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In the present paper, the prediction of the performance of a centrifugal pump is investigated based on proper orthogonal decomposition (POD) reduced-order model (ROM). Firstly, the POD method is used to analyze the characteristics of the complex flow field inside the centrifugal pump, especially the eigenvalues of the velocity field and pressure field, and then the ROM of the flow field in the centrifugal pump is established then. After that, the established reduced-order model is used to predict the internal flow field. The results show that the POD mode field can reflect the spatial scale distribution of the flow field. The low-order flow field reflects the large-scale flow field structure, while the high-order flow field reflects the small-scale flow field structure. With the established ROM, the overall pump head (0D, 0-dimensional parameter) can be predicted with the relative error of less than 5%, and the standard root mean square error (normalized root mean square error, NRMSE) of pressure field and velocity field (3D, 3-dimensional parameter) is less than 4% and 8%, respectively. The efficiency and accuracy of both 0D and 3D parameters’ prediction motivate the fast prediction of multi-scale simulation of the large-scale industrial system in the future. more...

Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets., (Copyright © 2019 Elsevier B.V. All rights reserved.) more...