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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

ROCM+Intel-PathForward+RemoteSpaces Development

This report documents the completion of milestone STPRO4-25 Harden and optimize the ROCm based AMD GPU backend, develop a prototype backend for the Intel ECP Path Forward architecture, and improve the existing prototype Remote Memory Space capabilities. The ROCM code was hardened up to the point of passing all Kokkos unit tests - then AMD deprecated the programming model, forcing us to start over in FY20 with HIP. The Intel ECP Path Forward architecture prototype was developed with some initial capabilities on simulators - but plans changed, so that work will not continue. Instead SYCL will be developed as a backend for Aurora. Remote Spaces was improved. Development is ongoing part of a collaboration with NVIDIA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Milestone 1241

Harden and optimize the ROCm based AMD GPU backend, develop a prototype backend for the Intel ECP Path Forward architecture, and improve the existing prototype Remote Memory Space capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Parallelization and Performance Portability in Hydrodynamics Codes

With the eve of Exascale computing, performance and portability are at the forefront of all scientific codes. Adding more cores and more energy to a system is no longer a sustainable way to achieve performance, and extra effort must now be made to improve performance in all areas of code and code development. Using hydrodynamic codes as a basis, this work explores numerous techniques to achieve performance in different ways. Adaptive mesh refinement (AMR) is a necessary technique to improve memory optimization in mesh-based simulations. However it is invasive and conventionally difficult to integrate into existing applications, so we present a new branch of AMR to create a smooth transition to these optimizations, which not only improves performance, but also greatly reduces developer effort. We introduce the concept of this improvement as Phantom-Cell AMR, and assess theoretically the improvements, as well as present an application of its use. Other work included involves and investigation into an efficient data structure that ensures optimal memory layout for cache performance, with a target of making codes performant and portable across all architectures. All of the work targets both performance and portability, not just on CPU hardware, but specifically across GPU architectures. Parallel performance is key to all of the methods presented, but the research makes a great effort to improve the portability of all applications to prepare for current high performance computing systems and those on the horizon.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Co-Development of Oscillating Water Column (OWC) and Offshore Power Station

The project proposed an Oscillating Water Column (OWC) at a low power rating (1-5kW); to build a prototype and perform field testing within 1–3 years and achieving a commercial version of the OWC 3–5 years. We selected the OWC off-the-shelf components to secure the supply-chain sources and to warrant stable and minimum pricing. We want to curtail the capital cost by reducing the weight and volume of the overall system structure at the design level by designing a high-speed turbine generator and optimizing the airspeed amplification. We want to minimize the operational and maintenance (O&M) costs through simplified architecture, minimal parts, modular/plug-and-play, and easy access designs.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Low Greenhouse Gas (GHG) Vehicle Technologies Research, Development, Demonstration and Deployment Topic 5 Natural Gas Engine Enabling Technologies

A 10 liter natural gas engine has been developed with significant improvements in efficiency while maintaining ultra low NOx emissions and meeting all other EPA criteria emissions limits. This was accomplished through design and analysis of performance components specifically for operation with natural gas in contrast to current production engines which are a minimally modified diesel engine that retain most diesel design features including a flat deck swirl head. The architecture developed here includes a pent roof cylinder head with tumble charge motion and cooling passages specifically optimized for effective cooling around the spark plug and valve bridges. Also in contrast to current production natural gas engines, EGR was not used, in part to avoid the initial cost and warranty expense associated with EGR systems, but also for performance benefits of faster combustion, reduced risk of misfire and high open cycle efficiency due to the turbocharger’s ability to extract energy from the high temperature exhaust. The exhaust manifold uses high temperature material and thermal mechanical fatigue analysis was completed to ensure the ability to withstand high exhaust temperatures without EGR. Other features include dual overhead cam with late intake valve closing Miller cycle and 14:1 compression ratio steel pistons. Low NOx emissions are achieved with stoichiometric combustion and application of a close coupled plus underfloor three way catalysts. The program target of peak brake thermal efficiency of 42% has been demonstrated along with bsNOx of 0.02 g/hp-hr over HD-FTP and RMCSET emissions cycles.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1990

Active member control of a precision structure with an H(infinity) performance objective

This paper addresses the noncollocated control of active structures using active structural elements. A top level architecture for active structures is presented, and issues pertaining to robust control of structures are discussed. Controllers optimized for an H sub inf performance specification are implemented on a test structure and the results are compared with analytical predictions. Directions for further research are identified.

Fanson, J. L.↗
NASA NTRS1991

Rectilinear partitioning of irregular data parallel computations

New mapping algorithms for domain oriented data-parallel computations, where the workload is distributed irregularly throughout the domain, but exhibits localized communication patterns are described. Researchers consider the problem of partitioning the domain for parallel processing in such a way that the workload on the most heavily loaded processor is minimized, subject to the constraint that the partition be perfectly rectilinear. Rectilinear partitions are useful on architectures that have a fast local mesh network. Discussed here is an improved algorithm for finding the optimal partitioning in one dimension, new algorithms for partitioning in two dimensions, and optimal partitioning in three dimensions. The application of these algorithms to real problems are discussed.

