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At least 1,297 records · Page 72

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DOE OSTI2022

Doping Metallic Grain Boundaries to Control Atomic Structure and Damage Tolerance (Final Report)

The interfaces between crystals, known as grain boundaries, often act as sites for crack and void nucleation during plastic deformation of metallic materials. While it is known that interfacial character and structural state can greatly influence this damage nucleation process, the current level of control over such details is extremely limited. The objective of this project was to obtain a fundamental understanding of how metallic grain boundary structure can be controlled through intelligent doping, with the idea of inducing planned grain boundary phases or complexions. The effect of complexion structure on dislocation accommodation mechanisms was studied, to improve the field’s understanding of damage nucleation at interfaces. While mechanical damage was the primary focus, the project showed that amorphous complexions can also improve a material’s resistance to radiation damage, providing a single materials design concept that can address multiple important technological areas. This research used a combination of computational, experimental, and characterization techniques to isolate and understand the importance of nanoscale grain boundary structure and interfacial chemistry. The fundamental insights provided by this project enable the creation of advanced engineering metals with improved damage tolerance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Modeling of SRS Aluminum-clad Spent Nuclear Fuel in Standard DOE Sealed Canisters

At the Savannah River Site (SRS), approximately 7 MTHM of aluminum-clad spent nuclear fuel is currently in wet storage at L-basin. A significant portion of this fuel is slated to be dried and sealed in DOE standard canisters for long-term storage. This report will focus on a few fuel types, which should bound all other fuel stored at SRS. These consist of the reference fuel assembly, the MURR fuel and the HFIR fuel. These bound a hypothetical maximum scenario for MTR box fuel, a maximum for decay heat in an assembly, and a maximum for the aluminum surface area, respectively. The geometry for the packages were created from DOE specifications for storage of each of the fuels. As long as the minimum time from reactor discharge to sealed storage is 3 years, then the highest temperatures which occur within the sealed canisters do not exceed 100° C for all scenarios considered here. In the geometry considered, while the HFIR fuel has a large surface area, the packaging configuration leaves large void areas, such that the aluminum surface to free volume ratio is about 30% less than an ATR or MURR packaging configuration.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Foams in Hohlraums

Foam materials are starting to find application in laser-heated hohlraums used to drive inertial confinement fusion (ICF) implosions. The disparate scales of the solid foam elements (nm) compared to the typical problem scale (mm) make it expensive to resolve the individual foam elements in hydrodynamic calculations. In this project we developed two new models for treating foam materials – a 3D hydrodynamic model that treats the foam as bits of pre-expanded solid material separated by voids, and a subgrid model that captures the essential physics of laser-heated foams without requiring resolution of the individual solid elements. We also explored the microphysics of the transition from interpenetrating kinetic behavior to fully-collisional fluid-like behavior during the early time heating and expansion of foam elements through a combination of single-fluid and multi-fluid hydrodynamics calculations and particle-in-cell calculations. The model was benchmarked against these calculations and against experimental data on laser-heated chemical and additive-manufactured (AM) foams. Since there was no existing data on laser-heated AM foams, we performed the first experiments on such foams at the Jupiter Laser Facility. Samples of three different types of printed AM foams were heated using a single 0.5 micron laser. The foam densities ranged from 10 to 100 mg/cc (supercritical for 0.5 micron light). We measured the backscattered light (power and spectrum), the transmitted light, and the time-integrated temperature of the foam samples. This benchmarked subgrid foam model is a new capability that should allow routine calculation of ICF designs using foam elements, potentially opening up a new design space.

