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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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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Beaver Creek Soil Incubation Experiment July 2018

The data accompanies the manuscript Antecedent conditions determine the biogeochemical response of coastal soils to seawater exposure" that evaluates impact of periodic seawater inundations of soils from in a first-order coastal watershed in the Olympic Peninsula of Washington state, USA. Samples were collected in July 2018 and subjected to laboratory incubations. The files contain a read me file, data files including soil chemical characteristics, FTICR-MS molecular and data file, FTICR-MS derived processed data from FREDA, reference file for transformation analysis, LC-MS derived molecular feature abundance data, gas flux data for multiple regression and R codes. The files are related to the publication and have been used in the results and analyses section.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Novel Modified Optical Fibers for High Temperature In-Situ Miniaturized Gas Sensors in Advanced Fossil Energy Systems

This report covers the technical progress on the program “Novel Modified Optical Fibers for High Temperature In-Situ Miniaturized Gas Sensors in Advanced Fossil Energy Systems”, funded by the National Energy Technology Laboratory of the U.S. Department of Energy, and performed by the Materials Science & Engineering and Electrical & Computer Engineering Departments at Virginia Tech, and summarizes technical progress from July 1st, 2005 –June 30th, 2014. The objective of this program was to develop novel fiber materials for high temperature gas sensors based on evanescent wave absorption in optical fibers. This project focused on two primary areas: the study of a sapphire photonic crystal fiber (SPCF) for operation at high temperature and long wavelengths, and a porous glass based fiber optic sensor for gas detection. The sapphire component of the project focused on the development of a sapphire photonic crystal fiber, modeling of the new structures, fabrication of the optimal structure, development of a long wavelength interrogation system, testing of the optical properties, and gas and temperature testing of the final sensor. The fabrication of the 6 rod SPCF gap bundle (diameter of 70μm) with a hollow core was successfully constructed with lead-in and lead-out 50μm diameter fiber along with transmission and gas detection testing. Testing of the sapphire photonic crystal fiber sensor capabilities with the developed long wavelength optical system showed the ability to detect CO 2 at or below 1000ppm at temperatures up to 1000°C. Work on the porous glass sensor focused on the development of a porous clad solid core optical fiber, a hollow core waveguide, gas detection capabilities at room and high temperature, simultaneous gas species detection, suitable joining technologies for the lead-in and lead-out fibers and the porous sensor, sensor system sensitivity improvement, signal processing improvement, relationship between pore structure and fiber geometry to optical properties, and the development of a sensor packaging prototype for laboratory testing. Analysis and experiments determined that a bonding technique using a CO 2 laser is the most suitable joining technique. Pore morphology alteration showed that transmission improved with increasing annealing temperature (producing smaller pores), while the sensor response time increased and the mechanical strength decreased with increasing annealing temperature. Software was developed for data acquisition and signal processing to collect and interpret spectral gas absorption data. Gas detection on porous glass sensors was completed and the detection limit was evaluated using acetylene and was found to be around 1- 200ppm. A complete materials package for porous glass sensors was manufactured for testing.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Reinterpreting the development of extensive air showers initiated by nuclei and photons

Ultra-high energy cosmic rays (UHECRs) interacting with the atmosphere generate extensive air showers (EAS) of secondary particles. The depth corresponding to the maximum development of the shower, X{sub max}, is a well-known observable for determining the nature of the primary cosmic ray which initiated the cascade process. In this paper, we present an empirical model to describe the distribution of X{sub max} for EAS initiated by nuclei, in the energy range from 10{sup 17} eV up to 10{sup 21} eV, and by photons, in the energy range from 10{sup 17} eV up to 10{sup 19.6} eV. Our model adopts the generalized Gumbel distribution motivated by the relationship between the generalized Gumbel statistics and the distribution of the sum of non-identically distributed variables in dissipative stochastic systems. We provide an analytical expression for describing the X{sub max} distribution for photons and for nuclei, and for their first two statistical moments, namely (X{sub max}) and σ{sup 2}(X{sub max}). The impact of the hadronic interaction model is investigated in detail, even in the case of the most up-to-date models accounting for LHC observations. We also briefly discuss the differences with a more classical approach and an application to the experimental data based on information theory.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Impact of Glycolate Anion on Aqueous Corrosion in DWPF and Downstream Facilities

