Positron emission particle tracking in fluidized beds with secondary gas injection

Hensler, Timo; Tupy, Martin; Strer, Timo; Pöschel, Thorsten; Wirth, Karl‐Ernst

doi:10.1016/j.powtec.2015.04.005

Cited by 24 publications

(8 citation statements)

References 18 publications

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“…PEPT also allowed the measurement of solids circulation flux in a CFB by following the tracer velocity within a given distance in the downcomer, with an assumption that the flow was in a moving packed-bed pattern [195]. Similarly, the dimensionless solids flux across the jet boundaries in a bubbling fluidized bed was determined from the tracer trajectory data [196]. In addition, based on the PEPT measured data distribution, a 'phase diagram' was proposed to depict different flow modes (e.g.…”

Section: Positron Emission Particle Trackingmentioning

confidence: 99%

Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review

Sun

Yan

2016

Meas. Sci. Technol.

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Section: Positron Emission Particle Trackingmentioning

confidence: 99%

Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review

Sun

Yan

2016

Meas. Sci. Technol.

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“…In recent years, the application of γ-photon imaging technology in the industrial field has gradually progressed. In 2015, the Timo team in Germany used the method of single tracer particle detection to detect the density distribution of positrons at a cone angle and then measure the size of the cone angle [5]. In 2016, Liu Jiantang et al successfully tested the defect of an ant nest cavity with γ-photon imaging technology and solved the nondestructive testing of an irregular cavity [6].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction Algorithm for Regions of Interest in γ-Photon Images

Yao

Zhao

et al. 2021

IEEE Access

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As a nondestructive testing technology, γ-photon imaging shows immense potential in the industrial field. However, the limitations of γ-photon imaging theory and detection technology result in various problems, such as low image resolution and edge blur. The technology is particularly difficult to apply to industrial detection that requires high imaging speed and high resolution. Therefore, this study proposes a reconstruction algorithm for regions of interest (ROI) in γ-photon images. The proposed algorithm is suitable for fast industrial detection and is based on the reconstruction algorithm for sinusoidal graph data, that is, the ordered subset expectation maximization (OSEM) image reconstruction algorithm. It is an improvement of the traditional point-and-line system matrix (SM) model. In the application of the proposed algorithm, the probability weight of a pixel is determined by the solid angle of the crystal bar at both ends of the line of response (LOR) to the pixel it passes through. In this work, the known contour parameters of industrial parts are used to describe the area of nuclide distribution as the ROI. Only the pixels through which the LOR passed in the ROI are counted, and the probability weights of these pixels are calculated to construct the SM. Gaussian filters are added in each iteration to suppress the clutter of scattered noise inside the image. The effectiveness of the algorithm was verified in two model experiments. A closed cavity detection experiment on industrial hydraulic parts was also conducted to compare the image reconstruction effects before and after the improvement. Results showed that the proposed algorithm can effectively improve image resolution and image edge contours. In the tee pipe model experiment and cavity detection experiment on hydraulic parts, the image reconstruction speed increased by more than 6 and 10 times, respectively. Hence, the proposed algorithm provided a feasible solution for quickly obtaining images with clear edges and high resolution under a large aperture detector ring.INDEX TERMS γ-photon imaging, OSEM, ROI, system matrix, industrial nondestructive testing

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“…Currently, PNDTT is used primarily to analyse the characteristics of semiconductors, metals, high temperature superconductors, polymers and other industrial materials, as well as to conduct non-destructive testing of internal defects of these industrial materials. The German Timo team [5] has proposed a single tracer particle testing method for tracking the density distribution of a positron at a cone angle, in order to measure the cone angle. Yao [6] proposed a method that uses 18F-labelled fuel to observe a combustion flow field in a confined space.…”

Section: Introductionmentioning

confidence: 99%

Positron Image Super-Resolution Using Generative Adversarial Networks

et al. 2021

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Positron images generated by positron non-destructive testing technology under rapid detection scenes such as low concentration dose, low exposure time and short imaging time, which have some problems like low-resolution and poor definition. These issues cannot be solved for the being time. Therefore, this paper pins hope on carrying out super-resolution processing on the low-resolution positron image, in order to generate images with high-resolution and clear details. The existing super-resolution algorithms which built on real world images, on the base of unfitted for the scene of super-resolution of positron images, it is easy to lose the details of the images and introduce noise textures. To make the generated super-resolution images more capable of restoring the features of low-resolution images, this research proposed a positron image super-resolution reconstruction method based on generative adversarial networks. In order to improve the input information utilization rate, long skip connections were added into the generator. In addition, the discriminant model, where composed of an image discriminator and a feature discriminator, can stimulate the generator to generate clearer super-resolution images which contain more details. In attempting to solve the problem of dataset matching, a special positron image super-resolution dataset is constructed for network application scenarios. In the adversarial training stage, perceptual similarity loss and adversarial loss are used to replace the traditional mean squared error loss to improve the images perception quality. Experimental results show that the proposed model can reconstruct lowresolution images by four times super-resolution in 0.16 seconds. The super-resolution images obtained are superior to other algorithms in visual effect, which have clearer detail structure and higher objective performance values. Hence this model can meet the requirements of rapid non-destructive testing of industrial parts.

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Positron emission particle tracking in fluidized beds with secondary gas injection

Cited by 24 publications

References 18 publications

Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review

Non-intrusive measurement and hydrodynamics characterization of gas–solid fluidized beds: a review

Reconstruction Algorithm for Regions of Interest in γ-Photon Images

Positron Image Super-Resolution Using Generative Adversarial Networks

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