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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Analytical calculation of muon intensities under deep sea-water

The study of the energy loss of high energy muons through different materials, such as rock and sea-water can cast light on characteristics of lepton interactions. There are less ambiguities for the values of atomic number (Z) and mass number (A) in sea-water than in rock. Muon intensities should be measured as fundamental data and as background data for searching the fluxes of neutrino. The average range energy relation in sea-water is derived. The correction factors due to the range fluctuation is also computed. By applying these results, the intensities deep under sea are converted from a given muon energy spectra at sea-level. The spectra of conventional muons from eta, K decays have sec theta enhancement. The spectrum of prompt muons from charmed particles is almost isotropic. The effect of prompt muons is examined.

Inazawa, H.↗
DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

TRIO: Burst Buffer Based I/O Orchestration, In: 2015 IEEE International Conference on Cluster Computing

The growing computing power on leadership HPC systems is often accompanied by ever-escalating failure rates. Checkpointing is a common defensive mechanism used by scientific applications for failure recovery. However, directly writing the large and bursty checkpointing dataset to parallel file systems can incur significant I/O contention on storage servers. Such contention in turn degrades bandwidth utilization of storage servers and prolongs the average job I/O time of concurrent applications. Recently burst buffers have been proposed as an intermediate layer to absorb the bursty I/O traffic from compute nodes to storage backend. But an I/O orchestration mechanism is still desirable to efficiently move checkpointing data from burst buffers to storage backend. In this paper, we propose a burst buffer based I/O orchestration framework, named TRIO, to intercept and reshape the bursty writes for better sequential write traffic to storage servers. Meanwhile, TRIO coordinates the flushing orders among concurrent burst buffers to alleviate the contention on storage server. Our experimental results demonstrated that TRIO could efficiently utilize storage bandwidth and reduce the average job I/O time by 37% on average for data-intensive applications in typical checkpointing scenarios.

Computer Science↗
DOE OSTI2014

Synthetic Graph Generation for Data-Intensive HPC Benchmarking: Scalability, Analysis and Real-World Application

The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allows the emulation of a broad spectrum of application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report described the in-depth analysis of the generated synthetic graphs' properties at a variety of scales using different generator implementations and examines their applicability to replicating real world datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Towards understanding HPC users and systems: A NERSC case study

High performance computing (HPC) scheduling landscape currently faces new challenges due to thechanges in the workload. Previously, HPC centers were dominated by tightly coupled MPI jobs. HPCworkloads increasingly include high-throughput, data-intensive, and stream-processing applications. Asa consequence, workloads are becoming more diverse at both application and job levels, posing newchallenges to classical HPC schedulers. There is a need to understand the current HPC workloads andtheir evolution to facilitate informed future scheduling research and enable efficient scheduling in futureHPC systems.In this paper, we present a methodology to characterize workloads and assess their heterogeneity,at a particular time period and its evolution over time. We apply this methodology to the workloads ofthree systems (Hopper, Edison, and Carver) at the National Energy Research Scientific Computing Center(NERSC). We present the resulting characterization of jobs, queues, heterogeneity, and performance thatincludes detailed information of a year of workload (2014) and evolution through the systems’ lifetime(2010–2014).

Rodrigo, Gonzalo P.↗
NASA NTRS1988

Planetary astronomy and supporting laboratory research

The aim was to obtain form laboratory measurements the molecular parameters needed to interpret observations of planetary and cometary spectra, and to develop the analytical and computational techniques to interpret the observed spectra in terms of planetary atmospheres including solids and cometary ices. The gas phase molecular parameters measured include the intensities and half-widths of vib-rotational lines, total intensities of absorption bands, temperature dependencies, and absorption and pressure parameters in random-band models of absorption bands. Computation of line shapes of H2 quadrupole lines from quantum mechanical first principles for comparison with laboratory data and use in modeling of planetary atmospheres was accomplished. The solid phase measurements include band profile and quantitative intensity measurements and dependence on composition as well as thermal and photolytic processing which mimics the particular astrophysical environments. Work on GeH4, PH3, has made significant progress.

