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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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Design effort undertaken to solve spinout problem plaguing high-performance sailboards. Proposed skeg section designed by use of computer model of pressure field and boundary layer. Prevents ventilation by maintaining attached boundary-layer flow throughout operating environment. Cavitation also avoided by preventing valleys in pressure distribution while skeg operated throughout its range.
A low cost, modular, high performance, and compact Telemetry and Command Processor (TCP) is being built as the foundation of command and data handling subsystems for the next generation of satellites. The TCP product line will support command and telemetry requirements for small to large spacecraft and from low to high rate data transmission. It is compatible with the latest TDRSS, STDN and SGLS transponders and provides CCSDS protocol communications in addition to standard TDM formats. Its high performance computer provides computing resources for hosted flight software. Layered and modular software provides common services using standardized interfaces to applications thereby enhancing software re-use, transportability, and interoperability. The TCP architecture is based on existing standards, distributed networking, distributed and open system computing, and packet technology. The first TCP application is planned for the 94 SDIO SPAS 3 mission. The architecture enhances rapid tailoring of functions thereby reducing costs and schedules developed for individual spacecraft missions.
A computational study of the wing for the distributed electric propulsion X-57 Maxwell airplane configuration at cruise and takeoff/landing conditions was completed. Three unstructured-mesh, Navier-Stokes computational fluid dynamics methods, FUN3D, USM3D and Kestrel, were used to predict the performance buildup of components to the full X-57 configuration. The goal of the X-57 wing and distributed electric propulsion system design was to meet or exceed the required lift coefficient of 3.95 for a stall speed of 58 knots. The X-57 Maxwell airplane was designed with a small, high aspect ratio cruise wing that was designed for a high cruise lift coefficient of 0.75 at a cruise speed of 150 knots and altitude of 8,000 ft, with an angle of attack of approximately 0deg. The computational data indicates that the X-57 full aircraft drag would meet the cruise drag goal with a 25 count drag margin. The cruise configuration maximum lift coefficient is 2.07 and without including the stabilator is 1.86 at an angle of attack of 14 deg, predicted with the USM3D flow solver using the Spalart-Allmaras turbulence model. The maximum lift coefficient for the high-lift wing (with the 30deg flap deflection) without the stabilator contribution is 2.60 at an angle of attack of 13 deg. For high-lift blowing conditions with 13.7 hp/prop, the maximum lift coefficient excluding the stabilator is 4.426 at (alpha) = 13 deg. Therefore, the lift augmentation from the high-lift propellers is 1.7 and the total lift augmentation from the high-lift system (30 deg flap deflection and the high-lift blowing) is 2.38. The drag for the high-lift wing with 30 deg flap deflection is much higher than the cruise wing configuration, but the high-lift system is used only during a small portion of the flight envelope. The pitching moment is relatively constant for both blown and unblown conditions when the stabilator is excluded. Modeling the full geometry has indicated some adverse effects from the fuselage on the wing and stabilator. At high angles of attack, the solutions with the USM3D flow solver using the Spalart-Allmaras turbulence model indicates large flow separation on the wing upper surface between the two high-lift nacelles near the fuselage, and also a reduction in sectional lift on the stabilator in the first 50 percent of the stabilator semispan. However, the large flow separation near the fuselage is mostly eliminated in the solutions predicted with two codes, USM3D and Kestrel, using Hybrid Reynolds-averaged Navier Stokes/Large Eddy Simulation turbulence models.
Practical applications of quantum computing are currently limited by the number of qubits that can be set with reasonable fidelities for each system. Therefore, a distributed quantum computing system with multiple quantum computers coherently connected is highly demanding. To realize the internode communication of quantum information, the software interface, Quantum Message Passing Interface (QMPI), leveraging the framework built for classical MPI but taking advantage of quantum teleportation to communicate between different quantum nodes was proposed. In this project, we develop the QMPI with point-to-point and collective operations in Qiskit and characterize its performance by demonstrating the application implementations. Moreover, we developed a new technique for optimizing collective communication of the distributed quantum programs with Multi-Controlled Toffoli gates. This technique beats the state-of-the-art in terms of fidelity and the number of remote EPR pairs consumed in both simulations and experiments.
Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. Here, we identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small files.
