Stereo-DIC Challenge 2.0: The Tensile Experience - Update 2025
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Reu, Phillip L. [Sandia National Laboratories (SNL↗
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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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Material Response Measured Using DIC for Dynamic Modified Brazil Disk Impact Testing
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Hansen, Aaron [Sandia National Laboratories (SNL-N↗
Using 3D-DIC on Space Flight Hardware
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Nathaniel W Gardner↗
Dissolved Inorganic Carbon and Dissolved Organic Carbon Data for the East River Watershed, Colorado (2015-2025)
This data package contains mean values for dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) for water samples taken from the East River Watershed in Colorado. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. DOC and DIC concentrations in water samples were determined using a TOC-VCPH analyzer (Shimadzu Corporation, Japan). DOC was analyzed as non-purgeable organic carbon (NPOC) by purging HCl-acidified samples with carbon-free air to remove DIC prior to measurement. After the acidified sample has been sparged, it is injected into a combustion tube filled with oxidation catalyst heated to 680 oC. The DOC in samples is combusted to CO2 and measured by a non-dispersive infrared (NDIR) detector. The peak area of the analog signal produced by the NDIR detector is proportional to the DOC concentration of the sample. DIC was determined by acidifying the samples with HCl first, and then purging with carbon-free air to release CO2 for analysis by NDIR detector. Total dissolved nitrogen (TDN) was analyzed using a Shimadzu Total Nitrogen Module (TNM-L) combined with the TOC-L analyzer (Shimadzu Corporation, Japan). TNM-L is a non-specific measurement of total nitrogen (TN). All nitrogen species in samples are combusted to nitrogen monoxide and nitrogen dioxide, then reacted with ozone to form an excited state of nitrogen dioxide. Upon returning to ground state, light energy is emitted. Then, TDN is measured using a chemiluminescence detector. All data reported are the mean values upon minimum of three replicate measurements, with a relative standard deviation < 3%. All samples were analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (dic_npoc_data_2014-2025.zip) containing a total of 337 files: 336 data files of DIC and NPOC data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v6_20250901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v6_20250901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; and (4) PDF and docx files for the determiniation of Method Detection Limits (MDLs) for DIC and NPOC data, which has been updated in 2026-08. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 113 locations containing DIC/NPOC data. Update on 2020-10-07: Updated the data files to remove times from the timestamps, so that only dates remain. The data values have not changed. Update on 2021-04-11: Added Determination of Method Detection Limits (MDLs) for DIC, NPOC and TDN Analyses document, which can be accessed as a PDF or with Microsoft Word.Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated dissolved inorganic carbon and dissolved organic carbon data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-11-21. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Revised versions of the PDF and docx files for determination of MDLs for DIC and NPOC were added to replace previous versions. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available DIC and NPOC data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available DIC and NPOC data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-10, of the PDF and docx files for determination of MDLs for DIC and NPOC data were added to this dataset.
54 ENVIRONMENTAL SCIENCES↗
Image Correlation Pattern Optimization for Micro-Scale In-Situ Strain Measurements
The accuracy and precision of digital image correlation (DIC) is a function of three primary ingredients: image acquisition, image analysis, and the subject of the image. Development of the first two (i.e. image acquisition techniques and image correlation algorithms) has led to widespread use of DIC; however, fewer developments have been focused on the third ingredient. Typically, subjects of DIC images are mechanical specimens with either a natural surface pattern or a pattern applied to the surface. Research in the area of DIC patterns has primarily been aimed at identifying which surface patterns are best suited for DIC, by comparing patterns to each other. Because the easiest and most widespread methods of applying patterns have a high degree of randomness associated with them (e.g., airbrush, spray paint, particle decoration, etc.), less effort has been spent on exact construction of ideal patterns. With the development of patterning techniques such as microstamping and lithography, patterns can be applied to a specimen pixel by pixel from a patterned image. In these cases, especially because the patterns are reused many times, an optimal pattern is sought such that error introduced into DIC from the pattern is minimized. DIC consists of tracking the motion of an array of nodes from a reference image to a