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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Additive Manufacturing of Thermoset Cellular Structures

Magnum Venus Products (MVP) and Oak Ridge National Laboratory (ORNL) set out to develop and commercialize a 3D printer depositing thermoset polymers with a build volume accommodating mid-scale applications capable of deposition rate exceeding 10 lb/hr with resolution of 3 mm or less. The objective of this work was to demonstrate the ability to efficiently print light weight cellular structures for application in load bearing structural applications, thereby transforming the mid to large scale additive technology. The project was organized around three main tasks: 1. Thermoset printer development, 2. Material and process parameter development and 3. Demonstration. The MVP-ORNL team successfully carried out the project by creating the first thermoset 3D printer of this size in the world and by using it to demonstrate the ability to print cellular structures. The team was awarded the Award for Composites Excellence at the 2018 CAMX conference for this development.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Planned FY19 Creep and Fatigue Design Curve Testing of Alloy 709 Base Metal

This report summarizes the status of planned FY 2019 creep and fatigue testing of the first commercial heat of Alloy 709 base metal at Oak Ridge National Laboratory (ORNL). A comprehensive master creep testing matrix was generated to support the ASME code qualification of Alloy 709. The testing activities and research are carried out at Argonne National Laboratory, Idaho National Laboratory and ORNL. ORNL was tasked to carry out a subset of the creep Code Case testing matrix for electroslag remelt (ESR) with solution annealing (S/A) at 1100°C in FY 2019. In this reporting cycle, 25 creep tests were started and 9 of them produced rupture data. The remaining 16 tests of ESR with S/A at 1100°C, along with 6 long-term creep tests of argon-oxygen-decarburization with S/A at 1100°C, are ongoing.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantitative and interpretable order parameters for phase transitions from persistent homology

Here, we apply modern methods in computational topology to the task of discovering and characterizing phase transitions. As illustrations, we apply our method to four two-dimensional lattice spin models: the Ising, square ice, XY, and fully frustrated XY models. In particular, we use persistent homology, which computes the births and deaths of individual topological features as a coarse-graining scale or sublevel threshold is increased, to summarize multiscale and high-point correlations in a spin configuration. We employ vector representations of this information called persistence images to formulate and perform the statistical task of distinguishing phases. For the models we consider, a simple logistic regression on these images is sufficient to identify the phase transition. Interpretable order parameters are then read from the weights of the regression. This method suffices to identify magnetization, frustration, and vortex-antivortex structure as relevant features for phase transitions in our models. We also define “persistence” critical exponents and study how they are related to those critical exponents usually considered.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The Effect of Process History on Grain Boundaries and Dislocation Substructures on Functional Properties of Nb for SRF Cavities: Plastic Formability and Microstructural Evolution. Final report

While much progress has been made in improving the performance of cavities in the past decade, reproducible performance remains elusive. This renewal of “The cost of grain boundaries on the performance of superconducting cavities” examines in further depth the interactions between forming and annealing processes and functional performance at the scale of dislocation substructure and grain boundaries. There are five tasks identified in the renewal, which are: Characterization / analysis of current cavity technology: Effect of deformation, heat treatment, surface treatments, and residual damage on cavity function (Lee and Bieler) Characterization of dislocation mechanisms in single and bicrystals (Bieler) Quantifying effects of impurities and deformation mechanisms in Nb using density functional theory calculations (Solanki) Crystal plasticity constitutive model development (Pourboghrat and Eisenlohr) Modeling and measuring thermal conductivity in thin layers (Wright)

36 MATERIALS SCIENCE↗
DOE OSTI2022

Pressurized Tube Creep Testing of Graded Transition Joints (GTJ) for G91 and 347H Base Metals

This report briefly describes part of the research activities dedicated to a DOE FE project, graded transition joints (GTJ) development for G91 and 347H steels. The task was focused on pressurized tube creep testing on the 3D-printed GTJ materials. A specimen with reduced gage section is developed that includes G91, GTJ, and 347H subsections. A tapered GTJ subsection is introduced to address the technical challenge of mechanical testing with mismatched rupture times of the base metals. The report provides the descriptions of basic considerations, specimen preparation, testing system, and experimental results along with main findings.

