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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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Topics addressed cover: identification of large space structure dynamics; special-purpose architectures for computational fluid dynamics; fault-tolerant processor architectures; data flow techniques; software specification and verification tools; management of software development; software environment for concurrent computing; and parallel algorithms and architectures for the solution of partial differential equations.
We present a definitive assessment of the role of inverse Compton scattering of cosmic microwave background photons (IC/CMB) in the context of radio galaxies. Owing to the steep increase of the CMB radiation energy density, IC/CMB is supposed to become progressively more important with respect to radio synchrotron cooling as the redshift increases. For typical energies at play, this process will up-scatter the CMB photons into the X-ray band, and is thus expected to yield a redshift-dependent, concurrent X-ray brightening and radio dimming of the jet-powered structures. Here, we show how a conclusive proof of this effect hinges on high-resolution imaging data in which the extended lobes can be distinguished from the compact hotspots where synchrotron self-Compton dominates the X-ray emission regardless of redshift. We analyse Chandra and Very Large Array data of 11 radio galaxies between 1.3 ≲ z8 ≲ 4.3, and demonstrate that the emission from their lobes is fully consistent with the expectations from IC/CMB in equipartition. Once the dependence on size and radio luminosity are properly accounted for, the measured lobe X-ray luminosities bear the characteristic ∝(1 + z)4 proportionality expected of a CMB seed radiation field. Whereas this effect can effectively quench the (rest-frame) GHz radio emission from z ≳ 3 radio galaxies below ≲ 1 mJy, IC/CMB alone cannot be responsible for a deficit in high-z, radio-loud active galactic nuclei (AGNs) if – as we argue – such AGNs typically have bright, compact hotspots.
Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iterates between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. As a result, equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.
Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments
Efficient random number generation with high quality statistical properties and exact reproducibility of Monte Carlo simulations are important requirements in many areas of computational science. VecRNG is a package providing pseudo-random number generation (pRNG) in the context of a new library VecMath. This library bundles up several general-purpose mathematical utilities, data structures, and algorithms having both SIMD and SIMT (GPUs) support based on VecCore.~Several state-of-the-art RNG algorithms are implemented as kernels supporting parallel generation of random numbers in scalar, vector, and Cuda workflows. In this report, we will present design considerations, implementation details, and computing performance of parallel pRNG engines on both CPU and GPU. Reproducibility of propagating multiple particles in parallel for HEP event simulation is demonstrated, using GeantV-based examples, for both sequential and fine-grain track-level concurrent simulation wor kflows. Strategies for efficient uses of vectorized pRNG and non-overlapping streams of random number sequences in concurrent computing environments is discussed as well.
Predicting, responding to, or interpreting the chemical record preserved in debris derived from nuclear events can be challenging due to chemical fractionation. Chemical fractionation is where different species of the evolving radionuclide inventory segregate and/or are lost from the system over the timescales of debris formation. Both historic data and recent research suggest that the interaction and character of the local environment may exert controls on chemical fractionation by influencing the cooling and evolution of the associated fireball as well as the composition of the vapor term and resultant speciation. Prior to this work, an integrated platform permitting dynamic and concurrent consideration of physical and chemical evolution of early time post-detonation event environments did not exist. Our work merged historic data and experimental approaches to support development of a computational framework able to simulate fundamental processes (e.g., entrainment of local environment, oxidation chemistry, and cooling time scales) that may perturb the radionuclide inventory captured in post-detonation debris. Work with historic debris confirmed that entrained environmental material affect debris composition, structure, and radionuclide incorporation. Complementary work utilizing a readily controllable and tunable benchtop setup (a plasma flow reactor) simulated the late cooling of a nuclear fireball (e.g., T < 6000 K) and bounded the sensitivity of actinide speciation and particle size distribution to variations in oxygen concentration and cooling rates. Concurrent laser ablation and laser heating experiments were used to investigate the chemistry and physics of processes occurring in vaporized and/or rapidly heated actinides and other elements in the presence of oxygen. A more computationally efficient microphysical model was developed for predicting and evolving size distributions of particles forming from mixed vapor terms and simulating particle formation processes under a variety of extreme conditions. Continued study of historic nuclear event film confirmed that shockwave data and physics codes agree to within the uncertainty of the data. Good agreement was achieved for thermal emission from an airburst, however the paucity of low-temperature molecular opacity data for mixtures of air, bomb debris, entrained dirt, and water vapor complicate agreement for more elaborate scenarios. A multiphysics code (ALE3D) was modified to bring the necessary physics and chemistry, including these new data and insights, onto a single platform. Code development included improved initialization of large physical systems, modernization of chemistry capabilities, and modifications to enable inclusion of particle transport.