Valero, F. P. J.↗
NASA NTRSDate not supplied

Expedited Model Building Method for Rocket Nozzle Side Loading

Statistical analysis of test data is currently the preferred method of creating models for uncharacterized side loading in rocket nozzles. However, statistical analysis requires multiple data sets to be reliable; getting these sets requires building and testing multiple rocket motors, which is not feasible given the incredible time and cost required to do so. By using a combination of geometry, simplified finite element analysis (FEA) analogs, and algebra we can create a limited predictive model from only one test data set. Once created, the model requires only the input from one strain gauge located at each yaw and pitch attachment bracket for the actuation system. This process is not computationally intensive, therefore saving an abundance of time and budget over using full 3D FEA models to develop a similar predictive model. To develop the model, conversion factors between the strain at the gauge locations and corresponding actuator load is derived from the data. These conversion factors are used to create a “conversion ellipse” that bounds the constitutive properties of the nozzle as one lumped factor. Using a 2D cross-sectional analog of the nozzle exit plane, we create a function that allows us to determine the azimuthal location of the unexpected load based on the ratio between yaw and pitch strain readings. Combining this with the “conversion ellipse” yields a function that given the strain reading outputs an induced actuator load, regardless of the azimuthal location of the unexpected load. As previously mentioned, this predictive model is limited in its application as there is inaccuracy introduced by the use of data by only one test as well as simplifying assumptions. This approach makes a useful tool to quickly determine whether uncharacterized actuator loading events can damage actuation systems or induce loading that exceeds design limits.

Seetha A Kolli↗
DOE OSTI2016

Real-time microstructure imaging by Laue microdiffraction: A sample application in laser 3D printed Ni-based superalloys

Synchrotron-based Laue microdiffraction has been widely applied to characterize the local crystal structure, orientation, and defects of inhomogeneous polycrystalline solids by raster scanning them under a micro/nano focused polychromatic X-ray probe. In a typical experiment, a large number of Laue diffraction patterns are collected, requiring novel data reduction and analysis approaches, especially for researchers who do not have access to fast parallel computing capabilities. In this article, a novel approach is developed by plotting the distributions of the average recorded intensity and the average filtered intensity of the Laue patterns. Visualization of the characteristic microstructural features is realized in real time during data collection. As an example, this method is applied to image key features such as microcracks, carbides, heat affected zone, and dendrites in a laser assisted 3D printed Ni-based superalloy, at a speed much faster than data collection. Such analytical approach remains valid for a wide range of crystalline solids, and therefore extends the application range of the Laue microdiffraction technique to problems where real-time decision-making during experiment is crucial (for instance time-resolved non-reversible experiments).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Aircraft/lidar turbulence comparison

Very good agreement between remotely sensed winds using a ground-based Doppler lidar and in situ measurements with an instrumented aircraft is possible. Results show that turbulence intensities computed from time histories measured with the aircraft and time histories of the radial wind measured with lidar can be analyzed statistically to provide turbulence intensities and turbulence spectra which agree well with one another. The results further show that the second moment data, as presently compared with the NASA/MSFC algorithms, do not provide meaningful comparisons with turbulence intensities measured with the aircraft. This disagreement, however, must be investigated further in terms of the accuracy of the second moment data determined by both the lidar hardware and the algorithm for computing the second moment.

Huang, Kao-Huah↗
DOE OSTI2021

FY21 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

The development of algorithms for machine learning and data analysis for the 3013 Surveillance Program is a collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). For corrosion detection, Laser Confocal Microscope (LCM) or Wide Area 3D Measurement System (WAMS) data is extracted from large binary files, with software written to convert the data to physical attributes (e.g., height, color and grayscale values; all as functions of a location in a plane projection). A user-friendly Matlab Graphical User Interface (GUI) that reads data from either LCM or WAMS files was developed to integrate input data with software developed for processing and evaluation. The GUI can selectively download binary data, interrogate data attributes, label data, flag significant features, execute Machine Learning (ML) algorithms, output parameters for trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. Features can be called out by user-specified thresholds, manual labeling or machine learning algorithms when they have been completed. The ability to rapidly label data is important because of the volume of data required for training machine learning algorithms. The GUI has the flexibility to allow addition of improved ML algorithms, methods for data visualization, and statistical computations. Statistical analyses via the GUI include areas of pits within a defined range of pit depths, correlations between Red-Green-Blue (RGB) or grayscale intensity and relative surface height, covariances between values associated with features, and feature histograms. The development of supervised machine learning algorithms, however, has been hindered by a lack of training data. The machine learning algorithms for crack identification are being refined but require improvements to the true positive rate for crack detection. This shortcoming is an artifact of the limited training data currently available, perhaps more so than the structure of the neural networks. At present, the best results are had from a consensus over an ensemble of randomly generated Deep Neural Network (DNN) or Convolutional Neural Network (CNN) algorithms. Although the consensus accuracy method has yielded optimum true positive and true negative rates in excess of 80%, additional validation testing is necessary. In addition to the suite of LCM data that was initially used, and which represents the majority of the work presented in this report, WAMS image data was also reviewed at a preliminary level. The review included a comparison between image resolution and dynamic range for each method. WAMS (ZON file) image data was found to have a pixel pitch of 3.69μm compared to 1 μm for the LCM (vk4 file) data, which implies a lower resolution for the WAMS images. Conversely, the ratio of dynamic range of the WAMS data to the LCM data was approximately 41:20 for height data, suggesting that information from WAMS should more accurately determine the depth of pits. At present, the significance of the greater dynamic range of the WAMS data relative to the LCM data has not yet been evaluated.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗
DOE OSTI2024

