Mechanical & Industrial Engineering Dissertations Collection

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  • Publication
    Additive Manufacturing of Homopolymers and Polymer-Nanoparticle Composites using the Cold Spray Deposition Technique
    (2024-09) Sundara Rajan, Kashyap
    Cold spray is an additive manufacturing technique where solid powder particles bond with a target substrate upon high-velocity impact. It offers advantages over traditional coating methods, preserving materials' properties without harmful by-products. While extensively researched for metal-on-metal coatings, cold spray's application to polymers is relatively new. This dissertation comprises three studies focusing on cold spray of polymers. The first investigates coating metal substrates with polymeric powders, a topic with limited prior research. It systematically examines parameters such as particle and substrate temperature, substrate surface roughness, and the effect of priming on deposition efficiency and adhesion strength. The results indicate that there exists a deposition window of velocity, impact angle and temperature for good deposition, and the spray parameters need to be maintained within this window. The second study explores using polymer composites in cold spray. Nano-sized copper and silicon dioxide particles are added to high-density polyethylene (HDPE) to create composites. The research examines how concentration, particle size, and impact energy affect deposition. Results show that copper and SiO2 behave differently when added to HDPE, and there is not a monotonic increase in the efficiency of the cold spray process with the increase in the filler material. Instead, an optimal particle concentration is found for each composite powder that maximizes the deposition efficiency. This optimal concentration varies with particle size and type. The third study investigates adding carbon nanotubes (CNTs) to HDPE powders. CNTs are known for their exceptional properties including strength, toughness, and electrical and thermal conductivity. The research demonstrates that both the composite powder processing and the cold spray process itself significantly influence the morphology, distribution, and orientation of CNTs within the HDPE matrix, and consequently, the properties imparted by the CNTs. Notably, the cold spray process is found to positively impact CNT orientation in the final deposit, which can be inferred from the enhanced electrical conductivity of the CNT-HDPE deposit. These findings offer valuable insights into creating polymer composite coatings using cold spray and establish it as an efficient alternative to conventional coating techniques.
  • Publication
    Graphene-Enabled High-Performance Bioelectronics
    (2024-09) Zhang, Xiaoyu
    High-performance biological sensing and modulation are essential for uncovering fundamental biological processes and enabling a range of downstream biomedical applications. Miniaturized electronics enabled by nanomaterials, which offer advanced stability, resolution, and spatiotemporal specificity, have emerged as promising tools to achieve these goals. However, the complexity of biological systems, with their intricate and multi-scale interactions with nanomaterials, poses significant challenges for nanoelectronics in delivering biologically significant performance and functionality to meet diverse needs. Building on recent progress in nanoscience and bioelectronics, my PhD work has focused on understanding electrical transduction properties at nano-bio interfaces and applying this knowledge to develop graphene-enabled, high-performance bioelectronics to address these challenges across various biological systems, including micro-biofluids, biomolecules, and cells. For micro-biofluids, I will show that graphene single microelectrodes, which harvest charge from continuous aqueous flow, provide an effective biofluid flow sensing strategy. In particular, over six-months stability and sub-micrometer/second resolution in real-time quantification of whole-blood flows with multiscale amplitude-temporal characteristics are obtained in a graphene microfluidic chip. For biomolecules, I will demonstrate that oscillational DNA strands tethered to a graphene transistor, driven by an alternating electric field, induce transistor-current spectral characteristics that resist interference interactions. These spectral characteristics enable DNA sensing with ultrahigh specificity and a detection limit improvement of two orders of magnitude compared to typical methods. Besides, I will discuss a model suggesting that the high specificity and sensitivity of our approach are due to the inherent difference in pliability between unpaired and paired DNA strands. At the cellular scale, I will present a microdevice that integrates microelectrolytic pH modulation with graphene-nanoelectronic pH sensing functions, enabling real-time regulation of cell-microenvironmental pH with high spatial specificity and pH precision. In addition, I will show real-time pH-based control of bacteria motility and cardiomyocytic calcium signaling, providing insights into their dynamic responses to time-variable extracellular-pH modulations. Together, the unique capabilities of our graphene-enabled electronics open up new opportunities for high-performance sensing and modulation of complex biological systems across diverse spatiotemporal scales for both fundamental research and translational applications.
  • Publication
    Graphene-Based Nanomaterials for Advancing Biosensing and Biocontrol
    (2024-09) Fan, Xiao
    The development of techniques for detecting and regulating biomolecules and cells is crucial in clinical diagnostics and biological research. Elevated by significant advancements in materials science, electronics, and optics, the methodologies for detecting and modulating biomolecules and cells have achieved remarkable enhancements in sensitivity, specificity and reliability. However, combining all the necessary properties for electrode materials—including high transparency, high conductivity, biocompatibility, chemical stability, low impedance, and low cost— into a single material system remains a challenge for achieving high-performance biosensing and biocontrol. By synthesizing graphene-based nanomaterials with exceptional properties and applying them to interface with biological systems, this dissertation addresses this challenge and demonstrates advancements in biological sensing and control. For protein analysis, I utilize monolayer graphene microelectrodes in microfluidic devices for electrokinetic focusing and sensing of proteins. The electrolysis stability of graphene electrodes is >103× improved compared to typical microfabricated inert-metal microelectrodes. The reliable pH gradient between a pair of biased graphene microelectrodes enables the separation and concentration of specific proteins into narrow band (~100 micrometers) within minutes. The high optical transparency of graphene allows for label-free detection of the proteins at same processing position with sensitivity ~100× higher than those of the state-of-the-art label-free sensors. For cell action potential measurement, I synthesized whisker-like multiwalled carbon nanotubes on monolayer graphene surface via chemical vapor deposition using nanoparticle catalysts. The carbon nanotubes and substrate graphene share the same fermi level, and the hybrid material retains electrical mobility comparable to that of bare graphene. The carbon-nanotube structures enhance capacitive and resistive by 80× and 7×, respectively, serving as the primary transducing pathway and improving the signal-to-noise ratio for detecting extracellular cardiomyocytic action potentials by 2.5×. Furthermore, the enhancement of electric field at the carbon-nanotube structures enables low-bias (~4 V) nano-electroporation and the recording of intracellular action potentials. Future research in this area includes utilizing isoelectric focusing to analyze viruses and bacteria in our devices, and developing controllable capture and release systems to detect target molecules. Additionally, we aim to measure intracellular action potentials of neurons and investigate gene delivery to cancer cells using our CNT-graphene-based devices.
