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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 NTRS1994

ControlShell: A real-time software framework

The ControlShell system is a programming environment that enables the development and implementation of complex real-time software. It includes many building tools for complex systems, such as a graphical finite state machine (FSM) tool to provide strategic control. ControlShell has a component-based design, providing interface definitions and mechanisms for building real-time code modules along with providing basic data management. Some of the system-building tools incorporated in ControlShell are a graphical data flow editor, a component data requirement editor, and a state-machine editor. It also includes a distributed data flow package, an execution configuration manager, a matrix package, and an object database and dynamic binding facility. This paper presents an overview of ControlShell's architecture and examines the functions of several of its tools.

Schneider, Stanley A.↗
NASA NTRS1984

Dataflow computing approach in high-speed digital simulation

New computational tools and methodologies for the digital simulation of continuous systems were explored. Programmability, and cost effective performance in multiprocessor organizations for real time simulation was investigated. Approach is based on functional style languages and data flow computing principles, which allow for the natural representation of parallelism in algorithms and provides a suitable basis for the design of cost effective high performance distributed systems. The objectives of this research are to: (1) perform comparative evaluation of several existing data flow languages and develop an experimental data flow language suitable for real time simulation using multiprocessor systems; (2) investigate the main issues that arise in the architecture and organization of data flow multiprocessors for real time simulation; and (3) develop and apply performance evaluation models in typical applications.

Ercegovac, M. D.↗
DOE OSTI2019

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. Here, we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. Finally, the effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Visualizing Electrical Power Systems as Flow Fields

We describe a method for visualizing data flows on large networks. We transform data flow on fixed networks into a vector field, which can be directly visualized using scientific flow visualization techniques. We evaluated the method on power flowing through two transmission power networks: a small, regional, IEEE test system (RTS-96) and a large national-scale system (the Eastern Interconnection). For the larger and more complex transmission system, the method illustrates features of the power flow that are not accessible when visualizing the power transmission with traditional network visualization techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

The Nuclear Energy Advanced Modeling and Simulation Safeguards and Separations Reprocessing Plant Toolkit

This report details the progress made in the development of the Reprocessing Plant Toolkit (RPTk) for the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. RPTk is an ongoing development effort intended to provide users with an extensible, integrated, and scalable software framework for the modeling and simulation of spent nuclear fuel reprocessing plants by enabling the insertion and coupling of user-developed physicochemical modules of variable fidelity. The NEAMS Safeguards and Separations IPSC (SafeSeps) and the Enabling Computational Technologies (ECT) supporting program element have partnered to release an initial version of the RPTk with a focus on software usability and utility. RPTk implements a data flow architecture that is the source of the system's extensibility and scalability. Data flows through physicochemical modules sequentially, with each module importing data, evolving it, and exporting the updated data to the next downstream module. This is accomplished through various architectural abstractions designed to give RPTk true plug-and-play capabilities. A simple application of this architecture, as well as RPTk data flow and evolution, is demonstrated in Section 6 with an application consisting of two coupled physicochemical modules. The remaining sections describe this ongoing work in full, from system vision and design inception to full implementation. Section 3 describes the relevant software development processes used by the RPTk development team. These processes allow the team to manage system complexity and ensure stakeholder satisfaction. This section also details the work done on the RPTk ``black box'' and ``white box'' models, with a special focus on the separation of concerns between the RPTk user interface and application runtime. Section 4 and 5 discuss that application runtime component in more detail, and describe the dependencies, behavior, and rigorous testing of its constituent components.

