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- All Subjects: Mechanical Engineering
- Creators: Wang, Liping
- Creators: Ren, Yi
Description
Production from a high pressure gas well at a high production-rate encounters the risk of operating near the choking condition for a compressible flow in porous media. The unbounded gas pressure gradient near the point of choking, which is located near the wellbore, generates an effective tensile stress on the porous rock frame. This tensile stress almost always exceeds the tensile strength of the rock and it causes a tensile failure of the rock, leading to wellbore instability. In a porous rock, not all pores are choked at the same flow rate, and when just one pore is choked, the flow through the entire porous medium should be considered choked as the gas pressure gradient at the point of choking becomes singular. This thesis investigates the choking condition for compressible gas flow in a single microscopic pore. Quasi-one-dimensional analysis and axisymmetric numerical simulations of compressible gas flow in a pore scale varicose tube with a number of bumps are carried out, and the local Mach number and pressure along the tube are computed for the flow near choking condition. The effects of tube length, inlet-to-outlet pressure ratio, the number of bumps and the amplitude of the bumps on the choking condition are obtained. These critical values provide guidance for avoiding the choking condition in practice.
ContributorsYuan, Jing (Author) / Chen, Kangping (Thesis advisor) / Wang, Liping (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Tesla turbo-machinery offers a robust, easily manufactured, extremely versatile prime mover with inherent capabilities making it perhaps the best, if not the only, solution for certain niche applications. The goal of this thesis is not to optimize the performance of the Tesla turbine, but to compare its performance with various working fluids. Theoretical and experimental analyses of a turbine-generator assembly utilizing compressed air, saturated steam and water as the working fluids were performed and are presented in this work. A brief background and explanation of the technology is provided along with potential applications. A theoretical thermodynamic analysis is outlined, resulting in turbine and rotor efficiencies, power outputs and Reynolds numbers calculated for the turbine for various combinations of working fluids and inlet nozzles. The results indicate the turbine is capable of achieving a turbine efficiency of 31.17 ± 3.61% and an estimated rotor efficiency 95 ± 9.32%. These efficiencies are promising considering the numerous losses still present in the current design. Calculation of the Reynolds number provided some capability to determine the flow behavior and how that behavior impacts the performance and efficiency of the Tesla turbine. It was determined that turbulence in the flow is essential to achieving high power outputs and high efficiency. Although the efficiency, after peaking, begins to slightly taper off as the flow becomes increasingly turbulent, the power output maintains a steady linear increase.
ContributorsPeshlakai, Aaron (Author) / Phelan, Patrick (Thesis advisor) / Trimble, Steve (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis evaluates the viability of an original design for a cost-effective wheel-mounted dynamometer for road vehicles. The goal is to show whether or not a device that generates torque and horsepower curves by processing accelerometer data collected at the edge of a wheel can yield results that are comparable to results obtained using a conventional chassis dynamometer. Torque curves were generated via the experimental method under a variety of circumstances and also obtained professionally by a precision engine testing company. Metrics were created to measure the precision of the experimental device's ability to consistently generate torque curves and also to compare the similarity of these curves to the professionally obtained torque curves. The results revealed that although the test device does not quite provide the same level of precision as the professional chassis dynamometer, it does create torque curves that closely resemble the chassis dynamometer torque curves and exhibit a consistency between trials comparable to the professional results, even on rough road surfaces. The results suggest that the test device provides enough accuracy and precision to satisfy the needs of most consumers interested in measuring their vehicle's engine performance but probably lacks the level of accuracy and precision needed to appeal to professionals.
ContributorsKing, Michael (Author) / Ren, Yi (Thesis director) / Spanias, Andreas (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Solar energy has become one of the most popular renewable energy in human’s life because of its abundance and environment friendliness. To achieve high solar energy conversion efficiency, it usually requires surfaces to absorb selectivity within one spectral range of interest and reflect strongly over the rest of the spectrum. An economic method is always desired to fabricate spectrally selective surfaces with improved energy conversion efficiency. Colloidal lithography is a recently emerged way of nanofabrication, which has advantages of low-cost and easy operation.
In this thesis, aluminum metasurface structures are proposed based on colloidal lithography method. High Frequency Structure Simulator is used to numerically study optical properties and design the aluminum metasurfaces with selective absorption. Simulation results show that proposed aluminum metasurface structure on aluminum oxide thin film and aluminum substrate has a major reflectance dip, whose wavelength is tunable within the near-infrared and visible spectrum with metasurface size. As the metasurface is opaque due to aluminum film, it indicates strong wavelength-selective optical absorption, which is due to the magnetic resonance between the top metasurface and bottom Al film within the aluminum oxide layer.
