X-ray Computed Tomography Scanning Research Articles

Optimizing Voltage for Effective X-ray Computed Tomography Scan: A Study on Varied Soil Bulk Densities and Container Sizes

Numerous studies have highlighted the role of X-ray computed tomography (X-ray CT) in understanding root architecture. Nevertheless, setting definitive scanning parameters for diverse soils in varied container sizes remains challenging. This study investigates the influence of X-ray CT system voltage on the penetration capability in diverse soils and container sizes, focusing on two key parameters: (1) gray values, which indicate X-ray attenuation and contribute to image contrast, and (2) signal-to-noise ratio, a measure of image clarity. Five soil samples were collected from various depths within a soil profile to encompass bulk density values ranging from 1.34 to 1.84 g·cm−3 to conduct the experiment. Containers with dimensions of 6 × 6 × 6 cm³, 8 × 8 × 6 cm³, 10 × 10 × 6 cm³, 12 × 12 × 6 cm³, 14 × 14 × 6 cm³, and 16 × 16 × 6 cm³ were used. Voltage levels spanning 75 to 225 kV, in 25-kV increments, were applied to each sample. The observed gray values of the X-ray images were fitted using a logistic model of three parameters. Results showed that increasing voltage leads to enhanced penetration up to a plateau point, irrespective of soil density or container size. This plateau could potentially yield higher quality scans, given that lower voltages result in subdued gray values and reduced image contrast. Notably, it was observed that soil properties, including mineral composition, directly affect image gray values. This study established optimal voltage settings for specific soil types at fixed densities, offering valuable insights for researchers investigating soil–root interactions. Although the current findings are based on five soils, a more extensive sampling encompassing diverse soil textures and densities is necessary for a comprehensive understanding of X-ray penetration behavior across various soil types.

Three-dimensional source position verification in image-guided high-dose-rate brachytherapy using an XCT-based gel dosimeter.

Comprehensive quality assurance (QA) for a seamless workflow of high-dose-rate brachytherapy, from imaging to planning and irradiation, is uncommon, and QA of the source dwell position is performed in one- or two-dimensions. Gel dosimetry using magnetic resonance imaging (MRI) is effective in verifying the three-dimensional distribution of doses for image-guided brachytherapy (IGBT). However, MRI scanners are not readily accessible, and MRI scanning is time-consuming. Nevertheless, X-ray computed tomography (XCT) is available for IGBT planning. In this study, we designed and developed an efficient method for QA for a seamless workflow of IGBT with a new commercially available XCT-based polymer gel dosimeter. To enable direct insertion of brachytherapy applicators, the gelatinizing agent of the dosimeter was modified. A cylindrical polyvinyl chloride jar was filled with the modified gel dosimeter, which was subsequently used to determine the reproducibility of source dwell positions, detectability of source positional errors from intentionally introduced catheter length offsets, effect of looped source transfer tubes on the average displacement, extent of inter-observer variation, and gel robustness following multiple needle-insertions. Three ProGuide sharp needles were inserted into the jar. The dwell time at each point was determined to identify the irradiated volume with a diameter of approximately 10mm on XCT images. All the times were the same. The plan was delivered using an afterloader with an Ir-192 radioactive source, and the irradiated gel dosimeter was scanned using an XCT scanner. The subtracted images were generated from pre- and post-irradiated images. Volumes with incremented Hounsfield units were manually identified and contoured. The centroid of the volume was defined as the measured source dwell position. Subsequently, planned source dwell positions were extracted from the DICOM file of the plan. Finally, the source dwell positions in plan and irradiated gel were compared in three axes. The hardness of the dosimeter was 1250% greater than that of the previously reported gel dosimeter. Source dwell positions were visually identified in the XCT image. Testing of CT acquisition, planning, irradiation, and analysis was completed in approximately 1h. In the reproducibility test of source dwell positions, created by inserting three needles (each with three source dwell positions), the average displacements of the source positions from the first source dwell position were within 0.5mm in all three directions. In the detectability test, displacements were less than 1mm in the x-y plane but greater than 1mm in the z-axis, which was the source path direction. When errors of 1-3mm were intentionally introduced, the measured displacement was within 0.7mm of the median (range: 0.21-1.65mm) of intentional errors. When the transfer tube was looped, the source dwell position displaced by approximately 1mm. After 20 needle-insertions, the source dwell position displacement was within 1mm. The maximum inter-observer variation of contouring was 0.57mm. The XCT-based gel dosimeter enabled verification of three-dimensional source dwell positions for a seamless workflow of IGBT with high precision and efficiency.

