Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data needs”

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 577 records · Page 32

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

59 BASIC BIOLOGICAL SCIENCES↗

CLAS12 remote data-stream processing using ERSAP framework

Implementing a physics data processing application is relatively straightforward with the use of current containerization technologies and container image runtime services, which are prevalent in most high-performance computing (HPC) environments. However, the process is complicated by the challenges associated with data provisioning and migration, impacting the ease of workflow migration and deployment. Transitioning from traditional file-based batch processing to data-stream processing workflows is suggested as a method to streamline these workflows. This transition not only simplifies file provisioning and migration but also significantly reduces the necessity for extensive disk space. Data-stream processing is particularly effective for real-time processing during data acquisition, thereby enhancing data quality assurance. This paper introduces the integration of the JLAB CLAS12 event reconstruction application within the ERSAP data-stream processing framework that facilitates the execution of streaming event reconstruction at a remote data center and enables the return streaming of reconstructed events to JLAB while circumventing the need for temporary data storage throughout the process.

Gyurjyan, Vardan↗

Applications of Federated Learning in Semiconductor Manufacturing [Poster]

As semiconductor manufacturers explore advanced data analytics and modeling techniques and data hungry machine learning models increase in popularity due to their accuracy in solving generalized problems and ability to learn complex relationships, federated learning emerges as a privacy preserving machine learning technique for preserving data privacy and ensuring intellectual property protection. Federated Learning is a machine learning technique focused on training models using distributed data that never needs to be centrally stored, allowing the use of advanced machine learning techniques without compromising data privacy, and in the semiconductor manufacturing industry advanced machine learning techniques can reduce cost and time, but maintaining data privacy is essential to maintaining a competitive advantage. This paper systematically reviews existing literature on applications of federated learning in the semiconductor manufacturing industry with a focus on identifying common themes, algorithms, and gaps within the literature to drive future research directions. The findings reveal five key themes, including improvements in quality assurance, virtual models, privacy preservation, reliable data practices, and emerging trends and developments. By identifying key themes in literature on federated learning and semiconductor manufacturing and analyzing gaps and discussed methodologies, this study highlights several potential future research directions to expand the application of federated learning techniques in the semiconductor manufacturing domain.

42 ENGINEERING↗

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗

Evolving radon diffusion through earthen barriers at uranium waste disposal sites

Field measurements of Rn-222 fluxes from the tops and bottoms of compacted clay radon barriers were used to calculate effective Rn diffusion coefficients (D Rn ) at four uranium waste disposal sites in the western United States to assess cover performance after more than 20 years of service. Values of D Rn ranged from 7.4 × 10 -7 to 6.0 × 10 -9 m 2 /s, averaging 1.42 × 10 -7 . Water saturation (S W ) from soil cores indicated that there was relatively little control of D Rn by S W , especially at higher moisture levels, in contrast to estimates from most steady-state diffusion models. Further, this is attributed to preferential pathways intrinsic to construction of the barriers or to natural process that have developed over time including desiccation cracks, root channels, and insect burrows in the engineered earthen barriers. A modification to some models in which fast and slow pathway D Rn values are partitioned appears to give a good representation of the data; 4% of the fast pathway was needed to fit the data regression. For locations with high S w and highest D Rn (and fluxes) at each site, the proportion of fast pathway ranged from 1.7% to 34%, but for many locations with lower fluxes, little if any fast pathway was needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Autonomous Radiation Mapping and Quantification using an Unmanned Ground Vehicle. Part I - Environment Mapping

When characterizing facilities, advanced autonomous systems are safe, efficient, and cost-effective tools, which can safely deploy state-of-the-art instrumentation without exposing workers to radiation risks. One main focus of the research is the visual mapping of the environment.This paper presents a framework for the mapping aspect that utilizes lidar and SLAM technologies coupled with a monocular camera to create a point cloud of the environment. Upon completion, the present research work aims to provide an autonomous mobile robot the capability to localize itself and aid the user with visualizing the radiation data. Challenges: C++ node needs to properly synchronize image and point cloud data. 3D lidar has resolution of ± 3 cm. Proper camera calibration needed. Transforms need to be precise for proper alignment of image and point-cloud. Results: Voxelated point clouds. Accurate colors from calibrated cameras including details smaller than lidar resolution. Developed Robotics Operating System (ROS) package for ease of transfer to other robot platforms.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Research challenges and directions in HVAC fault prevalence