36 MATERIALS SCIENCE↗
NASA NTRS2015

Status: Crewmember Noise Exposures on the International Space Station

The International Space Station (ISS) provides a unique environment where crewmembers from the US and our international partners work and live for as long as 6 to 12 consecutive months. During these long-durations ISS missions, noise exposures from onboard equipment are posing concerns for human factors and crewmember health risks, such as possible reductions in hearing sensitivity, disruptions of crew sleep, interference with speech intelligibility and voice communications, interference with crew task performance, and reduced alarm audibility. It is crucial to control acoustical noise aboard ISS to acceptable noise exposure levels during the work-time period, and to also provide a restful sleep environment during the sleep-time period. Acoustic dosimeter measurements, obtained when the crewmember wears the dosimeter for 24-hour periods, are conducted onboard ISS every 60 days and compared to ISS flight rules. NASA personnel then assess the acoustic environment to which the crewmembers are exposed, and provide recommendations for hearing protection device usage. The purpose of this paper is to provide an update on the status of ISS noise exposure monitoring and hearing conservation strategies, as well as to summarize assessments of acoustic dosimeter data collected since the Increment 36 mission (April 2013). A description of the updated noise level constraints flight rule, as well as the Noise Exposure Estimation Tool and the Noise Hazard Inventory implementation for predicting crew noise exposures and recommending to ISS crewmembers when hearing protection devices are required, will be described.

Limardo-Rodriguez, Jose G.↗
DOE OSTI2022

A deep learning approach for semantic segmentation of unbalanced data in electron tomography of catalytic materials

In computed TEM tomography, image segmentation represents one of the most basic tasks with implications not only for 3D volume visualization, but more importantly for quantitative 3D analysis. In case of large and complex 3D data sets, segmentation can be an extremely difficult and laborious task, and thus has been one of the biggest hurdles for comprehensive 3D analysis. Heterogeneous catalysts have complex surface and bulk structures, and often sparse distribution of catalytic particles with relatively poor intrinsic contrast, which possess a unique challenge for image segmentation, including the current state-of-the-art deep learning methods. To tackle this problem, we apply a deep learning-based approach for the multi-class semantic segmentation of a γ-Alumina/Pt catalytic material in a class imbalance situation. Specifically, we used the weighted focal loss as a loss function and attached it to the U-Net’s fully convolutional network architecture. We assessed the accuracy of our results using Dice similarity coefficient (DSC), recall, precision, and Hausdorff distance (HD) metrics on the overlap between the ground-truth and predicted segmentations. Our adopted U-Net model with the weighted focal loss function achieved an average DSC score of 0.96 ± 0.003 in the γ-Alumina support material and 0.84 ± 0.03 in the Pt NPs segmentation tasks. We report an average boundary-overlap error of less than 2 nm at the 90th percentile of HD for γ-Alumina and Pt NPs segmentations. The complex surface morphology of γ-Alumina and its relation to the Pt NPs were visualized in 3D by the deep learning-assisted automatic segmentation of a large data set of high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) tomography reconstructions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In Situ, Real-Time Diagnostic to Improve the Liquid Metal Jetting Additive Manufacturing Process [Poster]

Liquid Metal Jetting (LMJ) is a metal additive manufacturing technique that involves jetting molten metal droplets at high frequencies to build solid metal parts. As an alternative to industry standard Lazer Powder Bed Fusion (LPBF) and Direct Metal Writing (DMW) techniques, LMJ poses significant advantages including no powder feed stock, no post sintering process, very high deposition rates, and the capability to print various metals. This summer, I was tasked with improving my old system to process at much higher resolution, Increasing the capture rate, and implementing this diagnostic on various LMJ setups. This system provides a vital longitudinal study to understand individual droplets in the LMJ process.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗
DOE OSTI2014

High Explosive Compatibility: Aging and Compatibility of LX-21

The tasks performed in FY14 to support our overall goals include: 1) Select a mature formulation for future aging and compatibility experiments 2) Perform gross compatibility testing for a number of LX-21/material combinations using three different evaluation techniques (DSC, TGA, and CRT) 3) Evaluate moisture sorption and diffusion characteristics of LX-21 4) Get the infrastructure and procedures in place to begin long -term accelerated aging experiments with LX-21.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exxon Mobil-NETL Testing of DAC Materials