Glycolic acid is being evaluated as an alternate reductant in the preparation of high level waste for the Defense Waste Processing Facility (DWPF) at the Savannah River Site (SRS). During processing, the glycolic acid may not be completely consumed with small quantities of the glycolate anion being carried forward to other high level waste (HLW) facilities. The SRS liquid waste contractor requested an assessment of the impact of the glycolate anion on the corrosion of the materials of construction (MoC) throughout the waste processing system since this impact had not been previously evaluated. A literature review revealed that corrosion data were not available for the MoCs in glycolic-bearing solutions applicable to SRS systems. Data on the material compatibility with only glycolic acid or its derivative products were identified; however, data were limited for solutions containing glycolic acid or the glycolate anion. For the proprietary coating systems applied to the DWPF concrete, glycolic acid was deemed compatible since the coatings were resistant to more aggressive chemistries than glycolic acid. Additionally, similar coating resins showed acceptable resistance to glycolic acid.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Workshop to Identify Opportunities in Biological and Environmental Research Uniquely Enabled by the APS Upgrade

The development of synchrotron-based x-ray science has enabled tremendous scientific advances, driven both by discoveries of new fundamental ways to derive insight from the interaction of x-rays with matter and by the evolution of x-ray light sources themselves. Over the last 20-plus years, the U.S. Department of Energy’s (DOE’s) Advanced Photon Source (APS) at Argonne has yielded a multitude of transformational insights into science questions across numerous areas relevant to the biological, geological, geochemical, biogeochemical, and environmental sciences. Recent developments in accelerator technology, particularly the multi-bend achromat lattice, have led to plans to dramatically upgrade the APS, allowing an upgraded APS to offer orders-of-magnitude improvements in key parameters such as coherence and brightness, while retaining high-energy capabilities. These enhanced parameters are directly relevant to almost all hard x-ray imaging, scattering, and spectroscopy experiments (Eriksson et al., 2014). The APS Upgrade Project exceeds the capabilities of today’s synchrotrons by 2 to 3 orders of magnitude in brightness and coherent flux in the hard x-ray range, enabling a transformational range of new probes for the structure, properties, and functionality of matter. With vastly improved coherent flux, nearly all techniques can become microscopies. For example, frontiers inaccessible today will be opened by x-ray instruments that provide three-dimensional (3-D) real-space resolution down to nanometers across, up to millimeters of field of view, providing the ability to follow in real time relevant processes and trace element sensitivity down to a few atoms. In addition to the unprecedented spatial resolution across a large field of view, and with appropriate time resolution, the ability for high-throughput, four-dimension/five-dimension data collection enables unprecedented statistical analysis of complex biological and environmental systems in changing environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Nanomaterial datasets to advance tomography in scanning transmission electron microscopy

Electron tomography in materials science has flourished with the demand to characterize nanoscale materials in three dimensions (3D). Access to experimental data is vital for developing and validating reconstruction methods that improve resolution and reduce radiation dose requirements. This work presents five high-quality scanning transmission electron microscope (STEM) tomography datasets in order to address the critical need for open access data in this field. The datasets represent the current limits of experimental technique, are of high quality, and contain materials with structural complexity. Included are tomographic series of a hyperbranched Co 2 P nanocrystal, platinum nanoparticles on a carbon nanofibre imaged over the complete 180° tilt range, a platinum nanoparticle and a tungsten needle both imaged at atomic resolution by equal slope tomography, and a through-focal tilt series of PtCu nanoparticles. A volumetric reconstruction from every dataset is provided for comparison and development of post-processing and visualization techniques. Researchers interested in creating novel data processing and reconstruction algorithms will now have access to state of the art experimental test data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

METABOLIC: high-throughput profiling of microbial genomes for functional traits, metabolism, biogeochemistry, and community-scale functional networks

