Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data intensive computing”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Modeling the Proton Radiation Belt With Van Allen Probes Relativistic Electron-Proton Telescope Data

An empirical model of the proton radiation belt is constructed from data taken during 2013-2017 by the Relativistic Electron-Proton Telescopes on the Van Allen Probes satellites. The model intensity is a function of time, kinetic energy in the range 18-600 megaelectronvolts, equatorial pitch angle, and L shell of proton guiding centers. Data are selected, on the basis of energy deposits in each of the nine silicon detectors, to reduce background caused by hard proton energy spectra at low L. Instrument response functions are computed by Monte Carlo integration, using simulated proton paths through a simplified structural model, to account for energy loss in shielding material for protons outside the nominal field of view. Overlap of energy channels, their wide angular response, and changing satellite orientation require the model dependencies on all three independent variables be determined simultaneously. This is done by least squares minimization with a customized steepest descent algorithm. Model uncertainty accounts for statistical data error and systematic error in the simulated instrument response. A proton energy spectrum is also computed from data taken during the 8 January 2014 solar event, to illustrate methods for the simpler case of an isotropic and homogeneous model distribution. Radiation belt and solar proton results are compared to intensities computed with a simplified, on-axis response that can provide a good approximation under limited circumstances.

Selesnick, R. S.↗
NASA NTRS2012

CFD Simulations for the Effect of Unsteady Wakes on the Boundary Layer of a Highly Loaded Low-Pressure Turbine Airfoil (L1A)

This report is the third part of a three-part final report of research performed under an NRA cooperative Agreement contract. The first part was published as NASA/CR-2012-217415. The second part was published as NASA/CR-2012-217416. The study of the very high lift low-pressure turbine airfoil L1A in the presence of unsteady wakes was performed computationally and compared against experimental results. The experiments were conducted in a low speed wind tunnel under high (4.9%) and then low (0.6%) freestream turbulence intensity for Reynolds number equal to 25,000 and 50,000. The experimental and computational data have shown that in cases without wakes, the boundary layer separated without reattachment. The CFD was done with LES and URANS utilizing the finite-volume code ANSYS Fluent (ANSYS, Inc.) under the same freestream turbulence and Reynolds number conditions as the experiment but only at a rod to blade spacing of 1. With wakes, separation was largely suppressed, particularly if the wake passing frequency was sufficiently high. This was validated in the 3D CFD efforts by comparing the experimental results for the pressure coefficients and velocity profiles, which were reasonable for all cases examined. The 2D CFD efforts failed to capture the three dimensionality effects of the wake and thus were less consistent with the experimental data. The effect of the freestream turbulence intensity levels also showed a little more consistency with the experimental data at higher intensities when compared with the low intensity cases. Additional cases with higher wake passing frequencies which were not run experimentally were simulated. The results showed that an initial 25% increase from the experimental wake passing greatly reduced the size of the separation bubble, nearly completely suppressing it.

Vinci, Samuel, J.↗
DOE OSTI2018

A simple model for estimating changes in rainfall erosivity caused by variations in rainfall patterns

Highlights: • A simple relationship was developed for predicting daily erosivity in changing climates. • The relationship depends on precipitation amount and maximum 0.5-h intensity I{sub 0.5} . • The relationship was calibrated using measured climate data and had a R{sup 2} of 0.99. • Simulated daily and annual erosivities were predicted with a R{sup 2} of 0.99 using the relationship. • The relationship was tested using I{sub 24}, showing a R{sup 2} of 0.78 for the simulated data. A major challenge when coupling soil loss models with precipitation forecasts from Global Circulation Models (GCMs) is that their time resolutions do not generally agree. Precipitation forecasts from GCM must be scaled down; however, the distribution of the rainfall intensity, which can affect soil loss as much as precipitation amounts, is usually not considered in this process. Therefore, the objective of this study was to develop a statistical equation for computing event-based rainfall erosivity under changing precipitation patterns using the least amount of information possible. For this purpose, an empirical equation for predicting event-based rainfall erosivity was developed using the product of the total precipitation P and the maximum 0.5-h rainfall intensity, I{sub 0.5}. This equation was calibrated using measured precipitation data from 28 sites in Central Chile and then tested with simulated data with different rainfall patterns from the CLIGEN (CLImate GENerator) weather generator. More than 53,000 rainfall events were analyzed, where the equation consistently provided R{sup 2} values of 0.99 for every dataset used, revealing its robustness when used in potential climate change scenarios in the study site. However, because computing I{sub 0.5} requires estimating precipitation at a high time resolution, the relationship was recalibrated and tested using 1 through 24-h maximum rainfall intensities. Using these intensities, the equation provided erosivity estimates with R{sup 2} ranging from 0.78 to 0.99, where better results were obtained as the resolution of the data increased. This study provides the methodology for building and testing the proposed equation and discusses its advantages and limitations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Findings on Subtask 3.1 - Bakken Rich Gas Enhanced Oil Recovery Project

