SEARCH · Engineering Papers

Results for “data analytics”

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.

SourceResearch connectionFrom yearThrough year

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

At least 523 records · Page 29

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Data Fusion to Enhance Quality Control and Analysis with Instruments at the Marine and Coastal Research Laboratory

Deploying environmental monitoring instruments in the marine environment can be challenging, facing challenges around device survivability, biofouling and corrosion, and consistent data collection. This project explores the use of data fusion – the process of integrating multiple data sources to produce more consistent, accurate, and useful information – to build a consistent long-term monitoring system at the Marine and Coastal Research Laboratory (MCRL) in Sequim, Washington. Unused instruments that had been acquired from past projects were inventoried and deployments planned on the MCRL pier and floating dock. A total of 8 instruments were deployed including a tide gauge, hydrophone, acoustic Doppler current profiler (ADCP), photosynthetically active radiation (PAR) sensors, meteorological station, and three water quality sensors. Deployments were planned to be well-protected around the pier structure and a maintenance schedule was created for cleaning and recalibration. An automated data pipeline was created to aggregate data on edge computers that push data to Amazon Web Services (AWS) cloud storage every 15 minutes, performing automated quality control and data transformations using the Time Series Data Analytical Toolkit (TSDAT). Continued efforts are underway to maintain this system into the future, take a data-driven approach to maintenance scheduling, improve the reliability of the system, and share the data with a variety of end-users.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

DataFed

There is a need to easily and uniformly capture, access, enrich, organize, search, and collaborate on working scientific data sets across geographically distributed experiment, computation, and data analytics facilities. DataFed provides a set of services and applications enabling simple and uniform cross-facility scientific data management with federated (distributed) data repositories. This solution greatly reduces effort required by scientist to organize and/or move data between facilities/institutes, Reduces likelihood of misidentification and subsequent misuse of data, and improves scientific reproducibility through deep, domain-specific context and provenance capture.

Stansberry, DaleV.↗

DOE OSTI2018

ShareAnalytics

ShareAnalytics is a lightweight extensible platform for collaborative data analytics and facility characterization via Non-intrusive Load Monitoring (NILM). It consists of three core components: a full stack web application, a dashboard tool for analyzing streaming data and a High Performance Computing (HPC) cluster leveraging slurm for resource management to perform real time analysis. The entire framework is implemented in Docker for ease of deployment and to allow the user to test the platform before making a significant investment in hardware if they require additional computing resources. While many other platforms for analyzing streaming data exists, ShareAnalytics is unique in that HPC capabilities and facility characterization via scored analytics are fully integrated into the platform.

Oesch, TimothyS↗

DOE OSTI2015

Chemical Composition Analysis of Simulated Waste Glass T10-G-16A

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Chemical Composition Analysis of Simulated Waste Glass T10-G-16A

In this report, SRNL provides chemical composition analyses of a simulated LAW glass designated T10-G-16A. The measured chemical composition data are reported and compared with the targeted values for each component. No issues were identified in reviewing the analytical data. The measured concentrations of Al 2 O 3 , CaO, Na 2 O, and SiO 2 in T10-G-16A deviated from the targeted composition. It is of potential interest to note that these deviations result in a glass composition that is shifted toward the SiO 2 corner of the Al 2 O 3 -Na 2 O-SiO 2 phase diagram, away from the nepheline phase field. The results of these analyses will be used in developing a better understanding of the properties and performance of this glass.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

EDX ClaiMM: Digital Resources for the Critical Minerals and Materials Community

Securing critical mineral supply chains is essential for transitioning to a clean energy economy and for maintaining national security. Big-data analytics can serve as a cost-effective means of identifying new domestic critical mineral resources but only if data can be easily located and digested. Using ArcGIS Enterprise Sites, EDX ClaiMM was developed to increase the accessibility of critical minerals data, reducing time spent on data collection and integration. Hosted tools provide rapid visualization and exploration of key datasets, unlocking insights to support resource assessments.

