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NASA NTRS2000

Use Computer-Aided Tools to Parallelize Large CFD Applications

Porting applications to high performance parallel computers is always a challenging task. It is time consuming and costly. With rapid progressing in hardware architectures and increasing complexity of real applications in recent years, the problem becomes even more sever. Today, scalability and high performance are mostly involving handwritten parallel programs using message-passing libraries (e.g. MPI). However, this process is very difficult and often error-prone. The recent reemergence of shared memory parallel (SMP) architectures, such as the cache coherent Non-Uniform Memory Access (ccNUMA) architecture used in the SGI Origin 2000, show good prospects for scaling beyond hundreds of processors. Programming on an SMP is simplified by working in a globally accessible address space. The user can supply compiler directives, such as OpenMP, to parallelize the code. As an industry standard for portable implementation of parallel programs for SMPs, OpenMP is a set of compiler directives and callable runtime library routines that extend Fortran, C and C++ to express shared memory parallelism. It promises an incremental path for parallel conversion of existing software, as well as scalability and performance for a complete rewrite or an entirely new development. Perhaps the main disadvantage of programming with directives is that inserted directives may not necessarily enhance performance. In the worst cases, it can create erroneous results. While vendors have provided tools to perform error-checking and profiling, automation in directive insertion is very limited and often failed on large programs, primarily due to the lack of a thorough enough data dependence analysis. To overcome the deficiency, we have developed a toolkit, CAPO, to automatically insert OpenMP directives in Fortran programs and apply certain degrees of optimization. CAPO is aimed at taking advantage of detailed inter-procedural dependence analysis provided by CAPTools, developed by the University of Greenwich, to reduce potential errors made by users. Earlier tests on NAS Benchmarks and ARC3D have demonstrated good success of this tool. In this study, we have applied CAPO to parallelize three large applications in the area of computational fluid dynamics (CFD): OVERFLOW, TLNS3D and INS3D. These codes are widely used for solving Navier-Stokes equations with complicated boundary conditions and turbulence model in multiple zones. Each one comprises of from 50K to 1,00k lines of FORTRAN77. As an example, CAPO took 77 hours to complete the data dependence analysis of OVERFLOW on a workstation (SGI, 175MHz, R10K processor). A fair amount of effort was spent on correcting false dependencies due to lack of necessary knowledge during the analysis. Even so, CAPO provides an easy way for user to interact with the parallelization process. The OpenMP version was generated within a day after the analysis was completed. Due to sequential algorithms involved, code sections in TLNS3D and INS3D need to be restructured by hand to produce more efficient parallel codes. An included figure shows preliminary test results of the generated OVERFLOW with several test cases in single zone. The MPI data points for the small test case were taken from a handcoded MPI version. As we can see, CAPO's version has achieved 18 fold speed up on 32 nodes of the SGI O2K. For the small test case, it outperformed the MPI version. These results are very encouraging, but further work is needed. For example, although CAPO attempts to place directives on the outer- most parallel loops in an interprocedural framework, it does not insert directives based on the best manual strategy. In particular, it lacks the support of parallelization at the multi-zone level. Future work will emphasize on the development of methodology to work in a multi-zone level and with a hybrid approach. Development of tools to perform more complicated code transformation is also needed.

Jin, H.↗

NASA NTRS2004

Project Golden Gate: towards real-time Java in space missions

This paper describes the problem domain and our experimentation with the first commercial implementation of the Real Time Specification for Java. The two main issues explored in this report are: (1) the effect of RTSJ's non-heap memory on the programming model, and (2) performance benchmarking of RTSJ/Linux relative to C++/VxWorks.

lander↗

NASA NTRS2008

Self-Deploying Trusses Containing Shape-Memory Polymers

Composite truss structures are being developed that can be compacted for stowage and later deploy themselves to full size and shape. In the target applications, these smart structures will precisely self-deploy and support a large, lightweight space-based antenna. Self-deploying trusses offer a simple, light, and affordable alternative to articulated mechanisms or inflatable structures. The trusses may also be useful in such terrestrial applications as variable-geometry aircraft components or shelters that can be compacted, transported, and deployed quickly in hostile environments. The truss technology uses high-performance shape-memory-polymer (SMP) thermoset resin reinforced with fibers to form a helical composite structure. At normal operating temperatures, the truss material has the structural properties of a conventional composite. This enables truss designs with required torsion, bending, and compression stiffness. However, when heated to its designed glass transition temperature (Tg), the SMP matrix acquires the flexibility of an elastomer. In this state, the truss can be compressed telescopically to a configuration encompassing a fraction of its original volume. When cooled below Tg, the SMP reverts to a rigid state and holds the truss in the stowed configuration without external constraint. Heating the materials above Tg activates truss deployment as the composite material releases strain energy, driving the truss to its original memorized configuration without the need for further actuation. Laboratory prototype trusses have demonstrated repeatable self-deployment cycles following linear compaction exceeding an 11:1 ratio (see figure).

