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DOE OSTI2016

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

2024 Incubator Final Report: Active Hybrid Mooring

This Incubator project has defined and developed a representation of an active hybrid mooring (AHM) system in Simulink to assess whether the speed and accuracy of AHM is sufficient to warrant further investigation in a larger project. We first built a representative mooring system for the VolturnUS-S 15MW floating offshore wind turbine (FOWT) in 1,000-meter water depth to provide baseline loads and displacements needed in the AHM system, with the results showing off-the-shelf actuators would be practical. We then defined model configurations for the numerical and physical substructures, using standalone MoorDyn v2 with a novel input strategy and chained hydraulic actuators represented in Simulink Simscape, respectively. The remainder of the project has focused on verifying the existing models for speed and accuracy against OpenFAST simulations, increasing their complexity to enable six degree of freedom (6DOF) operation, and connecting the numerical and physical substructures into a single holistic model. The verification phase of the project has shown promising results, though further refinement is needed to resolve accuracy discrepancies and for robust performance across a wider range of metocean conditions. The numerical model we developed is computationally stable and operates faster than real time using a time step of 0.01 seconds or shorter, indicating the model is likely fast enough for our AHM design to operate successfully. Additionally, the Simscape model of a hydraulic actuator operates with less than 0.3% relative error when actuating mooring loads from an OpenFAST simulation of the VolturnUS-S 1,000-meter mooring system. The aforementined refinements will be performed in the next year as a Continuing Incubator, with key remaining tasks include programming the predictor-corrector loop to minimize movement error, testing against more complex nonlinear wave cases, and expanding to enable loads from connected shared mooring lines.

17 WIND ENERGY↗

DOE OSTI2017

How does the cosmic large-scale structure bias the Hubble diagram?

The Hubble diagram is one of the cornerstones of observational cosmology. It is usually analysed assuming that, on average, the underlying relation between magnitude and redshift matches the prediction of a Friedmann-Lemaître-Robertson-Walker model. However, the inhomogeneity of the Universe generically biases these observables, mainly due to peculiar velocities and gravitational lensing, in a way that depends on the notion of average used in theoretical calculations. In this article, we carefully derive the notion of average which corresponds to the observation of the Hubble diagram. We then calculate its bias at second-order in cosmological perturbations, and estimate the consequences on the inference of cosmological parameters, for various current and future surveys. We find that this bias deeply affects direct estimations of the evolution of the dark-energy equation of state. However, errors in the standard inference of cosmological parameters remain smaller than observational uncertainties, even though they reach percent level on some parameters; they reduce to sub-percent level if an optimal distance indicator is used.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

2D HSQC Chemical Shifts of Impurities from Biomass Pretreatment

Two dimensional (2D) heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) is a powerful analytical method which can be used to elucidate the structure of biomass. During processing, biomass is typically subjected to some form of chemical treatment which can be performed with a variety of compounds. The presence of these compounds, even in trace amounts, has the potential to contaminate the sample and lead to misinterpretation of the HSQC spectra. Here we report the chemical shifts of 29 compounds commonly used in biomass processing which have the potential to contaminate the biomass samples and lead to the misinterpretation of peaks associated with biomass (Populus trichocarpa ) pretreated via autohydrolysis. The identification of these chemical shifts could serve as a valuable tool in preventing errors in characterizing biomass via HSQC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Estimating the impacts of natural gas power generation growth on solar electricity development: PJM's evolving resource mix and ramping capability

