This paper describes the hardware and software upgrades, operation, and performance of the high intensity diffractometer for residual stress analysis (HIDRA) instrument, a residual stress mapping neutron diffractometer located at the High Flux Isotope Reactor at Oak Ridge National Laboratory in Oak Ridge Tennessee, USA. Following a major upgrade in 2018, the new instrument has a single 3He multiwire 2D 30 × 30 cm2 position sensitive detector, yielding a field of view of 17° 2θ. The increase in the field of view (from 4° 2θ) from the previous model instrument has contributed to the tremendous improvement in the out of plane solid angle such that the 3D count rate could be obtained easily. Accordingly, the hardware, software, Data Acquisition System (DAS), and so on have also been updated. Finally, all these enhanced features of HIDRA have been ably demonstrated by conducting multi directional diffraction measurements in the quenched 750-T74 aluminum, and the evolved and improved strain/stress mappings are presented.

The High Intensity Diffractometer for Residual stress Analysis (HIDRA) is the engineering diffractometer at the High Flux Isotope Reactor designed for spatially resolved characterization of residual stresses in large-scale engineering components. Prior to October 2019, the instrument was referred to as the second Generation Neutron Residual Stress Facility (NRSF2) as described previously.1 With this upgrade, the instrument has fundamentally changed; consequently, it has been renamed to reflect that change. HIDRA can be configured using a variety of instrument optics (slits or collimators) and standard or custom sample environments as dictated by the experimental needs. The flexible configuration allows the instrument to meet the needs of the user community, that is to focus on addressing early stage, applied, and industrial materials science and engineering research. HIDRA is particularly well-suited for 3-D strain, texture, and phase mapping of common engineering alloys, including ferritic and austenitic steels, aluminum, and nickel-based alloys.

Strain mapping instruments, e.g., HIDRA, utilize incident and diffracted beam optics to measure the diffraction data from a specific volume within a sample, referred to as a gauge volume (GV). Mapping experiments routinely utilize GVs between 1 and 25 mm3, where the GV is selected to optimize the counting time and precision of strain measurement. Diffraction spectra are measured at multiple locations to generate an array of spatially dependent data. Each dataset is measured for a pre-determined time to realize a desired peak height above the background. This desired peak height above the background corresponds with a peak fitting quality to provide the desired precision of d-spacing measurement. These data are used to characterize spatial-dependent strains (macro and micro), texture, phase fraction, and residual stress. We note that proper alignment of the sample is critical to ensure that GV remains fully within the sample to avoid anomalous shifts in peak position. See Ref. 2 for more information about anomalous peak shifts due to partially buried GVs. HIDRA integrates experiment planning tools that help mitigate the risk of measuring data for partially buried volumes.

The previous NRSF2 instrument was primarily utilized to investigate the impact of materials processes on residual stresses; for example, users have studied stress and microstructural variations caused by welding (thermal and friction stir), heat-treating, forging, stamping, extrusion, and additive manufacturing.3–8 This paper presents details of the upgraded instrument hardware and control software and the gains in performance over NRSF2.

The new layout of the HIDRA instrument is shown in Fig. 1(a). The theodolites previously used for optical alignment have been replaced with digital cameras. Table I gives an overview of the components of the HIDRA engineering diffractometer. The sample goniometer and the monochromator remain unchanged from NRSF2(1). HIDRA represents significant changes over the previous NRSF2 instrument. All components except the sample stages and the monochromator have been replaced by new hardware: a 2D position sensitive detector, optics improvements, and motor controllers/collision sensors. In addition, new data acquisition software and experimental planning tools, as detailed in the following subsections, provide enhanced measurement capabilities for users.

FIG. 1.

(a) A diagram of the HIDRA instrument, each upgraded system is denoted, and (b) the new DENEX 300 × 300 mm2 active area detector.

FIG. 1.

(a) A diagram of the HIDRA instrument, each upgraded system is denoted, and (b) the new DENEX 300 × 300 mm2 active area detector.

Close modal
TABLE I.

Components of the HIDRA engineering diffractometer.