Exascale workloads are expected to incorporate data-intensive processing in close coordination with traditional physics simulations. These emerging scientific, data-analytics and machine learning applications need to access a wide variety of datastores in flat files and structured databases. Programmer productivity is greatly enhanced by mapping datastores into the application process's virtual memory space to provide a unified “in-memory” interface. Currently, memory mapping is provided by system software primarily designed for generality and reliability. However, scalability at high concurrency is a formidable challenge on exascale systems. Also, there is a need for extensibility to support new datastores potentially requiring HPC data transfer services. In this article, we present UMap , a scalable and extensible userspace service for memory-mapping datastores. Furthermore, through decoupled queue management, concurrency aware adaptation, and dynamic load balancing, UMap enables application performance to scale even at high concurrency. We evaluate UMap in data-intensive applications, including sorting, graph traversal, database operations, and metagenomic analytics. Our results show that UMap as a userspace service outperforms an optimized kernel-based service across a wide range of intra-node concurrency by 1.22-1.9 × . We performed two case studies to demonstrate UMap 's extensibility. First, a new datastore residing in remote memory is incorporated into UMap as an application-specific plugin. Second, we present a persistent memory allocator Metall built atop UMap for unified storage/memory.
Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected graphs, pose significant challenges in terms of execution performance. To tackle this, distributed-memory solutions such as partitioning the graph to concurrently train multiple replicas of GNNs are in practice. However, approaches requiring a partitioned graph usually suffer from communication overhead and load imbalance, even under optimal partitioning and communication strategies due to irregularities in the neighborhood minibatch sampling. This paper proposes practical trade-offs for improving the sampling and communication overheads for representation learn- ing on distributed graphs (using popular GraphSAGE architecture) by developing a parameterized prefetch and eviction scheme on top of the state-of-the-art Amazon DistDGL distributed GNN framework, demonstrating about 15–40% improvement in end-to-end training performance on the NERSC Perlmutter supercomputer for various OGB datasets.
Current epoxy resins utilized in high performance structural composites absorb moisture from high humidity environments. Such moisture absorption causes plasticization of the resin to occur with concurrent swelling and lowering of the glass transition temperature. Similar effects are observed in composites. Data are presented showing the effects of absorbed moisture on Hercules AS/3501-5 graphite/epoxy composites. Prediction of moisture content and distribution in composites, along with reduction in mechanical properties, are discussed.
Interactions among engineering disciplines and subsystems in engineering system design are surveyed and specific instances of such interactions are described. Examination of the interactions that a traditional design process in which the numerical values of major design variables are decided consecutively is likely to lead to a suboptimal design. Supporting numerical examples are a glider and a space antenna. Under an alternative approach introduced, the design and its sensitivity data from the subsystems and disciplines are generated concurrently and then made available to the system designer enabling him to modify the system design so as to improve its performance. Examples of a framework structure and an airliner wing illustrate that approach.
Interactions among engineering disciplines and subsystems in engineering system design are surveyed and specific instances of such interactions are described. Examination of the interactions that a traditional design process in which the numerical values of major design variables are decided consecutively is likely to lead to a suboptimal design. Supporting numerical examples are a glider and a space antenna. Under an alternative approach introduced, the design and its sensitivity data from the subsystems and disciplines are generated concurrently and then made available to the system designer enabling him to modify the system design so as to improve its performance. Examples of a framework structure and an airliner wing illustrate that approach.
A processor core is configured to execute a parent task that is described by a data structure stored in a memory. A coprocessor is configured to dispatch a child task to the at least one processor core in response to the coprocessor receiving a request from the parent task concurrently with the parent task executing on the at least one processor core. In some cases, the parent task registers the child task in a task pool and the child task is a future task that is configured to monitor a completion object and enqueue another task associated with the future task in response to detecting the completion object. The future task is configured to self-enqueue by adding a continuation future task to a continuation queue for subsequent execution in response to the future task failing to detect the completion object.
A processor core is configured to execute a parent task that is described by a data structure stored in a memory. A coprocessor is configured to dispatch a child task to the at least one processor core in response to the coprocessor receiving a request from the parent task concurrently with the parent task executing on the at least one processor core. In some cases, the parent task registers the child task in a task pool and the child task is a future task that is configured to monitor a completion object and enqueue another task associated with the future task in response to detecting the completion object. The future task is configured to self-enqueue by adding a continuation future task to a continuation queue for subsequent execution in response to the future task failing to detect the completion object.
Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.
The Anglo-Australian Observatory (AAO) is building a system that will provide a two-degree field of view at prime focus. A robot positioner will be used to locate up to 400 optical fibers at pre-determined positions in this field. While observations are being made using one set of 400 fibers, the robot will be positioning a second set of fibers in a background field that can be moved in to replace the first when the telescope is moved to a new position. The fibers feed two spectrographs each with a 1024 square CCD detector. The software system being produced to control this involves Vaxes for overall control and data recording, UNIX workstations for fiber configuration calculations and on-line data reduction, and VME systems running VxWorks for real-time control of critical parts such as the positioner robot. The system has to be able to interact with the observatory's present data acquisition systems, which use the ADAM system. As yet, the real-time parts of ADAM have not been ported to Unix, and so we are having to produce a smaller-scale system that is similar but inherently distributed (which ADAM is not). We are using this system as a testbed for ideas that we hope may eventually influence an ADAM II system. The system we are producing is based on a message system that is designed to be able to handle inter-process and inter-processor messages of any length, efficiently, and without ever requiring a task to block (i.e., be unresponsive to 'cancel' messages, enquiry messages), other than when deliberately waiting for external input - all of which will be through such messages. The essential requirement is that a message 'send' operation should never be able to block. The messages will be hierarchical, self-defining, machine-independent data structures. This allows us to provide very simple monitoring of messages for diagnostic purposes, and allows general purpose interface programs to be written without needing to share precise byte by byte message format definitions. Programs in this system have interfaces defined simply in terms of named actions and their parameters. Real-time control programs are required to be able to handle a number of such actions concurrently; data reduction programs will normally only need to handle one action at a time ('process an image', 'display a spectrum', etc).
Monte Carlo radiation transport (MCRT) methods have been used to simulate radiation environments for many decades by tracking individual particles through a model to accumulate statistical information. MCRT geometry is historically formed using the constructive solid geometry (CSG). Recently, significant work has been performed to support simulations using computer-aided design (CAD)-based tessellated surfaces to support highly complex geometries. Ray tracing acceleration data structures from the rendering and visualization community are applied to accelerate particle tracking in CAD-based models. Despite these efforts, CSG representations provide the superior performance in surface intersection operations during particle flight. Concurrently, pseudo Monte Carlo methods have become prevalent in rendering applications to support more realistic models for scattering media, motivating innovations that are advantageous for MCRT simulations. Finally, the authors’ work extends these innovations by employing Intel’s Embree ray tracing kernel within a geometry toolkit for Monte Carlo to improve the simulation performance using CAD-based models by factors of 1.5 to 2.
Analyses of data collected in Antarctica since 1963 were conducted to identify features of behavior and performance likely to occur during long-duration missions in space.The influence of mission duration and station latitude on POMS mood scores was examined in 450 American men and women who overwintered in Antarctica between 1991 and 1998. The influence of crewmember social characteristics, personality traits, interpersonal needs, and station environments on measures of behavior and performance at the end of the austral winter was examined in 657 American men who overwintered between 1963 and 1974. Both data sets were used to examine the influence of crew social structure on individual performance. Results: Seasonal variations in mood appear to be associated with the altered diurnal cycle and psychological segmentation of the mission. Concurrent measures of personality, interpersonal needs, and coping styles are better predictors of depressed mood and peer-supervisor performance evaluations than baseline measures because of the unique features of the station social and physical environments and the absence of resources typically used to cope with stress elsewhere. Individuals in crews with a clique structure report significantly more depression, anxiety, anger, fatigue and confusion than individuals in crews with a core-periphery structure. Depressed mood is inversely associated with severity of station physical environment, supporting the existence of a positive or "salutogenic" effect for individuals seeking challenging experiences in extreme environments.
The specific requirements for spacecraft computing systems are considered. These requirements are partly related to the constraints of limited resources of power, weight, and volume. Another important factor is the requirement of extremely high reliability. These reliability requirements have led to introduction of automated redundancy techniques on board the spacecraft. The various redundant computers check each other and provide recovery procedures when a computer is found to have failed. Past and future capabilities are considered along with distributed processing requirements. System considerations are discussed, taking into account suboptimum computer throughput, sensitivity to software modifications, hierarchic timing, I/O granularity, restricted communications, synchronous functions, hierarchic control, and concurrent error detection. A description is presented of the Unified Data System (UDS), which consists of a set of standard microcomputers connected by several buses. Attention is also given to synchronization and timing, the executive control structure, the programming language, and the executive program.