Abstract Expansion of distributed solar photovoltaic (PV) and natural gas‐fired generation capacity in the United States has put a renewed spotlight on methods and tools for power system planning and grid modernization. This article investigates the impact of increasing natural gas‐fired electricity generation assets on installed distributed solar PV systems in the Pennsylvania–New Jersey–Maryland (PJM) Interconnection in the United States over the period 2008–2018. We developed an empirical dynamic panel data model using the system‐generalized method of moments (system‐GMM) estimation approach. The model accounts for the impact of past and current technical, market and policy changes over time, forecasting errors, and business cycles by controlling for PJM jurisdictions‐level effects and year fixed effects. Using an instrumental variable to control for endogeneity, we concluded that natural gas does not crowd out renewables like solar PV in the PJM capacity market; however, we also found considerable heterogeneity. Such heterogeneity was displayed in the relationship between solar PV systems and electricity prices. More interestingly, we found no evidence suggesting any relationship between distributed solar PV development and nuclear, coal, hydro, or electricity consumption. In addition, considering policy effects of state renewable portfolio standards, net energy metering, differences in the PJM market structure, and other demand and cost‐related factors proved important in assessing their impacts on solar PV generation capacity, including energy storage as a non‐wire alternative policy technique. This article is categorized under: Photovoltaics > Economics and Policy Fossil Fuels > Climate and Environment Energy Systems Economics > Economics and Policy

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Impacts of Bulk Microphysics Scheme Structural Choices on Simulations of Rain Initiation Through Drop Coalescence

This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.

320 (cloud physics and chemistry)↗

DOE OSTI2019

Investigating the Effect of the Closure in Partially-Averaged Navier–Stokes Equations

For this study, the importance of the turbulence closure to the modeling accuracy of the partially-averaged Navier–Stokes equations (PANS) is investigated in prediction of the flow around a circular cylinder at Reynolds number of 3900. A series of PANS calculations at various degrees of physical resolution is conducted using three Reynolds-averaged Navier–Stokes equations (RANS)-based closures: the standard, shear-stress transport (SST), and turbulent/nonturbulent (TNT) k–ω models. The latter is proposed in this work. The results illustrate the dependence of PANS on the closure. At coarse physical resolutions, a narrower range of scales is resolved so that the influence of the closure on the simulations accuracy increases significantly. Among all closures, PANS–TNT achieves the lowest comparison errors. The reduced sensitivity of this closure to freestream turbulence quantities and the absence of auxiliary functions from its governing equations are certainly contributing to this result. It is demonstrated that the use of partial turbulence quantities in such auxiliary functions calibrated for total turbulent (RANS) quantities affects their behavior. On the other hand, the successive increase of physical resolution reduces the relevance of the closure, causing the convergence of the three models toward the same solution. Finally, this outcome is achieved once the physical resolution and closure guarantee the precise replication of the spatial development of the key coherent structures of the flow.

42 ENGINEERING↗

DOE OSTI2016

SU-F-J-140: Using Handheld Stereo Depth Cameras to Extend Medical Imaging for Radiation Therapy Planning

Purpose: A correct body contour is essential for the accuracy of dose calculation in radiation therapy. While modern medical imaging technologies provide highly accurate representations of body contours, there are times when a patient’s anatomy cannot be fully captured or there is a lack of easy access to CT/MRI scanning. Recently, handheld cameras have emerged that are capable of performing three dimensional (3D) scans of patient surface anatomy. By combining 3D camera and medical imaging data, the patient’s surface contour can be fully captured. Methods: A proof-of-concept system matches a patient surface model, created using a handheld stereo depth camera (DC), to the available areas of a body contour segmented from a CT scan. The matched surface contour is then converted to a DICOM structure and added to the CT dataset to provide additional contour information. In order to evaluate the system, a 3D model of a patient was created by segmenting the body contour with a treatment planning system (TPS) and fabricated with a 3D printer. A DC and associated software were used to create a 3D scan of the printed phantom. The surface created by the camera was then registered to a CT model that had been cropped to simulate missing scan data. The aligned surface was then imported into the TPS and compared with the originally segmented contour. Results: The RMS error for the alignment between the camera and cropped CT models was 2.26 mm. Mean distance between the aligned camera surface and ground truth model was −1.23 +/−2.47 mm. Maximum deviations were < 1 cm and occurred in areas of high concavity or where anatomy was close to the couch. Conclusion: The proof-of-concept study shows an accurate, easy and affordable method to extend medical imaging for radiation therapy planning using 3D cameras without additional radiation. Intel provided the camera hardware used in this study.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Tomographic Volumetric Additive Manufacturing of Silica Glass with In Situ Error Calculation and Projection Tiling