Background Advances in microbiome science are being driven in large part due to our ability to study and infer microbial ecology from genomes reconstructed from mixed microbial communities using metagenomics and single-cell genomics. Such omics-based techniques allow us to read genomic blueprints of microorganisms, decipher their functional capacities and activities, and reconstruct their roles in biogeochemical processes. Currently available tools for analyses of genomic data can annotate and depict metabolic functions to some extent; however, no standardized approaches are currently available for the comprehensive characterization of metabolic predictions, metabolite exchanges, microbial interactions, and microbial contributions to biogeochemical cycling. Results We present METABOLIC (METabolic And BiogeOchemistry anaLyses In miCrobes), a scalable software to advance microbial ecology and biogeochemistry studies using genomes at the resolution of individual organisms and/or microbial communities. The genome-scale workflow includes annotation of microbial genomes, motif validation of biochemically validated conserved protein residues, metabolic pathway analyses, and calculation of contributions to individual biogeochemical transformations and cycles. The community-scale workflow supplements genome-scale analyses with determination of genome abundance in the microbiome, potential microbial metabolic handoffs and metabolite exchange, reconstruction of functional networks, and determination of microbial contributions to biogeochemical cycles. METABOLIC can take input genomes from isolates, metagenome-assembled genomes, or single-cell genomes. Results are presented in the form of tables for metabolism and a variety of visualizations including biogeochemical cycling potential, representation of sequential metabolic transformations, community-scale microbial functional networks using a newly defined metric “MW-score” (metabolic weight score), and metabolic Sankey diagrams. METABOLIC takes ~ 3 h with 40 CPU threads to process ~ 100 genomes and corresponding metagenomic reads within which the most compute-demanding part of hmmsearch takes ~ 45 min, while it takes ~ 5 h to complete hmmsearch for ~ 3600 genomes. Tests of accuracy, robustness, and consistency suggest METABOLIC provides better performance compared to other software and online servers. To highlight the utility and versatility of METABOLIC, we demonstrate its capabilities on diverse metagenomic datasets from the marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lakes, wastewater, and the human gut. Conclusion METABOLIC enables the consistent and reproducible study of microbial community ecology and biogeochemistry using a foundation of genome-informed microbial metabolism, and will advance the integration of uncultivated organisms into metabolic and biogeochemical models. METABOLIC is written in Perl and R and is freely available under GPLv3 at https://github.com/AnantharamanLab/METABOLIC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Geospatial Information, Metadata, and Maps for Global River Corridor Science Focus Area Sites (v5)

This dataset provides geospatial information, metadata, and maps for the Pacific Northwest National Laboratory (PNNL) River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) sites. The RC-SFA works to transform understanding of spatial and temporal dynamics in river corridor hydrobiogeochemical functions from molecular reaction to watershed and basin scales. The knowledge we gain is used to formulate and test hypotheses and to improve mechanistic representation of river corridor processes and their response to disturbances in multiscale models of integrated hydrobiogeochemical function. The data provided includes Site ID, latitude, longitude, stream name, and common ID (COMID) for sites used across the RC-SFA. The COMID can be used to find and download data from NHDPlus (https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus) and other platforms. The sites included are non-exhaustive. Sites (including past sites) will be added to this data package in the future. Data generated from the RC SFA can be accessed at https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA. This data package was originally published in April 2023. It was updated in June 2023 (v2; modified files), December 2023 (v3; modified files), January 2025 (v4; modified files), and December 2025 (v5; modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder. The data folder consists of (1) file-level metadata; (2) data dictionary; (3) readme; (4) methods codes; (5) geospatial information for all RC SFA sites including International Generic Sample Number (IGSN); (6) maps of all sites and sites in Washington State, USA; and (7) a subfolder with the shapefile of all sites. All files are .csv, .pdf, .shp, .cpg, .dbf, .prj, .qmd, or .shx. We thank the Confederated Tribes and Bands of the Yakama Nation for access to field locations where some data were collected in Washington state. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Irradiation-Accelerated Corrosion of Reactor Core Materials. Final Report

This project aims to understand how radiation accelerates corrosion of reactor core materials. The combination of high temperature, chemically aggressive coolants, a high radiation flux and mechanical stress poses a major challenge for the life extension of current light water reactors, as well as the success of most all GenIV concepts. Of these four drivers, the combination of radiation and corrosion places the most severe demands on materials, for which an understanding of the fundamental science is simply absent. Only a few experiments have been conducted to understand how corrosion occurs under irradiation, yet the limited data indicates that the effect is large; irradiation causes order of magnitude increases in corrosion rates. Without a firm understanding of the mechanisms by which radiation and corrosion interact in film formation, growth, breakdown and repair, the extension of the current LWR fleet beyond 60 years and the success of advanced nuclear energy systems are questionable. The proposed work will address the process of irradiation-accelerated corrosion that is important to all current and advanced reactor designs, but remains very poorly understood. An improved understanding of the role of irradiation in the corrosion process will provide the community with the tools to develop predictive models for in-reactor corrosion, and to address specific, important forms of corrosion such as irradiation assisted stress corrosion cracking.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