Schueler, Robert M.↗

NASA NTRSDate not supplied

Segmented Hexagonal Antenna Reflector Concentrically Stacked Using Shape Memory Composite Tubular Hinges

A new architecture for solid surface reflector antennas scalable to sizes greater than 10 m is presented. The design combines the compact, lightness and simplicity characteristics of advanced deployable structures to create sub-reflectors that can be assembled in space into larger units using a robotic arm and operations under development by a related parallel project. The seven-panel hexagonal sub-reflector is divided into hexagonal panels of that efficiently stack concentrically and vertically. The central panel is connected to each side panel on the back side by a pair of tubular shape memory composite hinges that enable the required deployment kinematics. A secondary mechanism ultimately closes the interpanel gap. The focus of the paper is on the development of the sub-reflector elements, namely the tubular hinges that use embedded heaters and sensors for actuation, the actuation mechanisms, and the lightweight sandwich construction panels. A finite element analysis parametric study assessed how design features of the hinge affect its stowage and deployment dynamics. The component fabrication and testing plans for the two-panel assembly breadboard model are outlined.

deployable reflectors↗

NASA NTRSDate not supplied

Segmented Hexagonal Antenna Reflector Concentrically Stacked Using Shape Memory Composite Tubular Hinges

deployable reflectors↗

DOE OSTI2022

Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Scaling kinetic Monte-Carlo simulations of grain growth with combined convolutional and graph neural networks

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we suggest a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160 3 ), our method reduces memory usage and runtime in inference by 117× and 115×, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder’s ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. Training data are generated from stochastic grain growth simulations, providing realistic variability for learning robust microstructure evolution. Comprehensive system validation confirms that the model is accurate, robust, and scalable.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Performance Impact and Trade-Offs for Tuning Key Architectural Parameters on CPU+GPU Systems

In this work, we performed an initial design space exploration of an accelerated processing unit (APU)—a hybrid CPU+GPU architecture that integrates both compute units (CUs) and memory into a unified system. This integration aims to reduce data movement, enhance memory locality, and improve energy efficiency by enabling the CPU and GPU to share memory directly. This effort focused on the interplay of key design components—cache line size, the number of CUs, and main memory technology—and the trade-offs of each configuration were analyzed. This paper highlights the various configurations’ impact on memory accesses, data reuse, and power utilization. The results provide valuable insights that can be leveraged to optimize APU architectures for high-performance and energy-efficient computing and thus create a balanced architecture. This optimization can be achieved by adopting dynamic cache management, runtime CU scaling, and advanced memory integration, highlighting the potential of APUs to address critical challenges in compute, data movement, and memory power consumption.

Asifuzzaman, Kazi [ORNL] (ORCID:0000000240044791)↗

NASA NTRS1973

The light-sensitive MNOS memory transistor.

The behavior theory of the light-sensitive MNOS memory transistor is developed and supported by presented experimental evidence. It is shown that the nitride, oxide, and silicon space-charge current-field relationships necessary for comparison of theory and experiment can be obtained from steady-state current-voltage measurements on the MNOS device.

Sewell, F. A., Jr.↗

NASA NTRS1994

Interpreter composition issues in the formal verification of a processor-memory module

This report describes interpreter composition techniques suitable for the formal specification and verification of a processor-memory module using the HOL theorem proving system. The processor-memory module is a multichip subsystem within a fault-tolerant embedded system under development within the Boeing Defense and Space Group. Modeling and verification methods were developed that permit provably secure composition at the transaction-level of specification, significantly reducing the complexity of the hierarchical verification of the system.