A plume of the contaminant hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) with concentrations greater than the New Mexico tap water drinking standard (9.66 ppb) is present in the regional aquifer near the southwestern boundary of Los Alamos National Laboratory (LANL). A risk assessment is performed for exposure to regional aquifer groundwater, with long-term predictions of RDX concentrations provided by a calibrated, probabilistic, numerical fate and transport model. The structure of the model is hierarchical, with the RDX Regional Aquifer (RA) groundwater model acting as the primary tool for analysis of downgradient RDX concentrations. The RA model is deeply informed by the conceptual site model (CSM) and is calibrated using site RDX concentration data and hydraulic head measurements, along with other analyses. The model is calibrated using data through December 2019. Where data are scarce other lines of evidence are used to inform inputs, including the multiphase RDX Vadose Zone (VZ) model and Pipe and Disk analytical screening tool. Model inputs are described with informative prior distributions using a robust approach to development that incorporates all available lines of evidence for every parameter used as an input to the RA model. Calibration is performed using a classical nonlinear optimization routine, which is then used to initialize a Bayesian calibration. The Bayesian calibration constrains the uncertainty in the classical calibration, ultimately providing posterior distributions for all model parameters. The challenges of the calibration include high-dimensional parameter space, including spatially heterogeneous hydraulic conductivities, comparatively sparse data, and low RDX concentrations. Posterior parameter distributions developed in the calibration process are then used for stochastic predictive model runs into the future. The result of the forward runs is spatially and temporally explicit estimates of head and concentration with uncertainty at all points in space and time. The probabilistic modeling approach presented here includes innovative computational and statistical methods that leverage high-performance computing (HPC) resources. It makes use of prior modeling work performed at the chromium plume site in the central LANL area, with extensive updates. The risk assessment will ultimately be used to support decision-making at the site, using multiple appropriately weighted sources of information, as well as uncertainty, sensitivity, and value-of-information analyses. (authors)
Due to limited crew availability to support science and the large number of experiments to be operated simultaneously, telescience is key to a successful International Space Station (ISS) science program. Crew, operations personnel at NASA centers, and researchers at universities and companies around the world must work closely together to perform scientific experiments on-board ISS. NASA has initiated use of Voice over Internet Protocol (VoIP) to supplement the existing HVoDS mission voice communications system used by researchers. The Internet Voice Distribution System (IVoDS) connects researchers to mission support "loops" or conferences via Internet Protocol networks such as the high-speed Internet 2. Researchers use IVoDS software on personal computers to talk with operations personnel at NASA centers. IVoDS also has the capability, if authorized, to allow researchers to communicate with the ISS crew during experiment operations. NODS was developed by Marshall Space Flight Center with contractors A2 Technology, Inc. FVC, Lockheed- Martin, and VoIP Group. IVoDS is currently undergoing field-testing with full deployment for up to 50 simultaneous users expected in 2002. Research is currently being performed to take full advantage of the digital world - the Personal Computer and Internet Protocol networks - to qualitatively enhance communications among ISS operations personnel. In addition to the current voice capability, video and data-sharing capabilities are being investigated. Major obstacles being addressed include network bandwidth capacity and strict security requirements. Techniques being investigated to reduce and overcome these obstacles include emerging audio-video protocols and network technology including multicast and quality-of-service.
The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Stress-based failure probability has been developed and analyzed to assess the integrity of tri-structural isotropic (TRISO) fuel particles during fuel life cycles. While simple, stress-based approaches to failure probability leveraging the Weibull statistical distribution entails a number of drawbacks when stress concentration occurs near crack tips, including finite element mesh size dependency. In this report, we use an interaction integral approach to the computation of stress intensity factors in functionally graded materials (FGM) for axisymmetric models. The inner pyrolytic carbon (IPyC) cracking induced silicon carbide (SiC) failure is one of the dominated failure modes in TRISO failure analysis. In this study, we consider a crack in the IPyC layer perpendicular to the SiC layer. The interface between these two TRISO layers is considered to be porous, which we simulate considering a transition of mechanical properties over the porous length. These aspects are considered in the computation of stress intensity factor (SIF) from a fracture mechanics approach and compared with the known stress-based failure probability approach.
We report on the first year of the OSiRIS project (NSF Award #1541335, UM, IU, MSU and WSU) which is targeting the creation of a distributed Ceph storage infrastructure coupled together with software-defined networking to provide high-performance access for wellconnected locations on any participating campus. The projects goal is to provide a single scalable, distributed storage infrastructure that allows researchers at each campus to read, write, manage and share data directly from their own computing locations. The NSF CC*DNI DIBBS program which funded OSiRIS is seeking solutions to the challenges of multi-institutional collaborations involving large amounts of data and we are exploring the creative use of Ceph and networking to address those challenges. While OSiRIS will eventually be serving a broad range of science domains, its first adopter will be the LHC ATLAS detector project via the ATLAS Great Lakes Tier-2 (AGLT2) jointly located at the University of Michigan and Michigan State University. Part of our presentation will cover how ATLAS is using the OSiRIS infrastructure and our experiences integrating our first user community. The presentation will also review the motivations for and goals of the project, the technical details of the OSiRIS infrastructure, the challenges in providing such an infrastructure, and the technical choices made to address those challenges. We will conclude with our plans for the remaining 4 years of the project and our vision for what we hope to deliver by the projects end.