A Stochastic Intracellular Model of Anthrax Infection With Spore Germination Heterogeneity

We present a stochastic mathematical model of the intracellular infection dynamics of Bacillus anthracis in macrophages. Following inhalation of B. anthracis spores, these are ingested by alveolar phagocytes. Ingested spores then begin to germinate and divide intracellularly. This can lead to the eventual death of the host cell and the extracellular release of bacterial progeny. Some macrophages successfully eliminate the intracellular bacteria and will recover. Here, a stochastic birth-and-death process with catastrophe is proposed, which includes the mechanism of spore germination and maturation of B. anthracis . The resulting model is used to explore the potential for heterogeneity in the spore germination rate, with the consideration of two extreme cases for the rate distribution: continuous Gaussian and discrete Bernoulli. We make use of approximate Bayesian computation to calibrate our model using experimental measurements from in vitro infection of murine peritoneal macrophages with spores of the Sterne 34F2 strain of B. anthracis . The calibrated stochastic model allows us to compute the probability of rupture, mean time to rupture, and rupture size distribution, of a macrophage that has been infected with one spore. We also obtain the mean spore and bacterial loads over time for a population of cells, each assumed to be initially infected with a single spore. Our results support the existence of significant heterogeneity in the germination rate, with a subset of spores expected to germinate much later than the majority. Furthermore, in agreement with experimental evidence, our results suggest that most of the spores taken up by macrophages are likely to be eliminated by the host cell, but a few germinated spores may survive phagocytosis and lead to the death of the infected cell. Finally, we discuss how this stochastic modelling approach, together with dose-response data, allows us to quantify and predict individual infection risk following exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2016

Toward Improving the Type IV Cracking Resistance in Cr-Mo Steel Weld Through Thermo-Mechanical Processing

Detailed microstructure characterization of Grade 91 (Modified 9Cr-1Mo, ASTM A387) steel subjected to a thermo-mechanical treatment (TMT) process was performed to rationalize the cross-weld creep properties. A series of thermo-mechanical processing in the austenite phase region, followed by isothermal aging at temperatures at 973 to 1173 K (700 to 900ºC) was applied to the Grade 91 steel to promote precipitation kinetics of MX (M: Nb and V, X: C and N) in the austenite matrix. Detailed characterization of the base metals after standard tempering confirmed the presence of fine MX dispersion within the tempered martensitic microstructure in steels processed at/and above 1073 K (800 ºC). Relatively low volume fraction of M 23 C 6 precipitates was observed after processing at 1073 K (800 ºC). The cross-weld creep strength after processing was increased with respect to the increase of MX dispersion, indicating that these MX precipitates maintained during weld thermal cycles in the fine grained heat affected zone (FGHAZ) region and thereby contribute to improved creep resistant of welds in comparison to the welds made with the standard “normalization and tempering” processes. Lastly, the steels processed in this specific processing condition showed improved cross-weld creep resistance and sufficient room-temperature toughness. The above data is also analysed based on existing theories of creep deformation based on dislocation climb mechanism.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗
DOE OSTI2012

Optical and electronic properties of highly stable and textured hydrogenated ZnO:Al thin films

Highlights: ► We investigate the impact of hydrogen treatment at high temperature of ZnO:Al film. ► Electrical properties of the ZnO:Al films improved due to hydrogen annealing. ► Optical properties of the ZnO:Al films enhanced due to hydrogen annealing. ► ZnO:Al film properties strongly depend on the hydrogen treatment temperature. -- Abstract: We have experimentally investigated the effects of hydrogen-annealing on the structural, electrical, and optical properties of Al-doped ZnO (ZnO:Al) thin films prepared by RF magnetron sputtering at room temperature. From the X-ray diffraction observations, the orientation of ZnO:Al films was found to be a c-axis in the hexagonal structure. We found that intentionally incorporated hydrogen plays an important role in n-type conduction as a donor, improving free carrier concentration and electrical stability. We simultaneously obtained improved optical transmission and enhanced absorption edge of the ZnO:Al film due to hydrogen-annealing. Our experimental data suggest the hydrogen-annealing process as an important role in the enhancement of electrical and optical properties, which is promising as a back reflector material for thin-film solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Machine learning in electronic-quantum-matter imaging experiments

