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Biomedical Engineering and Bioengineering Commons™
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Articles 1 - 5 of 5
Full-Text Articles in Biomedical Engineering and Bioengineering
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There …
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition …
A Validated Software Application To Measure Fiber Organization In Soft Tissue, Erica E. Morrill, Azamat N. Tulepbergenov, Christina J. Stender, Roshani Lamichhane, Raquel J. Brown, Trevor J. Lujan
A Validated Software Application To Measure Fiber Organization In Soft Tissue, Erica E. Morrill, Azamat N. Tulepbergenov, Christina J. Stender, Roshani Lamichhane, Raquel J. Brown, Trevor J. Lujan
Mechanical and Biomedical Engineering Faculty Publications and Presentations
The mechanical behavior of soft connective tissue is governed by a dense network of fibrillar proteins in the extracellular matrix. Characterization of this fibrous network requires the accurate extraction of descriptive structural parameters from imaging data, including fiber dispersion and mean fiber orientation. Common methods to quantify fiber parameters include fast Fourier transforms (FFT) and structure tensors, however, information is limited on the accuracy of these methods. In this study, we compared these two methods using test images of fiber networks with varying topology. The FFT method with a band-pass filter was the most accurate, with an error of 0.71 …
An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova
An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova
Mechanical and Biomedical Engineering Faculty Publications and Presentations
There is a growing interest to apply the immersed boundary method to compute wind fields over arbitrarily complex terrain. The computer implementation of an immersed boundary module into an existing flow solver can be accomplished with minor modifications to the rest of the computer program. However, a versatile preprocessor is needed at the first place to extract the essential geometric information pertinent to the immersion of an arbitrarily complex terrain inside a 3D Cartesian mesh. Errors in the geometric information can negatively impact the correct implementation of the immersed boundary method as part of the solution algorithm. Additionally, the distance …
Multi-Level Parallelism For Incompressible Flow Computations On Gpu Clusters, Dana A. Jacobsen, Inanc Senocak
Multi-Level Parallelism For Incompressible Flow Computations On Gpu Clusters, Dana A. Jacobsen, Inanc Senocak
Mechanical and Biomedical Engineering Faculty Publications and Presentations
We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues faced when merging fine-grain parallelism on the GPU using CUDA with coarse-grain parallelism that use either MPI or MPI-OpenMP for communications. We present three different strategies to overlap computations with communications, and systematically assess their impact on parallel performance on two different GPU clusters. Our …