The proposed sample is fabricated based on colloidal lithography method. Monolayer polystyrene particles of 500 nm are successfully prepared and transferred onto silicon substrate. Scanning electron microscope is used to check the surface topography. Aluminum thin film with 20-nm or 50-nm thickness is then deposited on the sample. After monolayer particles are removed, optical properties of samples are measured by micro-scale optical reflectance and transmittance microscope. Measured and simulated reflectance of these samples do not have frequency selective properties and is not sensitive to defects. The next step is to fabricate the Al metasurface on Al_2 O_3 and Al films to experimentally demonstrate the selective absorption predicted from the numerical simulation.
In this thesis, aluminum metasurface structures are proposed based on colloidal lithography method. High Frequency Structure Simulator is used to numerically study optical properties and design the aluminum metasurfaces with selective absorption. Simulation results show that proposed aluminum metasurface structure on aluminum oxide thin film and aluminum substrate has a major reflectance dip, whose wavelength is tunable within the near-infrared and visible spectrum with metasurface size. As the metasurface is opaque due to aluminum film, it indicates strong wavelength-selective optical absorption, which is due to the magnetic resonance between the top metasurface and bottom Al film within the aluminum oxide layer.
The proposed sample is fabricated based on colloidal lithography method. Monolayer polystyrene particles of 500 nm are successfully prepared and transferred onto silicon substrate. Scanning electron microscope is used to check the surface topography. Aluminum thin film with 20-nm or 50-nm thickness is then deposited on the sample. After monolayer particles are removed, optical properties of samples are measured by micro-scale optical reflectance and transmittance microscope. Measured and simulated reflectance of these samples do not have frequency selective properties and is not sensitive to defects. The next step is to fabricate the Al metasurface on Al_2 O_3 and Al films to experimentally demonstrate the selective absorption predicted from the numerical simulation.
ContributorsGuan, Chuyun (Author) / Wang, Liping (Thesis advisor) / Azeredo, Bruno (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
With the substantial development of intelligent robots, human-robot interaction (HRI) has become ubiquitous in applications such as collaborative manufacturing, surgical robotic operations, and autonomous driving. In all these applications, a human behavior model, which can provide predictions of human actions, is a helpful reference that helps robots to achieve intelligent interaction with humans. The requirement elicits an essential problem of how to properly model human behavior, especially when individuals are interacting or cooperating with each other. The major objective of this thesis is to utilize the human intention decoding method to help robots enhance their performance while interacting with humans. Preliminary work on integrating human intention estimation with an HRI scenario is shown to demonstrate the benefit. In order to achieve this goal, the research topic is divided into three phases. First, a novel method of an online measure of the human's reliance on the robot, which can be estimated through the intention decoding process from human actions，is described. An experiment that requires human participants to complete an object-moving task with a robot manipulator was conducted under different conditions of distractions. A relationship is discovered between human intention and trust while participants performed a familiar task with no distraction. This finding suggests a relationship between the psychological construct of trust and joint physical coordination, which bridges the human's action to its mental states. Then, a novel human collaborative dynamic model is introduced based on game theory and bounded rationality, which is a novel method to describe human dyadic behavior with the aforementioned theories. The mutual intention decoding process was also considered to inform this model. Through this model, the connection between the mental states of the individuals to their cooperative actions is indicated. A haptic interface is developed with a virtual environment and the experiments are conducted with 30 human subjects. The result suggests the existence of mutual intention decoding during the human dyadic cooperative behaviors. Last, the empirical results show that allowing agents to have empathy in inference, which lets the agents understand that others might have a false understanding of their intentions, can help to achieve correct intention inference. It has been verified that knowledge about vehicle dynamics was also important to correctly infer intentions. A new courteous policy is proposed that bounded the courteous motion using its inferred set of equilibrium motions. A simulation, which is set to reproduce an intersection passing case between an autonomous car and a human driving car, is conducted to demonstrate the benefit of the novel courteous control policy.
