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210 records · Page 12

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗

Re-calibration of PBX9501 SURF model

PBX 9501 is a plastic bonded explosive composed of 95 wt % HMX and a binder; see [Gibbs and Popolato, 1980, pp. 109–119]. SURF is a reactive burn model for shock initiation and propagation of detonation waves. It has previously been calibrated for PBX 9501. Here the SURF model is recalibrated for PBX 9501; specifically, lot 730-010 at ρ = 1.837 g/cc 3 . The new calibration uses the Davis reactants and products EOS calibrated for the AWSD model [Aslam et al., 2020]. The burn rate in the shock initiation regime is fit to the Pop plot from 5 embedded gauge shock-to-detonation transition (SDT) experiments from [Gustavsen et al., 1999, see fig 12 and table 5]. In the propagation regime, the burn rate is fit to curvature effect data (detonation speed as function of front curvature); see [Aslam, 2007]. Also the burn parameters are adjusted to fit the gap-stick experiment [Hill et al., 2018]. Simulating the detonation wave speed in this experiment requires a model that is accurate for initiation with complex shock loading; in particular, a pressure decreasing gradient behind a curved lead shock. This is more difficult than calibrating to the standard SDT experiments which are 1-D and driven by a sustained shock. Simulations of the gap-stick experiment for PBX 9501 with the SURF model will be discussed in a subsequent report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Cardiac atrophy after bed rest and spaceflight

Cardiac muscle adapts well to changes in loading conditions. For example, left ventricular (LV) hypertrophy may be induced physiologically (via exercise training) or pathologically (via hypertension or valvular heart disease). If hypertension is treated, LV hypertrophy regresses, suggesting a sensitivity to LV work. However, whether physical inactivity in nonathletic populations causes adaptive changes in LV mass or even frank atrophy is not clear. We exposed previously sedentary men to 6 (n = 5) and 12 (n = 3) wk of horizontal bed rest. LV and right ventricular (RV) mass and end-diastolic volume were measured using cine magnetic resonance imaging (MRI) at 2, 6, and 12 wk of bed rest; five healthy men were also studied before and after at least 6 wk of routine daily activities as controls. In addition, four astronauts were exposed to the complete elimination of hydrostatic gradients during a spaceflight of 10 days. During bed rest, LV mass decreased by 8.0 +/- 2.2% (P = 0.005) after 6 wk with an additional atrophy of 7.6 +/- 2.3% in the subjects who remained in bed for 12 wk; there was no change in LV mass for the control subjects (153.0 +/- 12.2 vs. 153.4 +/- 12.1 g, P = 0.81). Mean wall thickness decreased (4 +/- 2.5%, P = 0.01) after 6 wk of bed rest associated with the decrease in LV mass, suggesting a physiological remodeling with respect to altered load. LV end-diastolic volume decreased by 14 +/- 1.7% (P = 0.002) after 2 wk of bed rest and changed minimally thereafter. After 6 wk of bed rest, RV free wall mass decreased by 10 +/- 2.7% (P = 0.06) and RV end-diastolic volume by 16 +/- 7.9% (P = 0.06). After spaceflight, LV mass decreased by 12 +/- 6.9% (P = 0.07). In conclusion, cardiac atrophy occurs during prolonged (6 wk) horizontal bed rest and may also occur after short-term spaceflight. We suggest that cardiac atrophy is due to a physiological adaptation to reduced myocardial load and work in real or simulated microgravity and demonstrates the plasticity of cardiac muscle under different loading conditions.

Flight Experiment↗

Micromechanical and fatigue in situ synchrotron characterization of an additively manufactured superalloy with porosity

Additive manufacturing (AM) has the potential to transform component production, but its widespread adoption is constrained by defects, such as pores, that compromise structural integrity. Here, this study investigates the influence of porosity on the micromechanical and fatigue response of AM Inconel 718 (IN718), a widely used aerospace superalloy. One baseline specimen with minimal stochastic porosity and another intentionally seeded with lack of fusion (LOF) pores were examined using high-energy X-ray diffraction microscopy (HEDM) and micro-computed tomography during cyclic loading. Fatigue cracks in the LOF specimen initiated more frequently and at lower cycle counts than in the baseline specimen. The number fraction of fatigue cracks that grew was comparatively higher in the LOF specimen. Grain-level metrics, including stress and diffraction spot widths, were quantified using far-field HEDM. Across the thousands of grains detected in both specimens, the pores in the LOF specimen increased the variability of stress and spot width evolution in the azimuthal direction, the latter serving as a surrogate measure of plastic deformation. However, no correlations emerged between these metrics and grain proximity to fatigue crack initiation sites or pores, underscoring the limitations of grain-averaged metrics for predicting fatigue. Nonetheless, the rich dataset reported here provides a foundation for future modeling efforts.