Nicol, David M.↗
NASA NTRS2010

NDARC-NASA Design and Analysis of Rotorcraft Theoretical Basis and Architecture

The theoretical basis and architecture of the conceptual design tool NDARC (NASA Design and Analysis of Rotorcraft) are described. The principal tasks of NDARC are to design (or size) a rotorcraft to satisfy specified design conditions and missions, and then analyze the performance of the aircraft for a set of off-design missions and point operating conditions. The aircraft consists of a set of components, including fuselage, rotors, wings, tails, and propulsion. For each component, attributes such as performance, drag, and weight can be calculated. The aircraft attributes are obtained from the sum of the component attributes. NDARC provides a capability to model general rotorcraft configurations, and estimate the performance and attributes of advanced rotor concepts. The software has been implemented with low-fidelity models, typical of the conceptual design environment. Incorporation of higher-fidelity models will be possible, as the architecture of the code accommodates configuration flexibility, a hierarchy of models, and ultimately multidisciplinary design, analysis and optimization.

Johnson, Wayne↗
NASA NTRS2018

Aladyn - Adaptive Neural Network Molecular Dynamics Simulation Code: Computational Materials Mini-Application

This report provides an overview and commands description of the Computational Materials mini-application, Aladyn. Aladyn is a simple molecular dynamics code written in FORTRAN 2008, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of quantum mechanics-based energy calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. The Aladyn code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing many core architectures based on graphic processing units (GPUs). The effort is related to the High Performance Computing Incubator (HPCI) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
DOE OSTI2017

Age, Gender, and Fine-Grained Ethnicity Prediction using Convolutional Neural Networks for the East Asian Face Dataset

This paper examines the difficulty associated with performing machine-based automatic demographic prediction on a sub-population of Asian faces. We introduce the Wild East Asian Face dataset (WEAFD), a new and unique dataset to the research community. This dataset consists primarily of labeled face images of individuals from East Asian countries, including Vietnam, Burma, Thailand, China, Korea, Japan, Indonesia, and Malaysia. East Asian turk annotators were uniquely used to judge the age and fine grain ethnicity attributes to reduce the impact of the other race effect and improve quality of annotations. We focus on predicting age, gender and fine-grained ethnicity of an individual by providing baseline results with a convolutional neural network (CNN). Finegrained ethnicity prediction refers to predicting ethnicity of an individual by country or sub-region (Chinese, Japanese, Korean, etc.) of the East Asian continent. Performance for two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that ease network optimization by promoting region based feature extraction.

Srinivas, Nisha↗
DOE OSTI2017

DMS Advanced Applications for Accommodating High Penetrations of DERs and Microgrids: Preprint

Efficient and effective management of the electrical distribution system requires an integrated system approach for Distribution Management Systems (DMS), Distributed Energy Resources (DERs), Distributed Energy Resources Management System (DERMS), and microgrids to work in harmony. This paper highlights some of the outcomes from a U.S. Department of Energy (DOE), Office of Electricity (OE) project, including 1) Architecture of these integrated systems, and 2) Expanded functions of two example DMS applications, Volt-VAR optimization (VVO) and Fault Location, Isolation and Service Restoration (FLISR), to accommodate DER. For these two example applications, the relevant DER Group Functions necessary to support communication between DMS and Microgrid Controller (MC) in grid-tied mode are identified.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

Space Networking↗
DOE OSTI2023

Performance Comparison of Object Detection Networks for Shrapnel Identification in Ultrasound Images

Ultrasound imaging is a critical tool for triaging and diagnosing subjects but only if images can be properly interpreted. Unfortunately, in remote or military medicine situations, the expertise to interpret images can be lacking. Machine-learning image interpretation models that are explainable to the end user and deployable in real time with ultrasound equipment have the potential to solve this problem. We have previously shown how a YOLOv3 (You Only Look Once) object detection algorithm can be used for tracking shrapnel, artery, vein, and nerve fiber bundle features in a tissue phantom. However, real-time implementation of an object detection model requires optimizing model inference time. Here, we compare the performance of five different object detection deep-learning models with varying architectures and trainable parameters to determine which model is most suitable for this shrapnel-tracking ultrasound image application. We used a dataset of more than 16,000 ultrasound images from gelatin tissue phantoms containing artery, vein, nerve fiber, and shrapnel features for training and evaluating each model. Every object detection model surpassed 0.85 mean average precision except for the detection transformer model. Overall, the YOLOv7tiny model had the higher mean average precision and quickest inference time, making it the obvious model choice for this ultrasound imaging application. Other object detection models were overfitting the data as was determined by lower testing performance compared with higher training performance. In summary, the YOLOv7tiny object detection model had the best mean average precision and inference time and was selected as optimal for this application. Next steps will implement this object detection algorithm for real-time applications, an important next step in translating AI models for emergency and military medicine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A Case Study of LLVM-Based Analysis for Optimizing SIMD Code Generation