AIDM-CT: software for cone-beam X-ray tomography and deep-learning-based analysis

ABSTRACT Non-destructive testing (NDT) with high-resolution cone-beam X-ray computed tomography (XCT) plays a crucial role in revealing the 3D distribution and morphology of defects within an object. It’s necessary to generalise an intelligent and customised XCT-based NDT protocol, providing solutions for reconstruction quality improvement and complex defect quantitative analysis for users. We analysed the key techniques of XCT and developed the systematic software for data acquisition, reconstruction, intelligent super-resolution and segmentation in our advanced imaging and data mining laboratory (AIDM-CT). The experimental results demonstrated the software efficiently achieves the control of XCT scanning, artefact correction algorithms related to cone-beam geometrics, beam hardening, ring and radial artefacts, deep-learning based slice super-resolution and feature segmentation, and quantitative analysis. The software is programmed by Python language to provide the friendly multi-function graphical user interface (GUI), in which the programming of reconstruction and corrections combined with Computer Unified Device Architecture (CUDA) acceleration to guarantee the high efficiency of XCT task. Particularly, the multi-functional super resolution module (MF-GAN) is proposed to optimise the quality of CT image and the improved U-Net segmentation module (CBAM U-Net) is also developed to fulfill the high-precision quantitative non-destructive testing of homogeneous and variform microdefects in our AIDM-CT software.

Very fine roots differ among switchgrass (Panicum virgatum L.) cultivars and differentially affect soil pores and carbon processes

Switchgrass (Panicum virgatum L.) is a promising feedstock for biofuel production, with diverse cultivars representing several ecotypes adapted to different environmental conditions within the contiguous USA. Multiple field studies have demonstrated that monoculture switchgrass cultivation leads to slow to negligible soil carbon (C) gains, an outcome unexpected for such a deep-rooted perennial. We hypothesize that different switchgrass cultivars have disparate impacts on soil C gains, and one of the reasons is variations in physical characteristics of their roots, where roots directly and indirectly influence formation of soil pores. We tested this hypothesis at Great Lakes Bioenergy Research Center's research site in Michigan using two lowland cultivars (Alamo and Kanlow) and four upland cultivars (Southlow, Cave-in-Rock, Blackwell, and Trailblazer). Three types of soil samples were collected: 20 cm diameter (Ø) intact cores used for root analyses; 5 cm Ø intact cores subjected to X-ray computed tomography scanning used for pore characterization; and disturbed soil samples used for microbial biomass C (MBC) and soil C measurements. Path analysis was used to explore interactive relationships among roots, soil pores, and their impact on MBC, and ultimately, on soil C contents across six cultivars. The abundance of very fine roots (<200 μm Ø) was positively associated with fractions of pores in the same size range, but negatively with distances to pores and particulate organic matter. Higher abundance of such roots also led to greater MBC, while greater volumes of medium pores (50–200 μm Ø) and shorter distances to pores increased MBC. Results suggest that the greater proportion of very fine roots is a trait that can potentially stimulate soil C gains, with pore characteristics serving as links for the relationship between such roots and C gains. However, at present, ten years of cultivation generated no differences in soil C among the studied cultivars.