Recently, there has been a growing interest in enabling fast data analytics by leveraging system capabilities from large-scale high-performance computing (HPC) systems. OpenSHMEM is a popular run-time system on HPC systems that has been used for large-scale compute-intensive scientific applications. In this paper, we propose to leverage OpenSHMEM to design a distributed in-memory key-value store for fast data analytics. Accordingly, we have developed SHMEMCache on top of OpenSHMEM to leverage its symmetric global memory, efficient one-sided communication operations and general portability. We have also evaluated SHMEMCache through extensive experimental studies. Our results show that SHMEMCache has accomplished significant performance improvements over hte original Memcached in terms of latency and throughput. Our evaluation on the Titan supercomputer has also demonstrated that SHMEMCache can scale to 1024 nodes.
This paper presents the results of conceptual level thermal analyses of a High Speed Civil Transport (HSCT) wing using p-version finite elements. The work was motivated by a thermal analysis of a HSCT wing structure which showed the importance of radiation heat transfer throughout the structure. The analysis also showed that refining a traditional finite element mesh to accurately capture the temperature distribution on the internal structure led to very large meshes with unacceptably long execution times. Further study indicated using p-version finite elements might improve computation performance for this class of problem. Methods for determining internal radiation heat transfer were then developed and demonstrated on test problems representative of the geometry found in an aircraft wing structure. This paper presents the results of the application of these new methods to the analysis of a high speed aircraft wing. Results for both a wing box model as well as a full wing model are presented. 'Me reduced wing box model allows for a comparison of the traditional finite element method with mesh refinement (h-refinement) to the new p-version finite elements while the full wing model demonstrates the applicability and efficiency of p-version finite elements for large models.
By comparing the detailed wall static pressure distributions for 9 inch and 11 inch long fins generating a crossing shock configuration at M = 2.93, the high resolution results of the 9 inch fins are shown to be free of exit effects. Analysis of the static pressure profiles have delineated the limited regions where the single fin results are valid. The characteristics of the complex interaction, with varying shock wave strength, have been described. The data provide a critical test for computational fluid dynamics which, in its initial phase, has performed poorly in predicting the measured wall static pressure distributions.
This report details recent progress for the ASCR funded project “Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows”. We refer to the project as IPPD/2, reflecting the 2017 renewal under expanded scope and partners In IPPD/2, we increased our research scope to include data motion. We are focusing on three major aspects: a) observe how data is generated, distributed, and used; b) analyze how data is (repeatedly) consumed with a focus both on repeated patterns and anomalies; and c) explore how to optimize data motion. This new work on data motion will augment and complement IPPD/2’s research that focused on the computational aspects of tasks. We leverage and extend our existing tools and demonstrate our work on the Belle II workflow suite as well as on workflows from NSLS-II. The highlights of our work are as follows: Provenance for Workflows: Provenance is used to provide information enabling quality control, re-run computational workflows, and reproduce results. IPPD/2 has been building a scalable provenance management system that enables the capture of provenance from the high-level workflow through all relevant system levels in one integrated environment. Leveraging this work, our recent efforts have included using provenance as an enabling technique. Workload characterization: Leveraging provenance and analysis, we characterize data movement within network, storage, and memory over a variety of workloads. This characterization enables an understanding by performance analysts and application developers of the range of behaviors that could be expected. Performance Prediction for Workflows: The goal of modeling distributed workflows is to understand performance bottlenecks and enable more intelligent task scheduling to optimize selected metrics of interest (e.g., task throughput or output data rate). IPPD/2 has utilized both analytical and AI/ML modeling methodologies for performance modeling. Advanced Scheduling and Fault Modeling for Workflows: Scheduling of large-scale scientific workflows on geographically distributed resources is a challenging problem. To improve workflow throughput, we combined novel scheduling algorithms with task predictions from performance modeling and fault modeling. Dynamically Alleviating Bottlenecks in Workflows: Exploiting our provenance, analysis, and modeling efforts, we have explored and developed several techniques for dynamically detecting and alleviating bottlenecks in data movement. In particular, we have spent considerable effort demonstrating our techniques on production-like workflow configurations.