The optical, thermal, and chemical properties of glass make it an ideal material for use in a wide range of applications, from optics and chemistry reactionware to consumer products. Tomographic volumetric additive manufacturing (tVAM) creates 3D objects by selectively gelling material within a rotating vial of photo-sensitive material by exposure to a series of tomographically-determined images. Here, in this study, tVAM is investigated for fabrication of complex, centimeter-scale, 3D silica glass parts. By optimizing the post-processing procedure, fully-dense glass parts with up to 2 cm 2 cross sectional area are achieved. The changes in optical properties during printing are leveraged for in situ metrology during printing, and the non-Newtonian nature of the glass-filled photoresin system to facilitate larger fabrication volumes via tiled projections. Printed structures are finally demonstrated for applications as MEMS components, optics, fluidics, and consumer products.

Materials science↗

DOE OSTI2019

Reconstructing large-scale structure with neutral hydrogen surveys

Upcoming 21-cm intensity surveys will use the hyperfine transition in emission to map out neutral hydrogen in large volumes of the universe. Unfortunately, large spatial scales are completely contaminated with spectrally smooth astrophysical foregrounds which are orders of magnitude brighter than the signal. This contamination also leaks into smaller radial and angular modes to form a foreground wedge, further limiting the usefulness of 21-cm observations for different science cases, especially cross-correlations with tracers that have wide kernels in the radial direction. Here in this paper, we investigate reconstructing these modes within a forward modeling framework. Starting with an initial density field, a suitable bias parameterization and non-linear dynamics to model the observed 21-cm field, our reconstruction proceeds by {combining} the likelihood of a forward simulation to match the observations (under given modeling error and a data noise model) {with the Gaussian prior on initial conditions and maximizing the obtained posterior}. For redshifts z=2 and 4, we are able to reconstruct 21cm field with cross correlation, r c > 0.8 on all scales for both our optimistic and pessimistic assumptions about foreground contamination and for different levels of thermal noise. The performance deteriorates slightly at z=6. The large-scale line-of-sight modes are reconstructed almost perfectly. We demonstrate how our method also provides a technique for density field reconstruction for baryon acoustic oscillations, outperforming standard methods on all scales. We also describe how our reconstructed field can provide superb clustering redshift estimation at high redshifts, where it is otherwise extremely difficult to obtain dense spectroscopic samples, as well as open up a wealth of cross-correlation opportunities with projected fields (e.g. lensing) which are restricted to modes transverse to the line of sight.

21 cm↗

DOE OSTI2024

Getting allometry right at the Oak Ridge free‐air CO 2 enrichment experiment: Old problems and new opportunities for global change experiments

Societal Impact Statement Free‐air CO 2 enrichment (FACE) experiments provide essential data on forest responses to increasing atmospheric CO 2 for evaluations of climate change impacts on humanity. Understanding and reducing the uncertainty in the experimental results is critical to ensure scientific and public confidence in the models and policy initiatives that derive therefrom. One source of uncertainty is the estimation of tree biomass using mathematical relationships between biomass and easily obtained and non‐destructive measurements (allometry). We evaluated the robustness of the allometric relationships established at the beginning of a FACE experiment and discuss the challenges and opportunities for the new generation of FACE experiments. Summary Long‐term field experiments to elucidate forest responses to rising atmospheric CO 2 concentration require allometric equations to estimate tree biomass from non‐destructive measurements of tree size. We analyzed whether the allometric equations established at the beginning of a free‐air CO 2 enrichment (FACE) experiment in a Liquidambar styraciflua plantation were still valid at the end of the 12 year experiment. Aboveground woody biomass was initially predicted by an equation that included bole diameter, taper, and height, assuming that including taper and height as predictors would accommodate changes in tree structure that might occur over time and in response to elevated CO 2 . At the conclusion of the FACE experiment, we harvested 23 trees, measured dimensions and dry mass of boles and branches, and extracted and measured the woody root mass of 10 trees. Although 10 of the harvested trees were larger than the trees used to establish the allometric relationship, measured aboveground woody biomass was well predicted by the original allometry. The initial linear equation between bole basal area and woody root biomass underestimated final root biomass by 28%, but root biomass was just 21% of total wood mass, and errors in aboveground and belowground estimates were offsetting. The allometry established at the beginning of the experiment provided valid predictions of tree biomass throughout the experiment. New allometric approaches using terrestrial laser scanning should reduce an important source of uncertainty in decade‐long forest experiments and in assessments of centuries‐long forest biomass accretion used in evaluating carbon offsets and climate mitigation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Human adipose-derived mesenchymal stromal cells from face and abdomen undergo replicative senescence and loss of genetic integrity after long-term culture

