mkite: A distributed computing platform for high-throughput materials simulations
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36 MATERIALS SCIENCE↗
SEARCH · Engineering Papers
Results for “materials database”
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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Critical statistical assessment of data in metal additive manufacturing
Obtaining high quality data reflecting the relationships between the additive manufacturing (AM) process parameters, material microstructure and mechanical properties is crucial for the use of machine learning in AM. A database of over 4,000 data entries of metal AM was created thanks to a large number of literature studies on key process parameters and indicators of build quality. Meta-analysis reveals critical biases in the literature. Firstly, majority of studies report only high quality builds, these imbalances in reporting result in weak correlation between process parameters, properties and consolidation, limiting the ability of machine learning models to generalize beyond optimized conditions. Nevertheless, the trained models accurately predict yield strength ($R^2 = 0.85$), suggesting that certain process–property relationships are effectively captured within these models. Secondly, quantitative microstructural data are largely absent, limiting the learning of the microstructure-mechanical properties relationships. Finally, current process window identification is based largely on the consolidation, despite significant uncertainty in its measurement. It is important to identify the process map on the basis of not only the consolidation, but also mechanical behaviour under loading. Such a identification shows that 316 L and Inconel have much larger process map (i.e. highly printable) in comparison to the AlSi10Mg and Ti6Al4V.
Additive manufacturing↗
Synthesis and properties of anhydrous rare-earth phosphates, monazite and xenotime: a review
The synthesis methods, crystal structures, and properties of anhydrous monazite and xenotime (REPO 4 ) crystalline materials are summarized within this review. For both monazite and xenotime, currently available Inorganic Crystal Structure Database data were used to study the effects of incorporating different RE cations on the unit cell parameters, cell volumes, densities, and bond lengths. Domains of monazite-type and xenotime-type structures and other AXO4 compounds (A = RE; X = P, As, V) are discussed with respect to cation sizes. Reported chemical and radiation durabilities are summarized. Different synthesis conditions and chemicals used for single crystals and polycrystalline powders, as well as first-principles calculations of the structures and thermophysical properties of these minerals are also provided.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
AI-assisted detector design for the EIC (AID(2)E)
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.
97 MATHEMATICS AND COMPUTING↗
Design sensitivity analysis using EAL. Part 2: Shape design parameters
A numerical implementation of shape design sensitivity analysis of built-up structures is presented, using the versatility and convenience of an existing finite element structural analysis code and its data base management system. This report is a continuation of a previous report on conventional design parameters. The finite element code used in the implementation presented is the Engineering Analysis Language (EAL), which is based on a hybrid analysis method. It has been shown that shape design sensitivity computations can be carried out using the database management system of EAL, without writing a separate program and a separate data base. The material derivative concept of continuum mechanics and an adjoint variable method of design sensitivity analysis are used to derive shape design sensitivity information of structural performances. A domain method of shape design sensitivity analysis and a design component method are used. Displacement and stress functionals are considered as performance criteria.
Dopker, B.↗
In-Field Diffuse Ultraviolet Spectroscopy and Imaging of the Stardust Sample Return Capsule
In-field diffuse Ultraviolet (UV) spectroscopy and imaging systems were developed for the purposes of evaluating the surface chemical composition of spacecraft thermal control coatings and materials. The investigation of these systems and the compilation of an associated UV reflectance and luminescence database were conducted using the Stardust Sample Return Capsule (SRC), located at the Johnson Space Center. Spectral responses of the surfaces of the Stardust forebody and aftbody in both reflectance and fluorescence modes were examined post-flight. In this paper, we report on two primary findings of in-field diffuse UV spectroscopy and imaging: (1) deduction of the thermal history of thermal control coatings of the forebody and (2) bond line variations in the aftbody. In the forebody, the thermal history of thermal control coatings may be deduced from the presence of particular semiconducting defect states associated with ZnO, a common emissivity constituent in thermal control coatings. A spatial dependence of this history was mapped for these regions. In the aftbody, luminescing defect states, associated with Si and SiO2 color centers were found along regions of bond variability.
