Matching Items (3)
Description
As the explorations beyond the Earth's boundaries continue to evolve, researchers and engineers strive to develop versatile technologies capable of adapting to unknown space conditions. For instance, the utilization of Screw-Propelled Vehicles (SPVs) and robotics that utilize helical screws propulsion to transverse planetary bodies is a growing area of interest. An example of such technology is the Extant Exobiology Life Surveyor (EELS), a snake-like robot currently developed by the NASA Jet Propulsion Laboratory (JPL) to explore the surface of Saturn’s moon, Enceladus. However, the utilization of such a mechanism requires a deep and thorough understanding of screw mobility in uncertain conditions. The main approach to exploring screw dynamics and optimal design involves the utilization of Discrete Element Method (DEM) simulations to assess interactions and behavior of screws when interacting with granular terrains. In this investigation, the Simplified Johnson-Kendall-Roberts (SJKR) model is implemented into the utilized simulation environment to account for cohesion effects similar to what is experienced on celestial bodies like Enceladus. The model is verified and validated through experimental and theoretical testing. Subsequently, the performance characteristics of screws are explored under varying parameters, such as thread depth, number of screw starts, and the material’s cohesion level. The study has examined significant relationships between the parameters under investigation and their influence on the screw performance.
ContributorsAbdelrahim, Mohammad (Author) / Marvi, Hamid (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Arizona State University (Publisher)
Created2023
Description
In material science, microstructure plays a key role in determining properties, which further determine utility of the material. However, effectively measuring microstructure evolution in real time remains an challenge. To date, a wide range of advanced experimental techniques have been developed and applied to characterize material microstructure and structural evolution on different length and time scales. Most of these methods can only resolve 2D structural features within a narrow range of length scale and for a single or a series of snapshots. The currently available 3D microstructure characterization techniques are usually destructive and require slicing and polishing the samples each time a picture is taken. Simulation methods, on the other hand, are cheap, sample-free and versatile without the special necessity of taking care of the physical limitations, such as extreme temperature or pressure, which are prominent
issues for experimental methods. Yet the majority of simulation methods are limited to specific circumstances, for example, first principle computation can only handle several thousands of atoms, molecular dynamics can only efficiently simulate a few seconds of evolution of a system with several millions particles, and finite element method can only be used in continuous medium, etc. Such limitations make these individual methods far from satisfaction to simulate macroscopic processes that a material sample undergoes up to experimental level accuracy. Therefore, it is highly desirable to develop a framework that integrate different simulation schemes from various scales
to model complicated microstructure evolution and corresponding properties. Guided by such an objective, we have made our efforts towards incorporating a collection of simulation methods, including finite element method (FEM), cellular automata (CA), kinetic Monte Carlo (kMC), stochastic reconstruction method, Discrete Element Method (DEM), etc, to generate an integrated computational material engineering platform (ICMEP), which could enable us to effectively model microstructure evolution and use the simulated microstructure to do subsequent performance analysis. In this thesis, we will introduce some cases of building coupled modeling schemes and present
the preliminary results in solid-state sintering. For example, we use coupled DEM and kinetic Monte Carlo method to simulate solid state sintering, and use coupled FEM and cellular automata method to model microstrucutre evolution during selective laser sintering of titanium alloy. Current results indicate that joining models from different length and time scales is fruitful in terms of understanding and describing microstructure evolution of a macroscopic physical process from various perspectives.
issues for experimental methods. Yet the majority of simulation methods are limited to specific circumstances, for example, first principle computation can only handle several thousands of atoms, molecular dynamics can only efficiently simulate a few seconds of evolution of a system with several millions particles, and finite element method can only be used in continuous medium, etc. Such limitations make these individual methods far from satisfaction to simulate macroscopic processes that a material sample undergoes up to experimental level accuracy. Therefore, it is highly desirable to develop a framework that integrate different simulation schemes from various scales
to model complicated microstructure evolution and corresponding properties. Guided by such an objective, we have made our efforts towards incorporating a collection of simulation methods, including finite element method (FEM), cellular automata (CA), kinetic Monte Carlo (kMC), stochastic reconstruction method, Discrete Element Method (DEM), etc, to generate an integrated computational material engineering platform (ICMEP), which could enable us to effectively model microstructure evolution and use the simulated microstructure to do subsequent performance analysis. In this thesis, we will introduce some cases of building coupled modeling schemes and present
the preliminary results in solid-state sintering. For example, we use coupled DEM and kinetic Monte Carlo method to simulate solid state sintering, and use coupled FEM and cellular automata method to model microstrucutre evolution during selective laser sintering of titanium alloy. Current results indicate that joining models from different length and time scales is fruitful in terms of understanding and describing microstructure evolution of a macroscopic physical process from various perspectives.
ContributorsChen, Shaohua (Author) / Jiao, Yang (Thesis advisor) / Wang, Qinghua (Committee member) / Emady, Heather (Committee member) / Gel, Aytekin (Committee member) / Arizona State University (Publisher)
Created2018
Description
The current research is based on the principles of three-dimensional discrete element method (3D – DEM) through simulations, by using heat transfer models in EDEM, to investigate the effects of fill level, rotation rate and particle size on the steady-state conduction heat transfer in rotary drums. The high heat and mass transfer rates obtained through rotary drums make them very useful for powder mixing and heating processes in metallurgical, cement, mining, pharmaceutical, detergent and other particulate processing applications. However, these complex processes are difficult to model and operate since the particles can have a wide range of properties, and there is currently no way to predict the optimal operating conditions for a given material.
Steady-state heat transfer by conduction forms the basis for understanding other steady-state and unsteady-state heat transfer in a rotary drum – conduction, convection and radiation. Statistical analysis is carried out to determine the effects of these process parameters and find optimal operating conditions, which will thereby improve the heat transfer efficiency in rotary drums. A stainless-steel drum with a diameter of 6 inches and a length of 3 inches was modeled in EDEM with silica beads of sizes 2 mm, 3 mm and 4 mm at fill levels of 10%, 17.5% and 25%, and at rotation rates of 2 rpm, 5 rpm and 10 rpm. It was found that the heating uniformity increased with decreasing particle size, decreasing fill level and increasing rotation rate. This research is the first step towards studying the other heat transfer modes and various other process parameters. Better understanding of the various heat transfer modes, when used in combination for heating the particles, will be beneficial in improving the operating efficiency, reducing material costs and leading to significant energy conservation on a global scale.
Steady-state heat transfer by conduction forms the basis for understanding other steady-state and unsteady-state heat transfer in a rotary drum – conduction, convection and radiation. Statistical analysis is carried out to determine the effects of these process parameters and find optimal operating conditions, which will thereby improve the heat transfer efficiency in rotary drums. A stainless-steel drum with a diameter of 6 inches and a length of 3 inches was modeled in EDEM with silica beads of sizes 2 mm, 3 mm and 4 mm at fill levels of 10%, 17.5% and 25%, and at rotation rates of 2 rpm, 5 rpm and 10 rpm. It was found that the heating uniformity increased with decreasing particle size, decreasing fill level and increasing rotation rate. This research is the first step towards studying the other heat transfer modes and various other process parameters. Better understanding of the various heat transfer modes, when used in combination for heating the particles, will be beneficial in improving the operating efficiency, reducing material costs and leading to significant energy conservation on a global scale.
ContributorsBheda, Bhaumik (Author) / Emady, Heather (Thesis advisor) / Muhich, Christopher (Committee member) / Nielsen, David (Committee member) / Arizona State University (Publisher)
Created2020