Body fat depots are heterogeneous concerning their embryonic origin, structure, exposure to environmental stressors, and availability. Thus, investigating adipose-derived mesenchymal stromal cells (ASCs) from different sources is essential to standardization for future therapies. In vitro amplification is also critical because it may predispose cell senescence and mutations, reducing regenerative properties and safety. Here, we evaluated long-term culture of human facial ASCs (fASCs) and abdominal ASCs (aASCs) and showed that both met the criteria for MSCs characterization but presented differences in their immunophenotypic profile, and differentiation and clonogenic potentials. The abdominal tissue yielded more ASCs, and these had higher proliferative potential, but facial cells displayed fewer mitotic errors at higher passages. However, both cell types reduced clonal efficiency over time and entered replicative senescence around P12, as evaluated by progressive morphological alterations, reduced proliferative capacity, and SA-β-galactosidase expression. Loss of genetic integrity was detected by a higher proportion of cells showing nuclear alterations and γ-H2AX expression. Our findings indicate that the source of ASCs can substantially influence their phenotype and therefore should be carefully considered in future cell therapies, avoiding, however, long-term culture to ensure genetic stability.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Perspectives on Systematic Cloud Microphysics Scheme Development With Machine Learning

Cloud microphysics—the collection of processes that govern the small‐scale formation, evolution, and interactions of liquid droplets and ice crystals in clouds and precipitation—remains a major source of uncertainty in weather and climate models. Although too small in scale to be explicitly resolved in any large‐eddy simulation, weather, or climate model, the representation of cloud microphysical processes has significant impact at the climate scale. Current microphysical schemes are limited by both parametric uncertainty, linked to uncertainty in physical parameter values, and structural uncertainty, arising from incomplete physical understanding of the processes at play or approximations made for computational efficiency. Recent advances in the application of machine learning (ML) to the physical sciences show significant potential for minimizing these limitations by leveraging high‐fidelity simulations and observations. Here we outline the challenges that must be addressed to apply ML toward cloud microphysics scheme development. This perspectives paper synthesizes recent progress in using data‐driven methods, including ML, to improve cloud microphysics parameterizations and highlights opportunities to address key uncertainties. We discuss the roles of aleatoric (irreducible, or statistical) and epistemic (reducible, or systematic) errors in contributing to microphysics parameterization uncertainty. ML can leverage observations to improve microphysical schemes via bottom‐up and top‐down constraints. Methods such as differentiable programming and ML‐enhanced sampling strategies and the creation of large scale benchmark data sets promise to bridge the gap between observations and models and to improve the consistency of cloud microphysical representation across temporal and spatial scales.