Pugel, D. Elizabeth↗
Non-Volatile Residue (NVR) Contamination from Dry Handling and Solvent Cleaning
This slide presentation reviews the testing for Non-Volatile Residue contamination transferred to surfaces from handling and solvent cleaning. Included in the presentation is a list of the items tested, formal work instructions dealing with NVR. There is an explanation of the Gravimetric determination method used to test the NVR in a variety of items, i.e., Gloves, Swabs, Garments, Bagging material, film and Wipes. Another method to test for contamination from NVR is the contact transfer method. The use of this method for testing gloves, garments, bagging material and film is explained. Certain equations use in NVR analysis and the use of a database for testing of NVR in consumables are reviewed.
Sovinski, Marjorie F.↗
A High Temperature Cyclic Oxidation Data Base for Selected Materials Tested at NASA Glenn Research Center
The cyclic oxidation test results for some 1000 high temperature commercial and experimental alloys have been collected in an EXCEL database. This database represents over thirty years of research at NASA Glenn Research Center in Cleveland, Ohio. The data is in the form of a series of runs of specific weight change versus time values for a set of samples tested at a given temperature, cycle time, and exposure time. Included on each run is a set of embedded plots of the critical data. The nature of the data is discussed along with analysis of the cyclic oxidation process. In addition examples are given as to how a set of results can be analyzed. The data is assembled on a read-only compact disk which is available on request from Materials Durability Branch, NASA Glenn Research Center, Cleveland, Ohio.
Barrett, Charles A.↗
Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems
This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.
Additive manufacturing↗
Cross Section Sensitivity and Propagated Errors in HZE Exposures
It has long been recognized that galactic cosmic rays are of such high energy that they tend to pass through available shielding materials resulting in exposure of astronauts and equipment within space vehicles and habitats. Any protection provided by shielding materials result not so much from stopping such particles but by changing their physical character in interaction with shielding material nuclei forming, hopefully, less dangerous species. Clearly, the fidelity of the nuclear cross-sections is essential to correct specification of shield design and sensitivity to cross-section error is important in guiding experimental validation of cross-section models and database. We examine the Boltzmann transport equation which is used to calculate dose equivalent during solar minimum, with units (cSv/yr), associated with various depths of shielding materials. The dose equivalent is a weighted sum of contributions from neutrons, protons, light ions, medium ions and heavy ions. We investigate the sensitivity of dose equivalent calculations due to errors in nuclear fragmentation cross-sections. We do this error analysis for all possible projectile-fragment combinations (14,365 such combinations) to estimate the sensitivity of the shielding calculations to errors in the nuclear fragmentation cross-sections. Numerical differentiation with respect to the cross-sections will be evaluated in a broad class of materials including polyethylene, aluminum and copper. We will identify the most important cross-sections for further experimental study and evaluate their impact on propagated errors in shielding estimates.
Heinbockel, John H.↗
Potential Alternatives Report for Validation of Alternatives to Aliphatic Isocyanate Polyurethanes
Identifying and selecting alternative materials and technologies that have the potential to reduce the identified HazMats and hazardous air pollutants (HAPs), while incorporating sound corrosion prevention and control technologies, is a complicated task due to the fast pace at which new technologies emerge and rules change. The alternatives are identified through literature searches, electronic database and Internet searches, surveys, and/or personal and professional contacts. Available test data was then compiled on the proposed alternatives to determine if the materials meet the test objectives or if further)laboratory or field-testing will be required. After reviewing technical information documented in the PAR, government representatives, technical representatives from the affected facilities, and other stakeholders involved in the process will select the list of viable alternative coatings for consideration and testing under the project's Joint Test Protocol entitled Joint Test Protocol for Validation of Alternatives to Aliphatic Isocyanate Polyurethanes and Field Test Plan entitled Field Evaluations Test Plan for Validation of Alternatives to Aliphatic Isocyanate Polyurethanes, both prepared by ITB. Test results will be reported in a Joint Test Report upon completion oftesting. The selection rationale and conclusions are documented in this PAR. A cost benefit analysis will be prepared to quantify the estimated capital and process costs of coating alternatives and cost savings relative to the current coating processes, however, some initial cost data has been included in this PAR. For this coatings project, isocyanates, as found in aliphatic isocyanate polyurethanes, were identified as the target HazMat to be eliminated. Table 1-1 lists the target HazMats, the related process and application, current specifications, and affected programs.