Numerical failure modelling of natural fibre composite coupons using X-ray computed tomography based modelling

Natural fibre composites offer versatile applications across industries while being superior in sustainability aspects compared to other composite types. To unlock their full potential, it is essential to understand their complex failure involving fibre failure, matrix cracking, and debonding at the fibre-matrix interface. Efforts to address these challenges focus on advanced numerical models, probing behaviour from micro to macro scales. However, these models face complexities in handling intricate failure modes given the non-uniform nature of natural fibres. To overcome these challenges, image-based modelling using X-ray computed tomography scans is proposed. This work’s novelty lies in integrating detailed microstructure information with a nonlinear calibration procedure to accurately model damage and failure in natural fiber composites. It marks a significant step toward developing a virtual testing model, paving the way for assessing composites with varying fiber content or fiber types.

Automatic detection of hidden defects and qualification of additively manufactured parts using X-ray computed tomography and computer vision

Additive manufacturing (AM) has the unique capability to produce parts with complex three-dimensional structures with features that are internal to the part and hidden from view. Such internal features are difficult to inspect and may have defects that affect the function of a part. X-ray computed tomography (CT) is one of the only methods for nondestructive inspection (NDI) of the interior of AM parts. This paper explores how machine learning (ML) methods can be used to analyze CT scans of AM parts to automatically identify the presence of defects in geometric features. We designed a nozzle part with internal three-dimensional (3D) channels and introduced five different synthetic defects whose presence could affect the nozzle performance. A resin-based AM process fabricated 155 nozzle parts that included both defect-free parts and parts with synthetic defects. CT scans were collected for each part and processed into 68,510 image cross sections. The extracted images were used to train an ML model based on the ResNet34 architecture. The model can automatically identify and classify defects in individual CT slice images with over 98% accuracy. High model accuracy is possible with training on as few as 30 parts. The research demonstrates the potential of ML methods to automatically identify hidden defects and qualify AM parts using X-ray CT scans.

Quantifying the effect of scanning parameters on digital volume correlation analysis for in situ X-ray imaging of concrete

The potential effects of X-ray Computed Tomography (XCT) scan settings on the kinematic field computed via Digital Volume Correlation (DVC) are discussed in this paper. A parametric sensitivity study was conducted, including a series of acquired XCT scans, probing the same volume of interest (VOI) of a concrete specimen, and applying various combinations of XCT scan settings. Different values were adopted for the number of projections acquired, the exposure time and the pixel size. The DVC results revealed a linear correlation between the mean normalised correlation residuals, designating the quality of the correlation and the reliability of the computed kinematic field and the scanning parameters. Gaussian and median filters were applied to the raw data to explore the effect of image noise smoothness in the DVC analysis. The significant reduction noted for the mean normalised correlation residuals was not accompanied by similar levels of change for the corresponding error uncertainties, as defined by the displacement magnitude. The concept was tested against a realistic loading scenario, where the DVC kinematic fields, computed for various XCT “recipes”, were compared. Slim differences in the distribution of the mean minimum principal strain were observed.

Microwave self-healing characteristics of the internal voids in steel slag asphalt mixture subjected to salt-freeze-thaw cycles

Microwave heating technology can be used to repair freeze-thaw (F-T) cycle damage and cracks caused by the use of snow melting salts and de-icers in winter. This study utilized three different gradations of asphalt mixtures: AC-13, SMA-13, and OGFC-13, and enhanced their microwave absorption performance with steel slag (SS). The heating uniformity was evaluated through surface and internal temperature tests. Additionally, the mechanisms of damage and healing of the asphalt mixtures under salt freeze-thaw (S-F-T) damage conditions were explored using S-F-T cycles test and X-ray computed tomography scans (CT). The results showed that OGFC-13 exhibits a significantly higher rate of temperature increase compared to the other two types due to its stronger wave absorption capacity. The thermal hysteresis caused by steel slag leads to uneven spatial temperature distribution. The study recommends an optimal microwave heating time of 40 s for SS asphalt mixtures, which has minimal impact on the mixture structure, by defining an evaluation method for the uniformity index and local overheat ratio. After undergoing S-F-T cycles, the porosity of all three asphalt mixtures increased, particularly at the top and bottom of the specimens. Among them, OGFC-13 exhibited the poorest durability under S-F-T conditions, with the S-F-T cycles having a significant impact on medium-sized voids (0.15–0.6 mm). After microwave heating, AC-13 and SMA-13 showed good repair effects, with porosity levels returning to their initial states. For large voids (>2.36 mm) in OGFC, microwave heating may not effectively reduce the number of voids. Additionally, the healing rate of mixture specimens at certain mid-height ranges is higher due to elevated surface temperatures in these areas, making it especially effective in repairing F-T damage. This study shows that microwave heating of SS asphalt mixtures can improve the durability of pavement in cold regions. However, implementation challenges include the need for specialized equipment and potential uneven heating. Future research should explore optimizing microwave heating techniques to minimize local overheating and investigate the long-term performance of microwave-repaired asphalt pavements under various environmental conditions.

