Article
Pharmacology & Pharmacy
M. Abrami, M. Grassi, D. Masiello, G. Pontrelli
Summary: The prediction of drug dissolution profiles is crucial for understanding drug's pharmacokinetic behavior and the bioavailability of dosage forms. A mathematical model has been developed in this study to describe the dissolution process of irregularly shaped particles, taking into account both surface kinetics and convective diffusion. The model theoretically shows that the dissolution rate is nonlinearly dependent on the surface curvature, and considers the subsequent recrystallization process in the bulk fluid. The simplicity and ability to determine model parameters using common techniques are the main advantages of this model, allowing the evaluation of the importance of solid wettability on the dissolution process. The proposed model has been demonstrated to be important in describing the experimental dissolution data of theophylline monohydrate.
EUROPEAN JOURNAL OF PHARMACEUTICS AND BIOPHARMACEUTICS
(2022)
Article
Geosciences, Multidisciplinary
Marco Ceia, Roseane Missagia, Irineu Lima Neto
Summary: This paper analyzed four carbonate rock datasets to investigate the relationship between misfits of the Nur's model and experimental data with porosity and pressure variations through best-fitting regressions. A modification on the Nur's model accounting for pressure influence on porosity was proposed, significantly improving accuracy with average relative errors 50% smaller than original estimates. The performance of the modified model varied for different carbonate textures, highlighting the influence of siliciclastic content on the predictability error.
JOURNAL OF APPLIED GEOPHYSICS
(2022)
Article
Environmental Sciences
Zhen Wang, Ming Dou, Pengju Ren, Bin Sun, Ruipeng Jia, Yuze Zhou
Summary: The settling velocity of microplastics in aquatic environments is influenced by factors such as shape irregularity, density, and particle size. Irregular microplastics have a significantly lower settling velocity compared to perfectly spherical microplastics. A model was proposed to predict the correlation between the settling velocity of microplastics and their shape, density, particle size, and water density.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Yuangui Zhang, Bangrang Di, Feng Gao, Lei Li
Summary: Background porosity has a significant influence on seismic anisotropy and shear wave splitting in fractures. The experimental results show that increasing porosity leads to a decrease in P-wave anisotropy, while shear wave splitting remains nearly constant. These results are consistent with predictions from equivalent medium theories.
APPLIED SCIENCES-BASEL
(2023)
Article
Geosciences, Multidisciplinary
Abrar Alabbad, Jack Dvorkin, Yazeed Altowairqi, Zhou F. Duan
Summary: A rock physics-based seismic interpretation workflow has been developed to extract volumetric rock properties from seismic data, successfully linking elastic properties with volumetric properties through rock physics models in different case studies. Additionally, a geology-driven relationship was derived to accurately relate pore fluid to porosity in rocks.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Engineering, Mechanical
Efstathios E. Michaelides, Zhigang Feng
Summary: The knowledge of closure equations for the drag coefficients of nonspherical particles is crucial for numerical codes. However, the existing correlations for drag coefficients have high uncertainty. This review paper critically examines the understanding of size and shape of particles, presents shape factors and descriptors used in past correlations, and provides information on the accuracy and applicability of various drag coefficient correlations for nonspherical particles.
JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME
(2023)
Article
Geosciences, Multidisciplinary
Prabal Shankar Das, Rima Chatterjee, Sumangal Dasgupta
Summary: This study used a rock physics template (RPT) approach to model the impacts of mechanical compaction and chemical diagenesis on porosity reduction in the Barail Sandstone. This study also analyzed the effects of diagenesis, rock frame constituents, and pore fluids on seismic amplitude response using a probabilistic amplitude variation with angle (AVA) approach.
NATURAL RESOURCES RESEARCH
(2022)
Article
Energy & Fuels
Hani Salman Al-Mukainah, Syed Rizwanullah Hussaini, Jack Petrovich Dvorkin
Summary: This work discusses how to generate a physically valid transform between porosity and electrical resistivity at different spatial scales using elemental data obtained from small natural rock fragments. By utilizing process-based upscaling, the authors demonstrate, through examples, the feasibility of obtaining a coarse-scale transform between these two physical properties, proving that the method is generalizable to address similar questions in different situations.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Eric J. Goldfarb, Ken Ikeda, Richard A. Ketcham, Masa Prodanovic, Nicola Tisato
Summary: This study introduces a segmentation-less digital rock physics method that estimates rock properties by preserving the scaling relationship between voxel values in CT attenuation units and calibrating with known densities. The method allows quick and accurate estimation of density and porosity without the need for external laboratory calibration.
COMPUTERS & GEOSCIENCES
(2022)
Article
Astronomy & Astrophysics
Ya N. Istomin, A. A. Gunya
Summary: We propose a model of the global structure of the electromagnetic fields in the Fermi bubbles (FBs), which allows for the acceleration of protons to ultrahigh energies. The magnetic and electric fields in the FBs resemble those found in jets ejected from active galactic nuclei. The supermassive black hole in the center of the Galaxy can energize the FB and keep it active for a long time.
