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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Smoky Mountain Data Challenge 2020: An Open Call to Solve Data Problems in the Areas of Neutron Science, Material Science, Urban Modeling and Dynamics, Geophysics, and Biomedical Informatics

The 2020 Smoky Mountains Computational Sciences and Engineering Conference enlists research scientists from across Oak Ridge National Laboratory (ORNL) to be data sponsors and help create data analytics challenges for eminent data sets at the laboratory. This work describes the significance of each of the seven data sets and their as- sociated challenge questions. The challenge questions for each data set were required to cover multiple difficulty levels. An international call for participation was sent to students, and researchers asking them to form teams of up to four people to apply novel data analytics techniques to these data sets.

Parete-Koon, Suzanne↗
DOE OSTI2017

Streamlining and Transparently Automating Complex Waste Determinations at Los Alamos National Laboratory - 17286

Environmental programs at Los Alamos National Laboratory (LANL) generate a large amount of sampling activities that can result in waste that needs to be characterized and properly disposed. Since 2011, sampling results are managed in the cloud-based Environmental Information Management (EIM) database, which contains over 16 million current and historical data records. In many cases, the database's analytical records are used to characterize and dispose waste from environmental operations. Three factors contribute to streamlining of the waste disposition operations: (1) if possible, use of the existing analytical data for waste characterization to avoid additional laboratory analysis; (2) reduction in time spent on data evaluation for various waste acceptance criteria; and (3) choice of the proper and least expensive disposal alternative. Use of the existing analytical data, when allowed by law, creates some paradoxical problems: when chemical analysis is requested with the purpose of characterizing waste, it creates only 100 to 400 analytical records. However, use of exiting analytical data at LANL may lead to evaluation of tens of thousands of analytical records, with many records for each single analyte. This occurs because data were collected for environmental characterization and not waste characterization. To streamline data analysis for waste disposition using existing data, LANL has created a Waste Data Summary (WDS) module in the site's EIM database. Recognizing that waste determination is performed by groups outside the environmental program at LANL, the WDS allows for logically guided data selection for a specific waste determination (solids, liquids, radiological, etc.) and reduces potentially thousands of rows of data into simple formatted data reports that are easy to comprehend and process. This allows the waste evaluators to review data using multiple approaches to make their own determination on the most appropriate disposal method. There are four basic principles of WDS operation: (1) The highest detected analytical result is evaluated for the selected samples (or the lowest undetected result if there are no detected results); (2) All approved disposal pathways are evaluated; (3) Any failures of the Waste Acceptance Criteria are visibly highlighted to focus the attention of the evaluator; and (4) The decision of the disposal path is left to the trained waste evaluator (WDS does not choose or suggest any disposal paths). The summary results are presented as the set of short data reports. These reports can be divided into two groups: general comparison reports and disposal pathway specific forms. The general comparison forms show all Resource Conservation and Recovery Act (RCRA) analytes with pass/fail evaluation and with preference for toxicity characteristic leaching procedure (TCLP) results (or 20x rule) for solid samples, the potential RCRA hazard codes assignment for detected analytes, and all rad results. The detected chemical and radioactivity results are compared to the background values and to Soil Screening and Soil Action limits (SSL/SAL limits). Disposal pathway specific forms follow Waste Acceptance Criteria requirements for all disposal pathways available at LANL. The programming of the WDS tries to maximize existing data by analyte substitutions (like using gross alpha for necessary alpha emitting isotopes) or by making calculations when analytical results do not agree with legal requirements (like total chlordane in the RCRA regulations, which is most often analyzed and reported as individual isomers). All forms may be annotated by the waste evaluator to provide additional information to support decision making. Upon final decision of a disposal path, the complete dataset used in the evaluation and the WDS forms with annotations is 'frozen' as an un-editable PDF export to document the data used in the waste disposal determination. The streamlined process results in less work for waste evaluators and reduces overall labor and analytical costs. By comparison, a full review of a 10,000 record parent dataset may take a waste evaluator over a week to review, whereas with the WDS module, the task can be accomplished in less than one hour with clear, reproducible results. These results can be reviewed and checked at any time by quality assurance personnel, thus ensuring transparency in the decision making process. LANL believes this approach has broad applicability to other sites where waste determination is based on review of existing data collected for more generalized environmental purposes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Data and Analytics to Inform Energy Retrofit of High Performance Buildings