High-energy X-ray diffraction microscopy↗

Sustainably Sourced, Thermally Resistant, Radiation Hard Biopolymer

This material represents a breakthrough in the production, manufacturing, and application of thermal protection system (TPS) materials and radiation shielding, as this represents the first effort to develop a non-metallic, non-ceramic, biomaterial-based, sustainable TPS with the capability to also act as radiation shielding. Until now, the standing philosophy for radiation shielding involved carrying the shielding at liftoff or utilizing onboard water sources. This shielding material could be grown onboard and applied as needed prior to different radiation landscapes (commonly seen during missions involving gravitational assists). The material is a bioplastic material. Bioplastics are any combination of a biopolymer and a plasticizer. In this case, the biopolymer is a starch-based material and a commonly accessible plasticizer. Starch molecules are composed of two major polymers: amylase and amylopectin. The biopolymer phenolic compounds are common to the ablative thermal protection system family of materials. With similar constituents come similar chemical ablation processes, with the potential to have comparable, if not better, ablation characteristics. It can also be used as a flame-resistant barrier for commercial applications in buildings, homes, cars, and heater firewall material. The biopolymer is observed to undergo chemical transformations (oxidative and structural degradation) at radiation doses that are 1,000 times the maximum dose of an unmanned mission (10-25 Mrad), indicating that it would be a viable candidate for robust radiation shielding. As a comparison, the total integrated radiation dose for a three-year manned mission to Mars is 0.1 krad, far below the radiation limit at which starch molecules degrade. For electron radiation, the biopolymer starches show minimal deterioration when exposed to energies greater than 180 keV. This flame-resistant, thermal-insulating material is non-hazardous and may be sustainably sourced. It poses no hazardous waste threats during its lifecycle. The material composition is radiation-tolerant up to megarad doses, indicating its use as a radiation shielding material. It is lightweight, non-metallic, and able to be mechanically densified, permitting a tunable gradient of thermal and radiation protection as needed. The dual-use (thermal and radiation shielding), sustainable nature of this material makes it suitable for both industrial applications as a sustainable/green building material, and for space applications as thermal protection material and radiation shield.

Pugel, Diane↗

Fluidized Bed Gasification For Conversion of Biomass and Waste Materials to Renewable Hydrogen

This project was undertaken in order to study the potential for hydrogen production, at low cost, from mixtures of biomass and municipal solid waste (MSW). This approach allows for the production of hydrogen with a very low fossil carbon burden, while taking advantage of tipping fees (associated with MSW) to improved process economics. The team sourced three primary feedstocks (wood, MSW, and waste plastics) and characterized them comprehensively using established techniques with a long track record in the field of gasification. All three primary feedstocks were highly reactive and lost most of their mass during initial devolatilization. The production of tars, including heavy tars, was quite high, and was most problematic in the case of the MSW and Waste Plastics feedstocks. Little practical difference was identified between the MSW and Waste Plastics materials, and the addition of bed-forming materials (dolomite and brown alumina) to the feedstocks was found to reduce production of tars during devolatilization under thermogravimetric analysis and/or Fischer Assay conditions. A series of four tests in a lab-scale bubbling-fluidized-bed gasifier, at 50 psig of pressure and about 825 C, confirmed these findings. Pellet feedstocks, broken into fragments, were used for these tests, and pellets comprised of 50% MSW and 50% biomass were found to be the best option in terms of fossil carbon burden, economic potential, and gasification characteristics. Tests were then undertaken in a pilot-scale gasifier facility based on the GTI U-Gas fluidized-bed gasification technology. The feedstock handling train of the 20 TPD U-Gas pilot-scale gasifier located in Des Plaines, IL, was operated under simulated gasification conditions, and the 50/50 pellets were found to be very robust and unproblematic. An improved design for the forward end of the feedstock injection screw of the gasifier was developed and installed. The design approach was based on improved passive cooling of the front-most shroud at the end of the screw, since this approach was found in comprehensive modeling studies to be more than sufficient to accomplish the project objectives associated with this phase of the work, while also avoiding thermal gradients that could have caused heat-stress-induced damage to the refractory around the feedstock inlet if an active cooling approach had been applied. Careful technoeconomic analysis (TEA) of two possible 1000 TPD facilities was carried out. The TEA of conversion of MSW with corn stover in one case, and MSW with woody feedstock in the other case, showed that both had the potential to provide hydrogen at about $1/kg (minimum selling price, 2018 dollar basis). Of the two TEA cases, the one that was based on the conversion of wood in the southeastern USA was found to have slightly better economic potential. The other TEA case was based on a real location in Nebraska and called for corn stover feedstock conversion along with MSW. An underserved communities outreach program plan was developed in cooperation with personnel from the Nebraska Public Power District.