36 MATERIALS SCIENCE↗

DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Neutron Imaging Using Grating Interferometry: Exploiting phase contrast and dark-field imaging for <1μm feature detection in bulk materials (Summary Report)

Radiography is an important non-destructive characterization tool for many LANL missions. Contrast in conventional radiography results from attenuation of the incoming x-ray or neutron beam by the object. Neutrons have a great benefit in detecting light elements (e.g. hydrogen) in the presence of heavier elements and penetrate deeper than X-rays into many materials, but neutron radiography resolution is typically limited to ~50 μm. Cracks, voids, pores and a variety of other features smaller than this resolution are therefore undetectable in attenuation based neutron imaging. However, novel neutron imaging modalities leveraging off of interferometric methods, so-called neutron grating interferometry (NGI), are able to spatially map sub-micron features and interfacial features while also exploiting the neutron’s sensitivity to light elements and isotope specific contrast to enable material characterization otherwise impossible.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Q4FY20 Quarterly Report. LL20-AM-sensor

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Project is 13 months into the 30 month on complete project plan. We are continuing effort on 3 main technical thrusts: algorithm development, multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In FY20 we underspent the initial budget, partly due to work restrictions from Covid-19 pandemic, but are working to make up lost time

42 ENGINEERING↗

DOE OSTI2020

Tardigrade: Summary (LL20-AM-sensor)

Tardigrade is an effort to create small, passive remote sensor for material detection and identification. The microbolometer based sensor design uses custom bandpass filter bands combined with tuned thermal expansion materials in a ruggedized lens housing to provide passive mechanical correction for optical changes across a wide temperature range. This requires developing computational tools to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Current efforts in the project are in 3 areas: material design algorithm development (transitioning into full 3D material design space), multi-material additive manufacturing process exploration, and sensor/optics design and engineering.

42 ENGINEERING↗

DOE OSTI2021

LL20-AM-sensor (Quarterly Report)

42 ENGINEERING↗

DOE OSTI2021

Q2FY21 Quarterly Report LL20-AM-sensor

42 ENGINEERING↗

DOE OSTI2021

Associating damage nucleation and distribution with grain boundary characteristics in Ta [Poster]

The goal of the project was to develop a void nucleation criterion along with growth information as a function of material microstructure to provide input to damage models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Dual-Purpose Canister Filling Demonstration Project Progress Report

This report discusses the initial progress made at the Oak Ridge National Laboratory to support direct disposal of dual-purpose canisters (DPCs) using filler materials to demonstrate that the probability of criticality in DPCs during disposal to be below the probability for inclusion in a repository performance assessment. In the initial phase of a multi-phase effort that will result in a full-scale demonstration, a computational fluid dynamics (CFD) model was developed to gauge the filling process and to uncover any unforeseen issues. The initial filling simulations of the lower region (mouse holes) of a prototypic DPC show successful removal of the inner space voids and smooth, even progression of the liquid level. In the initial phase, flow through a pipe that is similar to the drain pipe in a DPC will be investigated separately to gain valuable insight of flow regime inside a pipe. The initial experimental setups for validating the computational filling model have been designed, and the various assembly parts are being procured. The experience gained from the initial experiments will be applied to the next steps toward a full-scale demonstration and to the validation of multiphysics filling simulation models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

ZPPR-15 Small Reactivity Worth Experiments

In addition to measuring the worth of sodium voiding and the worth of simulated control rods, the ZPPR staff measured the Doppler worth of four standard samples, the worth of axial expansion and the worth of radial bowing in ZPPR-15. Doppler sample worths were measured in ZPPR-15A, ZPPR-15B and ZPPR-15D. The Doppler samples were natural UO 2 , depleted uranium metal, depleted U-10Zr alloy and 33% enriched UO 2 . These samples were placed in a sample capsule in the inner core of ZPPR-15 and heated to various temperatures between 300 K and 1100 K. The resulting changes in reactivity relative to the reference configuration were measured to determine the Doppler worths of the samples. Special segmented drawers were constructed to measure the worth of axial expansion in ZPPR15. The segments were connected to each other, and the back segment was connected to a spring-loaded cable. Pulling the cable introduced small gaps between drawer segments to simulate axial expansion, and releasing the cable eliminated those gaps. The worth of this simulated axial expansion was measured in ZPPR-15A, ZPPR-15B and ZPPR-15D. The worth of radial bowing was measured in ZPPR-15, but the experimenters regarded the result as unsatisfactory. Radial bowing was measured by two methods in ZPPR-17A, so the ZPPR-17A bowing measurements were analyzed in place of the unpublished ZPPR-15 radial bowing measurement. The first ZPPR-17A measurement consisted of rearranging the plates in 96 core drawers at the core-radial blanket boundary to simulate outward motion of the fuel during bowing. The second ZPPR-17A measurement used a new bowing oscillator to move the core plates a small distance vertically in a special drawer. The published uncertainties for the Doppler worth, axial expansion worth and radial bowing worth measurements are the statistical uncertainties in the measurements. In reality, the published uncertainty for each of these measurements is just one component of the total uncertainty. There are additional uncertainties specific to each measurement and a common uncertainty related to conversion from the natural measurement units, cents, to pcm for calculations. A full uncertainty analysis was performed for each measurement. The significant uncertainties were quantified, and a total uncertainty was determined for each measurement. The ZPPR-15 and ZPPR-17A experimental records were used to create detailed as-built Monte Carlo models of the Doppler worth, axial expansion and radial bowing measurement configurations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Q3FY21 Quarterly Report LL20-AM-sensor