deformed image. Every pixel in the images has an associated intensity (grayscale) value, with discretization depending on the bit depth of the image. Because individual pixel matching by intensity value yields a non-unique scale-dependent problem, subsets around each node are used for identification. A correlation criteria is used to find the best match of a particular subset of a reference image within a deformed image. The reader is referred to references for enumerations of typical correlation criteria. As illustrated by Schreier and Sutton and Lu and Cary systematic errors can be introduced by representing the underlying deformation with under-matched shape functions. An important implication, as discussed by Sutton et al., is that in the presence of highly localized deformations (e.g., crack fronts), error can be reduced by minimizing the subset size. In other words, smaller subsets allow the more accurate resolution of localized deformations. Contrarily, the choice of optimal subset size has been widely studied and a general consensus is that larger subsets with more information content are less prone to random error. Thus, an optimal subset size balances the systematic error from under matched deformations with random error from measurement noise. The alternative approach pursued in the current work is to choose a small subset size and optimize the information content within (i.e., optimizing an applied DIC pattern), rather than finding an optimal subset size. In the literature, many pattern quality metrics have been proposed, e.g., sum of square intensity gradient (SSSIG), mean subset fluctuation, gray level co-occurrence, autocorrelation-based metrics, and speckle-based metrics. The majority of these metrics were developed to quantify the quality of common pseudo-random patterns after they have been applied, and were not created with the intent of pattern generation. As such, it is found that none of the metrics examined in this study are fit to be the objective function of a pattern generation optimization. In some cases, such as with speckle-based metrics, application to pixel by pixel patterns is ill-conditioned and requires somewhat arbitrary extensions. In other cases, such as with the SSSIG, it is shown that trivial solutions exist for the optimum of the metric which are ill-suited for DIC (such as a checkerboard pattern). In the current work, a multi-metric optimization method is proposed whereby quality is viewed as a combination of individual quality metrics. Specifically, SSSIG and two auto-correlation metrics are used which have generally competitive objectives. Thus, each metric could be viewed as a constraint imposed upon the others, thereby precluding the achievement of their trivial solutions. In this way, optimization produces a pattern which balances the benefits of multiple quality metrics. The resulting pattern, along with randomly generated patterns, is subjected to numerical deformations and analyzed with DIC software. The optimal pattern is shown to outperform randomly generated patterns.
Bomarito, G. F.↗
An Internal Digital Image Correlation Technique for High-Strain Rate Dynamic Experiments
Full-field, quantitative visualization techniques, such as digital image correlation (DIC), have unlocked vast opportunities for experimental mechanics. However, DIC has traditionally been a surface measurement technique, and has not been extended to perform measurements on the interior of specimens for dynamic, full-scale laboratory experiments. This limitation restricts the scope of physics which can be investigated through DIC measurements, especially in the context of heterogeneous materials. The focus of this study is to develop a method for performing internal DIC measurements in dynamic experiments. The aim is to demonstrate its feasibility and accuracy across a range of stresses (up to 650 MPa), strain rates (10 3 - 10 6 s -1 ), and high-strain rate loading conditions (e.g., ramped and shock wave loading). Internal DIC is developed based on the concept of applying a speckle pattern at an inner-plane of a transparent specimen. The high-speed imaging configuration is coupled to the traditional dynamic experimental setups, and is focused on the internal speckle pattern. During the experiment, while the sample deforms dynamically, in-plane, two-dimensional deformations are measured via correlation of the internal speckle pattern. In this study, the viability and accuracy of the internal DIC technique is demonstrated for split-Hopkinson (Kolsky) pressure bar (SHPB) and plate impact experiments. The internal DIC experimental technique is successfully demonstrated in both the SHPB and plate impact experiments. In the SHPB setting, the accuracy of the technique is excellent throughout the deformation regime, with measurement noise of approximately 0.2% strain. In the case of plate impact experiments, the technique performs well, with error and measurement noise of 1% strain. The internal DIC technique has been developed and demonstrated to work well for full-scale dynamic high-strain rate and shock laboratory experiments, and the accuracy is quantified. Here, the technique can aid in investigating the physics and mechanics of the dynamic behavior of materials, including local deformation fields around dynamically loaded material heterogeneities.
