Search NASA⌕ Search

SEARCH · Search NASA

Results for “Computer Science - Operating Systems”

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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Basic Research Needs for Microelectronics

Since the invention of the integrated circuit in 1958, advances in microelectronics have followed Moore’s law and other scaling laws. These have led to over a billion-fold increase in the number of transistors on a chip. The Department of Energy’s Office of Science (DOE-SC) programs have always been at the cutting edge of microelectronics, making major contributions to the scientific understanding, materials, and advanced instrumentation that enabled innovations to promote scaling. This has driven transformative advances in microelectronics for the challenging demands of DOE’s high performance computing and science facilities. Now, strong evidence exists that scaling is approaching its physical and economic limits, and yet, the growth of data-centric computing and sensor networks is redefining computing workloads and microelectronics needs. In addition, greatly improved microelectronics are needed for the nation’s electricity grid if it is to be energy-efficient, resilient to natural phenomena and intentional attack, and agile in adapting to fluctuations in demand and power generation. Sustained and rapid progress in microelectronics science and technology from millivolt to megavolt scales is thus essential if we are to continue pushing the boundaries of science within DOE, and more significantly, to continue to lead the global information and power technology revolution. In October 2018, DOE-SC convened a workshop to address Basic Research Needs for Microelectronics by a “co-design” approach. Participants focused on scientific issues associated with advanced microelectronics technologies for applications relevant to the DOE mission, including computing, power grid management, and science facility workloads. (Topics of direct relevance to quantum information science and quantum computing were outside the scope of this workshop.) The workshop was organized around microelectronics needs for three areas: 1) future DOE-SC facilities, 2) high performance computing beyond exascale (a billion billion calculations per second), and 3) power control, conversion, and detection for a modernized electrical grid and related high power applications. Panels were formed around each of these areas, with a fourth panel formed to address Crosscutting Research. Workshop participants were asked to focus on a co-design innovation ecosystem in which materials, chemistries, devices, systems, architectures, algorithms, and software are researched and developed in a closely integrated fashion, with feedback and interdisciplinary collaboration at each interface in this microelectronics technology hierarchy. The panels identified the five Priority Research Directions on the following page.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

Abstract Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Reliable density functional calculations for the electronic structure of thermoelectric material ZnSb

In this paper, we present the results of systematic test calculations for the electronic structure of thermoelectric material ZnSb using a first-principles full-potential all electron computational method. We used a linear combination of atomic orbitals (LACO) formalism, based on density functional theory (DFT). The exchange-correlation interaction potential of the many electron system was described by using a generalized gradient approximation (GGA). We compared the calculated indirect and direct band gaps as well as the effective masses of holes and electrons in ZnSb with experimental measurement results. The calculated indirect band gap of ZnSb is 0.56 eV, which agrees very well with the experimentally measured values of 0.50 eV ~ 0.61 eV. The calculated direct band gap at X point is 0.89 eV. The calculated effective masses of electrons and holes in ZnSb also agree with experimental data. The systematical test calculations as well as the comparisons of the calculated results with experimental measurements show that the obtained electronic structure of ZnSb would be reliable. We did not observe a major deficiency of the first-principles DFT calculation for the electronic structure of ZnSb, using full-potential all electron LACO method. The reported electronic structure of single crystal ZnSb from this work may provide a fundamental knowledge base for further research and applications for this important thermoelectric material.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterizing Machine Learning I/O Workloads on Leadership Scale HPC Systems

High performance computing (HPC) is no longer solely limited to traditional workloads such as simulation and modeling. With the increase in the popularity of machine learning (ML) and deep learning (DL) technologies, we are observing that an increasing number of HPC users are incorporating ML methods into their workflow and scientific discovery processes, across a wide spectrum of science domains such as biology, earth science, and physics. This gives rise to a diverse set of I/O patterns than the traditional checkpoint/restart-based HPC I/O behavior. The details of the I/O characteristics of such ML I/O workloads have not been studied extensively for large-scale leadership HPC systems. This paper aims to fill that gap by providing an in-depth analysis to gain an understanding of the I/O behavior of ML I/O workloads using darshan - an I/O characterization tool designed for lightweight tracing and profiling. We study the darshan logs of more than 23, 000 HPC ML I/O jobs over a time period of one year running on Summit - the second-fastest supercomputer in the world. This paper provides a systematic I/O characterization of ML I/O jobs running on a leadership scale supercomputer to understand how the I/O behavior differs across science domains and the scale of workloads, and analyze the usage of parallel file system and burst buffer by ML I/O workloads.