A Surface Determination Technique for Dimensional and Geometrical Analysis in Industrial X-ray Computed Tomography

Industrial X-ray computed tomography (XCT) is a nondestructive technique that can measure workpieces with non-accessible internal features or multimaterial components and assess the dimensional properties of assemblies in assembled states. Surface determination is one of its most crucial steps, which consists of determining boundary surfaces between a solid material and the surrounding air or between different solid materials. It allows for extracting surface points and assessing different features of the object from the data acquired through XCT scans. This task is particularly complex because of challenges associated with material properties, artefacts and noise. The main objective of this work is to assess not just the dimensional but also the geometric characteristics of industrial parts, which requires a more accurate definition of surface points. Therefore, we propose a new surface determination technique (SDT) in XCT to achieve subvoxel accuracy in determining surface points. We demonstrated the effectiveness and stability of our method by comparing it with other SDTs documented in the literature or with results from commercial software. The validation involved measuring various attributes, such as diameter, cylindricity and flatness, of a multi-stepped aluminium part calibrated by a coordinate measuring machine.

Video frame interpolation neural network for 3D tomography across different length scales

Three-dimensional (3D) tomography is a powerful investigative tool for many scientific domains, going from materials science, to engineering, to medicine. Many factors may limit the 3D resolution, often spatially anisotropic, compromising the precision of the information retrievable. A neural network, designed for video-frame interpolation, is employed to enhance tomographic images, achieving cubic-voxel resolution. The method is applied to distinct domains: the investigation of the morphology of printed graphene nanosheets networks, obtained via focused ion beam-scanning electron microscope (FIB-SEM), magnetic resonance imaging of the human brain, and X-ray computed tomography scans of the abdomen. The accuracy of the 3D tomographic maps can be quantified through computer-vision metrics, but most importantly with the precision on the physical quantities retrievable from the reconstructions, in the case of FIB-SEM the porosity, tortuosity, and effective diffusivity. This work showcases a versatile image-augmentation strategy for optimizing 3D tomography acquisition conditions, while preserving the information content.

Design of an experimental approach based on the contrast-to-noise ratio measurement for X-ray computed tomography parameters optimization applied to a carbon fiber-reinforced polymer materials scan

This paper proposes a systematic approach for the optimization of scan parameters for industrial X-ray computed tomography (XCT), as regard its specific application as diagnostic tool on carbon fiber-reinforced polymer materials (CFRP). This procedure allows the system operator to overcome suboptimal scan results due to a subjective choice of XCT parameters. In this work, XCT scan quality has been measured in terms of contrast-to-noise ratio (CNR) metric, by calculating it on collected XCT 2D projection images. A four-factor five-level central composite design (CCD) was implemented to perform experiments, and a quadratic polynomial model was chosen to describe the effects of XCT scanning parameters combination on CNR measurement and finally to predict optimal results. Analysis of variance was carried out to evaluate the significance of the model on the response, reporting a R2 of 97.1%, and response surface analyses were also performed for CNR optimization purposes. In order to validate the CCD results, different XCT parameters combinations, coming from the CCD analysis on projection images, were used to run different scans, and, as result, the optimal CNR predicted from the model was also reflected in an optimal CNR measured on the reconstructed XCT images.