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimization for DNNs tremendously important, especially for edge devices. Owing to the large parameter space and cost of evaluating each parameter in the search space, manually tuning of DNN hyperparameters is impractical. Automatic joint DNN and hardware hyperparameter optimization is indispensable for such problems. Bayesian optimization-based approaches have shown promising results for hyperparameter optimization of DNNs. However, most of these techniques have been developed without considering the underlying hardware, thereby leading to inefficient designs. Further, the few works that perform joint optimization are not generalizable and mainly focus on CMOS-based architectures. In this work, we present a novel pseudo agent-based multiobjective hyperparameter optimization (PABO) for maximizing the DNN performance while obtaining low hardware cost. Compared to the existing methods, our work poses a theoretically different approach for joint optimization of accuracy and hardware cost and focuses on memristive crossbar based accelerators. PABO uses a supervisor agent to establish connections between the posterior Gaussian distribution models of network accuracy and hardware cost requirements. The agent reduces the mathematical complexity of the co-optimization problem by removing unnecessary computations and updates of acquisition functions, thereby achieving significant speed-ups for the optimization procedure. PABO outputs a Pareto frontier that underscores the trade-offs between designing high-accuracy and hardware efficiency. Our results demonstrate a superior performance compared to the state-of-the-art methods both in terms of accuracy and computational speed (~100x speed up).
Laser powder bed fusion (LPBF) additive manufacturing (AM) offers a variety of advantages over traditional manufacturing, however its usefulness for manufacturing of high-performance components is currently hampered by internal defects (porosity) created during the LPBF process that have an unknown impact on global mechanical performance. By inducing porosity distributions through variations in print energy density and inspecting the resulting tensile samples using computed tomography, nearly 50,000 pores across 75 samples were identified. Porosity characteristics were quantitatively extracted from inspection data and compared with mechanical properties to understand the strength of relationships between porosity and global tensile performance. Useful porosity characteristics were identified for prediction of part performance. Results indicate that ductility and strain at ultimate tensile strength are the global tensile properties most significantly impacted by porosity and can be predicted with reasonable accuracy using simple porosity shape descriptors such as volume, diameter, and surface area. Moreover, it was found that the largest pores influenced behavior most significantly. Specifically, pores in excess of 125 µm in diameter were found to be a sufficient threshold for property estimation. These results establish an initial understanding of the complex defect-performance relationship in AM 316L stainless steel and can be leveraged to develop certification standards and improve confidence in part quality and reliability for the broader set of engineering alloys.
Instabilities of shallow gas-charged seabed are potential geological hazards in ocean engineering. In practice, the conventional field sampling techniques failed to obtain undisturbed gas-bearing sediments from the seabed for laboratory mechanical testing because of sensitive gas exsolution and escape from sediments. However, preparation of representative remoulded gas-charged specimens is a challenging issue, because it is rather difficult to quantitatively control the gas content and obtain uniform distribution of gas bubbles within the specimen. Given the above problems, this work proposes a reliable approach to reconstitute the high-saturation specimen of gas-charged sediments in the laboratory by an improved multifunction integrated triaxial apparatus (MITA). This apparatus is developed based on an advanced stress path triaxial system by introducing a temperature-controlled system and a wave-monitoring system. The temperature-controlled system is used to accurately mimic the in situ environments of sediments in the seabed. The wave-monitoring system is utilized to identify exsolution point of free gas and examine the disturbance of gas to specimens during gas exsolution. The detailed procedure of gassy specimen preparation is introduced. Then, the quality of prepared specimens using our improved apparatus is validated by the high-resolution micro-X-ray computed tomography (μCT) scanning test, from which bubble occurrence and size distribution within the gassy sand specimen can be obtained; and preliminary mechanical tests on gassy sand specimens with various initial saturation degrees are performed. The proposed specimen preparation procedure succeeds in proving the postulated occurrence state of gas bubbles in coarse-grained sediments and accurately controlling the gas content.
Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.
High Energy Physics (HEP) software environments often involve ~ hundreds of external packages and libraries, and similar numbers of experiment-specific, science-critical packages—many under continuous development. Managing coherent releases of the external software stack is challenging enough, but managing the highly-collaborative—and distributed—development of a large body of code against such a stack adds even more complexity and room for error. Spack is a popular Python-based package management tool with a specific focus on the needs of High Performance Computing (HPC) systems and system administrators whose strength is orchestrating the discrete download, build, testing, and installation of pre-packaged or tagged third-party software against similarly stable dependencies. As such it is becoming increasingly popular within HEP as that community makes increasing use of HPC facilities, and as efforts to develop future HPC systems utilize Spack to provide scientific software on those platforms. SpackDev is a system to facilitate the simultaneous development of interconnected sets of packages. Intended to handle packages without restriction to one internal build system, SpackDev is integrated with Spack as a command extension in order to leverage features such as dependency calculations and build system configuration, and is generally applicable outside HEP. We describe SpackDev’s features and development over the last two years, initial experience using SpackDev in the context of the LArSoft liquid argon detector toolkit, and work remaining before it can be considered a fully-functional multi-package build system for HEP experiments utilizing Spack.