Buildings consume more than one-third of the world?s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis ? energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20percent of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Detectable Changes in U1a Data [Slides]

This is an update presentation on U1a data analytics for the Advanced Data Analytics for Proliferation Detection Tech Exchange Meeting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Part 2: Forensic attribution profiling of Russian VX in food using liquid chromatography-mass spectrometry

This work is part two of a three-part series in this issue of a Sweden-United States collaborative effort towards the understanding of the chemical attribution signatures of Russian VX (VR) in synthesized samples and complex food matrices. We describe the sourcing of VR present in food based on chemical analysis of attribution signatures by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate data analysis. Analytical data was acquired from seven different foods spiked with VR batches that were synthesized via six different routes in two separate laboratories. The synthesis products were spiked at a lethal dose into seven food matrices: water, orange juice, apple purée, baby food, pea purée, liquid eggs and hot dog. After acetonitrile sample extraction, the samples were analyzed by LC-MS/MS operated in MRM mode. A multivariate statistical calibration model was built on the chemical attribution profiles from 118 VR spiked food samples. Using the model, an external test-set of the six synthesis routes employed for VR production was correctly identified with no observable major impact of the food matrices to the classification. The overall performance of the statistical models was found to be exceptional (94%) for the test set samples retrospectively classified to their synthesis routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integration and validation of some modules for modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures

Three modules are integrated into the built-in OpenFOAM rhoCentralFoam solver towards accurate and efficient modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures within the OpenFOAM 10.0 framework. The first module is the mixture-averaged diffusion model. The second module is the built-in OpenFOAM Lagrangian solver coupled with optimised droplet drag coefficient and convective heat transfer coefficient sub-models. The last module is a sparse stiff chemistry solver based on dynamic adaptive hybrid integration (AHI-S). The optimised droplet sub-models are first verified in correct implementation for subsequent simulations in this work. Further, they show good accuracy against experimental and analytical data in the modelling of ammonia droplet acceleration and cooling in the flowing and/or low-temperature air. The accuracy and efficiency gains related to the mixture-averaged diffusion model and the AHI-S chemistry solver are examined by simulating 1-D detonation propagation in ammonia droplet-free/laden ammoniaoxygen mixtures. Numerical results of detonation propagation speed, gaseous temperature, density, and species distributions around the induction zone show good agreement with experimental data and analytical solutions. Compared to the built-in OpenFOAM diffusion model, the mixture-averaged diffusion model provides different numerical predictions of pulsating instabilities in detonation propagation. It shows better accuracy in depicting the detonation structure within the droplet-free section attributed to improved multi-component diffusion modelling. Compared to the built-in OpenFOAM solver EulerImplicit (backward Euler), the AHI-S chemistry solver reduces the computational cost by around 50%. It achieves satisfactory accuracy in calculating detonation propagation speed within the droplet-free section with the optimal efficiency when the safety factor, β, equals 0.5.

42 ENGINEERING↗
DOE OSTI2024

Peregrine Software Development: Report on the Code Conversion From Python to C++

This work package seeks to convert the Peregrine software tool from its original Python implementation to a production version based on the C++ language. Peregrine is a powerful research platform with a multitude of advanced data analytics and data visualization functionalities. Developed by scientists to explore multimodal and multidimensional data related to the production of components using powder bed additive manufacturing processes, the tool implements state-of-the-art algorithms to assist machine users in making build or part quality determinations. Given that Peregrine is data-intensive, the goal of this conversion is to enhance the tool’s flexibility and interactivity and reduce the number of code dependencies to facilitate its deployment as part of the ongoing technology transfer campaign. This brief document provides an overview of Peregrine’s functionalities and capabilities, along with a detailed description of the core functionalities that have been implemented to date in the new C++ version. This document serves as a development update at the end of the first year of the ongoing conversion and will be regularly updated as progress continues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A Decision Analysis Tool for Climate Impacts, Adaptations, and Vulnerabilities