Total in-place oil for the Bakken petroleum system (BPS) (which includes the Bakken and Three Forks Formations) has been estimated to be 600 billion barrels (bbl). However, BPS wells have decline rates as high as 85% over the first 3 years of their lives, and primary recovery factors typically range from 3% to 10% of original oil in place. Given the low initial recovery rates, even small incremental productivity improvements could dramatically increase technically recoverable oil in the BPS. One potential solution is enhanced oil recovery (EOR) using gas injection, such as carbon dioxide (CO2) or hydrocarbon (HC) gases. While commonly used in conventional reservoirs, CO2 EOR in unconventional tight oil reservoirs has been limited to pilot tests. EOR using rich gas (mixture of methane, ethane, and propane) has also been employed in numerous pilots in several unconventional plays and has recently been successfully applied in the Eagle Ford play. If successful, large-scale gas-based EOR in the BPS could dramatically increase oil productivity and recovery factors and extend the life of the play for decades. While CO2 may be a technically suitable working fluid for EOR in the BPS, supplies are limited and costs for using CO2 in EOR pilots are prohibitively high. Meanwhile, produced gas flaring has presented challenges for BPS operators in North Dakota. Analysis conducted by the North Dakota Pipeline Authority indicates that the current gas-gathering infrastructure in North Dakota is insufficient to accommodate all of the associated gas that is produced from the BPS. The geographically isolated location of North Dakota relative to large natural gas markets, combined with suppressed natural gas prices, has made it economically challenging for industry to invest capital in expanding gas-gathering infrastructure in the state. These circumstances led to a research program conducted by the Energy & Environmental Research Center (EERC) in partnership with Liberty Resources Management Company LLC (LR) to examine the potential to use rich gas injection for EOR and mitigate flaring. A rich gas EOR pilot test was designed and executed by LR at its Stomping Horse development area in Williams County, North Dakota. From July 2018 through May 2019, a total of 160 million standard cubic feet (MMscf) of rich produced gas was injected into the BPS using five different wells in a sequential injection strategy. LR’s Leon–Gohrick drill spacing unit (DSU) was used as the test site. Regulatory oversight was provided by the North Dakota Industrial Commission (NDIC). Technical support was provided by the EERC through a series of laboratory, modeling, and field-based activities, and additional post-pilot research activities incorporated learnings from the test, developed new laboratory data, improved fracture modeling methods, and developed machine learning and big data analytics. The results from the Stomping Horse rich gas EOR pilot activities indicate that developing an effective, economical EOR approach for the BPS will require more field tests. Another key lesson learned from the Stomping Horse tests is that detailed pre- and posttest data on reservoir conditions and fluids production are essential. Robust reservoir characterization provides information that is crucial to creating realistic geomodels and conducting valid dynamic simulations of potential EOR scenarios. A detailed understanding of the completions and production history of offset wells is also necessary for valid test result interpretations. This knowledge is essential to designing the operational parameters of injectivity tests and interpreting the results. A conformance control strategy is also essential to success. Laboratory-based examinations of rich gas interactions with reservoir fluids and rocks were conducted, with an emphasis on determining the ability to mobilize oil in the tight reservoir rocks and shales of the BPS. Injection fluid composition was shown to have a positive impact on reducing reservoir oil minimum miscibility pressure (MMP), reducing interfacial tension (IFT), and altering wettability. IFT and contact angle measurements demonstrated that wettability can be altered in the presence of rich gas, suggesting the potential to improve oil recovery. Iterative modeling of surface infrastructure and reservoir performance using data generated by the various project activities was conducted. A geologic model of the Stomping Horse area was built; history-matched oil, gas, and water production was used in simulations of various EOR scenarios. Early programmatic modeling results were used to support LR’s design and operation of the EOR pilot and to provide insight regarding optimization of future commercial-scale BPS EOR design and operations. Post-pilot modeling focused on alternative methods of understanding complex fracture networks and accelerating simulation time. These led to improved simulation run times and provide excellent history-matching results. Several of these iterative models were used as the bases for developing algorithms into machine learning and big data analytics. History matching in reservoir simulation is time-consuming and computer processing-intensive. Machine learning algorithms were created, and an automated history-matching tool was developed. A large set of synthetic reservoir simulations were created to generate well responses (oil, gas, and water production, well bottomhole pressure [BHP], and tracer or propane breakthrough) for a set of EOR operating parameters that included offset well status (open or closed), injectate (rich gas or propane), injection rate, and injection well BHP. A user interface was developed to provide real-time visualization. Machine learning-based models were developed to provide rapid forecasting of well performance given a set of user-defined EOR operating parameters. These predictive models allow the user to modify the offset well status, injection rate, and injection well BHP and rapidly forecast future production performance. The combination of real-time visualization tools with real-time forecasting tools provides a framework for real-time control—operational changes that the EOR site operator can enact (e.g., changing gas injection rates) to affect the observed performance and potentially improve the EOR outcome. There is great reason to be optimistic about the future of EOR in the Bakken. The results of the laboratory studies suggest significant potential for high rates of oil mobilization using produced field gas injection under the right conditions. The results of the lab studies, combined with rigorous statistical analysis of well production data and associated modeling efforts, confirm the notion that fluid mobility within the reservoir is controlled by fractures. As more knowledge is gained about the nature and distribution of fracture networks in the Bakken, the industry will be in a better position to predict and, ultimately, influence fluid mobility. New field tests are necessary to develop a more complete understanding of those conditions. Thoughtful and creatively engineered field tests within a well-characterized geologic setting will yield the fundamental knowledge needed to take Bakken oil production to the next level. This subtask was cofunded through the EERC–U.S. Department of Energy Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by the North Dakota Industrial Commission’s Oil and Gas Research Program and Computer Modelling Group.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2019

Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

With the development of data-driven techniques, building energy prediction on a district level has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m 2 , compared to 12.05% and 0.63 kWh/m 2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m 2 for the support vector regression model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

pnnl/metagenomic_analysis

Metagenomic analysis is a computationally intensive task, this problem is compounded when many hundreds of samples need to be analyzed. We needed a program that would process the metagenomic sequencing data, manage computing resources, and combine the results into a unified output. We developed a script that prepares raw metagenomic sequencing data for analysis, configures analysis parameters, launches analysis jobs, keeps track of analysis progress, and combines analysis output. The analysis itself itself is done by the functionality provided by mothur (https://www.mothur.org/).

Avila, Andrew↗
NASA NTRS1970

Elimination of redundancy in telemetered data

Procedure for estimating the intensity of a Poisson process can be readily programmed for a digital computer, and does not require an a priori probability space. Reduction of the sampling rate is possible without sacrificing significant information.

Vu-Son, C.↗
DOE OSTI2014

A Study of Successive Over-relaxation Method Parallelization over Modern HPC Languages

Successive over-relaxation (SOR) is a computationally intensive, yet extremely important iterative solver for solving linear systems. Due to recent trends of exponential growth in amount of data generated and increasing problem sizes, serial platforms have proved to be insucient in providing the required computational power. In this paper, we present parallel implementations of red-black SOR method using three modern programming languages namely Chapel, D and Go. We employ SOR method for solving 2D steady-state heat conduction problem. We discuss the optimizations incorporated and the features of these languages which are crucial for improving the program performance. Experiments have been performed using 2, 4, and 8 threads and performance results are compared with serial execution. The analysis of results provides important insights into working of SOR method.