This paper presents a methodology for using LLVM-based tools to tune the DCA++ (dynamical cluster approximation) application that targets the new ARM A64FX processor. The goal is to describe the changes required for the new architecture and generate efficient single instruction/multiple data (SIMD) instructions that target the new Scalable Vector Extension instruction set. During manual tuning, the authors used the LLVM tools to improve code parallelization by using OpenMP SIMD, refactored the code and applied transformation that enabled SIMD optimizations, and ensured that the correct libraries were used to achieve optimal performance. By applying these code changes, code speed was increased by 1.98× and 78 GFlops were achieved on the A64FX processor. The authors aim to automatize parts of the efforts in the OpenMP Advisor tool, which is built on top of existing and newly introduced LLVM tooling.

Huber, Joseph↗
DOE OSTI2021

Characterization and structural analysis of a thermophilic GH11 xylanase from compost metatranscriptome

Xylanase is efficient for xylan degradation and widely applied in industries. We found a GH11 family xylanase (Xyn11A) with high thermostability and catalytic activity from compost metatranscriptome. This xylanase has the optimal reaction temperature at 80 °C with the activity of 2907.3 U/mg. The X-ray crystallographic structure shows a typical “right hand” architecture, which is the characteristics of the GH11 family enzymes. Comparing it with the mesophilic XYN II, a well-studied GH11 xylanase from Trichoderma reesei, Xyn11A is more compact with more H-bonds. Our mutagenic results show that the electrostatic interactions in the thumb and palm region of Xyn11A could result in its high thermostability and activity. Introducing a disulfide bond at the N-terminus further increased its optimal reaction temperature to 90 °C with augmented activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Strong and Weak Scaling of the Sierra/SD Eigenvector Problem to a Billion Degrees of Freedom

Sierra/SD is a structural dynamics finite element software package that is known for its scalability and performance on DOE supercomputers. While there are historical documents demonstrating weak and strong scaling on DOE systems such as Redsky, no such formal studies have been done on modern architectures. This report demonstrates that Sierra/SD still scales on modern architectures. Non structured meshes in the shape of an I-Beam are solved in sizes ranging from fifty thousand degrees of freedom in serial up to one and a half billion degrees of freedom on over eighteen thousand processors using only default solver options. The report serves as a baseline for users to estimate computation cost of finite element analyses in Sierra/SD, understand how solver options relate to computational costs, and pick optimal processor counts to solve a given problem size, as well as a baseline for evaluating computational cost and scalability on next generation architectures.

42 ENGINEERING↗
DOE OSTI2022

Gamma Ray Source Localization for Time Projection Chamber Telescopes Using Convolutional Neural Networks

Diverse phenomena such as positron annihilation in the Milky Way, merging binary neutron stars, and dark matter can be better understood by studying their gamma ray emission. Despite their importance, MeV gamma rays have been poorly explored at sensitivities that would allow for deeper insight into the nature of the gamma emitting objects. In response, a liquid argon time projection chamber (TPC) gamma ray instrument concept called GammaTPC has been proposed and promises exploration of the entire sky with a large field of view, large effective area, and high polarization sensitivity. Optimizing the pointing capability of this instrument is crucial and can be accomplished by leveraging convolutional neural networks to reconstruct electron recoil paths from Compton scattering events within the detector. In this investigation, we develop a machine learning model architecture to accommodate a large data set of high fidelity simulated electron tracks and reconstruct paths. We create two model architectures: one to predict the electron recoil track origin and one for the initial scattering direction. We find that these models predict the true origin and direction with extremely high accuracy, thereby optimizing the observatory’s estimates of the sky location of gamma ray sources.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

Visual information processing II; Proceedings of the Meeting, Orlando, FL, Apr. 14-16, 1993

Various papers on visual information processing are presented. Individual topics addressed include: aliasing as noise, satellite image processing using a hammering neural network, edge-detetion method using visual perception, adaptive vector median filters, design of a reading test for low-vision image warping, spatial transformation architectures, automatic image-enhancement method, redundancy reduction in image coding, lossless gray-scale image compression by predictive GDF, information efficiency in visual communication, optimizing JPEG quantization matrices for different applications, use of forward error correction to maintain image fidelity, effect of peanoscanning on image compression. Also discussed are: computer vision for autonomous robotics in space, optical processor for zero-crossing edge detection, fractal-based image edge detection, simulation of the neon spreading effect by bandpass filtering, wavelet transform (WT) on parallel SIMD architectures, nonseparable 2D wavelet image representation, adaptive image halftoning based on WT, wavelet analysis of global warming, use of the WT for signal detection, perfect reconstruction two-channel rational filter banks, N-wavelet coding for pattern classification, simulation of image of natural objects, number-theoretic coding for iconic systems.

Huck, Friedrich O.↗