This study provides a review of the current state of knowledge, gaps, and potential value in research on the prevalence of faults in commercial buildings. Two separate efforts were made in this study: (1) we performed a literature review to determine the extent of currently available fault prevalence data for heating, ventilation, and air-conditioning (HVAC) systems, and (2) we conducted dozens of interviews with subject matter experts and stakeholders to determine the HVAC fault data that would be of greatest value. Through the literature review and interviews, we discovered unmet needs for empirical data on the prevalence of faults at the desired level of granularity, consistency, and scale; this lack of data leads us to recommend future work studying commercial buildings’ HVAC fault prevalence, with robust fault taxonomy and a variety of meaningful fault prevalence metrics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Hubble WFC3 Spectroscopy of the Habitable-zone Super-Earth LHS 1140 b

Atmospheric characterization of temperate, rocky planets is the holy grail of exoplanet studies. These worlds are at the limits of our capabilities with current instrumentation in transmission spectroscopy and challenge our state-of-the-art statistical techniques. Here we present the transmission spectrum of the temperate super-Earth LHS 1140b using the Hubble Space Telescope (HST). The Wide Field Camera 3 (WFC3) G141 grism data of this habitable-zone (T {sub eq} = 235 K) super-Earth (R = 1.7 R {sub ⊕}) shows tentative evidence of water. However, the signal-to-noise ratio, and thus the significance of the detection, is low and stellar contamination models can cause modulation over the spectral band probed. We attempt to correct for contamination using these models and find that, while many still lead to evidence for water, some could provide reasonable fits to the data without the need for molecular absorption although most of these cause features in the visible ground-based data which are nonphysical. Future observations with the James Webb Space Telescope would be capable of confirming, or refuting, this atmospheric detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Expanding Benchmarks for Nuclear Data Validation [Slides]

The presentation discusses benchmarking in comparison to experimental truth, the integral experiments that were performed, and that benchmarks are evaluated experiments. It also discusses validation testing, and that the ultimate goal of the validation end product is to improve evaluated nuclear data for applications It does state that validation highlights errors in the Nuclear Data Pipeline that affect applications, for example: Missing Cd Capture Gammas in ENDF/B-VII.1. It acknowledges that additional types of experiments are needed to test data used in applications, and that validation experiments do not have to be complicated and expensive. The presentation also discusses different types of measurements including activation foil and fission chamber measurements, reactor kinetics measurements, and subcritical measurements. The presentation concludes with an experiment summary and states that all applications need validation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput—83% of theoretical peak—on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.

Data refactoring↗

Data reduction for low energy nuclear physics experiments using data frames

Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Codebase release 2.0 for sauce

Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

Marshall, Caleb (ORCID:0000000211942920)↗

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

54 ENVIRONMENTAL SCIENCES↗

Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs

Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: ideally, methods that can scale data volumes adaptively so as to enable negotiation of performance and fidelity tradeoffs in different situations. Multigrid-based hierarchical data representations hold promise as a solution to this problem, allowing for flexible conversion between different fidelities so that, for example, data can be created at high fidelity and then transferred or stored at lower fidelity via logically simple and mathematically sound operations. However, the effective use of such representations has been hindered until now by the relatively high costs of creating, accessing, reducing, and otherwise operating on such representations. We describe here highly optimized data refactoring kernels for GPU accelerators that enable efficient creation and manipulation of data in multigrid-based hierarchical forms. We demonstrate that our optimized design can achieve up to 250 TB/s aggregated data refactoring throughput—83% of theoretical peak—on 1024 nodes of the Summit supercomputer. We showcase our optimized design by applying it to a large-scale scientific visualization workflow and the MGARD lossy compression software.