MonochromatorDouble focusing popovicci-stoica silicon9,10
Incident wavelengths 1.45; 1.54; 1.73; 1.89; 2.27; 2.67 Å 
Detector Denex 3He 2D PSD 
Size: 30 × 30 cm2 (17° × 17°) 
Sample to detector distance = 1 m 
Pixel resolution: 1.5 mm horizontal; 3 mm vertical 
Detector 2θ range 65°–120° accessible 2θ axis rotation 17° 2θ detector field of view 
Maximum flux at the sample 3 × 107 n/cm2/s (Si 331 and Si 400) 
Instrument optics available Slits 
Incident width (mm): 0.3–5 
Incident height (mm): 0.3–20 
Diffracted width(mm): 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 2.5, 3, 4, 5 
Radial collimator 
Diffracted width (mm): 2 
Peak location precision 0.0003° 2θ (∼100–150 με depends on material) 
Sample orienting and controlled environments Huber Eulerian cradle 
Phi-chi stage 
Controlled atmosphere furnace (up to 1200 °C in air or inert gases) 
Other user defined environments (contact instrument staff) 
MonochromatorDouble focusing popovicci-stoica silicon9,10
Incident wavelengths 1.45; 1.54; 1.73; 1.89; 2.27; 2.67 Å 
Detector Denex 3He 2D PSD 
Size: 30 × 30 cm2 (17° × 17°) 
Sample to detector distance = 1 m 
Pixel resolution: 1.5 mm horizontal; 3 mm vertical 
Detector 2θ range 65°–120° accessible 2θ axis rotation 17° 2θ detector field of view 
Maximum flux at the sample 3 × 107 n/cm2/s (Si 331 and Si 400) 
Instrument optics available Slits 
Incident width (mm): 0.3–5 
Incident height (mm): 0.3–20 
Diffracted width(mm): 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 2.5, 3, 4, 5 
Radial collimator 
Diffracted width (mm): 2 
Peak location precision 0.0003° 2θ (∼100–150 με depends on material) 
Sample orienting and controlled environments Huber Eulerian cradle 
Phi-chi stage 
Controlled atmosphere furnace (up to 1200 °C in air or inert gases) 
Other user defined environments (contact instrument staff) 

HIDRA uses the DENEX 300 TN 3He multiwire detector, as shown in Fig. 1(b). The 3He detector provides high gamma discrimination, which is necessary at HIDRA due to the high background in the instrument vicinity due to the proximity of the HFIR core, the HB-2 beam, and scatter from adjacent instruments. This detector has a 300 × 300 mm2 active area positioned one meter from the instrument center of rotation, which results in a field of view of 17° 2θ, an improvement over the previous detector coverage of 4.4° 2θ. A 17° field of view allows for the simultaneous measurement of multiple peaks in many engineering materials using a single detector setting. Increasing the out-of-plane coverage improves the count rate by a factor of ∼3. The 17° out-of-plane field of view expansion allows for out-of-plane data reduction, enabling complete pole figure coverage with fewer rotations in the out of plane rotation of the sample (ψ). Figure 2 shows a comparison of NRSF2’s detector array with the HIDRA detector. These data were taken using the Si 422 (1.54 Å) monochromator setting and a 2 mm (wide) by 15 mm (height) incident aperture. The sample is a Fe (BCC) powder utilized for calibration in an aluminum container. No receiving slits were used in the measurement. We note that the increase in detector counting does translate to the apparent increase in both the peak intensity and background by a factor of ∼3. The increased count rate is a capability that allows for smaller gauge volumes, more measurement points in a mapping experiment, increased allowable penetration (larger samples), and/or more samples in limited beamtime. The narrower peak seen in the new DENEX detector can also allow for higher precision measurement of peak location, which increases the precision in the d-spacing measured and, therefore, improves the strain resolution achievable by the instrument.

FIG. 2.

Comparison of the 211 peaks for BCC ferritic steel powder measured using NRSF2 and HIDRA. The peak height scales by 3.2 and the peak width changes from 0.80° to 0.50° from NRSF2 to HIDRA, respectively. The incident slits were 2 × 15 mm2 in each case, but the HIDRA data were taken without a diffracted slit, and the satellite peaks at 78.5° and 86.5° are artifacts (Al 311) of the aluminum container shifted with respect to the goniometer center of rotation.

FIG. 2.