36 MATERIALS SCIENCE↗

DOE OSTI2021

Developing a New Polyolefin Precursor for Low-Cost, High-Strength Carbon Fiber

Current high-strength carbon fibers (CFs) are mostly derived from PAN polymer precursor, which are very expensive with limited usage in the high-end products. On the other hand, it has also stimulated research interest to develop new low-cost precursors and manufacturing process. Despite numerous studies, researchers have yet to develop a new polymeric precursor that is less expensive, melt-spinnable, and offers a high C-yield. In this three-year R&D project, we proposed three consecutive research phases, including (i) the development of new polyolefin-based precursors that can be thermally transformed to C material in one heating step under inert atmosphere and achieved a C-yield >80%, which is more than 60% higher than that of current PAN precursor, (ii) With the suitable new polyolefin-based precursor, we can focus on CF manufacturing process, including fiber spinning, stabilization, and carbonization, to form high quality carbon fibers with the desirable polymorphous morphology, and (iii) the investigation of thermal conversion process under tension for converting polyolefin-based precursor fibers to high tensile strength carbon fibers with specific graphite crystallite size, orientation of basal line, order-disorder ratio, fiber diameter, and reduced structural defect (voids). composites.

08 HYDROGEN↗

DOE OSTI2021

Behind the Meter Solar+Storage: Market data and trends [Slides]

As the distributed solar market evolves toward more dynamic forms of deployment, interest in paired solar-plus-storage applications continues to gain steam, but details on the current state of the market are relatively sparse. To fill that void, Berkeley Lab has released an in-depth analysis of this budding market segment. This report draws on the Lab’s Tracking the Sun dataset to characterize trends in deployment, system sizing and equipment selection, installer-market development, and system pricing. The report also provides indicative analyses of the financial and resilience value that host customers in several key markets presently receive by pairing storage with solar.

14 SOLAR ENERGY↗

DOE OSTI2021

Performance of Pulsed Thermal Tomography Imaging with Machine Learning-Based Classification of Defects in Additively Manufactured Structures

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermal Tomography Imaging (PTT) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTT capabilities in detecting and visualizing microscopic defects in metallic specimens. The thermal tomography (TT) algorithm obtains depth reconstructions of spatial effusivity from the data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring of images with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images. CNN is trained on a database of TT images created for a set of simulated metallic structures with elliptical subsurface voids. Test of CNN performance demonstrate the ability to classify radii and angular orientation of subsurface defects in TT images. In addition, we have shown that CNN trained on elliptical defects is capable of classifying irregular-shaped defects obtained from scanning electron microscopy (SEM) of stainless steel sections printed with LPBF.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Tardigrade (NA-22 Quarterly Report)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 24 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Tardigrade (Quarterly Report Q1 FY2022)

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. The project is 27 months into the 36-month plan. We are continuing effort on 3 main technical thrusts: material design algorithm development (transitioning into full 3D design), multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In the last quarter, we have focused on developing the code for 3D Topology Optimization, building data sets for 2D autoencoder training, and finishing lens housing engineering for the microbolometer based test sensor. We are exploring 2 potential multi-material AM technologies, a polymer-based technique in development at LLNL and a metal SLS system produced by Aconity, called the AeroSint deposition head.

36 MATERIALS SCIENCE↗