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
(2022)
Article
Materials Science, Multidisciplinary
Max Weiner, Matthias Schmidtchen, Ulrich Prahl
Summary: A new type of composite refractory with the ability to conduct electricity in high-temperature environments is developed within the FOR3010 Refrabund project. To support the development of its material properties and production technology, a new simulation approach is introduced to describe irregularly shaped particles of at least two different phases.
ADVANCED ENGINEERING MATERIALS
(2022)
Article
Geochemistry & Geophysics
Tobias B. Gram, Frederik P. Ditlevsen, Klaus Mosegaard, Ida L. Fabricius
Summary: The study investigates the impact of water flooding on the strength and elasticity of chalk reservoirs, showing changes in porosity and Biot's coefficient at different stages of testing. The results suggest that water flooding promotes pore collapse, leading to elastoplastic deformation in chalk.
GEOPHYSICAL PROSPECTING
(2021)
Article
Energy & Fuels
Faisal Altawati, Hossein Emadi, Rayan Khalil
Summary: Unconventional resources like the Eagle Ford formation pose challenges due to their ultra-low permeability, requiring careful consideration of experimental parameters when evaluating physical and dynamic elastic properties. The study evaluated 24 Eagle Ford core samples, observing variations in porosity, permeability, and velocities within a certain range and establishing strong correlations between P- and S-wave velocities at different confining pressures. Applying cycles of confining pressure affected velocities and dynamic elastic moduli, showcasing the complexities of unconventional resources evaluation.
JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY
(2021)
Article
Materials Science, Multidisciplinary
Jhoan Guzman, Rafael de Moura Nobre, Enzo R. Nunes, D. L. Bayerlein, R. B. Falcao, Edwin Sallica-Leva, Joao Batista Ferreira Neto, Henrique Rodrigues Oliveira, Victor Lira Chastinet, Fernando J. G. Landgraf
Summary: The study explores the fabrication of Ti-53wt.%Nb alloy parts using irregularly shaped powder in LPBF, demonstrating the possibility of achieving high density samples and an increase in hardness with higher energy density.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2021)
Article
Materials Science, Multidisciplinary
Jhoan Guzman, Rafael de Moura Nobre, D. L. Rodrigues Junior, Willy Ank de Morais, Enzo R. Nunes, D. L. Bayerlein, R. B. Falcao, Edwin Sallica-Leva, Henrique Rodrigues Oliveira, Victor Lira Chastinet, Fernando J. G. Landgraf
Summary: The study found that irregularly shaped powder has lower flowability and apparent density compared to spherical shaped powder. Samples of Nb47Ti alloy from different powder preparation methods showed variations in microhardness and porosity levels.
JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE
(2021)
Article
Geochemistry & Geophysics
Suihong Song, Tapan Mukerji, Jiagen Hou
Summary: This study improves the simulation method based on generative adversarial networks (GANs) to bridge the gap between remotely sensed geophysical information and geology. The generated geological facies models are realistic, diversified, and consistent with all input conditions.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Tongjun Chen, Zhijun Lin, Zuiliang Liu, Tapan Mukerji
Summary: This study conducted a comparative experiment to discuss the detectability and influencing factors of heterogeneous stress field distributions in underground coal mines in North China. The results showed that curvature variations and mined-out voids are the dominant factors affecting the uneven distribution of stress fields in coal mine panels.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geosciences, Multidisciplinary
Runhai Feng, Dario Grana, Tapan Mukerji, Klaus Mosegaard
Summary: Geological facies modeling is a key component in exploring and characterizing subsurface reservoirs. This work presents a deep learning approach based on generative adversarial networks for geological facies modeling. It introduces a Bayesian GANs approach to create facies models and analyze the model uncertainty. The proposed method is applied to different geological scenarios and successfully captures the variability of the data.
MATHEMATICAL GEOSCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Mingliang Liu, Tapan Mukerji
Summary: This paper introduces a method based on deep generative adversarial networks to generate high-resolution digital rock images and recover fine details. Experimental results demonstrate the effectiveness of this method and its potential to better characterize heterogeneous porous media and predict pore-scale flow and petrophysical properties.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Geochemistry & Geophysics
Liming Zhao, Tongjun Chen, Tapan Mukerji, Mingjin Zhang, Tao Xing
Summary: This article proposes a model based on Berryman and Milton's generalized Gassmann's equations to calculate the high-frequency saturated bulk modulus at different soft-pore fractions or crack densities in Mavko and Jizba's model. Experimental data validate the effectiveness of the proposed model.