Enabling Computational Technologies↗
DOE OSTI2019

An Integrated Hydrological Model for Long-Term Monitoring in Tims Branch Watershed, SC - 19435

The Tims Branch watershed in Savannah River Site (SRS), South Carolina is a small-scale watershed that has been impacted by contaminated runoff and discharge wastewater from nuclear research facilities located within the watershed over the past century. Innovative remediation technologies, such as tin injection and pump and treat, have been implemented to remove heavy metals and volatile organic compounds (VOCs) from the watershed. Today, the Tims Branch watershed serves as a test-bed for monitoring the performance of remediation activities. This study describes the development of the two-dimensional integrated surface water/groundwater model for Tims Branch watershed to assess the impact of cleanup activities on hydrological processes in this watershed. This model will serve as a tool to simulate the effect of climatic events on overland flow distribution and further on will be used to assess contaminant transport. This paper illustrates the application of the developed model for simulation of overland flow depth and velocity in Tims Branch watershed and streamflow in A-014 outfall tributary. The watershed includes the second order Tims Branch stream, several outfalls, and a network of tributaries. Simulation of non-calibrated overland flow was conducted for the years 2016 using available stream flow data within the watershed. Recent flow data from a newly deployed stage gauge was also used to assess the model results. The results show the model ability to simulate overland flow and stream flow and can be used as a tool to represent the hydrological conditions in the Tims Branch watershed. The results of this model simulation will be further used to develop contaminant transport model. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Communication device for implementing selective encryption in a software defined network

The present disclosure pertains to systems and methods for selectively encrypting data flows within a software defined network (SDN). In one embodiment, a communication device may be configured to receive a plurality of unencrypted data packets. The communication device may receive from an SDN controller a criterion used to identify at least one of the unencrypted data flows to be encrypted. Based on the criterion, an encryption subsystem may generate an encrypted data flow the unencrypted data packets based on an encryption key. In some embodiments, the encryption system may parse the packets and encrypt the data payloads without encrypting the routing information associated with the packet. In other embodiments, the encryption subsystem may be configured to encapsulate and encrypt the entire unencrypted data packet. In some embodiments, the encryption subsystem may further be configured to authenticate a sending device and/or to verify the integrity of a message.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Communication device for implementing selective encryption in a software defined network

The present disclosure pertains to systems and methods for selectively encrypting data flows within a software defined network (SDN). In one embodiment, a communication device may be configured to receive a plurality of unencrypted data packets. The communication device may receive from an SDN controller a criterion used to identify at least one of the unencrypted data flows to be encrypted. Based on the criterion, an encryption subsystem may generate an encrypted data flow the unencrypted data packets based on an encryption key. In some embodiments, the encryption system may parse the packets and encrypt the data payloads without encrypting the routing information associated with the packet. In other embodiments, the encryption subsystem may be configured to encapsulate and encrypt the entire unencrypted data packet. In some embodiments, the encryption subsystem may further be configured to authenticate a sending device and/or to verify the integrity of a message.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Recent progress with the top to bottom approach to vectorization in GeantV

SIMD acceleration can potentially boost by factors the application throughput. Achieving efficient SIMD vectorization for scalar code with complex data flow and branching logic, goes however way beyond breaking some loop dependencies and relying on the compiler. Since the refactoring effort scales with the number of lines of code, it is important to understand what kind of performance gains can be expected in such complex cases. We started to investigate a couple of years ago a top to bottom vectorization approach to particle transport simulation. Percolating vector data to algorithms was mandatory since not all the components can internally vectorize. Vectorizing low-level algorithms is certainly necessary, but not sufficient to achieve relevant SIMD gains. In addition, the overheads for maintaining the concurrent vector data flow and copy data have to be minimized. In the context of a vectorization R&D for simulation we developed a framework to allow different categories of scalar and vectorized components to co-exist, dealing with data flow management and real-time heuristic optimizations. The paper describes our approach on coordinating SIMD vectorization at framework level, making a detailed quantitative analysis of the SIMD gain versus overheads, with a breakdown by components in terms of geometry, physics and magnetic field propagation. We also present the more general context of this R&D work and goals for 2018.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

A CLIPS expert system for clinical flow cytometry data analysis

An expert system is being developed using CLIPS to assist clinicians in the analysis of multivariate flow cytometry data from cancer patients. Cluster analysis is used to find subpopulations representing various cell types in multiple datasets each consisting of four to five measurements on each of 5000 cells. CLIPS facts are derived from results of the clustering. CLIPS rules are based on the expertise of Drs. Stewart, Duque, and Braylan. The rules incorporate certainty factors based on case histories.