Chen, Jieyang↗

Characterization of D-T Generator Neutron Flux Spectrum for Cyclic Neutron Activation Analysis Experiments

Improving nuclear data for short-lived fission product yields will further our fundamental understanding of fission, which is needed across various scientific fields and applications. One method of attaining the needed product yield data is through cyclic neutron activation, which allows a target to be irradiated in a neutron environment and then transported for counting of the radionuclides produced, typically via g spectroscopy. Recently, such a system has been constructed and commissioned at Pacific Northwest National Laboratory. Targets are shuttled between the head of a D-T neutron generator and a counting station with a transit time of 2 seconds. As part of the characterization of this system, the neutron flux was studied using two activation targets. The neutron flux from the deuterium-tritium fusion generator was determined to be 9.95(33) x10^8 n/cm2-s with a peak energy of 14.9 MeV and a spread of approximately 4 decades between the epithermal and 14 MeV peak group flux.

cyclic neutron activation, gamma-ray spectroscopy,↗

Seismology in the Cloud: A New Streaming Workflow

Data-intensive research in seismology is experiencing a recent boom, driven in part by large volumes of available data and advances in the growing field of data science. However, there are significant barriers to processing large data volumes, such as long retrieval times from data repositories, complex data management, and limited computational resources. New tools and platforms have reduced the barriers to entry for scientific cluster computing, including the maturation of the commercial cloud as an accessible instrument for research. Here, we build a customized research cluster in the cloud to test a new workflow for large-scale seismic analysis, in which data are processed as a stream (retrieved on-the-fly and acted upon without storing), with data from the Incorporated Research Institutions for Seismology Data Management Center. We use this workflow to deploy a spectral peak detection algorithm over 5.6 TB of compressed continuous seismic data from 2074 stations of the USArray Transportable Array EarthScope network. Using a 50-node cluster in the cloud, we completed the noise survey in 80 hr, with an average data throughput of 1.7 GB per minute. By varying cluster sizes, we find the scaling of our analysis to be sublinear, due to a combination of algorithmic limitations and data center response times. The cloud-based streaming workflow represents an order-of-magnitude increase in acquisition and processing speed compared to a traditional download-store-process workflow, and offers the additional benefits of employing a flexible, accessible, and widely used computing architecture. It is limited, however, due to its reliance on Internet transfer speeds and data center service capacity, and may not work well for repeated analyses or those for which even higher data throughputs are needed. These research applications will require a new class of cloud-native approaches in which both data and analysis are in the cloud.

58 GEOSCIENCES↗

Integral Experiment Final Design for Thermal/Epithermal eXperiments (TEX) using Highly Enriched Uranium with Polyethylene at Low Temperature (IER-479 CED-2 Report)

The goal of IER-479 is to design uranium critical experiments that can be used to validate low temperature cross sections and criticality safety analyses over multiple neutron energy regimes. Currently, there are no benchmarks in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook at temperatures lower than room temperature (International Criticality Safety Benchmark Evaluation Project Handbook, 2019). However, there are many needs for validation of criticality safety analysis at lower temperatures, including meeting transportation requirements and operations conducted outside or in unheated facilities. Additionally, NCSP has funded North Carolina State (NCSU) to generate new thermal scattering laws, including at lower temperatures, and the lack of integral benchmarks impedes data testing of these new cross sections. To address these needs, this report will present a critical experiment design covering various fission energy regimes with a goal temperature of -40°C (-40°F), which is based on the lower bound of expected non-cryogenic operational temperatures. The goal of the U.S. Nuclear Criticality Safety Program’s (NCSP) Thermal/Epithermal eXperiments (TEX) is to design and conduct new critical experiments to address high priority nuclear data needs from the nuclear criticality safety and nuclear data communities. The TEX program includes two series of baseline experimental configurations, one based on plutonium fuel (plutonium-aluminum Zero Power Physics Reactor (ZPPR) plates) and the other based on uranium fuel (highly enriched uranium (HEU) plates), that are moderated with varying thickness of polyethylene to create assemblies which span the thermal, intermediate, and fast fission energy regimes. The configurations are designed to be easily modified (for example, to add diluent materials of interest) to allow for efficient generation of additional benchmark configurations and allow for added nuclear data testing utility when comparing modified configurations to baseline configurations. The goal of IER-479 is to use the TEX-HEU concept (stack of HEU plates and polyethylene moderators) to design a critical experiment that can be used to validate low temperature cross sections and criticality safety analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Investigating the Future of Scientific Data Search [Slides]

Searching for usable, actionable, data in a trustworthy manner is a challenge across scientific communities. Artificial Intelligence (AI) and Machine Learning (ML) techniques may be leveraged to increase the utility of scientific data by: Demystify unstructured data to aid curation & sharing Surfacing hard to find datasets. User Experience (UX) Research can help uncover scientists needs & challenges finding data and using AI/ML enabled tools.

97 MATHEMATICS AND COMPUTING↗