Yesenchak, Rachel↗

DOE OSTI2023

Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Missing Photovoltaic Data Imputation

The integration of the global Photovoltaic (PV) market with real time data-loggers has enabled large scale PV data analytical pipelines for power forecasting and long-term reliability assessment of PV fleets. Nevertheless, the performance of PV data analysis heavily depends on the quality of PV timeseries data. This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data. STDGAE exploits temporal correlation, spatial coherence, and value dependencies from domain knowledge to recover missing data. It is empowered by two modules. (1) To cope with sparse yet various scenarios of missing data, STD-GAE incorporates a domain-knowledge aware data augmentation module that creates plausible variations of missing data patterns. This generalizes STD-GAE to robust imputation over different seasons and environment. (2) STD-GAE nontrivially integrates spatiotemporal graph convolution layers (to recover local missing data by observed “neighboring” PV plants) and denoising autoencoder (to recover corrupted data from augmented counterpart) to improve the accuracy of imputation accuracy at PV fleet level. We have evaluated our proposed model on two realworld PV datasets. Experimental results show that STD-GAE can achieve a gain of 43.14% in imputation accuracy and remains less sensitive to missing rate, different seasons, and missing scenarios, compared with state-of-the-art data imputation methods such as MIDA and LRTC-TNN.

Fan, Yangxin↗

DOE OSTI2012

Karlsruhe Database for Radioactive Wastes (KADABRA) - Accounting and Management System for Radioactive Waste Treatment - 12275

The data management system KADABRA was designed according to the purposes of the Cen-tral Decontamination Department (HDB) of the Wiederaufarbeitungsanlage Karlsruhe Rueckbau- und Entsorgungs-GmbH (WAK GmbH), which is specialized in the treatment and conditioning of radioactive waste. The layout considers the major treatment processes of the HDB as well as regulatory and legal requirements. KADABRA is designed as an SAG ADABAS application on IBM system Z mainframe. The main function of the system is the data management of all processes related to treatment, transfer and storage of radioactive material within HDB. KADABRA records the relevant data concerning radioactive residues, interim products and waste products as well as the production parameters relevant for final disposal. Analytical data from the laboratory and non destructive assay systems, that describe the chemical and radiological properties of residues, production batches, interim products as well as final waste products, can be linked to the respective dataset for documentation and declaration. The system enables the operator to trace the radioactive material through processing and storage. Information on the actual sta-tus of the material as well as radiological data and storage position can be gained immediately on request. A variety of programs accessed to the database allow the generation of individual reports on periodic or special request. KADABRA offers a high security standard and is constantly adapted to the recent requirements of the organization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Applying mobile technology to data collection for environmental characterization and remediation, beyond the science - 15453

The rapid advancements of our time bring us new methods of analysis that enhance the accuracy and sensitivity of measurement, improving the science used to solve problems and meet our objectives. What is often overlooked in our industry are the technology advancements that can dramatically improve the speed, accuracy and completeness by which those data can be collected, communicated, managed and reported. In addition, in the time used to generate and collect the analytical data, the technology collects, manages and reports other process data that can be used to better manage and facilitate our business process improving efficiency and communications as well as providing the information needed to track and manage cost and schedule. Good information management practices, including the use of innovative technology solutions like mobile data collection, are the drivers for project implementation. However, experience has highlighted key success factors that will maximize the level of success and benefit realized by the project. These factors include: -A comprehensive, working knowledge of operational objectives; - Detailed, documented process and data work-flows; - Effective technology selection and/or development (right tool for the job); - Complete integration of the technology into day to day work practice accomplished through effective training and implementation planning. Once this formula is in place, and executed consistently, it can be applied to any environmental investigation and/or remediation scenario. The proof is in the calculated return on investment and captured lessons learned for each project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Data analysis and modeling pipelines for controlled networked social science experiments

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗

DOE OSTI2022

MISPR : an open-source package for high-throughput multiscale molecular simulations

Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure–Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure–property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies.

36 MATERIALS SCIENCE↗

DOE OSTI2018

CHD4-mediated loss of E-cadherin determines metastatic ability in triple-negative breast cancer cells

Highlights: • CHD4 is associated with poor prognosis in triple-negative breast cancer patients. • Increased CHD4 is positively correlated with cell motility and drug resistance of TNBC cells. • CHD4 should be a potential prognostic biomarker in TNBC patient survival. Triple-negative breast cancer (TNBC) is a subtype of cancer with aggressive behaviors (high recurrence and metastasis rate) and poor prognosis. Therefore, studying the determining factors that lead to malignant TNBCs is necessary to develop personalized therapy and improve survival rates. In this study, we first analyzed levels of chromodomain helicase DNA binding protein 4 (CHD4) in 60 TNBC patients by immunohistochemical staining. We then clarified the role of CHD4 in TNBC and non-TNBC cell lines. Our clinical data indicated that higher CHD4 expression is positively correlated with metastatic stage, tumor recurrence, and survival status. Consistent with the clinical analytical data, our in vitro data also indicated that high level of CHD4 is positively correlated with malignant behaviors in TNBC cells, such as cell motility and mortality. For further analyses, we found that E-cadherin, N-cadherin and fibronetin are involved in CHD4-mediated epithelial-mesenchymal transition (EMT). Silencing of CHD4 also increased drug sensitivity to cisplatin and PARP1 inhibitor, especially in TNBC cells. Altogether, our findings showed that CHD4 is not only a potential prognostic biomarker for TNBC patient survival, but is also a powerful candidate in the development of new anti-cancer agents in TNBC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Prediction of Power Measurements Using Adaptive Filters