Mittal, Sparsh↗
DOE OSTI2017

High-Performance Key-Value Store on OpenSHMEM

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Locally adaptive artificial viscosity strategies for Lagrangian hydrodynamics

To accurately model inviscid flow with shock waves using staggered-grid Lagrangian hydrodynamics, artificial viscosity is introduced to convert kinetic energy into internal energy, thereby providing a mechanism to generate the required entropy increase across shocks. In this paper, we propose a new method for constructing an adaptive, artificial viscosity in the context of one-dimensional, staggered-grid Lagrangian hydrodynamics. Our adaptive, artificial viscosity is defined in terms of two parameters that depend on density locally in a neighborhood around a shock, and hence, vary cell-by-cell. Our methodology is based on building a reference set of pre-computed optimal, globally constant artificial viscosity coefficients for a family of isolated shock test problems. For arbitrary flows, the evaluation of the unknown coefficients is automated by first estimating shock intensity locally, and second computing the corresponding adaptive parameter value via interpolation over the data from the pre-computed reference set of optimal, globally constant coefficient values. To illustrate the performance of our new approach, we compare our results against two existing methods. The first method is a limiter-based approach, which relies on estimating velocity gradients of the flow, and the second method is utilized in several commercial codes. Finally, we demonstrate that our new adaptive methodology produces more accurate results for a variety of tests with propagating shock waves, as well as for the aforementioned family of isolated shock problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing al-location policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

Ranganath, Kiran↗
DOE OSTI2020

Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated models

One of the most promising devices for realizing power production through nuclear fusion is the tokamak. To maximize performance, it is preferable that tokamak reactors achieve advanced operating scenarios characterized by good plasma confinement, improved magnetohydrodynamic (MHD) stability, and a largely non-inductively driven plasma current. Such scenarios could enable steady-state reactor operation with high \emph{fusion gain} --- the ratio of produced fusion power to the external power provided through the plasma boundary. Precise and robust control of the evolution of the plasma boundary shape as well as the spatial distribution of the plasma current, density, temperature, and rotation will be essential to achieving and maintaining such scenarios. The complexity of the evolution of tokamak plasmas, arising due to nonlinearities and coupling between various parameters, motivates the use of model-based control algorithms that can account for the system dynamics. In this work, a learning-based accelerated model trained on data from the National Spherical Torus Experiment Upgrade (NSTX-U) is employed to develop planning and control strategies for regulating the density and temperature profile evolution around desired trajectories. The proposed model combines empirical scaling laws developed across multiple devices with neural networks trained on empirical data from NSTX-U and a database of first-principles-based computationally intensive simulations. The reduced execution time of the accelerated model will enable practical application of optimization algorithms and reinforcement learning approaches for scenario planning and control development. An initial demonstration of applying optimization approaches to the learning-based model is presented, including a strategy for mitigating the effect of leaving the finite validity range of the accelerated model. The approach shows promise for actuator planning between experiments and in real-time.

machine learning↗
NASA NTRS1997

Women in Flight Research at NASA Dryden Flight Research Center from 1946 to 1995: Monographs in Aerospace History - Number 6

This monograph discusses the working and living environment of women involved with flight research at NASA Dryden Flight Research Center during the late 1940s and early 1950s. The women engineers, their work and the airplanes they worked on from 1960 to December 1995 are highlighted. The labor intensive data gathering and analysis procedures and instrumentation used before the age of digital computers are explained by showing and describing typical instrumentation found on the X-series aircraft from the X-1 through the X-15. The data reduction technique used to obtain the Mach number position error curve for the X-1 aircraft and which documents the historic first flight to exceed the speed of sound is described and a Mach number and altitude plot from an X-15 flight is shown.

Powers, Sheryll Goecke↗
NASA NTRS1999

Technology Assessment of High Capacity Data Storage Systems: Can We Avoid a Data Survivability Crisis?