Lamb, Kara D. [Columbia Univ., New York, NY (Unite↗

DOE OSTI2016

Fouling resistance prediction using artificial neural network nonlinear auto-regressive with exogenous input model based on operating conditions and fluid properties correlations

Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗

DOE OSTI2025

Author Correction: Genome-guided isolation of the hyperthermophilic aerobe Fervidibacter sacchari reveals conserved polysaccharide metabolism in the Armatimonadota

Correction to: Nature Communicationshttps://doi.org/10.1038/s41467-024-53784-3, published online 4 November 2024 In the version of this article initially published, Table 1 did not include the properties of the taxa being proposed or refer directly to another location in the main manuscript describing the properties. As such, the original manuscript did not comply with Rule 27 (2)(c) of the ICNP. Also, Table 1 listed the order Fervidibacterales as the nomenclatural type for the class Fervidibacteria, which violates latest emended version of Rule 15 stating that the nomenclatural type for a class must be a genus. Below we provide a modification of Table 1 containing protologues with these errors corrected. We have also changed the order of the taxa in the table to meet the most common ordering. (Table presented.) Taxon names proposed under the ICNP Proposed taxon Etymology Description Genus Fervidibacter Fer.vi.di.bac’ter. L. masc. adj. fervidus, hot, steaming; N.L. masc. n. bacter, a rod; N.L. masc. n. Fervidibacter, a hot rod Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic, with high-affinity and/or low-affinity terminal oxidases present in the genomes. The oxidative pentose phosphate pathway and the tricarboxylic acid cycle are complete in genomes belonging to the genus. Gram-stain-negative and diderm cell envelope structure. Ovoid- to rod-shaped morphology. Spores are not formed. The genus is a distinct phylogenetic lineage in the family Fervidibacteraceae, the order Fervidibacterales, and the class Fervidibacteria in the phylum Armatimonadota. The type species is Fervidibacter sacchariT. Species Fervidibacter sacchari sac’cha.ri. N.L. gen. n. sacchari, of sugar Hyperthermophilic, microaerophilic, facultatively anaerobic, and grows chemoheterotrophically on monosaccharides and polysaccharides. Cells are ovoid- to rod-shaped, Gram-stain negative, and are 0.9–1.3 µm in width and 1.6–3.6 µm in length. Grows between 65 and 87.5 °C and an optimum temperature of 80 °C, and a pH range of 6.5–8.6 with an optimum pH of 7.5. Grows at an optimum O2 concentration of 5–10%. Grows on D-arabinose, D-galactose, D-glucose, D-rhamnose, D-ribose, D-xylose, chondroitin sulfate, colloidal chitin, galactan, gellan gum, guar gum, karaya gum, locust bean gum, xantham gum, xyloglucan, β-glucan, glycogen, starch, AFEX-pretreated corn stover, miscanthus, sugarcane bagasse, acetate and casamino acids. Grows weakly on xyloglucan under fermentation conditions. The major fatty acids (>10%) are C16:0, C18:0 and/or cyclo-C17:0, and iso-C16:0. The major respiratory quinones (>10%) are MK-8 and MK-9. The isolate and genomes of the species have been recovered from geothermal springs in the Great Basin, Nevada, USA. GC content of genomes range between 51–52%. Subunits for both the high-affinity and low-affinity terminal oxidases are encoded in the genomes. Genomes also encode a Group 3d [NiFe] hydrogenase, which produces hydrogen as an electron sink for NAD+ regeneration. The type strain PD1T (= JCM 39283T = DSM 113467T) was isolated from Great Boiling Spring in Nevada, USA. Family Fervidibacteraceae Fer.vi.di.bac.te.ra’ce.ae. N.L. masc. n. Fervidibacter type genus of the family; L. suff. -aceae ending to denote a family; N.L. fem. pl. n. Fervidibacteraceae the family of the genus Fervidibacter Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic, with high-affinity and/or low-affinity terminal oxidases present in the genomes. The oxidative pentose phosphate pathway and the tricarboxylic acid cycle are complete in genomes belonging to the family. The family is a distinct phylogenetic lineage in the order Fervidibacterales and the class Fervidibacteria in the phylum Armatimonadota. The type genus is Fervidibacter. Order Fervidibacterales Fer.vi.di.bac.te.ra’les. N.L. masc. n. Fervidibacter type genus of the order; L. suff. -ales ending to denote an order; N.L. fem. pl. n. Fervidibacterales the order of the genus Fervidibacter Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic or strictly anaerobic. Phylogenomic placement of this lineage within the Fervidibacteria and relative evolutionary divergence supports delineation of this lineage as an order within the class Fervidibacteria and phylum Armatimonadota. The type genus is Fervidibacter. Class Fervidibacteria Fer.vi.di.bac.te’ri.a. N.L. masc. n. Fervidibacter type genus of the type order of the class; L. suff. -ia ending to denote a class; N.L. neut. pl. n. Fervidibacteria the class of the order Fervidibacterales Thermophilic or hyperthermophilic inhabitants of freshwater thermal environments. All members are likely polysaccharide-degrading chemoheterotrophs with numerous carbohydrate-active enzymes encoded in their genomes. Aerobic or strictly anaerobic. Phylogenomic placement of this lineage within the Armatimonadota and relative evolutionary divergence supports delineation of this lineage as a class within the Armatimonadota. The type genus is Fervidibacter. The error has not been corrected in the PDF or HTML versions of the Article.