Fura, David A.↗

NASA NTRS1994

Identification and control of structures in space

During the last phase of the project, emphasis has changed to flexible space robotics, by mutual agreement between Dr. R. C. Montgomery, NASA Technical Officer, and the Principal Investigator. Significant advances have been achieved over the period covered by this report. Research has been concerned with two main subjects: (1) the maneuvering and control of freely floating flexible space robots, and (2) the development of a theory for the motion of flexible multibody systems. Work on the first subject has resulted in two papers, both of them concerned with planar maneuvers. The first is concerned with the maneuvering and delivery of a payload to a certain point and in a certain orientation in space. The second is concerned with the docking maneuver with a target whose motion is not known a priori. Both papers will appear in the 'Journal of Guidance, Control, and Dynamics.' The second subject is concerned with the development of hybrid (ordinary and partial) differential equations for the three dimensional motion of flexible multibody systems, a subject of vital interest in flexible space robotics. The paper will appear in the 'Journal of Guidance, Control and Dynamics' in an issue dedicated to the memory of Lawrence W. Taylor, Jr. Abstracts and copies of the papers are hereby included.

Meirovitch, Leonard↗

NASA NTRS2010

NASA Tech Briefs, January 2010

Topics covered include: Cryogenic Flow Sensor; Multi-Sensor Mud Detection; Gas Flow Detection System; Mapping Capacitive Coupling Among Pixels in a Sensor Array; Fiber-Based Laser Transmitter for Oxygen A-Band Spectroscopy and Remote Sensing; Low-Profile, Dual-Wavelength, Dual-Polarized Antenna; Time-Separating Heating and Sensor Functions of Thermistors in Precision Thermal Control Applications; Cellular Reflectarray Antenna; A One-Dimensional Synthetic-Aperture Microwave Radiometer; Electrical Switching of Perovskite Thin-Film Resistors; Two-Dimensional Synthetic-Aperture Radiometer; Ethernet-Enabled Power and Communication Module for Embedded Processors; Electrically Variable Resistive Memory Devices; Improved Attachment in a Hybrid Inflatable Pressure Vessel; Electrostatic Separator for Beneficiation of Lunar Soil; Amorphous Rover; Space-Frame Antenna; Gear-Driven Turnbuckle Actuator; In-Situ Focusing Inside a Thermal Vacuum Chamber; Space-Frame Lunar Lander; Wider-Opening Dewar Flasks for Cryogenic Storage; Silicon Oxycarbide Aerogels for High-Temperature Thermal Insulation; Supercapacitor Electrolyte Solvents with Liquid Range Below -80 C; Designs and Materials for Better Coronagraph Occulting Masks; Fuel-Cell-Powered Vehicle with Hybrid Power Management; Fine-Water-Mist Multiple-Orientation-Discharge Fire Extinguisher; Fuel-Cell Water Separator; Turbulence and the Stabilization Principle; Improved Cloud Condensation Nucleus Spectrometer; Better Modeling of Electrostatic Discharge in an Insulator; Sub-Aperture Interferometers; Terahertz Mapping of Microstructure and Thickness Variations; Multiparallel Three-Dimensional Optical Microscopy; Stabilization of Phase of a Sinusoidal Signal Transmitted Over Optical Fiber; Vacuum-Compatible Wideband White Light and Laser Combiner Source System; Optical Tapers as White-Light WGM Resonators; EPR Imaging at a Few Megahertz Using SQUID Detectors; Reducing Field Distortion in Magnetic Resonance Imaging; Fluorogenic Cell-Based Biosensors for Monitoring Microbes; A Constant-Force Resistive Exercise Unit; GUI to Facilitate Research on Biological Damage from Radiation; On-Demand Urine Analyzer; More-Realistic Digital Modeling of a Human Body; and Advanced Liquid-Cooling Garment Using Highly Thermally Conductive Sheets.

Source record↗

NASA NTRS1990

Programming distributed memory architectures using Kali

Programming nonshared memory systems is more difficult than programming shared memory systems, in part because of the relatively low level of current programming environments for such machines. A new programming environment is presented, Kali, which provides a global name space and allows direct access to remote data values. In order to retain efficiency, Kali provides a system on annotations, allowing the user to control those aspects of the program critical to performance, such as data distribution and load balancing. The primitives and constructs provided by the language is described, and some of the issues raised in translating a Kali program for execution on distributed memory systems are also discussed.

Mehrotra, Piyush↗

DOE OSTI2021

Verification and validation of developed short-term forecasting models

Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2006

Space Exploration: A Risk for Neural Stem Cells

During spaceflights beyond low Earth orbit, astronauts are exposed to potentially carcinogenic and tissue damaging galactic cosmic rays, solar proton events, and secondary radiation that includes neutrons and recoil nuclei produced by nuclear reactions in spacecraft walls or in tissue (1). Such radiation risk may present a significant health risk for human exploration of the moon and Mars. Emerging evidence that generation of new neurons in the adult brain may be essential for learning, memory, and mood (2) and that radiation is deleterious to neurogenesis (3-5) underscores a previously unappreciated possible risk to the cognitive functions and emotional stability of astronauts exposed to radiation in space. Here we use a novel reporter mouse line to identify at-risk populations of stem and progenitor cells in the brain and find, unexpectedly, that quiescent stem-like cells (rather than their rapidly dividing progeny) in the hippocampus constitute the most vulnerable cell population. This finding raises concerns about the possible risks facing astronauts on long duration space missions.

