arXiv subjects

Dilworth Y. Parkinson

Publications and source records attributed to Dilworth Y. Parkinson.

3 recordsLinked to original sources

arXiv2026

High-resolution in situ analysis of biomass pyrolysis by combining quantitative synchrotron $μ$CT and 3D particle-resolved simulations

Understanding the coupling between transport, chemical kinetics, and structural response within solid fuel combustion requires in situ measurements at high spatial resolution. To examine the dynamics of biomass pyrolysis, we optimize synchrotron X-ray micro-computed tomography ($μ$CT), and conduct 3D particle-resolved simulations. A micro-focused heating cell with controlled heating and flow rate is employed, achieving temperatures of up to 1240 K and 3D imaging at 3.24 $μ$m spatial and sub-minute temporal resolutions. The pyrolysis of three biomass materials at sample heating rates of 10-14 K/min are examined. The $μ$CT measurements capture pore deformation, cracking, anisotropic shrinkage, and provide simultaneous thermogravimetric and thermovolumetric analyses to examine specific secondary pyrolysis pathways. Complementary 3D simulations considering detailed kinetics and structural response reproduce the key trends observed, and identify deficiencies in capturing simultaneous mass and volume losses during pyrolysis.

physics.flu-dyn↗

arXiv2023

tomoCAM: Fast Model-based Iterative Reconstruction via GPU Acceleration and Non-Uniform Fast Fourier Transforms

X-Ray based computed tomography (CT) is a well-established technique for determining the three-dimensional structure of an object from its two-dimensional projections. In the past few decades, there have been significant advancements in the brightness and detector technology of tomography instruments at synchrotron sources. These advancements have led to the emergence of new observations and discoveries, with improved capabilities such as faster frame rates, larger fields of view, higher resolution, and higher dimensionality. These advancements have enabled the material science community to expand the scope of tomographic measurements towards increasingly in-situ and in-operando measurements. In these new experiments, samples can be rapidly evolving, have complex geometries, and restrictions on the field of view, limiting the number of projections that can be collected. In such cases, standard filtered back-projections (FBP) for the reconstructions often result in poor-quality reconstructions. Iterative reconstruction algorithms, such as model-based iterative reconstructions (MBIR), have demonstrated considerable success in producing high-quality reconstructions under such restrictions, but typically require high-performance computing resources with hundreds of compute nodes to solve the problem in a reasonable time.

physics.med-ph↗

arXiv2020

Constrained Non-Linear Phase Retrieval for Single Distance X-ray Phase Contrast Tomography

X-ray phase contrast tomography (XPCT) is widely used for 3D imaging of objects with weak contrast in X-ray absorption index but strong contrast in refractive index decrement. To reconstruct an object imaged using XPCT, phase retrieval algorithms are first used to estimate the X-ray phase projections, which is the 2D projection of the refractive index decrement, at each view. Phase retrieval is followed by refractive index decrement reconstruction from the phase projections using an algorithm such as filtered back projection (FBP). In practice, phase retrieval is most commonly solved by approximating it as a linear inverse problem. However, this linear approximation often results in artifacts and blurring when the conditions for the approximation are violated. In this paper, we formulate phase retrieval as a non-linear inverse problem, where we solve for the transmission function, which is the negative exponential of the projections, from XPCT measurements. We use a constraint to enforce proportionality between phase and absorption projections. We do not use constraints such as large Fresnel number, slowly varying phase, or Born/Rytov approximations. Our approach also does not require any regularization parameter tuning since there is no explicit sparsity enforcing regularization function. We validate the performance of our non-linear phase retrieval (NLPR) method using both simulated and real synchrotron datasets. We compare NLPR with a popular linear phase retrieval (LPR) approach and show that NLPR achieves sharper reconstructions with higher quantitative accuracy.

eess.IV↗