Lewis, pattie↗
ElectroCat (Electrocatalysis Consortium) [Slides]
ElectroCat was created as part of the USDOE Energy Materials Network in February 2016. The mission of the collaboration is to develop and implement PGM-free catalysts and electrodes by streamlining access to unique synthesis and characterization tools across national labs, developing missing strategic capabilities, and curating a public database of information. This presentation provides an overview of their work.
30 DIRECT ENERGY CONVERSION↗
A Bayesian framework for adsorption energy prediction on bimetallic alloy catalysts
Abstract For high-throughput screening of materials for heterogeneous catalysis, scaling relations provides an efficient scheme to estimate the chemisorption energies of hydrogenated species. However, conditioning on a single descriptor ignores the model uncertainty and leads to suboptimal prediction of the chemisorption energy. In this article, we extend the single descriptor linear scaling relation to a multi-descriptor linear regression models to leverage the correlation between adsorption energy of any two pair of adsorbates. With a large dataset, we use Bayesian Information Criteria (BIC) as the model evidence to select the best linear regression model. Furthermore, Gaussian Process Regression (GPR) based on the meaningful convolution of physical properties of the metal-adsorbate complex can be used to predict the baseline residual of the selected model. This integrated Bayesian model selection and Gaussian process regression, dubbed as residual learning, can achieve performance comparable to standard DFT error (0.1 eV) for most adsorbate system. For sparse and small datasets, we propose an ad hoc Bayesian Model Averaging (BMA) approach to make a robust prediction. With this Bayesian framework, we significantly reduce the model uncertainty and improve the prediction accuracy. The possibilities of the framework for high-throughput catalytic materials exploration in a realistic setting is illustrated using large and small sets of both dense and sparse simulated dataset generated from a public database of bimetallic alloys available in Catalysis-Hub.org.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Accurate machine-learning predictions of coercivity in high-performance permanent magnets
Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH max . While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (H c ). Although it is possible to calculate H c theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H c predictions for materials such as Ce-doped Nd 2 Fe 14 B, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to H c . With DFT calculations, we predict the Nd-site-dependent magnetic anisotropy behavior in Nd 2 Fe 14 B, confirming that Nd 4g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T c ). Finally, both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict H c with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Integration of the QMSFRG Database into the HZETRN Code
Accurate nuclear interaction data bases are needed for describing the transport of space radiation in matter including space craft structures, atmospheres, and tissues. Transport models support the identification and development of new material concepts for human and electronic part protection. Quantum effects are manifested in nuclear reactions in several ways including interference effects between terms in the multiple scattering series, the many-body nuclear wave functions (for e.g. the roles of shell structure and Fermi momentum) and nuclear clustering. The quantum multiple scattering fragmentation model (QMSFRG) is a comprehensive model for generating nuclear interaction databases for galactic cosmic ray (GCR) transport. Other nuclear databases including the NUCFRG model and Monte-Carlo simulation codes such as FLUKA, LAHET, HETC, and GEANT ignore quantum effects. These codes fail to describe many important features of nuclear reactions and are thus inaccurate for the evaluation of materials for radiation protection. Previously we have shown that quantum effects are manifested through constructive interference in forward production spectra, the effects of Fermi momentum on production spectra, cluster nuclei knockout, and the nuclear response function. Quantum effects are especially important for heavy ions with mass numbers less than 20 that dominate radiation transport in human tissues and for the materials that are expected to be superior in space radiation protection. We describe the integration of the QMSFRG model into the HZETRN transport code. Integration milestones include proper treatment of odd-even charge-mass effects in nuclear fragmentation and the momentum distribution of nucleon production from GCR primary heavy ions. We have also modified the two-body amplitudes in the model to include nuclear medium effects. In order to include a comprehensive description of the GCR isotopic composition in materials, we have described the isotopic composition of the GCR by extending the 59-isotope version of HZETRN to an 120-isotope version. The isotopic composition of most primary GCR elements (including H, He, C, N, O, Ne, Mg, Si, Ar, Ca, Cr, and Fe) are included in the extended model. We discuss results for the high-energy neutron composition inside materials, and the charge and mass distribution for benchmark GCR problems.