36 MATERIALS SCIENCE↗
In-Cell Deployment and First Use of Digital Image Correlation for In-Situ Strain Analysis of Irradiated Nuclear Fuel Rods During LOCA Transient
Digital image correlation (DIC) is a noncontact, optical method increasingly used across industries and research environments for acquiring multidimensional strain data. At Oak Ridge National Laboratory’s (ORNL’s) Severe Accident Test Station (SATS), DIC has been extensively applied to study the thermomechanical response of nuclear fuel claddings, yielding fundamental insights into material behavior. However, these efforts have focused exclusively on unirradiated materials and relied on the SATS out-of-cell infrastructure. Efforts over the past two years have been made to extend these capabilities to ORNL’s Irradiated Fuels Examination Laboratory SATS system within the hot-cells to enable testing of irradiated fuel cladding materials. Integrating DIC into the hot-cell SATS infrastructure presents unique challenges, including enabling remote operation of optical equipment and adapting auxiliary hot-cell systems for DIC implementation. This report discusses design, stand-up testing, and application of DIC to an irradiated nuclear fuel cladding segment. A DIC testing rig was successfully built and validated out-of-cell through extensive surrogate tests and calibrations. The rig and specialized DIC furnace were installed in the hot-cell, and a DIC test was successfully conducted. Results from that test correspond to expectations for Zr-based alloys and showed similar uncertainties compared to out-of-cell tests.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Hydrology Outweighs Temperature in Driving Production and Export of Dissolved Carbon in a Snowy Mountain Catchment
Abstract Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed‐scale reactive‐transport model BioRT‐HBV and stream data from a snow‐dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m 2 /year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m 2 /year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m 2 /year, compared to 0.12 ± 0.02 gC/m 2 /year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m 2 /year) than in the deep subsurface (0.36 ± 0.02 gC/m 2 /year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly‐observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often‐overlooked but growing importance of deeper processes in a warming climate.
54 ENVIRONMENTAL SCIENCES↗
Investigation of Optimal Digital Image Correlation Patterns for Deformation Measurement
Digital image correlation (DIC) relies on the surface texture of a specimen to measure deformation. When the specimen itself has little or no texture, a pattern is applied to the surface which deforms with the specimen and acts as an artificial surface texture. Because the applied pattern has an effect on the accuracy of DIC, an ideal pattern is sought for which the error introduced into DIC measurements is minimal. In this work, a study is performed in which several DIC pattern quality metrics from the literature are correlated to DIC measurement error. The resulting correlations give insight on the optimality of DIC patterns in general. Optimizations are then performed to produce patterns which are well suited for DIC. These patterns are tested to show their relative benefits. Chief among these benefits are a reduction in error of approximately 30 with respect to a randomly generated pattern.
Bomarito, G. F.↗
Insights into the Processing of Carbon by Early Microbial Ecosystems
Interactions between Earth and the biosphere that were crucial for early biological evolution also influenced substantially the processes that circulate C between its reservoirs in the atmosphere, ocean, crust and mantle. The C-13 C-12 values of crustal carbonates and organics have recorded changes both in biological discrimination and in the relative rates of burial of organics and carbonates. A full interpretation of these patterns needs further isotopic studies of microbial ecosystems and individual anaerobes. Thus we measured carbon isotope discrimination during autotrophic and heterotrophic growth of pure cultures of sulfate-reducing bacteria and archaea (SRB and SRA). Discrimination during CO2 assimilation is significantly larger than during heterotrophic growth on lactate or acetate. SRB grown lithoautotrophically consumed less than 3% of available CO2 and exhibited substantial discrimination, as follows: Desulfobacterium autotrophicum (alpha 1.0100 to 1.0123), Desulfobacter hydrogenophilus (alpha = 0.0138), and Desulfotomuculum acetoxidans (alpha = 1.0310). Mixotrophic growth of Desulfovibrio desulfuricans on acetate and CO2 resulted in biomass with delta C-13 composition intermediate to that of the substrates. We have recently extended these experiments to include the thermophilic SRA Archeoglobus spp. Ecological forces also influence isotopic discrimination. Accordingly, we quantified the flow of C and other constituents in modern marine cyanobacterial mats, whose ancestry extends back billions of years. Such ecosystem processes shaped the biosignatures that entered sediments and atmospheres. At Guerrero Negro, BCS, Mexico, we examined mats dominated by Microcoleus (subtidal) and Lyngbya (intertidal to supratidal) cyanobacteria. During 24 hour cycles, we observed the exchange of O2 and dissolved inorganic C (DIC) between mats and the overlying water. Microcoleus mats assimilated near-equal amounts of DIC during the day as they released at night, but Lyngbya mats typically showed net uptake of DIC over the diel cycle. Patterns of O2 daytime release and nighttime uptake mirrored these DIC trends in both mat types. Nighttime DIC effluxes from Microcoleus mats were equivalent in the presence versus absence of O2, whereas nighttime DIC effluxes from Lyngbya mats dropped markedly in the absence of O2. Thus aerobic diagenesis was more important in Lyngbya mats than in Microcoleus mats, perhaps because trapped O2 bubbles persist only in Lyngbya mats at night and thus sustain populations of aerbes. In both mat types, effluxes of H2, CH4 and short-chain fatty acids were much greater at night in the absence of 02, emphasizing the importance of fermentation. Differences observed between Microcoleus versus Lyngbya mats forecast differences in their microbial populations and in their patterns of gene expression.