With the advent of smart grid concept, Internet of Things (IoT) and the deployment of smart meters, the cyberattack threats on power networks have increased due to the use of communication systems that can be accessed by adversaries. Attackers will have the ability to manipulate the outcomes of smart meters which in turn influence the core application of Energy Management System 9EMS): State Estimation (SE). Bad data analytic tools may fail to detect some attacks into measurements. Meanwhile, Machine Learning (ML) solutions have been proposed for detecting False Data Injection (FDI) attacks. However, there is a lack of ML time-series solutions presented in the state-of-the-art that is yet to be complex. In signal processing, time-series solutions do not only consider the signal, but also the statistics of the signal over time. Therefore, in this paper, a machine learning for time-series solutions is presented as an application to model the measurements of the power grid that are used in SE. The presented model takes into account adaptive linear and non-linear filters: Finite Impulse Response (FIR), and Infinite Impulse Response (IIR). The presented models are implemented and performed on the IEEE-118 bus system. The results indicate the advantage of applying those filters over the state-of-the-art machine learning solutions.

Hamad, Khaled↗

DOE OSTI2012

Argonne Leadership Computing Facility 2011 annual report: Shaping future supercomputing

The ALCF's Early Science Program aims to prepare key applications for the architecture and scale of Mira and to solidify libraries and infrastructure that will pave the way for other future production applications. Two billion core-hours have been allocated to 16 Early Science projects on Mira. The projects, in addition to promising delivery of exciting new science, are all based on state-of-the-art, petascale, parallel applications. The project teams, in collaboration with ALCF staff and IBM, have undertaken intensive efforts to adapt their software to take advantage of Mira's Blue Gene/Q architecture, which, in a number of ways, is a precursor to future high-performance-computing architecture. The Argonne Leadership Computing Facility (ALCF) enables transformative science that solves some of the most difficult challenges in biology, chemistry, energy, climate, materials, physics, and other scientific realms. Users partnering with ALCF staff have reached research milestones previously unattainable, due to the ALCF's world-class supercomputing resources and expertise in computation science. In 2011, the ALCF's commitment to providing outstanding science and leadership-class resources was honored with several prestigious awards. Research on multiscale brain blood flow simulations was named a Gordon Bell Prize finalist. Intrepid, the ALCF's BG/P system, ranked No. 1 on the Graph 500 list for the second consecutive year. The next-generation BG/Q prototype again topped the Green500 list. Skilled experts at the ALCF enable researchers to conduct breakthrough science on the Blue Gene system in key ways. The Catalyst Team matches project PIs with experienced computational scientists to maximize and accelerate research in their specific scientific domains. The Performance Engineering Team facilitates the effective use of applications on the Blue Gene system by assessing and improving the algorithms used by applications and the techniques used to implement those algorithms. The Data Analytics and Visualization Team lends expertise in tools and methods for high-performance, post-processing of large datasets, interactive data exploration, batch visualization, and production visualization. The Operations Team ensures that system hardware and software work reliably and optimally; system tools are matched to the unique system architectures and scale of ALCF resources; the entire system software stack works smoothly together; and I/O performance issues, bug fixes, and requests for system software are addressed. The User Services and Outreach Team offers frontline services and support to existing and potential ALCF users. The team also provides marketing and outreach to users, DOE, and the broader community.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2020

Meteorological Tower Network Expansion Pilot Project Siting Considerations to Ensure Data Quality and Representativeness of Three New Meteorological Towers at Los Alamos National Laboratory

Associated with the Meteorological Tower Network Expansion Pilot Project by the Environmental Protection and Compliance (EPC) Compliance Programs (CP) Meteorology and Air Quality (MAQ) team, the purpose of this technical report is to establish requisite justification for the siting of three 10-meter high instrumented meteorological towers at three new locations on the Los Alamos National Laboratory (LANL) site. These towers and their associated instrumentation are similar in scope to the existing 10-meter meteorological tower that is collecting meteorological data in Mortandad Canyon (MDCN). The conclusions and recommendations in this technical report will inform LANL management, EPC-CP MAQ staff, and other LANL divisions of the criteria employed for the selection of the final location for siting these three new towers. Meeting such comprehensive criteria will ensure that all data acquired will be spatially representative for meeting the diverse Safety Basis (SB), emergency management, and operations organizations programmatic needs and analytical data input requirements to its codes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