Article
Geosciences, Multidisciplinary
Mingliang Liu, Dario Grana, Tapan Mukerji
Summary: A computationally efficient method using randomized tensor decomposition is developed in this study to reduce model parameters and observations for efficient data assimilation in low-dimensional spaces.
MATHEMATICAL GEOSCIENCES
(2022)
Article
Geology
Ardiansyah Koeshidayatullah, Elizabeth J. Trower, Xiaowei Li, Tapan Mukerji, Daniel J. Lehrmann, Michele Morsilli, Khalid Al-Ramadan, Jonathan L. Payne
Summary: This study uses a convolutional neural network-based segmentation method to measure ooid sizes and provides a systematic sampling of ooid sizes throughout the Phanerozoic. The numerical model shows that typical-sized ooids can grow under a wide range of parameter combinations, while giant ooids can only form under specific conditions. The study also finds that the formation of giant ooids is associated with conditions favorable for rapid CaCO3 precipitation.
Review
Geochemistry & Geophysics
Dario Grana, Leonardo Azevedo, Leandro De Figueiredo, Patrick Connolly, Tapan Mukerji
Summary: Seismic reservoir characterization aims to predict rock and fluid properties based on seismic measurements, using geophysical models and mathematical methods. Seismic inversion estimates elastic properties, while rock-physics inversion estimates petrophysical properties. Deterministic and probabilistic methods can be applied to solve these problems.
Article
Geochemistry & Geophysics
Dario Grana, Brian Russell, Tapan Mukerji
Summary: This study uses canonical correlation analysis to infer the relationship between seismic data and petrophysical properties, and proposes a two-step inversion approach. This approach avoids the calibration of a rock-physics model and maximizes the correlation with petrophysical properties through parameterization. Additionally, a probabilistic method is introduced to propagate the uncertainty from the seismic to the petrophysical domain. The results from synthetic and real case studies demonstrate the high accuracy of this method compared to traditional approaches.
Article
Environmental Sciences
Suihong Song, Tapan Mukerji, Jiagen Hou, Dongxiao Zhang, Xinrui Lyu
Summary: In this article, a Generative Adversarial Network (GAN)-based 3D reservoir simulation framework, GANSim-3D, is presented. It is capable of generating multiple realistic and conditional 3D earth models directly from given conditioning data. The framework is trained on small-size data cubes and can be used for geomodeling of 3D reservoirs of large arbitrary sizes. The practical use and verification of the framework are demonstrated using a field karst cave reservoir in China.
WATER RESOURCES RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Ali Kashefi, Tapan Mukerji
Summary: We propose a novel physics-informed deep learning framework for solving steady-state incompressible flow on multiple sets of irregular geometries. This framework incorporates a point-cloud based neural network to capture the geometric features of computational domains and utilizes the mean squared residuals of the governing partial differential equations as the loss function to capture the physics. It allows for solving equations on a set of computational domains with irregular geometries and can predict solutions on domains with unseen geometries, resulting in cost savings.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Engineering, Civil
Suihong Song, Dongxiao Zhang, Tapan Mukerji, Nanzhe Wang
Summary: To address the challenging task of stochastic conditional geomodelling, we propose a deep-learning framework called GANSim-surrogate, which effectively integrates geological patterns and various types of data. The framework consists of a CNN generator, a CNN-based surrogate, and options for searching appropriate input latent vectors. Through validation on channelized reservoirs, the framework is proven to generate realistic and consistent models with all conditioning data, while also being computationally efficient.
JOURNAL OF HYDROLOGY
(2023)
Article
Materials Science, Multidisciplinary
Rasool Ahmad, Mingliang Liu, Michael Ortiz, Tapan Mukerji, Wei Cai
Summary: This study focuses on calculating the homogenized elastic properties of rocks using 3D micro-CT scanned images. To solve the problem of large micro-CT images, a hierarchical homogenization method is proposed, where the image is divided into smaller subimages. The subimages are individually homogenized and then assembled to find the final homogenized elastic constant. The error in the homogenized constant follows a power law scaling with respect to the subimage size, and this scaling is used for better approximation of large heterogeneous microstructures.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
(2023)
Article
Geochemistry & Geophysics
Mingliang Liu, Divakar Vashisth, Dario Grana, Tapan Mukerji
Summary: A differentiable physics model integrated with neural networks is developed for high-resolution reservoir monitoring. The proposed method effectively estimates reservoir properties and accurately quantifies uncertain parameters for CO2 storage monitoring.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2023)
Article
Geochemistry & Geophysics
Mingliang Liu, Rasool Ahmad, Wei Cai, Tapan Mukerji
Summary: Digital rock physics combines tomographic imaging techniques with numerical simulations to estimate effective rock properties. To address the computational challenge of large sample sizes, a hierarchical homogenization method with a data-driven surrogate model based on convolutional neural networks is proposed. The method reduces computational time and memory demand compared to conventional algorithms.
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
(2023)
