scholarly journals 3D pressure field reconstruction from time-resolved stereoscopic PIV measurements by relaxation of Taylor's hypothesis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Dominique Fratantonio ◽  
John James Charonko
Keyword(s):  
Pressure Field ◽  
National Laboratory ◽  
Reconstruction Algorithm ◽  
Turbulent Channel Flow ◽  
Reconstruction Method ◽  
Time Resolved ◽  
Material Derivative ◽  
Stereoscopic Piv ◽  
Air Jet ◽  
Taylor’S Hypothesis

This work presents reconstructions of 3D pressure fields starting from 2D3C stereoscopic-PIV (SPIV) measurements. In Fratantonio et al. (2021), we presented a new reconstruction algorithm, the “Instantaneous convection” method, capable of producing 3D velocity fields from time-resolved SPIV measurements. For reconstructions in flows with strong shear layers and high turbulence intensity, this method is able to provide time-resolved 3D velocity volumes that are more accurate than those that can be obtained from the more frequently employed reconstruction method based on the Taylor’s hypothesis and on the use of a mean convective field. Here we investigate the possibility of reconstructing the 3D pressure field from the timeresolved series of reconstructed 3D velocity data. A pseudo-tracking method is employed for computing the velocity material derivative, and the pressure field is then reconstructed by solving the 3D Poisson equation. The velocity and pressure reconstructions are validated on the Direct Numerical Simulation data of the turbulent channel flow taken from the John Hopkins Turbulence Database (JHTDB), and an application to experimental SPIV measurements of an air jet flow in coflow carried out at the Turbulent Mixing Tunnel (TMT) facility at Los Alamos National Laboratory is presented.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

Investigation of the 3D Unsteady Rotor Pressure Field in a HP Turbine Stage

2002 ◽  
Author(s):  
E. Valenti ◽  
J. Halama ◽  
R. De´nos ◽  
T. Arts
Keyword(s):  
Reynolds Number ◽  
Mach Number ◽  
Pressure Field ◽  
Pressure Ratio ◽  
Turbine Stage ◽  
Time Resolved ◽  
Mach Number Distribution ◽  
Rotor Surface ◽  
Exit Mach Number ◽  
Unsteady Pressure Measurements

This paper presents steady and unsteady pressure measurements at three span locations (15, 50 and 85%) on the rotor surface of a transonic turbine stage. The data are compared with the results of a 3D unsteady Euler stage calculation. The overall agreement between the measurements and the prediction is satisfactory. The effects of pressure ratio and Reynolds number are discussed. The rotor time-averaged Mach number distribution is very sensitive to the pressure ratio of the stage since the incidence of the flow changes as well as the rotor exit Mach number. The time-resolved pressure field is dominated by the vane trailing edge shock waves. The incidence and intensity of the shock strongly varies from hub to tip due to the radial equilibrium of the flow at the vane exit. The decrease of the pressure ratio attenuates significantly the amplitude of the fluctuations. An increase of the pressure ratio has less significant effect since the change in the vane exit Mach number is small. The effect of the Reynolds number is weak for both the time-averaged and the time-resolved rotor static pressure at mid-span, while it causes an increase of the pressure amplitudes at the two other spans.

scholarly journals Source reconstruction for bioluminescence tomography via L1∕2 regularization

2018 ◽  
Vol 11 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Jingjing Yu ◽  
Qiyue Li ◽  
Haiyu Wang
Keyword(s):  
Image Quality ◽  
Matching Pursuit ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Preclinical Research ◽  
Bioluminescence Tomography ◽  
Comparison Results ◽  
Stable Reconstruction

Bioluminescence tomography (BLT) is an important noninvasive optical molecular imaging modality in preclinical research. To improve the image quality, reconstruction algorithms have to deal with the inherent ill-posedness of BLT inverse problem. The sparse characteristic of bioluminescent sources in spatial distribution has been widely explored in BLT and many L1-regularized methods have been investigated due to the sparsity-inducing properties of L1 norm. In this paper, we present a reconstruction method based on L[Formula: see text] regularization to enhance sparsity of BLT solution and solve the nonconvex L[Formula: see text] norm problem by converting it to a series of weighted L1 homotopy minimization problems with iteratively updated weights. To assess the performance of the proposed reconstruction algorithm, simulations on a heterogeneous mouse model are designed to compare it with three representative sparse reconstruction algorithms, including the weighted interior-point, L1 homotopy, and the Stagewise Orthogonal Matching Pursuit algorithm. Simulation results show that the proposed method yield stable reconstruction results under different noise levels. Quantitative comparison results demonstrate that the proposed algorithm outperforms the competitor algorithms in location accuracy, multiple-source resolving and image quality.

