Tribosurface Interactions involving Particulate Media with DEM-calibrated Properties: Experiments and Modeling

• Main Author: Desai, Prathamesh
• Format: Others
• Published: Research Showcase @ CMU 2017
• Subjects: Additive Manufacturing (AM); Computational Fluid Dynamics (CFD); Discrete Element Method (DEM); GPU Computing; Machine Learning; Rock Drilling
• Online Access: http://repository.cmu.edu/dissertations/1123; http://repository.cmu.edu/cgi/viewcontent.cgi?article=2162&context=dissertations

While tribology involves the study of friction, wear, and lubrication of interacting surfaces, the tribosurfaces are the pair of surfaces in sliding contact with a fluid (or particulate) media between them. The ubiquitous nature of tribology is evident from the usage of its principles in all aspects of life, such as the friction promoting behavior of shoes on slippery water-lubricated walkways and tires on roadways to the wear of fingernails during filing or engine walls during operations. These tribosurface interfaces, due to the small length scales, are difficult to model for contact mechanics, fluid mechanics and particle dynamics, be it via theory, experiments or computations. Also, there is no simple constitutive law for a tribosurface with a particulate media. Thus, when trying to model such a tribosurface, there is a need to calibrate the particulate media against one or more property characterizing experiments. Such a calibrated media, which is the “virtual avatar” of the real particulate media, can then be used to provide predictions about its behavior in engineering applications. This thesis proposes and attempts to validate an approach that leverages experiments and modeling, which comprises of physics-based modeling and machine learning enabled surrogate modeling, to study particulate media in two key particle matrix industries: metal powder-bed additive manufacturing (in Part II), and energy resource rock drilling (in Part III). The physics-based modeling framework developed in this thesis is called the Particle-Surface Tribology Analysis Code (P-STAC) and has the physics of particle dynamics, fluid mechanics and particle-fluid-structure interaction. The Computational Particle Dynamics (CPD) is solved by using the industry standard Discrete Element Method (DEM) and the Computational Fluid Dynamics (CFD) is solved by using finite difference discretization scheme based on Chorin's projection method and staggered grids. Particle-structure interactions are accounted for by using a state-of-the art Particle Tessellated Surface Interaction Scheme and the fluid-structure interaction is accounted for by using the Immersed Boundary Method (IBM). Surrogate modeling is carried out using back propagation neural network. The tribosurface interactions encountered during the spreading step of the powder-bed additive manufacturing (AM) process which involve a sliding spreader (rolling and sliding for a roller) and particulate media consisting of metal AM powder, have been studied in Part II. To understand the constitutive behavior of metal AM powders, detailed rheometry experiments have been conducted in Chapter 5. CPD module of P-STAC is used to simulate the rheometry of an industry grade AM powder (100-250microns Ti-6Al-4V), to determine a calibrated virtual avatar of the real AM powder (Chapter 6). This monodispersed virtual avatar is used to perform virtual spreading on smooth and rough substrates in Chapter 7. The effect of polydispersity in DEM modeling is studied in Chapter 8. A polydispersed virtual avatar of the aforementioned AM powder has been observed to provide better validation against single layer spreading experiments than the monodispersed virtual avatar. This experimentally validated polydispersed virtual avatar has been used to perform a battery of spreading simulations covering the range of spreader speeds. Then a machine learning enabled surrogate model, using back propagation neural network, has been trained to study the spreading results generated by P-STAC and provide much more data by performing regression. This surrogate model is used to generate spreading process maps linking the 3D printer inputs of spreader speeds to spread layer properties of roughness and porosity. Such maps (Chapters 7 and 8) can be used by a 3D-printer technician to determine the spreader speed setting which corresponds to the desired spread layer properties and has the maximum spread throughout. The tribosurface interactions encountered during the drilling of energy resource rocks which involve a rotary and impacting contact of the drill bit with the rock formation in the presence of drilling fluids have been studied in Part III. This problem involves sliding surfaces with fluid (drilling mud) and particulate media (intact and drilled rock particles). Again, like the AM powder, the particulate media, viz. the rock formation being drilled into, does not have a simple and a well-defined constitutive law. An index test detailed in ASTM D 5731 can be used as a characterization test while trying to model a rock using bonded particle DEM. A model to generate weak concrete-like virtual rock which can be considered to be a mathematical representation of a sandstone has been introduced in Chapter 10. Benchtop drilling experiments have been carried out on two sandstones (Castlegate sandstone from the energy rich state of Texas and Crab Orchard sandstone from Tennessee) in Chapter 11. Virtual drilling has been carried out on the aforementioned weak concrete-like virtual rock. The rate of penetration (RoP) of the drill bit has been found to be directly proportional to the weight on bit (WoB). The drilling in dry conditions resulted in a higher RoP than the one which involved the use of water as the drilling fluid. P-SATC with the bonded DEM and CFD modules was able to predict both these findings but only qualitatively (Chapter 11)