Process simulation and inclusion characterization in stainless steel ingot casting

For the ingot casting process, fluid flow of steel plays an important role in quality control and industrial operations. Using CFD software (FLUENT), this study overviews the fluid flow pattern in the bottom-teeming ingot filling process with three different swirl-modified upgate designs. Turbulent flow and mass transfer are considered main factors in controlling the process. The motion of the slag phase was also considered. In addition to the modeling studies, inclusions in stainless steel poured into ingots with a traditional and a swirl-modified upgate system were investigated using an optical microscope, SEM-EDS and ASPEX automated feature analysis technology. The main inclusions observed were Al₂O₃, MnS, and oxide-sulfide. This work provides a comprehensive description and understanding of the morphology and distribution of inclusions in bottom-poured ingot casting. Fewer inclusions were observed at the center and mid-radius of the swirl-modified ingot than that of the traditional ingot. More inclusions were found at the center of the ingot than nearer the walls. The re-designed upgate system did have positive effect on the flow pattern in the ingot and indirectly cause fewer inclusions in size range of 0~10µm, but made no major difference beyond the change in the smallest size range. --Abstract, page iii

Measuring Short Text Semantic Similarity with Deep Learning Models

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken, which is a subfield of artificial intelligence (AI). The development of NLP applications is challenging because computers traditionally require humans to speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. We study the use of deep learning models, the state-of-the-art artificial intelligence (AI) method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic similarity for pairs of question sentence. In the proposed network, multiple channels of knowledge for pairs of question text can be utilized to improve the representation of text. Then a dense layer is used to learn a classifier for classifying duplicated question pairs. Through extensive experiments on the Quora test collection, our proposed approach has shown remarkable and significant improvement over strong baselines, which verifies the effectiveness of the deep models as well as the proposed deep multi-channel framework

3-D Stress Redistribution During Hydraulic Fracturing Stimulation And Its Interaction With Natural Fractures In Shale Reservoirs

The hydraulic fracturing (also called fracturing, or fracking) technique has been widely applied in many fields, such as the enhanced geothermal systems (EGS), the improvement of injection rates for geologic sequestration of CO2, and for the stimulations of oil and gas reservoirs, especially for unconventional reservoirs with extremely low permeability. The key point for the success of hydraulic fracturing operations in unconventional resources is to connect and reactivate natural fractures and create the effective fracture network for fluid flow from pores into the production wells. To understand hydraulic fracturing technology, we must to understand some other affecting factors, e.g. in-situ stress conditions, reservoir mechanical properties, natural fracture distribution, and redistribution of the stress regime around the hydraulic fracture. Therefore, an accurate estimation of the redistribution of pore pressure and stresses around the hydraulic fracture is necessary, and it is very important to find out the reactivations of pre-existing natural fractures during the hydraulic fracturing process. Generally, fracture extension as well as its surround pore pressure and stress regime are affected by: poro- and thermoelastic phenomena as well as by fracture opening under the combined action of applied pressure and in-situ stresses. In this thesis, the previous studies on the hydraulic fracturing modeling and simulations were reviewed; a comprehensive semi-analytical model was constructed to estimate the pore pressure and stress distribution around an injection induced fracture from a single well in an infinite reservoir. With Mohr-Coulomb failure criterion, the natural fracture reactivation potential around the hydraulic fracture were studied. Then, a few case studies were presented, especially with the application in unconventional natural fractured shale reservoirs. This work is of interest in interpretation of micro-seismicity in hydraulic fracturing and in assessing permeability variation around a stimulation zone, as well as in estimation of the fracture spacing during hydraulic fracturing operations. In addition, the results from this study can be very helpful for selection of stimulated wells and further design of the re-fracturing operations