Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data sciences”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A data science approach for analysis and reconstruction of spinodal-like composition fields in irradiated FeCrAl alloys

A statistical method for the analysis of continuously distributed data representative of composition fluctuations in irradiated FeCrAl alloys acquired using Energy Dispersive X-ray Spectroscopy (EDS) method is presented. Using probability distribution functions, direct and cross-covariances between the elemental compositions, the effects of alloy composition and irradiation dose were investigated on the spatial distribution and length scale of composition fluctuations at the nanoscale. We have observed that, for neutron-irradiated FeCrAl alloys, the distribution of Fe and Cr followed a left-skewed and right-skewed distribution, respectively for all (average) alloy compositions and irradiation doses. The analysis also revealed enhanced spatial gradients in the elemental compositions at higher irradiation dose. Direct and cross-covariance estimates of the experimental data were also utilized for reconstruction of composition data through fitting it to a parametric form of the covariance functions. Linear Model of Coregionalization was used to determine the parameters of the covariance functions. Subsequently, a spectral method was utilized for simulating a realization of the alloy compositions. Close correspondence was observed between the experimental and the reconstructed data which was analyzed using probability distribution functions and covariance functions. Composition space of the experimental and reconstructed data and dislocation velocities as a function of applied stress and line directions over the entire composition maps were also examined.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The Neurodata Without Borders ecosystem for neurophysiological data science

The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our open-source software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB’s impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem, which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗
DOE OSTI2024

Women in Data Science Livermore Datathon 2024

The WiDS Datathon 2024 focuses on a prediction task using a roughly 39k record dataset (split into training and test sets) representing patients and their characteristics (age, race, BMI, zip code), their diagnosis and treatment information (breast cancer diagnosis code, metastatic cancer diagnosis code, metastatic cancer treatments, … etc.), their geo (zip-code level) demographic data (income, education, rent, race, poverty, …etc), as well as toxic air quality data (Ozone, PM25 and NO2) that tie health outcomes to environmental conditions. Each row in the data corresponds a single patient and her Diagnosis Period.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence (University of Delaware)

This report summarizes the activities, technical accomplishments, and outcomes of the RAPIDS2 Institute project at the University of Delaware (UD). The RAPIDS2 Institute was a large multi-institution project with the objective of assisting SciDAC and Office of Science application teams in the use of DOE supercomputing resources to achieve scientific breakthroughs. The UD team contributed to this effort through work on formal software verification. This thrust aims to reduce software developer time and effort, especially regarding debugging and testing, and to increase confidence in the correctness of the results computed by the software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Operation and Maintenance of PV Systems: Data Science, Analysis, and Standards

This effort improves the effectiveness and reduce uncertainty in O&M cost through four primary objectives/tasks: 1) institutionalize standards for reliability and availability reporting for large PV power plants; 2) bridge systemic O&M knowledge gaps around important topics affecting O&M; 3) characterize systemic failure modes and patterns and accelerate O&M experiential learning cycles using field data; and 4) establish a baseline understanding of UPVS O&M cost drivers. Key results of this effort include publication of IEC standards, published topical papers on O&M topics, training, and characterize field data for climate- and service-related patterns (additional details below). Integrating these results serves to reduce performance risk and facilitate improvement in the way solar projects are operated and maintained. Results are well received and two publications are among the most successful SETO publications at NREL ("Model of Operation and Maintenance Costs for Photovoltaic Systems with over 40,000 downloads and "Best Practices in Operation and Maintenance of PV Systems, 3rd Ed." with over 90,000 downloads).

14 SOLAR ENERGY↗
DOE OSTI2023

Preliminary Results of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying the solvent extraction process and the use of centrifugal contactors. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, color, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data supports the development of safeguards by design, provides operator process awareness, and aids in the discovery of process anomalies. This paper highlights some of the preliminary results from initial data collection campaigns and shares some of the lessons learned.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview

A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Using Data-Science Approaches to Unravel Insights for Enhanced Transport of Lithium Ions in Single-Ion Conducting Polymer Electrolytes

Solid polymer electrolytes have yet to achieve the desired ionic conductivity (>1 mS/cm) near room temperature required for many applications. This target implies the need to reduce the effective energy barriers for ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer–Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules 2023, 56, 15, 6051), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify anions leading to the energy barriers <30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers <20 kJ/mol, we have performed density functional theory (DFT) based calculations to connect the chemical structures of boron-based anions via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion hopping. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity >1 mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Development of a Multi Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is designing and constructing a solvent extraction test bed, Beartooth, as part of a nuclear fuel cycle stewardship initiative. The test bed will provide facilities and equipment for testing novel extraction processes and give early career scientists opportunities to gain skills in performing separations chemistry. The test bed is being uniquely designed to enable machine learning capabilities for the characterization of chemical process operations. One of the intentions of incorporating machine learning methods is to provide process operators with enhanced situational awareness for the optimization of separations techniques and possible detection of a diversion event. As a result, the objective of this Laboratory Directed Research and Development project is to develop a multi-sensor data collections system and implement machine learning techniques on signals from an existing centrifugal contactor cascade to identify equipment usage and process events. This presentation will provide an overview of sensors used and experiments conducted to date.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