DesMarais, D.↗
Review of High‐Speed Digital Image Correlation: Advancements and Good Practices
This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.
experimental mechanics↗
Enabling Digital Image Correlation for Deployment in the Hot Fuels Examination Facility
Digital image correlation (DIC) is a non-contact image-based technique for displacement and strain measurement. It is also a full-field method that can provide excellent spatial resolution. DIC has been widely used for measurements in many styles of mechanical tests and is desirable in part because of this flexibility to measure nearly any material across a range of time and length scales. Its ability to measure localized strain for tests and in environments not accessible to other strain measurement techniques makes it an ideal option for several mechanical test cases that exist in hot cell facilities such as the Hot Fuel Examination Facility (HFEF) at the Idaho National Laboratory (INL). However, DIC has not been demonstrated at HFEF, and several barriers to implementation in the hot cell environment exist. This report outlines the possible paths for deploying DIC at HFEF, weighing the technical advantages and risks with each path. It also discusses the steps which must be taken to add this capability to the suite of post-irradiation examination (PIE) techniques available at HFEF. Finally, it concludes with recommendations for the best method to perform DIC at the facility.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Standardized Analysis Process Using Digital Image Correlation to Calculate In Situ Cladding Strain from Modified Burst Tests for Fuel Performance Code Validation
Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.
Reactivity-initiated accident↗
Ring Pull Strain Analysis Version 1.1
This report details an analysis package, Ring Pull Strain Analysis (RPSA), that can be used to present and quantify digital image correlation (DIC) data as it relates to a gaugeless ring pull test. Gaugeless ring pull is a testing technique for mechanical testing of small annular samples, usually cut from a thin-walled tube. DIC data is often necessary for this kind of test because bending moments present on the ring cause a non-uniform strain distribution and localized measurements are necessary. In addition, the annular geometry of a ring lends itself to a polar representation, which is not present with typical DIC analysis methods. RPSA was made to calculate and plot the polar representation of strain from standard pre-processed DIC data of a gaugeless ring pull test. Further analysis can be done on ring pull including a quasi-uniaxial tensile analysis and coating analysis, which are also performed by RPSA. In addition, due to the universality of DIC plotting and ring pull test analysis, RPSA can accommodate a wide variety of tests, though it is tailored for ring pull testing. This report details how RPSA works, including the theory, assumptions, and logic behind the calculations and the structure of the program.
36 MATERIALS SCIENCE↗
Stable isotopic biogeochemistry of carbon and nitrogen in a perennially ice-covered Antarctic lake
Lake Hoare (77 degrees 38' S, 162 degrees 53' E) is an amictic, oligotrophic, 34-m-deep, closed-basin lake in Taylor Valley, Antarctica. Its perennial ice cover minimizes wind-generated currents and reduces light penetration, as well as restricts sediment deposition into the lake and the exchange of atmospheric gases between the water column and the atmosphere. The biological community of Lake Hoare consists solely of microorganisms -- both planktonic populations and benthic microbial mats. Lake Hoare is one of several perennially ice-covered lakes in the McMurdo Dry Valleys that represent the end-member conditions of cold desert and saline lakes. The dry valley lakes provide a unique opportunity to examine lacustrine processes that operate at all latitudes, but under an extreme set of environmental conditions. The dry valley lakes may also offer a valuable record of catchment and global changes in the past and present. Furthermore, these lakes are modern-day equivalents of periglacial lakes that are likely to have been common during periods of glacial maxima at temperate latitudes. We have analyzed the dissolved inorganic carbon (DIC) of Lake Hoare for delta 13C and the organic matter of the sediments and sediment-trap material for delta 13C and delta 15N. The delta 13C of the DIC indicates that 12C is differentially removed in the shallow, oxic portions of the lake via photosynthesis. In the anoxic portions of the lake (27-34 m) a net addition of 12C to the DIC pool occurs via organic matter decomposition. The dissolution of CaCO3 at depth also contributes to the DIC pool. Except near the Canada Glacier where a substantial amount of allochthonous organic matter enters the lake, the organic carbon being deposited on the lake bottom at different sites is isotopically similar, suggesting an autochthonous source for the organic carbon. Preliminary inorganic carbon flux calculations suggest that a high percentage of the organic carbon fixed in the water column is remineralized as it falls through the water column. At nearby Lake Fryxell, the substantial (relative to Lake Hoare) glacial meltstream input overprints Fryxell's shallow-water biological delta 13C signal with delta 13C-depleted DIC. In contrast, Lake Hoare is not significantly affected by surface-water input and mixing, and therefore the delta 13C patterns observed arise primarily from biological dynamics within the lake. Organic matter in Lake Hoare is depleted in 15N, which we suggest is partially the result of the addition of relatively light inorganic nitrogen into the lake system from terrestrial sources.