Investigation of aggregate gradation on air voids distribution in porous asphalt concrete using X-ray CT scanning images

Porous asphalt concrete (PAC) is typically used as a paving material for porous asphalt pavement in rainy areas due to its abundant interconnected pores structure. Not all interconnected pores in PAC are valid for permeability, mainly influenced by the composition of aggregate gradation. The main purpose of this paper is to investigate the effect of aggregate gradation on pores structure distribution in PAC by X-ray computed tomography (CT) system and digital image processing (DIP) techniques. The distributions of porosity, number and average equivalent diameter of air voids in middle part are much more evenly compared to the both ends in PAC specimens, accounting for about 70 % of the total of specimen height. The average equivalent diameter of interconnected air voids mainly ranges from 0.5 mm to 5 mm in PAC, accounting for about 80 % of the total numbers. For PAC with similar porosity, the larger the normal maximum aggregate size (NMAS), the less the number and the larger the average equivalent diameter of air voids (interconnected air voids), the smaller the difference between the interconnected porosity and porosity. For PAC with same NMAS, as the porosity increase, the number and average equivalent diameter of air voids (interconnected air voids) are respectively decreased and increased gradually, the difference between the interconnected porosity and porosity is also showed a decreasing trend. And then, the correlated equation is established among porosity, interconnected porosity and the composition of aggregate gradation. The distribution of air voids (interconnected air voids) size could be well described by the two-parameter Weibull model, and the relationships are explored and developed between Weibull model parameters and the composition of aggregate gradation.

Pyrolysis front detection in carbon phenolic composites using x-ray computed tomography

Carbon phenolic composites are used as thermal protection systems (TPS) materials on space capsules to protect them from the hot aerothermal environment. The phenolic resin in the composite material decomposes (pyrolyzes) at low temperatures resulting in a pyrolysis front within the material. The detection of the pyrolysis front after exposure to heat has historically been achieved by physically sectioning cross-sections of the material. We combine the phase contrast retrieval method to reconstruct x-ray computed tomography scans along with image convolution to identify the pyrolysis front in carbon phenolic composites. Unlike the standard filtered back projection method that captures only the carbon phase, the phase contrast retrieval method uses both the attenuation coefficients and refractive indices to illuminate all three phases (carbon, resin, and voids) of carbon phenolic composites. Image convolution is applied on scans reconstructed using the phase contrast retrieval method to develop a density map of the composite to locate the pyrolysis front. The analysis is performed on a sample of phenolic impregnated carbon ablator that was tested in an arc-jet facility. For the sample analyzed, the depth of the pyrolysis front from the surface of the sample is calculated to be 2.150 ± 0.148 mm. Although the proposed approach is applied to detect the pyrolysis front, the tools can be used to illuminate the structure of any carbon phenolic composite, and we propose the use of the phase contrast retrieval method as a methodological standard to analyze carbon phenolic composites used on space capsules.

Comparative evaluation of gold nanoparticles as contrast agent in multimodality diagnostic imaging

A contrast agent is clinically employed to aid the visualization of organs and structures when imaged by radiation based diagnostic imaging modalities. Iodinated contrast agents have been used for decades, but they carry a heightened risk of adverse allergy reactions and contrast-induced nephropathy. This study investigated the contrast enhancement characteristics of gold nanoparticles (AuNPs) through diverse cancer diagnostic imaging modalities as a potential alternative to the iodine-based contrasts. Solutions of 1.9 nm and 15 nm AuNPs, ranging from 3 to 20 mM concentrations, were prepared, alongside a low osmolar non-ionic iodine contrast agent as a control. All samples were scanned and imaged using x-ray computed tomography scanner (CT-Scan), digital mammography, digital radiography, and fluoroscopy modalities. The contrast-to-noise ratio (CNR) was measured to study the relationship between the size, the concentrations, and the influence of the tube voltage over the image contrast enhancement efficacy of the agents. A lower concentration of AuNPs produced lower CNR values. In addition, the 1.9 nm size of AuNPs caused lower CNR than the 15 nm AuNPs when tested with the highest concentration of 20 mM. High CT's HU values for both sizes of AuNPs indicated substantial contrast enhancement compare to iodine. Although the 15 nm AuNPs might set off higher CNR, a better quality of image contrast enhancement was also observed compared to the 1.9 nm AuNPs. The findings also suggested that the contrast enhancement by AuNPs is highly dependent on the modalities' type and x-rays energy. In conclusion, AuNPs could be applied as a contrast agent in various diagnostic imaging modalities representing promising approach in cancer detection particularly in cases of patients having adverse reactions towards the iodine based contrast agents.