scholarly journals Time-resolved velocity and pressure field quantification in a flow-focusing device for ultrafast microbubble production

2021 ◽  
Vol 6 (11) ◽  
Author(s):  
Sarah Cleve ◽  
Christian Diddens ◽  
Tim Segers ◽  
Guillaume Lajoinie ◽  
Michel Versluis
Keyword(s):  
Pressure Field ◽  
Flow Focusing ◽  
Time Resolved

scholarly journals Stochastic 3D Carbon Cloth GDL Reconstruction and Transport Prediction

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 572
Author(s):  
Yuan Gao ◽  
Teng Jin ◽  
Xiaoyan Wu
Keyword(s):  
Reconstruction Algorithm ◽  
Gas Diffusion ◽  
Reconstruction Method ◽  
Carbon Cloth ◽  
Statistical Characterization ◽  
Stochastic Reconstruction ◽  
Classical Simulation ◽  
The Many ◽  
Transport Prediction ◽  
Structure Properties

This paper presents the 3D carbon cloth gas diffusion layer (GDL) to predict transport behaviors of anisotropic structure properties. A statistical characterization and stochastic reconstruction method is established to construct the 3D micro-structure using the data from the true materials. Statistics of the many microstructure characteristics, such as porosity, pore size distribution, and shape of the void, are all quantified by image-based characterization. Furthermore, the stochastic reconstruction algorithm is proposed to generate random and anisotropic 3D microstructure models. The proposed method is demonstrated by some classical simulation prediction and to give the evaluation of the transport properties. Various reconstructed GDLs are also generated to demonstrate the capability of the proposed method. In the end, the adapted structure properties are offered to optimize the carbon cloth GDLs.

Gravity anomaly reconstruction based on nonequispaced Fourier transform

Geophysics ◽  
2019 ◽  
Vol 84 (6) ◽  
pp. G83-G92
Author(s):  
Ya Xu ◽  
Fangzhou Nan ◽  
Weiping Cao ◽  
Song Huang ◽  
Tianyao Hao
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Gravity Data ◽  
Signal Reconstruction ◽  
Gaussian Function ◽  
Reconstruction Algorithm ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Grid Data ◽  
2D Data

Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

scholarly journals Selection Criteria for Determination of Optimal Reconstruction Method for Cu-64 Trastuzumab Dosimetry on Siemens Inveon PET Scanner

2019 ◽  
Vol 8 (4) ◽  
pp. 512 ◽  
Author(s):  
Seonhwa Lee ◽  
Hyeongi Kim ◽  
Ye-rin Kang ◽  
Hyungwoo Kim ◽  
Jung Young Kim ◽  
...  
Keyword(s):  
Effective Dose ◽  
Selection Criteria ◽  
Absorbed Dose ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Algorithms ◽  
Recovery Coefficient ◽  
Optimal Reconstruction ◽  
Iteration Number

The goal of this study was to suggest criteria for the determination of the optimal image reconstruction algorithm for image-based dosimetry of Cu-64 trastuzumab PET in a mouse model. Image qualities, such as recovery coefficient (RC), spill-over ratio (SOR), and non-uniformity (NU), were measured according to National Electrical Manufacturers Association (NEMA) NU4-2008. Mice bearing a subcutaneous tumor ( 200 mm 3 , HER2 NCI N87) were injected with monoclonal antibodies (trastuzumab) with Cu-64. Preclinical mouse PET images were acquired at 4 time points after injection (2, 15, 40 and 64 h). Phantom and Cu-64 trastuzumab PET images were reconstructed using various reconstruction algorithms (filtered back projection (FBP), 3D reprojection algorithm (FBP-3DRP), 2D ordered subset expectation maximization (OSEM 2D), and OSEM 3D maximum a posteriori (OSEM3D-MAP)) and filters. The absorbed dose for the tumor and the effective dose for organs for Cu-64 trastuzumab PET were calculated using the OLINDA/EXM program with various reconstruction algorithms. Absorbed dose for the tumor ranged from 923 mGy/MBq to 1830 mGy/MBq with application of reconstruction algorithms and filters. When OSEM2D was used, the effective osteogenic dose increased from 0.0031 to 0.0245 with an increase in the iteration number (1 to 10). In the region of kidney, the effective dose increased from 0.1870 to 1.4100 when OSEM2D was used with iteration number 1 to 10. To determine the optimal reconstruction algorithms and filters, a correlation between RC and NU was plotted and selection criteria (0.9 < RC < 1.0 and < 10% of NU) were suggested. According to the selection criteria, OSEM2D (iteration 1) was chosen for the optimal reconstruction algorithm. OSEM2D (iteration 10) provided 154.7% overestimated effective dose and FBP with a Butterworth filter provided 20.9% underestimated effective dose. We suggested OSEM2D (iteration 1) for the calculation of the effective dose of Cu-64 trastuzumab on an Inveon PET scanner.