08 HYDROGEN↗

Current capabilities for simulating the extreme distortion of thin structures subjected to severe impacts

The explicit transient dynamics technology in use today for simulating the impact and subsequent transient dynamic response of a structure has its origins in the 'hydrocodes' dating back to the late 1940's. The growth in capability in explicit transient dynamics technology parallels the growth in speed and size of digital computers. Computer software for simulating the explicit transient dynamic response of a structure is characterized by algorithms that use a large number of small steps. In explicit transient dynamics software there is a significant emphasis on speed and simplicity. The finite element technology used to generate the spatial discretization of a structure is based on a compromise between completeness of the representation for the physical processes modelled and speed in execution. That is, since it is expected in every calculation that the deformation will be finite and the material will be strained beyond the elastic range, the geometry and the associated gradient operators must be reconstructed, as well as complex stress-strain models evaluated at every time step. As a result, finite elements derived for explicit transient dynamics software use the simplest and barest constructions possible for computational efficiency while retaining an essential representation of the physical behavior. The best example of this technology is the four-node bending quadrilateral derived by Belytschko, Lin and Tsay. Today, the speed, memory capacity and availability of computer hardware allows a number of the previously used algorithms to be 'improved.' That is, it is possible with today's computing hardware to modify many of the standard algorithms to improve their representation of the physical process at the expense of added complexity and computational effort. The purpose is to review a number of these algorithms and identify the improvements possible. In many instances, both the older, faster version of the algorithm and the improved and somewhat slower version of the algorithm are found implemented together in software. Specifically, the following seven algorithmic items are examined: the invariant time derivatives of stress used in material models expressed in rate form; incremental objectivity and strain used in the numerical integration of the material models; the use of one-point element integration versus mean quadrature; shell elements used to represent the behavior of thin structural components; beam elements based on stress-resultant plasticity versus cross-section integration; the fidelity of elastic-plastic material models in their representation of ductile metals; and the use of Courant subcycling to reduce computational effort.

Key, Samuel W.↗

Mercury Speciation via Diffusive Gradients Thin-Films Technology

Objectives: Develop and test various diffusive gradient in thin films (DGT) samplers for mercury - SRNL is developing reactive DGTS (rDGTs). Test chemistry to differentiate methylmercury from total and/or inorganic mercury in environmental samples. Fabricate and test rDGT samplers for deployment. Deploy the rDGTs in variable settings. Mercury is a persistent-bioaccumulative-toxic environmental pollutant. Mercury exists in different species such as elemental, inorganic, and methyl. Mercury speciation determine mercury behavior and toxicity. For example, methylmercury strongly accumulates in biota such as fish resulting in potential human health impacts. DGTs are innovative samplers to measure water concentration by diffusion and capture. Types of Hg DGTs used: Total Hg, Inorganic Hg, and Methyl Hg. Deployment Sites: Upper Three Runs, Lower Three Runs, Steele Creek, East Fork Poplar Creek (SRNL Sites), Horizons Creek, NOA Creek, Bruners Creek (ORNL Sites). DGT concentration estimates closely matched alternative measurements for soluble mercury at each Oak Ridge site - DGTs did not respond to particulate mercury. DGTs provide a representative measure for biota uptake since they are left out longer which allows for an average exposure concentration. DMA-80 provided an efficient and quick analysis. Variability in site location made a difference in Hg species. DGT preparations and sampling wasn't as quick and simple as microcolumn technology. Copper reagent degraded agarose gel - more work and cleaner data are needed to assess speciation in rDGTs. Future Work: Use cross-linked polyacrylamide for the collection layer for a stronger plastic. Make a copper diffusion layer without agarose. Test copper separation in lab using realistic stream conditions such as high organic carbon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks

A key aspect of how the brain learns and enables decision-making processes is through synaptic interactions. Electrical transmission and communication in a network of synapses are modulated by extracellular fields generated by ionic chemical gradients. Emulating such spatial interactions in synthetic networks can be of potential use for neuromorphic learning and the hardware implementation of artificial intelligence. Here, in this work, we demonstrate that in a network of hydrogen-doped perovskite nickelate devices, electric bias across a single junction can tune the coupling strength between the neighboring cells. Electrical transport measurements and spatially resolved diffraction and nanoprobe X-ray and scanning microwave impedance spectroscopic studies suggest that graded proton distribution in the inhomogeneous medium of hydrogen-doped nickelate film enables this behavior. We further demonstrate signal integration through the coupling of various junctions.