STATISTICAL EVALUATION OF SMALL SCALE MIXING DEMONSTRATION SAMPLING AND BATCH TRANSFER PERFORMANCE - 12093

The ability to effectively mix, sample, certify, and deliver consistent batches of High Level Waste (HLW) feed from the Hanford Double Shell Tanks (DST) to the Waste Treatment and Immobilization Plant (WTP) presents a significant mission risk with potential to impact mission length and the quantity of HLW glass produced. DOE's Tank Operations Contractor, Washington River Protection Solutions (WRPS) has previously presented the results of mixing performance in two different sizes of small scale DSTs to support scale up estimates of full scale DST mixing performance. Currently, sufficient sampling of DSTs is one of the largest programmatic risks that could prevent timely delivery of high level waste to the WTP. WRPS has performed small scale mixing and sampling demonstrations to study the ability to sufficiently sample the tanks. The statistical evaluation of the demonstration results which lead to the conclusion that the two scales of small DST are behaving similarly and that full scale performance is predictable will be presented. This work is essential to reduce the risk of requiring a new dedicated feed sampling facility and will guide future optimization work to ensure the waste feed delivery mission will be accomplished successfully. This paper will focus on the analytical data collected from mixing, sampling, and batch transfer testing from the small scale mixing demonstration tanks and how those data are being interpreted to begin to understand the relationship between samples taken prior to transfer and samples from the subsequent batches transferred. An overview of the types of data collected and examples of typical raw data will be provided. The paper will then discuss the processing and manipulation of the data which is necessary to begin evaluating sampling and batch transfer performance. This discussion will also include the evaluation of the analytical measurement capability with regard to the simulant material used in the demonstration tests. The paper will conclude with a discussion of the analysis results illustrating the relationship between the pre-transfer samples and the batch transfers, which support the recommendation regarding the need for a dedicated feed sampling facility.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2012

Solar-Powered Air Stripping at the Rocky Flats Site, Colorado - 12361

The U.S. Department of Energy's Rocky Flats Site (the Site), near Denver, Colorado, is a former nuclear weapons facility that was constructed beginning in 1951. With the end of the Cold War, the Site was cleaned up and closed in 2005. Four gravity-driven groundwater treatment systems were installed during cleanup, and their continued operation was incorporated into the final remedy for the Site. All utilities, including electrical power, were removed as part of this closure, so all Site electrical power needs are now met with small solar-powered systems. The Mound Site Plume Treatment System (MSPTS) was installed in 1998 as an innovative system based on zero-valent iron (ZVI). Groundwater flow from the Mound source area containing elevated concentrations of volatile organic compounds (VOCs), primarily in the tetrachloroethene (PCE)-trichloroethene (TCE) family of chlorinated solvents, is intercepted by a collection trench and routed to twin ZVI treatment cells. Later, in 2005, remediation of VOC-contaminated soils at a second up-gradient source area included adding an electron donor to the backfill to help stimulate biodegradation. This reduced concentrations of primary constituents but caused down-gradient groundwater to contain elevated levels of recalcitrant degradation byproducts, particularly cis-1,2-dichloroethene and vinyl chloride. A gravel drain installed as part of the 2005 remediation directs contaminated groundwater from this second source area to the MSPTS for treatment. This additional contaminant load, coupled with correspondingly reduced residence time within the ZVI media due to the increased flow rate, resulted in reduced treatment effectiveness. Elevated concentrations of VOCs were then detected in MSPTS effluent, as well as in surface water at the downstream performance monitoring location for the MSPTS. Subsequent consultations with the Site regulators led to the decision to add a polishing component to reduce residual VOCs in MSPTS effluent. Initially, several alternatives such as commercial air strippers and cascade aerators were evaluated; resulting cost estimates exceeded $100,000. After several simpler alternatives were considered and prototype testing was conducted, the existing effluent metering manhole was converted to house a spray-nozzle based, solar-powered air stripper, at a cost of approximately $20,000. About two-thirds of this cost was for the solar power system, which was initially designed to only provide power for 12 hours per day. Performance data are being collected and adjustments made to optimize the design, determine maintenance requirements, and establish power needs for continuous operation. Analytical data confirm the air stripper is sharply reducing concentrations of residual contaminants. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