Paul, Arnab↗
DOE OSTI2018

Inverse Thermal Analysis of Ti-6Al-4V Friction Stir Welds Using Numerical-Analytical Basis Functions with Pseudo-Advection

Inverse thermal analysis of Ti-6Al-4V friction stir welds is presented that demonstrates application of a methodology using numerical-analytical basis functions and temperature-field constraint conditions. This analysis provides parametric representation of friction-stir-weld temperature histories that can be adopted as input data to computational procedures for prediction of solid-state phase transformations and mechanical response. These parameterized temperature histories can be used for inverse thermal analysis of friction stir welds having process conditions similar those considered here. Case studies are presented for inverse thermal analysis of friction stir welds that use three-dimensional constraint conditions on calculated temperature fields, which are associated with experimentally measured transformation boundaries and weld-stir-zone cross sections.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting electronic structures at any length scale with machine learning

The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An autoencoder compression approach for accelerating large-scale inverse problems

Abstract Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.

Mathematics↗
DOE OSTI2021

Characterization of growth and development of sorghum genotypes with differential susceptibility to Striga hermonthica

Two sorghum varieties, Shanqui Red (SQR) and SRN39, have distinct levels of susceptibility to the parasitic weed Striga hermonthica, which have been attributed to different strigolactone composition within their root exudates. Root exudates of the Striga-susceptible variety Shanqui Red (SQR) contain primarily 5-deoxystrigol, which has a high efficiency for inducing Striga germination. SRN39 roots primarily exude orobanchol, leading to reduced Striga germination and making this variety resistant to Striga. The structural diversity in exuded strigolactones is determined by a polymorphism in the LOW GERMINATION STIMULANT 1 (LGS1) locus. Yet, the genetic diversity between SQR and SRN39 is broad and has not been addressed in terms of growth and development. Here, we demonstrate additional differences between SQR and SRN39 by phenotypic and molecular characterization. A suite of genes related to metabolism was differentially expressed between SQR and SRN39. Increased levels of gibberellin precursors in SRN39 were accompanied by slower growth rate and developmental delay and we observed an overall increased SRN39 biomass. The slow-down in growth and differences in transcriptome profiles of SRN39 were strongly associated with plant age. Additionally, enhanced lateral root growth was observed in SRN39 and three additional genotypes exuding primarily orobanchol. In summary, we demonstrate that the differences between SQR and SRN39 reach further than the changes in strigolactone profile in the root exudate and translate into alterations in growth and development.

09 BIOMASS FUELS↗
DOE OSTI2015

Optical delay encoding for fast timing and detector signal multiplexing in PET

Purpose: The large number of detector channels in modern positron emission tomography (PET) scanners poses a challenge in terms of readout electronics complexity. Multiplexing schemes are typically implemented to reduce the number of physical readout channels, but often result in performance degradation. Novel methods of multiplexing in PET must be developed to avoid this data degradation. The preservation of fast timing information is especially important for time-of-flight PET. Methods: A new multiplexing scheme based on encoding detector interaction events with a series of extremely fast overlapping optical pulses with precise delays is demonstrated in this work. Encoding events in this way potentially allows many detector channels to be simultaneously encoded onto a single optical fiber that is then read out by a single digitizer. A two channel silicon photomultiplier-based prototype utilizing this optical delay encoding technique along with dual threshold time-over-threshold is demonstrated. Results: The optical encoding and multiplexing prototype achieves a coincidence time resolution of 160 ps full width at half maximum (FWHM) and an energy resolution of 13.1% FWHM at 511 keV with 3 × 3 × 5 mm{sup 3} LYSO crystals. All interaction information for both detectors, including timing, energy, and channel identification, is encoded onto a single optical fiber with little degradation. Conclusions: Optical delay encoding and multiplexing technology could lead to time-of-flight PET scanners with fewer readout channels and simplified data acquisition systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Endoluminal ultrasound applicators for MR-guided thermal ablation of pancreatic tumors: Preliminary design and evaluation in a porcine pancreas model