36 MATERIALS SCIENCE↗

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

forest structure↗

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

forest structure↗

Illuminating the pathways to carbon liberation: a systems approach to characterizing the consequential unknowns of carbon transformation and loss from thawing permafrost peatlands (Final Report)

The IsoGenie3 Project delivered new systems-level insights into carbon cycling in thawing permafrost landscapes, with an emphasis on methane and carbon dioxide emissions. From >200 samples from the site collected over a decade, co-analyzed for geochemistry and microbiology, the team recovered ~1,500 assembled microbial genomes and ~1,900 viral population genomes, revealing appreciable genetic novelty - from a new highly abundant bacterial phylum, to novel methane consumers and their activities, to rampant viral novelty. IsoGenie3 linked these organisms to carbon compound transformations (which define the cycling of organic matter in soils, and the loss of the greenhouse gases carbon dioxide and methane), and saw that the microbes at each stage of permafrost thaw had different genetic potential to degrade categories of compounds, expressed that genetic potential differently, and actually transformed carbon compounds into greenhouse gases in different ways. IsoGenie 3 identified that some of the thaw-stage differences were due to plant-microbiome relationships; the plant species across the thaw gradient contributed different carbon compounds into the soil, and hosted distinct microbiota (differing among parts of plants as well as species). Lastly, microbes in the saturated post-thaw conditions appeared likely to contribute to the mobilization and toxification of mercury released during thaw. In parallel with ongoing field sampling and analysis, hypotheses arising from field observations were tested via lab incubation experiments. When communities are taken out of their native habitats, they behave differently, and the team first rigorously quantified the magnitude of this effect on microbiome composition and functional capacity, organic matter composition, and gas production; overall the main system processes were maintained in the lab incubations under the conditions tested. Further, the microbial data could inform geochemical reaction network models of those processes. Then, the team ran experiments with additions of compounds, varying temperature, and “live” vs. “dead” peat (the latter having been gamma irradiated, with a few additional variants to control for methodological artifacts). From these, we (a) determined the importance of plant-derived soluble phenolic compounds in bogs’ extraordinary recalcitrance of organic matter, and carbon gas emissions skewed to carbon dioxide; (b) proposed an abiotic ‘tanning’ mechanism, which could contribute to Sphagnum’s inhibitory effect on anaerobic decomposition through alteration of N availability. IsoGenie3 illuminated longer-term and landscape-scale interactions of permafrost thaw and carbon cycling, advancing knowledge of the drivers of methane dynamics not only across in the permafrost-associated peatland (where hydrology and plant communities dictate microbiomes) but also their interconnected lakes (where sediment carbon quality and resident microbiota are determined by position within lake, and lake features). By leveraging observations of site methane dynamics extending well before this project, the team was able to construct a 44-year portrait of the interplay of permafrost thaw, hydrology, vegetation dynamics, and carbon gas emissions, and the doubling of the fully-thawed fens over this time. From the detailed study of this focal site, IsoGenie3 also aimed to improve model representation of these kinds of sites and processes. To improve predictions of methane transformations, we incorporated acetate and isotope dynamics into the ‘DNDC’ biogeochemistry model. In addition, recovered genomes were grouped into ‘functional groups’, i.e. the genomes that perform a specific function of interest, then used to parameterize maximum growth rate and optimum growth temperature (via signatures in their sequence composition) for the BioCrunch model. The BioCrunch model was then in turn used to test the impact of increasing functional resolution of the microbes, on the carbon gas emissions. Lastly for modeling, the ecosys model was parameterized from the microbial and other data, and used to evaluate drivers of e.g. change in methane emissions. Finally, this project also led to the development of a range of new methods and tools, a new metric of organic matter decomposability, as well as a graph-database solution to multidisciplinary data storage and querying. This project’s ongoing analyses at our focal site also contributed to broader advancements in understanding elements of genetic plasticity and methane metabolism, climate change microbiology and community assembly, global peatland geochemistry and Arctic lakes’ roles in climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