Nou, Nancy O↗

DOE OSTI2021

An innovative digital image correlation technique for in-situ process monitoring of composite structures in large scale additive manufacturing

As additive manufacturing (AM) continues to develop and become a standardized manufacturing method, there will be a continued need to provide in-situ monitoring during the manufacturing of polymer composite printed components. Thermal residual stress is a primary cause of failures such as interlayer disbonds or delamination, micro cracking, and dimensional instability, which can occur during or after the build. Here, we report a novel digital image correlation (DIC) adaptation to monitor thermal residual stresses during the entire print process for large-scale AM. In this work, DIC has been investigated (a) by the natural speckle produced by the polymer surface for correlation, (b) to monitor AM build, and (c) to evaluate the effect of thermal residual stress on warpage of the printed component. The natural speckle pattern of the AM material resulted in a respectable 3.57% error compared to the traditional painted speckle pattern of 3.05% error. DIC measured a 190% increase in vertical displacement at the edge of the wall compared to the center, indicating warpage during AM. This work is a step towards a non-intrusive residual stress measuring technique using DIC for large-scale AM.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Experimental Examination of Additively Manufactured Patterns on Structural Nuclear Materials for Digital Image Correlation Strain Measurements

Abstract Background There are a limited number of commercially available sensors for monitoring the deformation of materials in-situ during harsh environment applications, such as those found in the nuclear and aerospace industries. Such sensing devices, including weldable strain gauges, extensometers, and linear variable differential transformers, can be destructive to material surfaces being investigated and typically require relatively large surface areas to attach (> 10 mm in length). Digital image correlation (DIC) is a viable, non-contact alternative to in-situ strain deformation. However, it often requires implementing artificial patterns using splattering techniques, which are difficult to reproduce. Objective Additive manufacturing capabilities offer consistent patterns using programmable fabrication methods. Methods In this work, a variety of small-scale periodic patterns with different geometries were printed directly on structural nuclear materials (i.e., stainless steel and aluminum tensile specimens) using an aerosol jet printer (AJP). Unlike other additive manufacturing techniques, AJP offers the advantage of materials selection. DIC was used to track and correlate strain to alternative measurement methods during cyclic loading, and tensile tests (up to 1100 µɛ) at room temperature. Results The results confirmed AJP has better control of pattern parameters for small fields of view and facilitate the ability of DIC algorithms to adequately process patterns with periodicity. More specifically, the printed 100 μm spaced dot and 150 μm spaced line patterns provided accurate measurements with a maximum error of less than 2% and 4% on aluminum samples when compared to an extensometer and commercially available strain gauges. Conclusion Our results highlight a new pattern fabrication technique that is form factor friendly for digital image correlation in nuclear applications.

Novich, K. A. (ORCID:0000000204466022)↗