Data-driven integration of synthetic representative volume elements and machine learning for improved microstructure-property linkage and material performance in ceramics

Ceramic materials, characterized by their heat resistance, dielectric properties, and mechanical performance, are often compromised by brittleness due to microstructural inclusions. These inclusions, such as defects, secondary phases, and pores, are critically analyzed for their impact on material strength and fracture properties. In this study, the linkage between microstructure and properties in ceramic materials is explored through a methodological approach that combines experimental observations with physics-based and machine learning models. A data-driven approach has been employed, utilizing synthetic Representative Volume Elements (RVEs) derived from X-ray computed tomography scans of ceramics. The methodology involves an automated finite element (FE) simulation process for progressive failure analysis under uniaxial compression and tension. The analysis is conducted using statistical, data-driven, and machine learning techniques, including principal component analysis and k-means clustering, to assess the microstructural features' impact on material performance. The study focuses on the use of RVEs for accurately capturing essential microstructural characteristics and addresses the challenges in developing synthetic models and the limitations of simulation capabilities. The study's findings reveal that less uniform inclusion distribution and a higher standard deviation in inclusion size correlate to lower mechanical performance. By applying data-driven methods, this research contributes to the optimization of material performance and the establishment of structure-property relationships, with a particular emphasis on the influence of inclusions and defects on the mechanical behavior of ceramic materials.

Ichnological study of deep marine hyperpycnites: A case study from the Shenhu continental slope (northern South China Sea)

Ichnological analysis plays a pivotal role in the study of sedimentary basins as it records the evolution of paleoenvironments and the associated changes, providing crucial support for sedimentological and stratigraphic interpretations. Yet, rare ichnological studies on deep-marine hyperpycnites have been conducted, resulting in less conclusive ichnological characterization of deep-marine hyperpycnites, compared to pelagites/hemipelagites, turbidites, and contourites. This study conducted detailed investigation of trace fossils distribution within deep-marine hyperpycnites, using non-destructive X-ray computed tomography scanning. The sediment core S19 on the upper Shenhu slope in the northern South China Sea documented deep-marine hyperpycnites facies S3, S2L, and L, possibly representing the proximal to distal hyperpycnal lobe. In this study, hydrodynamic energy and salinity are suggested to be the dominant stress factors within proximal lobes, resulting in less bioturbation and scattered distribution of vertical trace fossils such as Skolithos and Siphonichnus. In the middle lobe, the environment is predominantly characterized by moderate to high hydrodynamic energy, a rich abundance of organic material, and occasional slight salinity fluctuations. These environmental factors have led to the prevalence of trace fossils such as Arenicolites, Conichnus, Nereites, Phycosiphon, Rosselia, Scolicia, and Thalassinoides, which are well-represented in this region of the lobe. The organic material concentrated within the distal lobe to fringe acts as the main stress factor promoting opportunistic organism colonization and the formation of deposit-feeding traces. Therefore, the represented traces within distal hyperpycnites are mainly Nereites and Phycosiphon. This study provides a detailed investigation into trace fossil distribution within deep-marine hyperpycnal lobes, enriching our knowledge of the ichnology of deep-marine hyperpycnites.

