Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

Morphology Decoder to Predict Heterogeneous Rock Permeability with Machine Learning Guided 3D Vision

<p><a></a><a>Permeability has a dominant influence on the flow behavior of a natural fluid, and without proper quantification, biological fluids (Hydrocarbons) and water resources become waste. During the first decades of the 21^st century, permeability quantification from nano-micro porous media images emerged, aided by 3D pore network flow simulation, primarily using the Lattice Boltzmann simulator. Earth scientists realized that the simulation process holds millions of flow dynamics calculations with accumulated errors and high computing power consumption. Therefore, accuracy and efficiency challenges obstruct planetary exploration. To effic­­­iently, consistently predict permeability with high quality, we propose the Morphology Decoder. It is a parallel and serial flow reconstruction of machine learning-driven semantically segmented heterogeneous rock texture images of 3D X-Ray Micro Computerized Tomography (μCT) and Nuclear Magnetic Resonance (MRI). For 3D vision, we introduce controllable-measurable-volume as new supervised semantic segmentation, in which a unique set of voxel intensity corresponds to grain and pore throat sizes. The morphology decoder demarks and aggregates the morphologies' boundaries in a novel way to quantify permeability. The morphology decoder method consists of five novel processes, which we describe in this paper, these novel processes are (1) Geometrical: 3D Permeability Governing Equation, (2) Machine Learning: Guided 3D Properties Recognition of Rock Morphology, (3) Analytical: 3D Image Properties Integration Model for Permeability, (4) Experimental: MRI Permeability Imager, and (5) Morphology Decoder (the process that integrates the other four novel processes).</a></p>

An Overview of High Yield Strength Twinning-Induced Plasticity Steels

Twinning-induced plasticity (TWIP) steel is a second-generation advanced high strength steel grade developed for automotive applications. TWIP steels exhibit an excellent combination of strength and ductility, mainly originating from the activation of deformation twinning. However, TWIP steels generally exhibit a relatively low yield strength (YS), which limits their practical applications. Thus, developing high YS TWIP steels without ductility loss is essential to increase their industrial applications. The present work summarizes and discusses the recent progress in improving the YS of TWIP steels, in terms of precipitation strengthening, solid solution strengthening, thermomechanical processing, and novel processes. Novel processes involving sub-boundary strengthening, multi-phase structure, and gradient structure as well as the control of thermomechanical processing (recovery annealing and warm rolling) and precipitation strengthening were found to result in an excellent combination of YS and total elongation.

Improved mechanical performances of unidirectional jute fibre composites developed with new fibre architectures

This study detailed two novel processes, the use of stitching and PVA sizing based jute fibre UD preforms, with bamboo slice hybridization for the manufacturing of high-mechanical-performance jute composites, and significant improvement was found.