ContributorsWang, Yiwei (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
In the development of autonomous ground vehicles (AGVs), how to guarantee vehicle lateral stability is one of the most critical aspects. Based on nonlinear vehicle lateral and tire dynamics, new driving requirements of AGVs demand further studies and analyses of vehicle lateral stability control strategies. To achieve comprehensive analyses and stability-guaranteed vehicle lateral driving control, this dissertation presents three main contributions.First, a new method is proposed to estimate and analyze vehicle lateral driving stability regions, which provide a direct and intuitive demonstration for stability control of AGVs. Based on a four-wheel vehicle model and a nonlinear 2D analytical LuGre tire model, a local linearization method is applied to estimate vehicle lateral driving stability regions by analyzing vehicle local stability at each operation point on a phase plane. The obtained stability regions are conservative because both vehicle and tire stability are simultaneously considered. Such a conservative feature is specifically important for characterizing the stability properties of AGVs.
Second, to analyze vehicle stability, two novel features of the estimated vehicle lateral driving stability regions are studied. First, a shifting vector is formulated to explicitly describe the shifting feature of the lateral stability regions with respect to the vehicle steering angles. Second, dynamic margins of the stability regions are formulated and applied to avoid the penetration of vehicle state trajectory with respect to the region boundaries. With these two features, the shiftable stability regions are feasible for real-time stability analysis.
Third, to keep the vehicle states (lateral velocity and yaw rate) always stay in the shiftable stability regions, different control methods are developed and evaluated. Based on different vehicle control configurations, two dynamic sliding mode controllers (SMC) are designed. To better control vehicle stability without suffering chattering issues in SMC, a non-overshooting model predictive control is proposed and applied. To further save computational burden for real-time implementation, time-varying control-dependent invariant sets and time-varying control-dependent barrier functions are proposed and adopted in a stability-guaranteed vehicle control problem.
Finally, to validate the correctness and effectiveness of the proposed theories, definitions, and control methods, illustrative simulations and experimental results are presented and discussed.
ContributorsHuang, Yiwen (Author) / Chen, Yan (Thesis advisor) / Lee, Hyunglae (Committee member) / Ren, Yi (Committee member) / Yong, Sze Zheng (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2021
Description
Biogas’s potential as a renewable fuel source has been an area of increased research in recent years. One issue preventing wide-spread use of biogas as a fuel is the trace amounts of impurities that damage fuel-burning equipment by depositing silicon, sulfur, calcium and other elements on their surface. This study aims to analyze the effects of a high concentration of L4 linear siloxane on solid oxide fuel cell performance until failure occurs. L4 siloxane has not been extensively researched previously, and this investigation aims to provide new data to support similar, though slower, degradation compared to D4, D5 and other siloxanes in solid oxide fuel cells. The experiments were conducted inside a furnace heated to 800℃ with an Ni-YSZ-supported (Nickel-yttria-stabilized zirconia) fuel cell. A fuel source with a flow rate of 20 mL/min of hydrogen gas, 10 mL/min of nitrogen gas and 0.15 mL/min of L4 siloxane was used. Air was supplied to the cathode. The effects of siloxane deposition on cell voltage and power density degradation and resistance increase were studied by using techniques like the current-voltage method, electrochemical impedance spectroscopy, and gas chromatography. The results of the experiment after reduction show roughly constant degradation of 8.35 mV/hr, followed after approximately 8 hours by an increasing degradation until cell failure of 130.45 mV/hr. The initial degradation and stagnation match previous research in siloxane deposition on SOFCs, but the sharp decline to failure does not. A mechanism for solid oxide fuel cell failure is proposed based on the data.
ContributorsRiley, Derall M. (Author) / Milcarek, Ryan J (Thesis advisor) / Wang, Liping (Committee member) / Phelan, Patrick E (Committee member) / Arizona State University (Publisher)
Created2021
Description
In convective heat transfer processes, heat transfer rate increases generally with a large fluid velocity, which leads to complex flow patterns. However, numerically analyzing the complex transport process and conjugated heat transfer requires extensive time and computing resources. Recently, data-driven approach has risen as an alternative method to solve physical problems in a computational efficient manner without necessitating the iterative computations of the governing physical equations. However, the research on data-driven approach for convective heat transfer is still in nascent stage. This study aims to introduce data-driven approaches for modeling heat and mass convection phenomena. As the first step, this research explores a deep learning approach for modeling the internal forced convection heat transfer problems. Conditional generative adversarial networks (cGAN) are trained to predict the solution based on a graphical input describing fluid channel geometries and initial flow conditions. A trained cGAN model rapidly approximates the flow temperature, Nusselt number (Nu) and friction factor (f) of a flow in a heated channel over Reynolds number (Re) ranging from 100 to 27750. The optimized cGAN model exhibited an accuracy up to 97.6% when predicting the local distributions of Nu and f. Next, this research introduces a deep learning based surrogate model for three-dimensional (3D) transient mixed convention in a horizontal channel with a heated bottom surface. Conditional generative adversarial networks (cGAN) are trained to approximate the temperature maps at arbitrary channel locations and time steps. The model is developed for a mixed convection occurring at the Re of 100, Rayleigh number of 3.9E6, and Richardson number of 88.8. The cGAN with the PatchGAN based classifier without the strided convolutions infers the temperature map with the best clarity and accuracy.