In a recent address at the California Science Center in Los Angeles, Vice President Al Gore articulated a Digital Earth Vision. That vision spoke to developing a multi-resolution, three-dimensional visual representation of the planet into which we can roam and zoom into vast quantities of embedded geo-referenced data. The vision was not limited to moving through space, but also allowing travel over a time-line, which can be set for days, years, centuries, or even geological epochs. A working group of Federal Agencies, developing a coordinated program to implement the Vice President's vision, developed the definition of the Digital Earth as a visual representation of our planet that enables a person to explore and interact with the vast amounts of natural and cultural geo-referenced information gathered about the Earth. One of the challenges identified by the agencies was whether the technology existed that would be available to permanently store and deliver all the digital data that enterprises might want to save for decades and centuries. Satellite digital data is growing by Moore's Law as is the growth of computer generated data. Similarly, the density of digital storage media in our information-intensive society is also increasing by a factor of four every three years. The technological bottleneck is that the bandwidth for transferring data is only growing at a factor of four every nine years. This implies that the migration of data to viable long-term storage is growing more slowly. The implication is that older data stored on increasingly obsolete media are at considerable risk if they cannot be continuously migrated to media with longer life times. Another problem occurs when the software and hardware systems for which the media were designed are no longer serviced by their manufacturers. Many instances exist where support for these systems are phased out after mergers or even in going out of business. In addition, survivability of older media can suffer from physical breakdown of components (e.g. tapes simply lose their magnetic properties after a long time in storage). As a result, a potential data survivability crisis is emerging. The scale of the crisis is comparable to that facing the Social Security System. Sometime in one or two decades, the exponential growth of data will become so great that many enterprises will not be able to migrate through their data to more permanent media during the lifetime of the media on which it resides. This will result in significant losses of data and their resultant impacts. To avoid this crisis, we need to plan and devote greater financial and intellectual resources are needed for the development and refinement of new storage media and migration technologies in order to preserve all data any organization determines worth saving permanently. This talk will explore technological solutions and suggested recommendations to address this technological data crisis.

Halem, Milton↗
NASA NTRS1991

Dynamics Explorer guest investigator

A data base of satellite particle, electric field, image, and plasma data was used to determine correlations between the fields and the particle auroral boundaries. A data base of 8 days of excellent coverage from all instruments was completed. The geomagnetic conditions associated with each of the selected data periods, the number of UV image passes per study day that were obtained, and the total number of UV images for each day are given in tabular form. For each of the days listed in Table 1, both Vector Electric Field Instrument (VEFI) electric potential data and LAPI integrated particle energy fluxes were obtained. On the average, between 8 and 11 passes of useful data per day were obtained. These data are displayed in a format such that either the statistical electric field model potential or the statistical precipitation energy flux could be superimposed. The Heppner and Maynard (1987) and Hardy et al. (1987) models were used for the electric potential and precipitation, respectively. In addition, the auroral image intensity along the Dynamics Explorer-2 satellite pass could be computed and plotted along with the LAPI precipitation data and Hardy et al. (1987) values.

Sojka, Jan J.↗
NASA NTRSDate not supplied

Computing rovibrational, vibrational, and cascade emission spectra for comparison to astronomical observations

Over the last several years, our group has been involved in developing approaches to compute highly accurate spectroscopic constants and vibrational frequencies for small transient molecules that may be used in the interpretation and assignment of high-resolution laboratory experiments as well as high-resolution astronomical spectra. Additionally, we have used the computed spectroscopic constants to simulate purely rotational and rovibrational spectra so that these may be compared directly with high-resolution astronomical observations, and we have worked on developing approaches that can be applied to much larger molecules, such as polycyclic aromatic hydrocarbon (PAH) molecules, where we can explicitly determine anharmonic corrections to vibrational frequencies as well as take into account intensity sharing due to resonances. In addition, for PAH molecules we have used the anharmonic data to compute a library of temperature-dependent anharmonic vibrational spectra, with proper inclusion of polyad resonances, which can then be used to model the cascade emission spectra of PAH molecules – the type of spectra directly observed by astronomers. Another part of our work in spectroscopic signatures involves computing highly accurate line lists for common molecules, such as CO2, SO2, and NH3, which occur in many astrophysical environments, including the atmospheres of exoplanents, and often need to have their lines identified in high-resolution observations in order to determine which lines are due to other molecules. In order to characterize the atmospheres of hot exoplanets, these line lists need to be very accurate and extend to very high energies. I will discuss our latest work in these areas of astrochemical spectroscopy research.

Timothy J Lee↗