Mixture-of-Experts for Multi-Domain Defect Identification in Non-Destructive Inspection

Composite materials are widely used in aircraft structures because of their superior mechanical properties. However, their complex failure modes require sophisticated inspection methods to ensure structural integrity. Ultrasonic testing (UT) is a common non-destructive inspection (NDI) technique for aircraft composites that can detect internal and external defects with high resolution and accuracy. Despite their effectiveness, traditional UT methods rely on the manual interpretation of ultrasonic signals, which is time-consuming, labor-intensive, and subjective. Furthermore, processing such large-scale data, particularly across materials of varying thicknesses, significantly increases the computational demands of deep learning model optimization. To overcome these challenges, we propose an efficient sparse mixture-of-experts (MoE) model with a multi-level loss function and introduce four novel training objectives to improve computational efficiency and accuracy in identifying surface defects in composite aircraft materials. Here, we evaluated our approach on material with multiple thicknesses or domains comprising various defects. Our experimental results demonstrate higher accuracy and F1-Score, with only 10% training epochs compared to baseline MoE.

composite materials↗
NASA NTRS1984

Optical systolic solutions of linear algebraic equations

The philosophy and data encoding possible in systolic array optical processor (SAOP) were reviewed. The multitude of linear algebraic operations achievable on this architecture is examined. These operations include such linear algebraic algorithms as: matrix-decomposition, direct and indirect solutions, implicit and explicit methods for partial differential equations, eigenvalue and eigenvector calculations, and singular value decomposition. This architecture can be utilized to realize general techniques for solving matrix linear and nonlinear algebraic equations, least mean square error solutions, FIR filters, and nested-loop algorithms for control engineering applications. The data flow and pipelining of operations, design of parallel algorithms and flexible architectures, application of these architectures to computationally intensive physical problems, error source modeling of optical processors, and matching of the computational needs of practical engineering problems to the capabilities of optical processors are emphasized.

Neuman, C. P.↗
NASA NTRS1988

Application of advanced computational technology to propulsion CFD

The Internal Fluid Mechanics Division of the NASA Lewis Research Center is combining the key elements of computational fluid dynamics, aerothermodynamic experiments, and advanced computational technology to bring internal computational fluid dynamics (ICFM) to a state of practical application for aerospace propulsion system design. This paper presents an overview of efforts under way at NASA Lewis to advance and apply computational technology to ICFM. These efforts include the use of modern, software engineering principles for code development, the development of an AI-based user-interface for large codes, the establishment of a high-performance, data communications network to link ICFM researchers and facilities, and the application of parallel processing to speed up computationally intensive and/or time-critical ICFM problems. A multistage compressor flow physics program is cited as an example of efforts to use advanced computational technology to enhance a current NASA Lewis ICFM research program.

Szuch, John R.↗
NASA NTRS1988

Infrared predetection dynamic range compression via photorefractive crystals

The theoretical basis and practical implementation of a predetection dynamic-range compression technique for IR sensor systems are discussed. The approach takes advantage of the nonlinear intensity dependence of the gain coefficient in photorefractive crystals. Its feasibility is demonstrated in numerical computations using the experimental data of Cheng and Partovi (1986) on two-wave mixing in GaAs at 1.15 micron wavelength.

Liu, Hua-Kuang↗
NASA NTRS1988

Application of advanced computational technology to propulsion CFD

The Internal Fluid Mechanics Division of the NASA Lewis Research Center is combining the key elements of computational fluid dynamics, aerothermodynamic experiments, and advanced computational technology to bring internal computational fluid dynamics (ICFM) to a state of practical application for aerospace propulsion system design. This paper presents an overview of efforts underway at NASA Lewis to advance and apply computational technology to ICFM. These efforts include the use of modern, software engineering principles for code development, the development of an AI-based user-interface for large codes, the establishment of a high-performance, data communications network to link ICFM researchers and facilities, and the application of parallel processing to speed up computationally intensive and/or time-critical ICFM problems. A multistage compressor flow physics program is cited as an example of efforts to use advanced computational technology to enhance a current NASA Lewis ICFM research program.

Szuch, John R.↗