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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Applications of massively parallel computers in telemetry processing

Telemetry processing refers to the reconstruction of full resolution raw instrumentation data with artifacts, of space and ground recording and transmission, removed. Being the first processing phase of satellite data, this process is also referred to as level-zero processing. This study is aimed at investigating the use of massively parallel computing technology in providing level-zero processing to spaceflights that adhere to the recommendations of the Consultative Committee on Space Data Systems (CCSDS). The workload characteristics, of level-zero processing, are used to identify processing requirements in high-performance computing systems. An example of level-zero functions on a SIMD MPP, such as the MasPar, is discussed. The requirements in this paper are based in part on the Earth Observing System (EOS) Data and Operation System (EDOS).

El-Ghazawi, Tarek A.↗
NASA NTRS1994

Neural networks applications to control and computations

Several interrelated problems in the area of neural network computations are described. First an interpolation problem is considered, then a control problem is reduced to a problem of interpolation by a neural network via Lyapunov function approach, and finally a new, faster method of learning as compared with the gradient descent method, was introduced.

Luxemburg, Leon A.↗
NASA NTRS1996

Basis And Application Of The CARES/LIFE Computer Program

Report discusses physical and mathematical basis of Ceramics Analysis and Reliability Evaluation of Structures LIFE prediction (CARES/LIFE) computer program, described in "Program for Evaluation of Reliability of Ceramic Parts" (LEW-16018).

Nemeth, Noel N.↗
DOE OSTI2022

Peak Prediction Using Multi Layer Perceptron (MLP) for Edge Computing ASICs Targeting Scientific Applications

High data rate detectors play an integral part in scientific research and their development is actively pursued at High Energy Physics (HEP) facilities around the world. Edge Machine Learning (ML) offers the ability to reduce data rates by integrating ML algorithms into Application Specific Integrated Circuits (ASICs) on the front end electronics. In this work, we explore a set of neural network architectures for predicting the peak amplitudes in the detector's sensor response. We have designed and synthesized several MLP based neural networks comparing their inference accuracy, power consumption, and area targeting for minimal latency. The neural networks are synthesized in a commercial 65nm process. The effect of quantizing the network's weights and biases on hardware performance and area is reported. We also conduct design space exploration to compare between design alternatives

47 OTHER INSTRUMENTATION↗
NASA NTRS1987

Task allocation in a distributed computing system

A conceptual framework is examined for task allocation in distributed systems. Application and computing system parameters critical to task allocation decision processes are discussed. Task allocation techniques are addressed which focus on achieving a balance in the load distribution among the system's processors. Equalization of computing load among the processing elements is the goal. Examples of system performance are presented for specific applications. Both static and dynamic allocation of tasks are considered and system performance is evaluated using different task allocation methodologies.

Seward, Walter D.↗
DOE OSTI2017

Proxy App Prospectus for ECP Application Development Projects

The Exascale Computing Project (ECP) includes 25 application development (AD) projects1 and five co-design centers funded by the Office of Science, NNSA, and National Cancer Institute. Their full code bases are large and complex, and can be difficult for co-design partners to assimilate for use in collaboration for Exascale challenges. To alleviate this challenge, proxy applications (“apps”) are being developed: these are small, self-contained, simplified codes that are intended to model the performance-critical computations within applications. Proxy apps also capture programming methods and styles that drive requirements for compilers and other elements of the tool chain. Within ECP, application teams, co-design centers, software technology (ST) projects and vendors all plan to use proxies as a major mechanism to drive collaborations and co-design solutions for exascale challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. Here we describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Comparison between computations and experimental data in unsteady three-dimensional transonic aerodynamics, including aeroelastic applications

Comparisons were made of computed and experimental data in three-dimensional unsteady transonic aerodynamics, including aeroelastic applications. The computer code LTRAN3, which is based on small-disturbance aerodynamic theory, was used to obtain the aerodynamic data. A procedure based on the U-g method was developed to compute flutter boundaries by using the unsteady aerodynamic coefficients obtained from LTRAN3. The experimental data were obtained from available NASA publications. All the studies were conducted for thin, unswept, rectangular wings with circular-arc cross sections. Numerical and experimental steady and unsteady aerodynamic data were compared for a wing with an aspect ratio of 3 and a thickness ratio of 5% at Mach numbers of 0.7 and 0.9. Flutter data were compared for a wing with an aspect ratio of 5. Two thickness ratios, 6% at Mach numbers of 0.715, 0.851, and 0.913, and 4% at Mach number of 0.904, were considered. Based on the unsteady aerodynamic data obtained from LTRAN3, flutter boundaries were computed; they were compared with those obtained from experiments and the code NASTRAN, which uses linear aerodynamics.

Guruswamy, P.↗
NASA NTRS2006

Distributed Computing Framework for Synthetic Radar Application

We are developing an extensible software framework, in response to Air Force and NASA needs for distributed computing facilities for a variety of radar applications. The objective of this work is to develop a Python based software framework, that is the framework elements of the middleware that allows developers to control processing flow on a grid in a distributed computing environment. Framework architectures to date allow developers to connect processing functions together as interchangeable objects, thereby allowing a data flow graph to be devised for a specific problem to be solved. The Pyre framework, developed at the California Institute of Technology (Caltech), and now being used as the basis for next-generation radar processing at JPL, is a Python-based software framework. We have extended the Pyre framework to include new facilities to deploy processing components as services, including components that monitor and assess the state of the distributed network for eventual real-time control of grid resources.

synthetic aperture radar (SAR)↗
DOE OSTI2015

Accelerated application development: The ORNL Titan experience

The use of computational accelerators such as NVIDIA GPUs and Intel Xeon Phi processors is now widespread in the high performance computing community, with many applications delivering impressive performance gains. However, programming these systems for high performance, performance portability and software maintainability has been a challenge. In this paper we discuss experiences porting applications to the Titan system. Titan, which began planning in 2009 and was deployed for general use in 2013, was the first multi-petaflop system based on accelerator hardware. To ready applications for accelerated computing, a preparedness effort was undertaken prior to delivery of Titan. In this paper we report experiences and lessons learned from this process and describe how users are currently making use of computational accelerators on Titan.

97 MATHEMATICS AND COMPUTING↗