Climate change related extreme events (such as flooding, storms, and drought) are already impacting millions of people globally at a cost of billions of dollars annually. Hence, there are urgent needs for urban areas to develop adaptation strategies that will alleviate the impacts of these extreme events. However, lack of appropriate decision support tools that match local applications is limiting local planning efforts. In this paper, we present a quantitative analysis and optimization system with customized decision support modules built on geographic information system (GIS) platform to bridge this gap. This platform is called Urban Climate Adaptation Tool (Urban-CAT). For all Urban-CAT models, we divide a city into a grid with tens of thousands of cells; then compute a list of metrics for each cell from the GIS data. These metrics are used as independent variables to predict climate impacts, compute vulnerability score, and evaluate adaptation options. Overall, the Urban-CAT system has three layers: data layer (that contains spatial data, socio-economic and environmental data, and analytic data), middle layer (that handles data processing, model management, and GIS operation), and application layer (that provides climate impacts forecast, adaptation optimization, and site evaluation). The Urban-CAT platform can guide city and county governments in identifying and planning for effective climate change adaptation strategies.

adaptation↗
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
DOE OSTI2016

Supporting Building Portfolio Investment and Policy Decision Making through an Integrated Building Utility Data Platform

The American Recovery and Reinvestment Act stimulus funding of 2009 for smart grid projects resulted in the tripling of smart meters deployment. In 2012, the Green Button initiative provided utility customers with access to their real-time1 energy usage. The availability of finely granular data provides an enormous potential for energy data analytics and energy benchmarking. The sheer volume of time-series utility data from a large number of buildings also poses challenges in data collection, quality control, and database management for rigorous and meaningful analyses. In this paper, we will describe a building portfolio-level data analytics tool for operational optimization, business investment and policy assessment using 15-minute to monthly intervals utility data. The analytics tool is developed on top of the U.S. Department of Energy’s Standard Energy Efficiency Data (SEED) platform, an open source software application that manages energy performance data of large groups of buildings. To support the significantly large volume of granular interval data, we integrated a parallel time-series database to the existing relational database. The time-series database improves on the current utility data input, focusing on real-time data collection, storage, analytics and data quality control. The fully integrated data platform supports APIs for utility apps development by third party software developers. These apps will provide actionable intelligence for building owners and facilities managers. Unlike a commercial system, this platform is an open source platform funded by the U.S. Government, accessible to the public, researchers and other developers, to support initiatives in reducing building energy consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

SIRIUS: Enabling Progressive Data Exploration for Extreme-Scale Scientific Data

Scientific simulations on high performance computing (HPC) platforms generate large quantities of data. To bridge the widening gap between compute and I/O, and enable data to be more efficiently stored and analyzed, simulation outputs need to be refactored, reduced, and appropriately mapped to storage tiers. However, a systematic solution to support these steps has been lacking in the current HPC software ecosystem. To that end, this paper develops SIRIUS, a progressive JPEG-like data management scheme for storing and analyzing big scientific data. It co-designs data decimation, compression, and data storage, taking the hardware characteristics of each storage tier into considerations. With reasonably low overhead, our approach refactors simulation data, using either topological or uniform decimation, into a much smaller, reduced-accuracy base dataset, and a series of deltas that is used to augment the accuracy if needed. The base dataset and deltas are compressed and written to multiple storage tiers. Data saved on different tiers can then be selectively retrieved to restore the level of accuracy that satisfies data analytics. Thus, SIRIUS provides a paradigm shift towards elastic data analytics and enables end users to make trade-offs between analysis speed and accuracy on-the-fly. This paper further develops algorithms to preserve statistics for data decimation, a common requirement for reducing data. Here, we assess the impact of SIRIUS on unstructured triangular meshes, a pervasive data model used in scientific simulations. In particular, we evaluate two realistic use cases: the blob detection in fusion and high-pressure area extraction in computational fluid dynamics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of a Unified Taxonomy for HVAC System Faults

Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Methodology for inferring reactor core power distribution from an optical fiber based gamma thermometer array

Here, a 4-step data analytic methodology has been devised for the purpose of inferring the distribution of power in a reactor core, based on the response of an array of optical fiber based gamma thermometers (OFBGTs). This data analytic methodology is crucial for the development of a system of OFBGTs for the purpose of calibrating local power range monitors in boiling water reactors. Such a system would be an improvement to the present calibration system in boiling water reactors, in terms of safety, efficiency, and permanence. The first step of this methodology is to establish an energy balance method. In this method, one uses MCNP to determine response functions, which allow one to convert from gamma thermometer response to power. The gamma thermometers and the reactor core are segmented, such that each gamma thermometer segment provides an estimate of power for each reactor core segment. The estimates of power for the reactor core segments (hereafter referred to as fuel assembly segments) are calculated based on the measured response of the gamma thermometers. The second step of the methodology is to employ a weighting scheme to combine the estimates of the power of the various fuel assembly segments, based on the response of the various OFGBT segments and the incremental dose rates of the various fuel assembly segments to the OFBGT segments. The third step is to iteratively calculate the estimates of the power of the fuel assembly segments, until a convergence criterion is met, which indicates that the calculation has converged. The fourth and final step of the methodology is to estimate the uncertainty of the power for each fuel assembly segment. The mathematical basis for this step is not the focus of this paper. We have used a 3-D homogeneous reactor model to demonstrate the data analytic methodology; and have found that the data analytic methodology operates as intended.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

A Conceptual Site Model for Nature and Extent of Contamination in a Riparian-Near Shore Area - 12410

The 100-K decision area is located along the Columbia River and includes source Operable Units (OUs), a groundwater OU, and the adjacent surface water, saturated sediment and aquatic biota. A conceptual site model (CSM) has been developed to evaluate concentrations of non-radiological substances and radionuclides detected in soil, water and sediments in a riparian near-shore area along the Columbia River. The CSM is used to determine if potential transport pathways exist to these media from Hanford Site sources by incorporating information from the physical system, surface hydrology, subsurface hydrogeology, analytical results, and ecological evaluation into the model. Six contaminants of ecological concern, mostly metals and non-radiological inorganics, have been identified in riparian and near-shore media. With few exceptions (notably chromium and hexavalent chromium), there are ambient sources for these constituents in soil, sediment and water that are unrelated to the Hanford Site. While the CSM documented analytical and biological conditions, this paper presents results focused on analytical measurements to document the potential for these contaminants to be related to a release from the Hanford Site. The purpose for preparing this CSM was to address, on a reactor decision area basis, the potential for Hanford Site contaminants in soil or groundwater to migrate to riparian or near-shore areas at concentrations that could be of concern for ecological receptors. This CSM supplements the analysis of River Corridor-wide ecological risks presented in the ecological risk assessment of the RCBRA. The RCBRA identified on a site-wide basis some contaminants of ecological concern that warranted further evaluation. Based on the results of the further evaluation contained in this CSM, with the exception of hexavalent chromium, detected concentrations of contaminants in riparian or near-shore media are not reliably detectable at levels of ecological concern, or are not associated with contamination in soil or groundwater resulting from Hanford Site operations. Therefore, for purposes of alternatives evaluation in the 100-K Feasibility Study, hexavalent chromium in groundwater should be considered the only contaminants of ecological concern. There are uncertainties in this evaluation related to analytical data quality. With the exception of most recently collected analytical data in groundwater, analytical methods had MDLs that were either close to or higher than aquatic criteria; when methods with adequate detection limits are used, the results show that contaminant concentrations are less than aquatic criteria. In many cases where analytical results were higher than aquatic criteria, it was determined that the results were based on unfiltered samples. Unfiltered analytical results are inappropriate for comparison with aquatic criteria; in general, filtered analytical results were less than aquatic criteria. In some cases where concentrations were reported higher than aquatic criteria, further evaluation of the data revealed it was qualified because of the presence of blank contamination. The analytical detection limit considerations and data quality issues further support the conclusion that contaminants concentrations in water were not reliably detected above levels of ecological significance. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster

Data analytic methods are being developed to address the problem of how to assign a sensor set in a nuclear facility such that a requisite level of process monitoring capability is realized and that the sensor set is sufficiently rich to determine the status of the individual sensors with respect to need for calibration. There is an awareness in the nuclear industry that data analytics combined with rich sensor sets represent a means to improve operations and reduce costs. In the industry the calibration problem has been previously approached as an empirical data-driven problem with several methods having been developed. However, the experience of the utilities over the past ten years with these methods indicates that the absence of physics-based information renders the data-driven approach less reliable. Complicating factors such as the inherent variability of operation (both equipment alignment and operating condition) can confound a pure data-driven approach while there are no rigorous guidelines for determining what constitutes an adequate sensor set. The solution under development to overcome these shortcomings supplements the data analytic method with process information in a so-called process-constrained data-analytic approach. Simple balance equations are written for generic components (e.g., mechanical pump, valve, and heat exchanger). These do not require a priori knowledge of process parameters, such as heat transfer coefficients or friction factors. All that is needed on the part of the utility user is to identify the components and how they are connected. This report describes the development of a parallel computing capability for determining the optimal sensor set. The optimal sensor set problem suffers from the curse of dimensionality. Computation time increases exponentially as the size of the system grows. To overcome this difficulty a pre-conditioner algorithm is developed to find an approximate solution close the actual solution. This serves as a seed for the full-blown algorithm and acts to constrain the space that must searched. The optimization algorithms are described and the implementation on a parallel computing platform is described. The application of the method to a use case we are solving in collaboration with our utility partner served to illustrate how the default sensor set in a nuclear plant may not provide sufficient coverage to infer sensor calibration status.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Streaming Analytics and Data Engineering

SADE is a software package for rapidly assembling analytic pipelines to manipulate data. The packages consists of the engine that manages the data and coordinates the movement of data between the tasks performing a function, a set of core libraries consisting of plugins that perform common tasks, and a framework to extend the system supporting the development of new plugins. Currently through configuration files, a pipeline can be defined that maps the routing of data through a series of plugins. Pipelines can be run in a batch mode or can process streaming data; they can be executed from the command line or run through a Windows background service. There currently exists over a hundred plugins, over fifty pipeline configurations, and the software is now being used by about a half-dozen projects.

Source record↗
DOE OSTI2021

Data Science Techniques, Assumptions, and Challenges in Alloy Clustering and Property Prediction

Data analytics methods have been increasingly applied to understanding materials chemistry, processing due to the manufacturing approach, and uni-axial and cyclic property relationships in the highly complex space of alloy design. There are several benefits to applying data analytics to this space, including the ability to manage non-linearities in the responses of the alloy attributes and the resulting mechanical properties. However, key difficulties in applying and understanding the results of data analytics include the often lack of reported assumptions and data processing steps necessary to improve interpretation and reproducibility in derived results. In this work, the methods used to generate clustering and correlation analyses for experimental 9% Cr ferritic-martensitic steel data were investigated and the resulting implications for mechanical property predictions were assessed. This work uses principal component analysis, partitioning around medoids, t-SNE, and k-means clustering to investigate trends in composition, processing and microstructure information with creep and tensile properties, building on work done previously using a smaller version of the same dataset. The initial assumptions, preprocessing steps and methods are investigated and outlined in order to depict the fine level of detail required to convey the steps taken to process data and produce analytical results. Here, the variations in the resulting analyses are explored due to the influence of new and more varied data.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