For centuries, the scientific discovery process has been based on systematic human observation and analysis of natural phenomena. Today, however, automated instrumentation and large-scale data acquisition are generating datasets of such large volume and complexity as to defy conventional scientific methodology. Radically different scientific approaches are needed, and machine learning (ML) shows great promise for research fields such as materials science. Given the success of ML in the analysis of synthetic data representing electronic quantum matter (EQM), the next challenge is to apply this approach to experimental data—for example, to the arrays of complex electronic-structure images17 obtained from atomic-scale visualization of EQM. Here we report the development and training of a suite of artificial neural networks (ANNs) designed to recognize different types of order hidden in such EQM image arrays. These ANNs are used to analyse an archive of experimentally derived EQM image arrays from carrier-doped copper oxide Mott insulators. In these noisy and complex data, the ANNs discover the existence of a lattice-commensurate, four-unit-cell periodic, translational-symmetry-breaking EQM state. Further, the ANNs determine that this state is unidirectional, revealing a coincident nematic EQM state. Here, strong-coupling theories of electronic liquid crystals are consistent with these observations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels

Abstract Three probabilistic methodologies are developed for predicting the long-term creep rupture life of 9–12 wt%Cr ferritic-martensitic steels using their chemical and processing parameters. The framework developed in this research strives to simultaneously make efficient inference along with associated risk, i.e., the uncertainty of estimation. The study highlights the limitations of applying probabilistic machine learning to model creep life and provides suggestions as to how this might be alleviated to make an efficient and accurate model with the evaluation of epistemic uncertainty of each prediction. Based on extensive experimentation, Gaussian Process Regression yielded more accurate inference ( $$Pearson\;correlation\;coefficent> 0.95$$ P e a r s o n c o r r e l a t i o n c o e f f i c e n t > 0.95 for the holdout test set) in addition to meaningful uncertainty estimate (i.e., coverage ranges from 94 to 98% for the test set) as compared to quantile regression and natural gradient boosting algorithm. Furthermore, the possibility of an active learning framework to iteratively explore the material space intelligently was demonstrated by simulating the experimental data collection process. This framework can be subsequently deployed to improve model performance or to explore new alloy domains with minimal experimental effort.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2019

Tool for Searching USEPA’s TMDL Reports Repository to Analyze TMDL Modeling State of the Practice

We report that total maximum daily load (TMDL) reports archived in the USEPA database can provide useful guidance for the development of new TMDLs and watershed management plans by aiding the selection process for the most appropriate modeling tool. The database contains more than 70,000 individual documents; therefore, a rapid screening tool is needed to elicit information about previous modeling studies that might help guide stakeholders and regulators in dealing with the TMDL application at hand, save time, and lead to a more cost-effective regulatory outcome. The paper introduces a smart web-based software tool for TMDL report selection based on different water management criteria. The tool uses an automated search method based on frequency of common water body impairments and models to categorize and select TMDL reports. Additionally, this tool provides better insight on the relationship between the modeling tools used and the impairments they address. This tool has proven useful in reviewing the state of integrated modeling (IM), applications of remote sensing (RS), application of basic versus mechanistic modeling, margin of safety (MOS) assessment, and the state of practice regarding relationships among impairments, models, and regions where TMDLs for various pollutants are being developed. Despite limitations on direct access to all TMDLs developed and reported to the EPA by the user, the tool can be improved over time to derive a better understanding of the relationships between these impairments, data, and the TMDL development process. Finally, although the MOS is not directly quantified in the current version of the TRS tool, this feature may be incorporated in future updates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Data analytics approach for melt-pool geometries in metal additive manufacturing

Modern data analytics was employed to understand and predict physics-based melt-pool formation by fabricating Ni alloy single tracks using powder bed fusion. An extensive database of melt-pool geometries was created, including processing parameters and material characteristics as input features. Correlation analysis provided insight for relationships between process parameters and melt-pools, and enabled the development of meaningful machine learning models via the use of highly correlated features. We successfully demonstrated that data analytics facilitates understanding of the inherent physics and reliable prediction of melt-pool geometries. This approach can serve as a basis for the melt-pool control and process optimization.

36 MATERIALS SCIENCE↗