This joint initiative is aimed at extending our understanding of real DAC testing conditions at the NETL DAC testbed facility. Of particular focus will be: • Small Scale testing of powdered and formulated materials (supplied by ExxonMobil) to evaluate various performance metrics under DAC process cycles • Pilot scale testing of formulated materials at larger scales ExxonMobil will work with NETL to shake down equipment, validate testing methods, and define best practices for data analysis. Three tasks are proposed: 1. Validation of multi-cycle test data on powdered and formulated materials to benchmark various performance metrics of these materials. 2. Steam regeneration of powdered samples at small scale to define baseline performance under commercially relevant conditions. 3. Pilot scale testing of larger formulated materials to test commercially relevant samples under actual process cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Compilation of PRF Canyon Floor Pan Sample Analysis Results

On September 28, 2015, debris collected from the PRF (236-Z) canyon floor, Pan J, was observed to exhibit chemical reaction. The material had been transferred from the floor pan to a collection tray inside the canyon the previous Friday. Work in the canyon was stopped to allow Industrial Hygiene to perform monitoring of the material reaction. Canyon floor debris that had been sealed out was sequestered at the facility, a recovery plan was developed, and drum inspections were initiated to verify no additional reactions had occurred. On October 13, in-process drums containing other Pan J material were inspected and showed some indication of chemical reaction, limited to discoloration and degradation of inner plastic bags. All Pan J material was sealed back into the canyon and returned to collection trays. Based on the high airborne levels in the canyon during physical debris removal, ETGS (Encapsulation Technology Glycerin Solution) was used as a fogging/lock-down agent. On October 15, subject matter experts confirmed a reaction had occurred between nitrates (both Plutonium Nitrate and Aluminum Nitrate Nonahydrate (ANN) are present) in the Pan J material and the ETGS fixative used to lower airborne radioactivity levels during debris removal. Management stopped the use of fogging/lock-down agents containing glycerin on bulk materials, declared a Management Concern, and initiated the Potential Inadequacy in the Safety Analysis determination process. Additional drum inspections and laboratory analysis of both reacted and unreacted material are planned. This report compiles the results of many different sample analyses conducted by the Pacific Northwest National Laboratory on samples collected from the Plutonium Reclamation Facility (PRF) floor pans by the CH2MHill’s Plateau Remediation Company (CHPRC). Revision 1 added Appendix G that reports the results of the Gas Generation Rate and methodology. The scope of analyses requested by CHPRC includes the determination of common anions, gamma spectrometry, metals, corrosivity, organics and alpha spectrometry (note: alpha spectrometry was cancelled during the performance of this work with concurrence from CHPRC). Results may help elucidate the components that led to the unexpected reaction in the canyon as well as inform the radiological and hazardous characteristics. The specific anions, gamma emitters, organics and metals requested by CHPRC are provided in the analytical reports sections. The individual analyses were conducted under the Plutonium Finishing Plant (PFP) Floor Pan Evaluation Project Quality Assurance Project Plan (PFP Floor Pan Evaluation QAPP, Revision 0.) developed by PNNL specifically for this project. The final reports for each analysis set are included in this compilation of the results. Each package was reviewed under the PFP Floor Pan Evaluation Project Quality Assurance Project Plan so no additional reviews were conducted in this compilation task. The Gas Generation Rates in Appendix G were conducted under the PNNL “How Do I…” quality assurance program and were NOT conducted under the Plutonium Finishing Plant (PFP) Floor Pan Evaluation Project Quality Assurance Project Plan (PFP Floor Pan Evaluation QAPP, Revision 0.).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Validation of Material Models For Automotive Carbon Fiber Composite Structures Via Physical And Crash Testing (VMM Composites Project)