Comparison of the 211 peaks for BCC ferritic steel powder measured using NRSF2 and HIDRA. The peak height scales by 3.2 and the peak width changes from 0.80° to 0.50° from NRSF2 to HIDRA, respectively. The incident slits were 2 × 15 mm2 in each case, but the HIDRA data were taken without a diffracted slit, and the satellite peaks at 78.5° and 86.5° are artifacts (Al 311) of the aluminum container shifted with respect to the goniometer center of rotation.

Close modal

Raw detector images of the neutron events on the 2D detector as shown in Fig. 3 provide important insights into the microstructure of samples not possible using the previous 1D detector from NRSF2. Figure 3(a) shows an example of a single peak diffracted image. The wider 2θ field-of-view (17° 2θ) enables simultaneous measurement of multiple peaks as shown in Figs. 3(b) and 3(d). The 2D detector allows the user to evaluate data quality, such as spotty Debye rings indicative of large grains with respect to the gauge volume [Fig. 3(c)] or orientation texture evident from comparing intensities between Figs. 3(b) and 3(d).

FIG. 3.

Images of the detector events on the 2D detector for (a) an ideal single peak dataset, (b) a multipeak dataset, (c) a dataset with large grains with respect to the gauge volume, and (d) a dataset where texture is evident.

FIG. 3.

Images of the detector events on the 2D detector for (a) an ideal single peak dataset, (b) a multipeak dataset, (c) a dataset with large grains with respect to the gauge volume, and (d) a dataset where texture is evident.

Close modal

A JJ-Xray type 3 Slit (Air Version) model IB-C30-AIR slit package with BN backed Cadmium blades was installed in the incident beam. The upgraded slit systems enable the use of different GVs during an experiment. Slit opening changes are computer controlled as part of the data acquisition system. The incident blade opening ranges from 0.3 to 10 mm wide and 0.3 to 25 mm in height, each at a resolution of 0.05 mm. Motorized slit centering translations allow precise placement of the incident beam over the center of rotation of the goniometer. The translation stage can move the incident slit assembly to any position between 30 and 240 mm from the instrument center of rotation with a resolution of 0.05 mm, allowing for repositioning and rotation of large samples without collision with the new beam optics.

New diffracted optics (receiving slit assembly and radial collimator) were designed for the upgraded detector geometry as well as leveraging 3D printing for optics for cost savings without sacrificing performance just as others have for neutron experiments.11 The optics can either utilize a slit placed close to the samples to define a suitable gauge volume or a radial collimator, which, due to the increased offset from the instrument center, enables the study of larger samples. Currently available receiving slits cover a range of 0.3–5 mm (width) by 0.3–20 mm (height) in discrete steps. The slits are held by a 3D printed assembly fabricated from a 40 wt. % boron-nitride doped thermoplastic polyurethane (HBN) for neutron absorption. The prototype radial collimator currently in use at HIDRA is made from printed acrylonitrile butadiene styrene (ABS) plastic with an outer shield of borated aluminum. The next generation is currently being fabricated from the HBN material already in use on the receiving slit assembly in collaboration with the Manufacturing Demonstration Facility at Oak Ridge National Laboratory (ORNL). The blades of the collimator are 0.4 mm in thickness. The radial collimator is collimated in 2θ, with two horizontal supports that shadow a small region of the 2D detector. Vertical collimator blades do not create a shadow as the radial collimator assembly oscillates about the center of diffraction. Figure 4 shows a comparison of the gauge volumes achievable using slits and or radial collimator. The incident slits used in each were 3 mm wide by 15 mm high. The gauge volume intensity was mapped using 0.3 mm Ni wire, which was rasterized across the measurement volume. Each gauge volume is well defined, and intensity is uniform across X and Y in each defined gauge volume.

FIG. 4.

Intensity map of the GV isolated by using either the (a) 3 mm incident and 3 mm diffracted slits or (b) a 3 mm incident slit and the 2 mm radial collimator. The dashed line shows the expected extent of the GV.

FIG. 4.

Intensity map of the GV isolated by using either the (a) 3 mm incident and 3 mm diffracted slits or (b) a 3 mm incident slit and the 2 mm radial collimator. The dashed line shows the expected extent of the GV.

