Data Using Dynamic Bayesian Networks

A data-driven machine learning approach to scientific sensor data assessment may automatically learn directly from the structural and temporal patterns in abundant observations. The project has developed data assessment and assimilation models to screen datasets using dynamic Bayesian network (DBN) and deep learning (DL) methods, which rely on correlations between variables, across time, and between spatial locations, to detect poor or invalid data with a high degree of confidence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Integration of Ag-CBRAM crossbars and Mott ReLU neurons for efficient implementation of deep neural networks in hardware

In-memory computing with emerging non-volatile memory devices (eNVMs) has shown promising results in accelerating matrix-vector multiplications. However, activation function calculations are still being implemented with general processors or large and complex neuron peripheral circuits. Here, we present the integration of Ag-based conductive bridge random access memory (Ag-CBRAM) crossbar arrays with Mott rectified linear unit (ReLU) activation neurons for scalable, energy and area-efficient hardware (HW) implementation of deep neural networks. We develop Ag-CBRAM devices that can achieve a high ON/OFF ratio and multi-level programmability. Compact and energy-efficient Mott ReLU neuron devices implementing ReLU activation function are directly connected to the columns of Ag-CBRAM crossbars to compute the output from the weighted sum current. We implement convolution filters and activations for VGG-16 using our integrated HW and demonstrate the successful generation of feature maps for CIFAR-10 images in HW. Our approach paves a new way toward building a highly compact and energy-efficient eNVMs-based in-memory computing system.

Mott insulators↗

DOE OSTI2025

Verifying infectious disease scenario planning for geographically diverse populations

In the face of the COVID-19 pandemic, the literature saw a spike in publications for epidemic models, and a renewed interest in capturing contact networks and geographic movement of populations. There remains a general lack of consensus in the modeling community around best practices for spatiotemporal epi-modeling, specifically as it pertains to the infection rate formulation and the underlying contact or mixing model. We mathematically verify several common modeling assumptions in the literature, to prove when certain choices can provide consistent results across different geographic resolutions, population densities and patterns, and mixing assumptions. The most common infection rate formulation, a computationally low cost per capita infection rate assumption, fails the consistency tests for heterogeneous populations and gravity-weighting assumptions. Future modeling efforts in spatiotemporal disease modeling should be wary of this limitation, particularly when working with more heterogeneous or sparse populations. Our results provide guidance for testing that a model preserves desirable properties even when model inputs mask potential problems due to symmetry or homogeneity. We also provide a recipe for performing this type of verification, strengthening decision support tools.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Integrating 5G Technology for Improved Process Monitoring and Network Slicing in ICS

Industrial Control Systems (ICS) are crucial for monitoring physical processes that support essential cyber-enabled services like power generation. The use of proprietary communication and lack of effective intrusion detection mechanisms pose constraints for efficient operation. Therefore, there is a need to modernize these systems with decentralized technologies like Edge Computing and 5G. However, integrating 5G and Edge Computing into large-scale ICS networks presents implementation and performance challenges. To address these challenges, this paper proposes an integrated ICS architecture that combines 5G and Edge Computing technologies with traditional ICS protocols. The objective is to minimize implementation and operational difficulties while improving the monitoring of physical processes and enabling robust intrusion detection. The proposed architecture outlines the necessary components, services, and communication protocols required for the integration of 5G and Edge Computing.

Aguayo, Jared M.↗