An investigation on enhancing the bonding properties of 3D printed concrete permanent formwork and post-casted concrete

Employing 3D-printed permanent formwork (3DPF) technology for rapidly manufacturing prefabricated components constructed with reinforced concrete represents a promising application of additive manufacturing technology in load-bearing constructions. Ensuring efficient bonding between 3DPF and post-casted concrete (PCC) is a prerequisite for the widespread adoption of this technology. This research aims to enhance the 3DPF-PCC interface through: physical (groove), chemical (cement paste) and composite methods. The interfacial bond strength was quantitatively analysed by splitting tensile strength and shear strength. X-ray computed tomography scanning was utilized for the quantitative characterization of pore structures. Digital image processing techniques were employed to monitor crack development during splitting tensile tests. The mechanism for strengthening the shear strength of the grooved interface was elucidated using a finite element model, and a modified formula for calculating the shear strength of the 3DPF-PCC grooved interface was proposed. The physical method improves shear strength by increasing interlocking structures significantly. The composite method weakens mechanical interlocking structures, increases interface porosity, and adversely affects interface bonding strength. It is recommended that future engineering applications adopt the physical process to improve mechanical interlocking structures and ensure interface bonding strength.

Identification of TRISO pebbles at arbitrary orientation using pairs of X-ray radiographs

TRISO (tri-structural isotropic) fuel is anticipated to be one of the fuel types for future small modular reactors. A TRISO-fuelled pebble in a pebble bed reactor (PBR) consists of thousands of TRISO fuel particles embedded inside a graphite sphere. The diameter of each of the fuel particles is approximately 1 mm. A PBR can contain hundreds of thousands of pebbles, each with a diameter of approximately 6 cm. Unlike in traditional reactor designs, which have a relatively small number of discrete fuel assemblies, the large number of pebbles inside a PBR makes the tracking of individual fuel pebbles difficult. The distribution of TRISO particles within the pebble provides a unique fingerprint that identifies a pebble and can be used to track it. This fingerprint can be determined using a standard X-ray radiograph or a more time-consuming X-ray computed tomography scan. When an individual radiograph is used, the pebble image is compared to a library of reference images of the pebbles. To obtain a good match, the orientation of the pebble must be close to the orientation used for the reference image. As a pebble exits the reactor in a random orientation, the pebble must be reorientated before its identification can take place. This paper discusses the addition of a reference particle in the non-fuelled region of the pebble, as well as the simultaneous acquisition of pairs of radiographs at 90° with respect to each other, to aid pebble reorientation and the development of an identification framework. The framework follows a hybrid approach in which the pebble is first reorientated using machine learning techniques before being identified using traditional computer vision techniques. Other effects such as the impact of transmutation of the elements inside the fuel and the radioactivity of irradiated pebbles are also discussed.

Resolution Enhancement by Variable Zoom Trajectory in X-Ray Computed Tomography

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.

Shear-induced permeability evolution of natural fractures in granite: Implications for stimulation of EGS reservoirs

Permeability evolution of natural fractures during shearing is one of the critical issues in enhanced geothermal system (EGS). In this study, we conducted a series of numerical simulations of shear-flow tests under different normal stresses to investigate the permeability evolution and shear behavior of natural fractures during shearing. All numerical simulations in this study are executed based on a coupled hydro-mechanical pore network model (PNM) for fluid flow within the discrete element method (DEM). The numerical model uses a 33 mm × 25 mm fracture surface profile extracted from X-ray computed tomography scanning data of joints in a rock core retrieved from a 4.2-km-deep well at the Pohang EGS site. The results demonstrate a positive correlation between fracture permeability and shear displacement when the normal stress is relatively high. The central aspect of permeability evolution lies in the variation of local aperture distribution along the pathway: an increase in the variance of fracture aperture distribution leads to an increase in fracture permeability. Permeability evolution of fractures is significantly affected by normal stress, fracture roughness, and relative flow direction. Larger fracture roughness facilitates the formation of high-permeability pathways. Higher local normal stress often corresponds to lower local permeability. Moreover, high normal stress amplifies the effect of fracture roughness on permeability evolution. These findings enhance our comprehensive understanding of hydraulic-mechanical coupling effects during EGS stimulation.