Close modal

HIDRA utilizes the open-source Experimental Physics and Industrial Control System (EPICS) for instrument control interfaced with Galil 4080 multiaxis controllers. Collisions can be detected upon the triggering of robotic collision sensors (SR-61 from ATI industrial automation) from which the incident and receiving optics are suspended. These sensors are noted in Fig. 1. This collision detection prevents damage to the instrument by stopping instrument motion and notifying the user that a collision has occurred. The SR-61 sensor-based collision system also allows for a quick restart with minimal realignment.

HIDRA’s instrument data acquisition system (DAS) software utilizes the neutron Event Distributor (nED) data acquisition system.12 The nED system records measured neutrons as event packets and records their detection time and pixel information in event NeXus files.13 ORNL’s streaming management service allows for the cataloging of experimental metadata that are accessible through ORNL’s ONCat system.14–16 Raw and reduced neutron data are available for users to view or download through the neutron analysis cluster as soon as the experimental run is completed. Data reduction is achieved through Python Residual Stress (pyRS).17 

The system utilized, pyRS, is an open-source python-based analysis program developed at ORNL for the reduction, analysis, and visualization of measured mapping data.17 Users can remotely access data through the neutron analysis cluster, or users can download data for local analysis.

One of the most important components of mapping strains using neutron diffraction is the reproducible and accurate transformation of the Sample Coordinate System (SCS) to the Instrument Coordinate System (ICS). This can be a significant issue in data analysis when combining data taken at various sample orientations or comparing multiple samples.

An experimental planning tool, referred to as the CUBOID tool, was developed for planning experimental measurement locations in a pseudo-rectilinear sample. The CUBOID tool establishes a SCS based upon the user establishing an origin and coordinates system vectors. The software maintains a coordinate system transformation between the ICS and the SCS. A user can then define points by giving ranges and step values based on the SCS axes, from which a Cartesian product generates a grid of mapping locations. The mapping grid is automatically reviewed to identify points that are redundant and/or exceed axes limits. Users are allowed to alter and correct these points before executing the measurement. Figure 5 shows the interface users utilize to enter the desired measurement points in the SCS.

FIG. 5.

A screen capture of the cuboid tool interface, which allows for the definition of points in the SCS, as well as count times for measurement. The interface denotes whether the SCS points defined are within the sample as defined earlier in the CUBOID tool. Cuboid X, Cuboid Y, and Cuboid Z in the table are points defined in the SCS; for example, the first row in Cuboid X will measure points from −50 to −35 in 5 mm steps. The total time of measurement is calculated, including the axes' motion time and detector readout time, and is presented on a subsequent tab before scan execution.

FIG. 5.

A screen capture of the cuboid tool interface, which allows for the definition of points in the SCS, as well as count times for measurement. The interface denotes whether the SCS points defined are within the sample as defined earlier in the CUBOID tool. Cuboid X, Cuboid Y, and Cuboid Z in the table are points defined in the SCS; for example, the first row in Cuboid X will measure points from −50 to −35 in 5 mm steps. The total time of measurement is calculated, including the axes' motion time and detector readout time, and is presented on a subsequent tab before scan execution.

Close modal

A robust estimation of the total time and finish time for the scan is calculated and presented to the user. This estimate considers the detector readout time, motion time, as well as collection time to determine the total time of the scan.

A major component of the CUBOID tool is the 3D visualization (as shown in Fig. 6). By defining the sample geometry, the visualization allows a user to view where, in the sample, the planned gauge volume locations are, as well as display the direction of motion between the mapping locations. Any point within a defined run can be excluded by the user through the combination of simple logical statements excluding geometrical points within the SCS or by defining specific points. Examples include samples with holes, as in Fig. 6, areas where data should not be taken, or excluding the first N runs of a data collection which needs to be resumed after an unexpected termination.

FIG. 6.

Example 3D visualization, which shows (a) a sample with SCS defined measurement locations and paths and (b) an excluded circle of points. Also shown are the example locations for the (c) intensity scans performed to correct for sample shape and the (d) resultant shape correction.

FIG. 6.

Example 3D visualization, which shows (a) a sample with SCS defined measurement locations and paths and (b) an excluded circle of points. Also shown are the example locations for the (c) intensity scans performed to correct for sample shape and the (d) resultant shape correction.