Purpose: Endoluminal ultrasound may serve as a minimally invasive option for delivering thermal ablation to pancreatic tumors adjacent to the stomach or duodenum. The objective of this study was to explore the basic feasibility of this treatment strategy through the design, characterization, and evaluation of proof-of-concept endoluminal ultrasound applicators capable of placement in the gastrointestinal (GI) lumen for volumetric pancreas ablation under MR guidance. Methods: Two variants of the endoluminal applicator, each containing a distinct array of two independently powered transducers (10 × 10 mm 3.2 MHz planar; or 8 × 10 × 20 mm radius of curvature 3.3 MHz curvilinear geometries) at the distal end of a meter long flexible catheter assembly, were designed and fabricated. Transducers and circulatory water flow for acoustic coupling and luminal cooling were contained by a low-profile polyester balloon covering the transducer assembly fixture. Each applicator incorporated miniature spiral MR coils and mechanical features (guiding tips and hinges) to facilitate tracking and insertion through the GI tract under MRI guidance. Acoustic characterization of each device was performed using radiation force balance and hydrophone measurements. Device delivery into the upper GI tract, adjacent to the pancreas, and heating characteristics for treatment of pancreatic tissue were evaluated in MR-guided ex vivo and in vivo porcine experiments. MR guidance was utilized for anatomical target identification, tracking/positioning of the applicator, and MR temperature imaging (MRTI) for PRF-based multislice thermometry, implemented in the real-time RTHawk software environment. Results: Force balance and hydrophone measurements indicated efficiencies of 48.8% and 47.8% and −3 dB intensity beam-widths of 3.2 and 1.2 mm for the planar and curvilinear transducers, respectively. Ex vivo studies on whole-porcine carcasses revealed capabilities of producing ablative temperature rise (ΔT > 15 °C) contours in pancreatic tissue 4–40 mm long and 4–28 mm wide for the planar transducer applicator (1–13 min sonication duration, ∼4 W/cm{sup 2} applied acoustic intensity). Curvilinear transducers produced more selective heating, with a narrower ΔT > 15 °C contour length and width of up to 1–24 mm and 2–7 mm, respectively (1–7 min sonication duration, ∼4 W/cm{sup 2} applied acoustic intensity). Active tracking of the miniature spiral coils was achieved using a Hadamard encoding tracking sequence, enabling real-time determination of each coil’s coordinates and automated prescription of imaging planes for thermometry. In vivo MRTI-guided heating trials in three pigs demonstrated capability of ∼20 °C temperature elevation in pancreatic tissue at 2 cm depths from the applicator, with 5–7 W/cm{sup 2} applied intensity and 6–16 min sonication duration. Dimensions of thermal lesions in the pancreas ranged from 12 to 28 mm, 3 to 10 mm, and 5 to 10 mm in length, width, and depth, respectively, as verified through histological analysis of tissue sections. Multiple-baseline reconstruction and respiratory-gated acquisition were demonstrated to be effective strategies in suppressing motion artifacts for clear evolution of temperature profiles during MRTI in the in vivo studies. Conclusions: This study demonstrates the technical feasibility of generating volumetric ablation in pancreatic tissue using endoluminal ultrasound applicators positioned in the stomach lumen. MR guidance facilitates target identification, device tracking/positioning, and treatment monitoring through real-time multislice PRF-based thermometry.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Energy Efficient Mobility Systems 2018 Annual Progress Report

Energy Efficient Mobility Systems (EEMS) Program activities during FY 2018 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

FY2019 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2019 focused on analytical research to understand the impacts that new mobility technologies and services will have at the vehicle, traveler, and overall transportation system-level. This research included the development of vehicle and transportation system simulation models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results for projects within four of the five EEMS activity areas: (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) High Performance Computing and Big Data Solutions for Mobility Data, (3) Advanced R&D Projects conducted by industry and academia, and (4) Core Modeling, Simulation, and Evaluation, Similarly, the remaining EEMS activity area – (5) Living Labs (managed under VTO’s Technology Integration Program). Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy’s (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed a large number of instruments to a region nearby the Sierra de Córdobas mountains in Argentina as part of the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign (1). During this campaign, four radars were installed at a site outside of Villa Yacanto as shown in Figure 1. As part of a post-campaign effort to improve the usability of these data, a significant activity was undertaken towards the calibration, correction, and improvement of the data quality of these radar datastreams. This process in ARM nomenclature is referred to as generating a “b1” datastream. While these “b1” standards may imply different corrections or standards for various ARM instruments, for radars it refers to a datastream that has been calibrated (and cross-calibrated), including a serious effort to deliver the highest-quality (well-characterized) data possible. This report details (i) the status/quality of the original “a1” (raw) data, (ii) the corrections and calibrations that are applied to generate these b1 datastreams, (iii) the details of the applied algorithms and how radar offset/calibration numbers were determined for the eventual corrections, and (iv) the new and flexible plug-in-based processing system designed during CACTI for radar b1 activities (current, future) that interfaces with ARM’s Data Integrator (ADI) and high-performance computing (HPC) system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Spectral Transfer Learning Using Information Geometry for a User-Independent Brain-Computer Interface

Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry,STIG),which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects. The STIG method is validated in both off-line and real-time feedback analysis during a rapid serial visual presentation task (RSVP). For detection of single-trial, event-related potentials (ERPs), the proposed method can significantly outperform existing calibration-free techniques as well as out perform traditional within-subject calibration techniques when limited data is available. Here, this method demonstrates that unsupervised transfer learning for single-trial detection in ERP-based BCIs can be achieved without the requirement of costly training data, representing a step-forward in the overall goal of achieving a practical user-independent BCI system.

60 APPLIED LIFE SCIENCES↗