Computed tomography reconstruction on distributed storage using hybrid regularization approach

2019 ◽  
Vol 33 (06) ◽  
pp. 1950063 ◽  
Author(s):  
Shailendra Tiwari ◽  
Kavkirat Kaur ◽  
Yadunath Pathak ◽  
Shivendraa Shivani ◽  
Kuldeep Kaur
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Distributed Storage ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Noise Robustness ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction

Computed Tomography (CT) is considered as a significant imaging tool for clinical diagnoses. Due to low-dose radiation in CT, the projection data is highly affected by Gaussian noise which may lead to blurred images, staircase effect, loss of basic fine structure and detailed information. Therefore, there is a demand for an approach that can eliminate noise and can provide high-quality images. To achieve this objective, this paper presents a new statistical image reconstruction method by proposing a suitable regularization approach. The proposed regularization is a hybrid approach of Complex Diffusion and Shock filter as a prior term. To handle the problem of prominent Gaussian noise as well as ill-posedness, the proposed hybrid regularization is further combined with the standard Maximum Likelihood Expectation Maximization (MLEM) reconstruction algorithm in an iterative manner and has been referred to as the proposed CT-Reconstruction (CT-R) algorithm here after. Besides, considering the large sizes of image data sets for medical imaging, distributed storage for images have been employed on Hadoop Distributed File System (HDFS) and the proposed MLEM algorithms have been deployed for improved performance.The proposed method has been evaluated on both the simulated and real test phantoms. The final results are compared with the other standard methods and it is observed that the proposed method has many desirable properties such as better noise robustness, less computational cost and enhanced denoising effect.

Real Time Neutron Diffraction Experiments Using Bragg Edges

1994 ◽  
Vol 376 ◽  
Author(s):  
Walter J Trela ◽  
Kay Meggers ◽  
Hans G Priesmeyer
Keyword(s):  
Phase Transition ◽  
High Resolution ◽  
National Laboratory ◽  
Uniaxial Stress ◽  
Alamos National Laboratory ◽  
Time Resolved ◽  
Los Alamos National Laboratory ◽  
Strain Measurements ◽  
Transmission Technique ◽  
Millisecond Time Scale

ABSTRACTTime resolved phase transition and strain experiments have been performed on the millisecond time scale using a Bragg-edge transmission technique that has been developed at the Los Alamos National Laboratory. The precision with which lattice parameters can be determined from edge positions is sufficient to perform high-resolution strain measurements in uniaxial stress.

CROWD-SOURCED VISUAL DATA COLLECTION FOR MONITORING INDOOR CONSTRUCTION IN 3D

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fouad Amer ◽  
Mani Golparvar-Fard
Keyword(s):  
Data Collection ◽  
3D Reconstruction ◽  
Structure From Motion ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Visual Data ◽  
Mapping Algorithm ◽  
3D Point Clouds ◽  
Localization And Mapping

Complete and accurate 3D monitoring of indoor construction progress using visual data is challenging. It requires (a) capturing a large number of overlapping images, which is time-consuming and labor-intensive to collect, and (b) processing using Structure from Motion (SfM) algorithms, which can be computationally expensive. To address these inefficiencies, this paper proposes a hybrid SfM-SLAM 3D reconstruction algorithm along with a decentralized data collection workflow to map indoor construction work locations in 3D and any desired frequency. The hybrid 3D reconstruction method is composed of a pipeline of Structure from Motion (SfM) coupled with Multi-View Stereo (MVS) to generate 3D point clouds and a SLAM (Simultaneous Localization and Mapping) algorithm to register the separately formed models together. Our SfM and SLAM pipelines are built on binary Oriented FAST and Rotated BRIEF (ORB) descriptors to tightly couple these two separate reconstruction workflows and enable fast computation. To elaborate the data capture workflow and validate the proposed method, a case study was conducted on a real-world construction site. Compared to state-of-the-art methods, our preliminary results show a decrease in both registration error and processing time, demonstrating the potential of using daily images captured by different trades coupled with weekly walkthrough videos captured by a field engineer for complete 3D visual monitoring of indoor construction operations.

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