Close modal

A shape correction can be utilized through mapping edge locations to correct for any deformation in the sample and deviation from rectilinear coordinates. These edge scans are performed through shape correction scans (Fig. 6) of the actual edge locations and determine if the GV is in the sample through diffracted intensity. This also allows for corrections where the sample axes are not aligned with the instrument axes. This procedure ensures the GV is always fully buried within the sample. Figure 6 shows how the measurement points are adjusted such that they all remain within the deformed sample. When a sample is rotated or remounted, the coordinate system transformation is modified for the new sample mounting, and the sample coordinate system measurement locations can be measured under a different orientation. These CUBOIDs can then be saved, edited, and loaded by users for future experiments.

HIDRA’s expanded instrument capabilities are demonstrated by mapping the stress state in a quenched aluminum bar (7050-T74). This sample stress state has been described in greater detail elsewhere.18 Sample CMRE-A-08 was selected from this work as a demonstration of the beamline capabilities.

Figure 7 provides details of the sample CMRE-A-08. The stress field is mapped along planes A–A by measuring strains between the 311 planes. A 3 × 3 × 3 mm3 gauge volume is utilized in the mapping experiment. This is a small gauge volume for an aluminum sample, as the 7050-aluminum sample is a weak scatterer. The new HIDRA detector allowed for the mapping of a dense two-dimensional (2D) map as detailed in Fig. 8. The count time per point was only 200 s to achieve a high-quality signal, allowing for a reasonable count time to measure the entire grid with high fidelity. The mapping grid (Fig. 8) is across the YZ plane (shown as A–A section in Fig. 7). The 2D grid is defined at 1.5 mm steps and a range of ±9.9 in Y and of ±22.9 in Z, resulting in a grid containing 448 individual points with the origin at the center of the XYZ of the sample. The scan was performed for three orthogonal directions (X, Y, Z).

FIG. 7.

(a) Image of sample CMRE-A-08 along with (b) dimensions (in mm) and definition of mapping directions. The plane of measurement is denoted by plane A–A [18].

FIG. 7.

(a) Image of sample CMRE-A-08 along with (b) dimensions (in mm) and definition of mapping directions. The plane of measurement is denoted by plane A–A [18].

Close modal
FIG. 8.

Screen capture of the CUBOID tool showing mapping locations in a virtual representation of the CMRE-A-08 sample. The tool allows for quick validation that the sample is aligned and that all points are at the desired locations.

FIG. 8.

Screen capture of the CUBOID tool showing mapping locations in a virtual representation of the CMRE-A-08 sample. The tool allows for quick validation that the sample is aligned and that all points are at the desired locations.

Close modal

The methodology for the calculation of strain and stress has been detailed in Refs. 3, 19, and 20, and the key equations are summarized as follows:

The atomic spacing (dhkl) can be determined by Bragg’s law,

λ=2dhklsinθ,
(1)

where λ is the incident neutron beam wavelength (1.73 Å), and θ is the measured angle of diffraction from the peak fitting.

Lattice strain, ϵhkl, is defined as the ratio between the difference in atomic spacing, dhkl, and the stress-free atomic spacing, d0, which can be determined via

ϵhkl=dhkld0d0.
(2)

The macro strain was determined by mapping variations in the 311 peak position at selected locations in the sample. The 311 plane was selected because the elastic modulus in this crystallographic direction is close to the bulk elastic modulus, and it is less sensitive to intergranular strains and texture and best represents the macrostress-strain response of the material.19 The unstressed lattice spacing, d0, 1.2216 ± 0.003 Å, was measured from a representative (4 mm3) cube and extracted from the same alloy using Electric Discharge Machining (EDM) to minimize any mechanical deformation from cutting and chemical changes due to excessive heating of the sample. This small cube is so small that it cannot sustain significant residual stress. The validity of this d0 was confirmed by testing the forces in the stress measurement balance across the sample.