Cucinotta, F. A.↗
BISON-FIPD integration enhanced low-burnup SFR metallic fuel swelling model evaluation framework
Experiments indicated that metallic fuel in sodium-cooled fast reactors (SFRs) rapidly swells radially and axially at low burnup. Despite that, prior studies have been focused on describing high burnup axial fuel elongation. With recent conventional and non-conventional metallic fuel concepts being considered for license applications, understanding multidimensional fuel swelling at a wide range of burnup levels is important to fuel analysis and qualification activities. Here, we report the development and demonstration efforts of a low-burnup SFR metallic fuel swelling model evaluation framework using the BISON advanced fuel performance code. The framework leverages the Integral Fast Reactor (IFR) program X423 experiment data through the ongoing integration project to enable standardized and automated use of legacy metallic fuel irradiation data maintained in the SFR fuel irradiation and physics database (FIPD) for BISON metallic fuel model verification and validation. In conclusion, the performance of the framework was demonstrated using the two representative metallic fuel swelling model sets implemented in BISON, with a series of insights about future advanced swelling model development.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction
Optical device design is typically an iterative optimization process based on a good initial guess from prior reports. Optical properties databases are useful in this process but difficult to compile because their parsing requires finding relevant papers and manually converting graphical emissivity curves to data tables. Here, we present two contributions: one is a dataset of thermal emissivity records with design-related parameters, and the other is a software tool for automated colored curve data extraction from scientific plots. We manually collected 64 papers with 176 figures reporting thermal emissivity and automatically retrieved 153 colored curve data records. The automated figure analysis software pipeline uses Faster R-CNN for axes and legend object detection, EasyOCR for axes numbering recognition, and k-means clustering for colored curve retrieval. Additionally, we manually extracted geometry, materials, and method information from the text to add necessary metadata to each emissivity curve. Finally, we analyzed the dataset to determine the dominant classes of emissivity curves and determine the underlying design parameters leading to a type of emissivity profile.
47 OTHER INSTRUMENTATION↗
Monolithic Cu-Cr-Nb Alloys for High Temperature, High Heat Flux Applications
Work during the prior four years of this grant has resulted in significant advances in the development of Cu-8 Cr4 Nb and related Cu-Cr-Nb alloys. The alloys are nearing commercial use in the Reusable Launch Vehicle (RLV) where they are candidate materials for the thrust cell liners of the aerospike engines being developed by Rocketdyne. During the fifth and final year of the grant, it is proposed to complete development of the design level database of mechanical and thermophysical properties and transfer it to NASA Glenn Research Center and Rocketdyne. The database development work will be divided into three main areas: Thermophysical Database Augmentation, Mechanical Testing and Metallography and Fractography. In addition to the database development, work will continue that is focussed on the production of alternatives to the powder metallurgy alloys currently used. Exploration of alternative alloys will be aimed at both the development of lower cost materials and higher performance materials. A key element of this effort will be the use of Thermo-Calc software to survey the solubility behavior of a wide range of alloying elements in a copper matrix. The ultimate goals would be to define suitable alloy compositions and processing routes to produce thin sheets of the material at either a lower cost, or, with improved mechanical and thermal properties compared to the current Cu-Cr-Nb powder metallurgy alloys.
Ellis, David L.↗