As automotive fuel economy requirements increase, the push for reducing overall vehicle weight will likely include the consideration of materials that have not previously been part of mainstream vehicle design and manufacturing, including carbon fiber composites. Vehicle manufacturers currently rely on computer-aided engineering (CAE) methods as part of the design and development process, so going forward, the ability to accurately and predictably model carbon fiber composites will be necessary. If composites are to be used for structural components, this need applies to both, crash and quasi-static modeling. This final report covers the results of a five-year, $6.89M, 50% cost-shared research project between Department of Energy (DOE) and the US Advanced Materials Partnership (USAMP) under Cooperative Agreement DE-EE-0005661 known as “Validation of Material Models for Automotive Carbon Fiber Composite Structures Via Physical and Crash Testing (VMM).” The objective of the VMM Composites Project was to validate and assess the ability of physics-based material models to predict crash performance of automotive primary load-carrying carbon fiber composite structures. Simulation material models that were evaluated included micro-mechanics based meso-scale models developed by the University of Michigan (UM) and micro-plane models by Northwestern University (NWU) under previous collaborations with the DOE and Automotive Composites Consortium/USAMP, as well as five commercial crash codes: LS-DYNA, RADIOSS, VPS/PAM-CRASH, Abaqus, and GENOA-MCQ. CAE predictions obtained from seven organizations were compared with experimental results from quasi-static testing and dynamic crash testing of a thermoset carbon fiber composite front-bumper and crush-can (FBCC) system gathered under multiple loading conditions. This FBCC design was developed to demonstrate progressive crush, virtual simulation, tooling, fabrication, assembly, non-destructive evaluation and crash testing advances in order to assess the correlation of the predicted results to the physical tests. The FBCC was developed to meet a goal of 30-35% mass reduction while aiming for equivalent energy absorption as a steel component for which baseline experimental results were obtained from testing in the same crash modes. The project also evaluated crash performance of thermoplastic composite structures fabricated from commercial prepreg materials and low cost carbon fiber sourced from Oak Ridge National Laboratory. The VMM Project determined that no set of predictions from a CAE supplier were found to be universally accurate among all the six crash modes evaluated. In general, crash modes that were most dependent on the properties of the prepreg were more accurate than those that were dependent on the behavior of the joints. The project found that current CAE modeling methods or best practices for carbon fiber composites have not achieved standardization, and accuracy of CAE is highly reliant on the experience of its users. Coupon tests alone are not sufficient to develop an accurate material model, but it is necessary to bridge the gap between the coupon data and performance of the actual structure with a series of subcomponent level tests. Much of the unreliability of the predictions can be attributed to shortcomings in our ability to mathematically link the effects of manufacturing and material variability into the material models. This is a subject of ongoing research in the industry. The final report is organized by key technical tasks to describe how the validation project developed, modeled and compared crash data obtained on the composite FBCC to the multiple sets of CAE predictions. Highlights of the report include a discussion of the quantitative comparison between predictions and experimental data, as well as an in-depth discussion of remaining technological gaps that exist in the industry, which are intended to spur innovations and improvements in CAE technology.

36 MATERIALS SCIENCE↗
DOE OSTI2023

LANL end of the year report (2023)

This report summarizes the advances made by LANL in the XMAT-H2 program over FY23. In summary LANL was tasked with leading efforts related to (1) the development of models predicting hydrogen (H) redistribution within polycrystals (H redistribution model), (2) the development of models for the mechanical performance of metals/steels under H environments (Model for H effects on plasticity), The development of a framework to quantify the uncertainty in model parameters (Uncertainty in model parameters), (3) small scale characterization of the mechanical response of 347H (Microstructure characterization).

08 HYDROGEN↗
DOE OSTI2023

Visualizing Uranium Crystallization from Melt: Experiment-Informed Phase Field Modeling and Machine Learning

The focus of this project was to observe and simulate the solidification of uranium metal at the crystallographic level from its molten state. Melting experiments were conducted at two different scales to observe microstructural evolution using either a laboratory-scale induction furnace (hundreds of grams of metal) or a microscope heating stage (hundreds of milligrams of metal), respectively. Experimental parameters and characterization data were then used to inform a phase field model of gamma-U crystal growth as dendrites with or without secondary phase impurities in the form of uranium carbide particles. Finally, training datasets were generated by the phase field model as inputs to a neural network, developed with the aim of providing a faster, cheaper surrogate model for microstructural simulations within a given parameter space. Progress is reported herein for each of these task areas. Ultimately, 1) an optical microscope heating stage capability has been stood-up for uranium metal solidification studies, 2) a phase field model was advanced to simulate multiple uranium grains growing in the presence of carbide impurity particles and 3) a neural network was constructed and optimized to predict the microstructure features of individually growing uranium crystals.

36 MATERIALS SCIENCE↗