Stresses, σhkl(ξ), were determined using the measured strains for the three orthogonal directions. The stresses can be calculated from

σhkl(ξ)=Ehkl1+νhklϵhklξ+νhkl12νhklϵ11+ϵ22+ϵ33,
(3)

where ϵhklξ is the strain in the direction of interest with a given x, y, and z sample position (ξ), and ɛ11, ɛ22, and ɛ33 are the three orthogonal strains of which ϵhklξ is one. Young’s Modulus and Poisson’s ratio are Ehkl and νhkl, respectively. Note that for a case where the principal axes are unknown, additional measurement directions will be needed to derive the full strain tensor.

The diffraction data were reduced and fit using pyRS, the HIDRA specific open-source analysis program.17 Results of the calculated residual stress values are shown/presented in Fig. 9. The maximum tensile stresses are shown to be in the Z direction. The stress map in the Z direction closely matches the stress profile reported previously both in magnitude and in stress profile shape.18 The stress profile is symmetric along the y direction and only slightly asymmetric along the z direction. This confirms a similar trend shown in the previous study. The Y direction of stress is also reported. As this profile is not able to be measured easily through the contour method, this direction is a unique capability of diffraction methods to gain a further understanding of stress profiles. The stress profile behaves as expected and exhibits similar short-axis symmetry and a slight asymmetry in the long axis.

FIG. 9.

Stress mapping results for the (a) Z direction and (b) Y direction of sample CMRE-A-08. The location of the maximum tensile stresses is clearly visible at y = 0 and z ≈ ±10.

FIG. 9.

Stress mapping results for the (a) Z direction and (b) Y direction of sample CMRE-A-08. The location of the maximum tensile stresses is clearly visible at y = 0 and z ≈ ±10.

Close modal

HIDRA has been commissioned and running since October 2019. The new detector allows for a ∼3× count rate increase, while also increasing the 2θ field of view from 4° to 17°, which enables the simultaneous measurement of multiple peaks. HIDRA’s EPICS conversion has brought the data acquisition system in line with current practice at ORNL neutron scattering facilities for data handling and reduction. Upgraded instrument optics have allowed for further automation of experiments, and collision detection systems detect accidental collisions and limit damage during an experiment. The integrated experimental planning tool is a unique capability for strain scanning diffractometers and has been shown to greatly aid in the data collection of complex multi-dimensional maps.

The example experiment presented shows, in practice, the strength of the CUBOID tool and the pyRS analysis software. Additionally, the increased count rate and narrower line profile from the new 2D PSD detector allow for the measurement of smaller gauge volumes and more detailed maps within a reasonable time using neutrons. This allows for high density maps of high strain gradients, which can arise in engineering materials and continue to approach the high spatial resolution of other methods such as the contour method.

This research used resources at the High Flux Isotope, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. The HDIRA instrument team would like to thank collaborators Tyler Smith, Justin Condon, and Vlastimil Kunc at the Manufacturing Demonstration Facility (MDF) for the 3D printing of the radial collimator being used at HIDRA as well as their collaboration in the materials developed for the Boron containing Thermoplastic (HBN) material in the printing of the optics. Additional thanks should be given to Mr. Christopher D’Elia and Dr. Michael Hill from the University of California, Davis, for supplying the quenched Aluminum bar sample to utilize as a benchmark vs their contour method data, details of which are published in Ref. 18. The authors would like to acknowledge the IDAQ team at HFIR including Rob Knudson, Mike Harrington, and Gary Taufer for the hardware and software integration and data acquisition software development, as well as the ORNL detector group including Kevin Berry and Vladislav Sedov for the detector integration work.

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

The authors have no conflicts to disclose.

J. R. Bunn: Conceptualization (lead); Data curation (equal); Investigation (lead); Project administration (equal); Resources (equal); Validation (equal); Visualization (equal); Writing – original draft (equal); Writing – review & editing (equal). C. M. Fancher: Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (lead); Validation (equal); Writing – original draft (equal); Writing – review & editing (equal). E. A. Payzant: Funding acquisition (equal); Project administration (supporting); Supervision (equal); Writing – review & editing (equal). P. A. Cornwell: Project administration (supporting); Resources (supporting); Writing – review & editing (supporting). W. B. Bailey: Project administration (supporting); Resources (supporting); Writing – review & editing (supporting). R. Gregory: Resources (supporting); Software (supporting); Writing – review & editing (equal).

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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