NASA Program Exobiology↗
Improving Structural Test and Analysis Correlation Using Digital Image Correlation Boundary Measurements
Simplified idealizations, such as clamped or simply supported, are commonly used as support boundary conditions in modeling and analysis of tested aerospace structures. However, these simplifications are not always appropriate and characterization of the response of non-rigid boundaries is often required to improve test and analysis correlation. While e analysis models are typically modified to better represent the test, this modification is often not practical due to the complexities of the boundary response. A study is conducted to examine the use of digital image correlation (DIC) to measure the boundary support structure response and then to adjust the test data to remove the effects of the boundary response in order to improve test and analysis correlation. A high-aspect ratio composite wing test is the subject of the study. Three sources of boundary flexibility contributing to rigid body rotation of the test wing are identified and quantified using both DIC and conventional test data. The wing displacement test data are adjusted using DIC data for these sources. Wing displacement test data is also adjusted using direct measurement of wing rotation from a DIC system that monitored the top surface, root region of the wing, and using available conventional instrumentation. The adjusted data exhibits improved test and analysis correlation and demonstrated the benefit of using DIC along with conventional instrumentation to collect and interpret test data.
Andrew E Lovejoy↗
Mid-day photomixotrophy by Roseiflexus spp. and implications for the 13 C content of hot spring cyanobacterial mats
Microbial mats inhabiting extreme environments have been studied as modern analogs of stromatolites. Mats in Octopus Spring and Mushroom Spring, Yellowstone National Park, are predominated by unicellular photoautotrophic cyanobacteria (Synechococcus spp.), which are thought to cross-feed filamentous photoheterotrophic bacteria (mainly Roseiflexus spp.), except under early morning anoxic conditions when Roseiflexus spp. have been shown to fix dissolved inorganic carbon (DIC). Transcription patterns, however, suggest that Roseiflexus spp. may perform photomixotrophy, in which DIC is incorporated together with organic compounds during the daytime. We investigated the roles played by Synechococcus spp. and Roseiflexus spp. in DIC and organic matter uptake in mid-day light and oxic mats. Mass spectrometry was used to show that 13 C-bicarbonate uptake under infrared (IR) light (utilized by anoxygenic phototrophs) or visible-minus-blue light (V-B) (used by cyanobacteria) was about two-thirds and one-third, respectively, of that incorporated in full light. Laser-ablation mass spectrometry analysis demonstrated that 13 C incorporation under V-B light was restricted to the uppermost portion of the mat, whereas 13 C incorporation under IR light was maximal in deeper mat layers. 13 C-acetate, -propionate, -lactate, and -glycolate were incorporated to an equal or greater extent under IR and full light. Incorporation of 13 C into peptides showed that both Synechococcus spp. and Roseiflexus spp. were active in DIC uptake, whereas Roseiflexus spp. exhibited greater uptake of 13 C-organic acids, especially glycolate and lactate, into peptides. Peptides of proteins of the 3-hydroxypropionate pathway were labeled. Thus, Roseiflexus spp. appears to exhibit photomixotrophy throughout the day.
Roseiflexus↗
Carbon and Oxygen Budgets of Subtidal and Intertidal Cyanobacterial Mats
Intertidal cyanobacterial mats (Lyngbya-dominated) are contrasted with mats (Microcoleus-dominated) that grow in subtidal (0.7m water depth) hypersaline (90-110 permil) environments. In benthic chamber experiments conducted in Oct., 1999, mats exhibited greater net uptake of dissolved inorganic carbon (DIC) from overlying water during the daylight period than Microcoleus mats (e.g., 200 vs 120 mmol C/m. at 26 deg C, respectively). Net DIC release at night was similar for both mats (approx. 80 mmol C/m). Daytime net O2 release by Lyngby mats exceeded that by Microcoleus mats (150 vs 100 mmol O2/m), and O2 uptake at night was comparable for both mats (60-80 mmol O2/m). Nonphotosynthetic populations are more prominent within the subtidal versus intertidal mats, and accordingly exhibited greater internal 02 uptake and DIC production during the day. Over 24 hours, Lyngby-dominated mats exhibited greater net uptake of DIC than subtidal Microcoleus mats, consistent with these intertidal mats being "pioneer" communities that constantly recover from periodic physical disruption in energetic environments. The Microcoleus-dominated mats achieve steady-state mat thicknesses by balancing primary production against diagenetic decomposition of cellular and extracellular organic constituents.
DesMarais, David J.↗