Finally, this study investigates how machine learning analyzes the mass transfer in 3D printed fluidic devices. Random forests algorithm is hired to classify the flow images taken from semi-transparent 3D printed tubes. Particularly, this work focuses on laminar-turbulent transition process occurring in a 3D wavy tube and a straight tube visualized by dye injection. The machine learning model automatically classifies experimentally obtained flow images with an accuracy > 0.95.
ContributorsKang, Munku (Author) / Kwon, Beomjin (Thesis advisor) / Phelan, Patrick (Committee member) / Ren, Yi (Committee member) / Rykaczewski, Konrad (Committee member) / Sohn, SungMin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autonomous systems inevitably must interact with other surrounding systems; thus, algorithms for intention/behavior estimation are of great interest. This thesis dissertation focuses on developing passive and active model discrimination algorithms (PMD and AMD) with applications to set-valued intention identification and fault detection for uncertain/bounded-error dynamical systems. PMD uses the obtained input-output data to invalidate the models, while AMD designs an auxiliary input to assist the discrimination process. First, PMD algorithms are proposed for noisy switched nonlinear systems constrained by metric/signal temporal logic specifications, including systems with lossy data modeled by (m,k)-firm constraints. Specifically, optimization-based algorithms are introduced for analyzing the detectability/distinguishability of models and for ruling out models that are inconsistent with observations at run time. On the other hand, two AMD approaches are designed for noisy switched nonlinear models and piecewise affine inclusion models, which involve bilevel optimization with integer variables/constraints in the inner/lower level. The first approach solves the inner problem using mixed-integer parametric optimization, whose solution is included when solving the outer problem/higher level, while the second approach moves the integer variables/constraints to the outer problem in a manner that retains feasibility and recasts the problem as a tractable mixed-integer linear programming (MILP). Furthermore, AMD algorithms are proposed for noisy discrete-time affine time-invariant systems constrained by disjunctive and coupled safety constraints. To overcome the issues associated with generalized semi-infinite constraints due to state-dependent input constraints and disjunctive safety constraints, several constraint reformulations are proposed to recast the AMD problems as tractable MILPs. Finally, partition-based AMD approaches are proposed for noisy discrete-time affine time-invariant models with model-independent parameters and output measurement that are revealed at run time. Specifically, algorithms with fixed and adaptive partitions are proposed, where the latter improves on the performance of the former by allowing the partitions to be optimized. By partitioning the operation region, the problem is solved offline, and partition trees are constructed which can be used as a `look-up table' to determine the optimal input depending on revealed information at run time.
ContributorsNiu, Ruochen (Author) / Yong, Sze Zheng S.Z. (Thesis advisor) / Berman, Spring (Committee member) / Ren, Yi (Committee member) / Zhang, Wenlong (Committee member) / Zhuang, Houlong (Committee member) / Arizona State University (Publisher)
Created2022
Description
The thermal conductivity of cadmium sulfide (CdS) colloidal nanocrystals (NCs) and magic-sized clusters (MSCs) have been investigated in this work. It is well documented in the literature that the thermal conductivity of colloidal nanocrystal assemblies decreases as diameter decreases. However, the extrapolation of this size dependence does not apply to magic-sized clusters. Magic-sized clusters have an anomalously high thermal conductivity relative to the extrapolated size-dependence trend line for the colloidal nanocrystals. This anomalously high thermal conductivity could probably result from the monodispersity of magic-sized clusters. To support this conjecture, a method of deliberately eliminating the monodispersity of MSCs by mixing them with colloidal nanocrystals was performed. Experiment results showed that mixtures of nanocrystals and MSCs have a lower thermal conductivity that falls approximately on the extrapolated trendline for colloidal nanocrystal thermal conductivity as a function of size.
ContributorsSun, Ming-Hsien (Author) / Wang, Robert (Thesis advisor) / Rykaczewski, Konrad (Committee member) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2022