Encinas, Juan M.↗

DOE OSTI2021

Understanding the Design Space of Sparse/Dense Multiphase Dataflows for Mapping Graph Neural Networks on Spatial Accelerators

Graph Neural Networks (GNNs) have garnered a lot of recent interest because of their success in learning representations from graph-structured data across several critical applications in cloud and HPC. Owing to their unique compute and memory characteristics that come from an interplay between dense and sparse phases of computations, the emergence of reconfigurable dataflow (aka spatial) accelerators offers promise for acceleration by mapping optimized dataflows (i.e., computation order and parallelism) for both phases. The goal of this work is to characterize and understand the design-space of dataflow choices for running GNNs on spatial accelerators in order for the compilers to optimize the dataflow based on the workload. Specifically, we propose a taxonomy to describe all possible choices for mapping the dense and sparse phases of GNNs spatially and temporally over a spatial accelerator, capturing both the intra-phase dataflow and the inter-phase (pipelined) dataflow. Using this taxonomy, we do deep-dives into the cost and benefits of several dataflows and perform case studies on implications of hardware parameters for dataflows and value of flexibility to support pipelined execution.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2010

NASA Electronic Parts and Packaging (NEPP) Program - Update

This slide presentation reviews the goals and mission of the NASA Electronic Parts and Packaging (NEPP) Program. The NEPP mission is to provide guidance to NASA for the selection and application of microelectronics technologies, to improve understanding of the risks related to the use of these technologies in the space environment and to ensure that appropriate research is performed to meet NASA mission assurance needs. The program has been supporting NASA for over 20 years. The focus is on the reliability aspects of electronic devices. In this work the program also supports the electronics industry. There are several areas that the program is involved in: Memories, systems on a chip (SOCs), data conversion devices, power MOSFETS, power converters, scaled CMOS, capacitors, linear devices, fiber optics, and other electronics such as sensors, cryogenic and SiGe that are used in space systems. Each of these area are reviewed with the work that is being done in reliability and effects of radiation on these technologies.

LaBel, Kenneth A.↗

NASA NTRSDate not supplied

Overview of Results from the MISSE 9-15 Polymers and Composites Experiment 1-4 (PCE 1-4)

Spacecraft in low Earth orbit (LEO) and other planetary orbits are subjected to harsh environmental conditions that can result in erosion, embrittlement and optical property degradation of susceptible materials threatening spacecraft performance and durability. To increase our understanding of space environmental effects on spacecraft materials, NASA Glenn Research Center has developed a series of experiments flown as part of the Materials International Space Station Experiment (MISSE) missions on the exterior of the International Space Station (ISS). Four Glenn experiments with 365 flight samples were flown on ISS’s MISSE-Flight Facility (MISSE-FF). These experiments are the Polymers and Composites Experiment-1 (PCE-1) flown as part of the MISSE-9 mission, the PCE-2 flown as part of the MISSE-10 mission, the PCE-3 flown as part of the MISSE-12 and MISSE-15 missions, and the PCE-4 flown as part of the MISSE-13 mission. The PCE 1-4 flight samples were flown in either ram, wake, zenith or nadir flight orientations. The primary objectives of the PCE 1-4 experiments are to determine the LEO atomic oxygen (AO) erosion yield, Ey (volume loss per incident oxygen atom) and radiation durability of spacecraft polymers, composites and coatings. In addition, each experiment has a wide variety of materials with numerous sample objectives. This presentation provides an overview of the PCE 1-4 experiments, the numerous sample collaborators, the space environmental exposure of the flight samples, and an overview of the post-flight observations and results. The results include AO fluence and contamination analyses for each mission flight orientation, erosion data for the PCE 1-4 AO Ey samples, optical property data, and tensile results for the MISSE-9 and MISSE-13 tensile samples. In addition, an introduction of results from other unique samples such as indium tin oxide (ITO) coated thermal control samples, shape memory polymer composites, melanin based composites, and specialty coatings, will be provided.

Atomic oxygen↗