Salzman, G. C.↗
NASA NTRS1983

NIMBUS-7 ERB MATGEN Science Document

The ERB algorithms and computer software data flow used to convert sensor data into equivalent radiometric data are described in detail. The NIMBUS satellite location, orientation and sensor orientation algorithms are given. The computer housekeeping and data flow and sensor/data status algorithms are also given.

Soule, H. V.↗
NASA NTRS1990

Highly parallel computation

Among the highly parallel computing architectures required for advanced scientific computation, those designated 'MIMD' and 'SIMD' have yielded the best results to date. The present development status evaluation of such architectures shown neither to have attained a decisive advantage in most near-homogeneous problems' treatment; in the cases of problems involving numerous dissimilar parts, however, such currently speculative architectures as 'neural networks' or 'data flow' machines may be entailed. Data flow computers are the most practical form of MIMD fine-grained parallel computers yet conceived; they automatically solve the problem of assigning virtual processors to the real processors in the machine.

Denning, Peter J.↗
NASA NTRS1979

Bumblebee program, aerodynamic data. Part 2: Flow fields at Mach number 2.0

Available flow field data which can be used in validating theoretical procedures for computing flow fields around supersonic missiles are presented. Tabulated test data are given which define the flow field around a conical-nosed cylindrical body in a crossflow plane corresponding to a likely tail location. The data were obtained at a Mach number of 2.0 for an angle of attack of 0 to 23 degrees. The data define the flow field for cases both with and without a forward wing present.

Barnes, G. A.↗
DOE OSTI2023

Internship Final Report on the unsupervised learning sensor fusion (ULSF) approach

This paper describes a summer internship project undertaken at Sandia National Labs (SNL), both current status and future work. The project was to explore various machine learning approaches for use on turbulent flow data. Specifically, unsupervised classification of turbulent flow data was explored. First, the usage of models in this field is discussed, and several issues in the common usage of the models are identified. Solutions to these issues are then proposed, in the form of a Bayesian filtering approach which probabilistically incorporates multiple sources of data to improve confidence in a result. Several types of sensors are suggested for this method, the incorporation of which range from semi-supervised learning approaches to fully unsupervised. These approaches are then tested on several turbulent flow cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep learning interfacial momentum closures in coarse-mesh CFD two-phase flow simulation using validation data

Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Restoration of the Apollo 15 Heat Flow Experiment Data from 1975 to 1977

The Apollo 15 Heat Flow Experiment (HFE) was conducted from July 1971 through January 1977. Two heat flow probes were deployed roughly 8.5 meters apart. Probe 1 and Probe 2 penetrated to 1.4-meters and 1-meter depths into the lunar regolith, respectively. Temperatures at different depths and the surface were logged with 7.25-minute intervals and transmitted to Earth. At the conclusion of the experiment, only data obtained from July 1971 through December 1974 were processed and archived at the National Space Science Data Center (NSSDC) by the principal investigator of the experiment, Marcus Langseth of Columbia University. Langseth died in 1997. It is not known what happened to the HFE data tapes he used. Current researchers have strong interests in re-examining the HFE data for the full duration of the experiment. We have recovered and processed large portions of the Apollo 15 HFE data from 1975 through 1977 by assembling data and metadata from various sources.

HFE↗
NASA NTRS1984

Effect of mild atherosclerosis on flow resistance in a coronary artery casting of man

An in-vitro flow study was conducted in a mildly atherosclerotic main coronary artery casting of man using sugar-water solutions simulating blood viscosity. Steady flow results indicated substantial increases in pressure drop, and thus flow resistance at the same Reynolds number, above those for Poiseuille flow by 30 to 100 percent in the physiological Reynolds number range from about 100 to 400. Time-averaged pulsatile flow data showed additional 5 percent increases in flow resistance above the steady flow results. Both pulsatile and steady flow data from the casting were found to be nearly equal to those from a straight, axisymmetric model of the casting up to a Reynolds number of about 200, above which the flow resistance of the casting became gradually larger than the corresponding values from the axisymmetric model.

Back, L. H.↗