  • Publication
    Numerical Investigation of Fluid-Structure Interactions between a Cylinder and Shear Thinning/Thickening and Viscoelastic Fluids
    (2024-09) Patel, Umang
    The interactions between flexibly-mounted rigid structure or flexible structure and internal or external fluid are called Fluid-Structure Interactions (FSI). These interactions cause movement of the structure, either static deformation or oscillatory motion. FSI systems are prevalent in aerospace engineering, civil engineering, ocean engineering, and biomedical engineering. Vortex- Induced Vibration (VIV) is a fundamental problem and it occurs commonly in the field of FSI. Flexible or flexibly-mounted bluff bodies placed in the flow shed vortices at a frequency that increases with the incoming flow velocity. When the shedding frequency matches the structure’s natural frequency, the structure oscillates. This is called VIV. Some of the situations where VIV can be observed include offshore structures, mooring lines, risers, marine cables, bridges, transmission lines, and aircraft control surfaces. The oscillations caused by VIV can be desirable or undesirable depending on the application. Therefore, understanding the physics behind this phenomenon is very important. In this work, we explore the VIV of a cylinder in the flow of non-Newtonian fluids by conducting a series of CFD simulations and show how non-Newtonian fluids significantly influence the response of a flexibly-mounted cylinder in comparison with Newtonian fluids. We study the effects of fluid properties such as fluid’s time constant λ (for inelastic shear-thinning and shear-thickening fluids) and relaxation time, maximum polymer extensibility, and viscosity ratio (for viscoelastic fluids) on the VIV amplitude and frequency, flow forces, and the wake patterns. When the results are compared using the Reynolds number, Re0, defined based on the zero-shear-rate viscosity of the fluids, shear-thinning fluids enhance the oscillations while shearthickening fluids suppress the oscillations. If, however, we define a characteristic Reynolds number, Rechar, based on a viscosity evaluated at the characteristic shear rate, U/D, then at a constant Re_char, the amplitude of response stays very similar for the shear-thinning, shear-thickening, and Newtonian fluids. Despite this similarity, the observed far wake is different: shear thinning amplifies the generation of vorticity and reduces the extent of the wake, whereas shear thickening limits the generation of vorticity and extends the wake. For viscoelastic fluids, a new mode of shedding is observed in the wake of the cylinder: In addition to the primary vortices that are also observed in the Newtonian flows, secondary vortices that are caused entirely by the viscoelasticity of the fluid are observed. For a system of a rigid cylinder forced to oscillate sinusoidally in the transverse direction in the flow, we calculate the lift coefficient in phase with the cylinder velocity to predict the range of different system parameters where self-excited oscillations might occur if the cylinder is allowed to oscillate freely. We study one-degree-of-freedom (1DOF crossflow) and two-degree-of-freedom (2DOF crossflow and inline) VIV of a cylinder in the flow of viscoelastic fluid, where both inertia and elasticity of the flow are important in governing the VIV response. For increased elasticity in the fluid, we observe significant polymer deformation in the upstream stagnation region resulting in a region of large elastic stress that acts as a wall around the cylinder. As a result, the stagnation point moves upstream of the cylinder creating a finite gap between the shear layers and the cylinder surface. The region of flow separation is both widened and extended further downstream. With increased elasticity, VIV is suppressed in both 1DOF and 2DOF cases. We show that higher harmonic forces that are typically observed for the 2DOF VIV responses in a Newtonian flow and cause increased fatigue damage in the structure, are suppressed by adding elasticity to the flow.
  • Publication
    Experimental Observations of Fluid-Structure Interaction Phenomena with Applications to Vertical-Axis Wind Turbines
    (2024-09) Benner, Bridget
    This work seeks to apply fundamental experiments to better understand the types of flow-induced dynamic instabilities that wind turbine blades may experience. To that end, this thesis further advances the knowledge of a classic vortex-induced vibration (VIV) case, that is one degree of freedom VIV of a flexibly-mounted cylinder in flow. The experiments shed light on the response of the cylinder as the degree of freedom is incrementally rotated from the purely crossflow direction (perpendicular to the flow) to the purely inline direction (parallel to the flow). A more applicable set of experiments is then discussed, the VIV experienced by a flexibly-mounted hydrofoil with a NACA 0021 cross section while the angle of attack and reduced velocities are both increased incrementally. When they are parked, vertical-axis wind turbine (VAWT) blades are known to encounter every possible angle of attack and the reduced velocity ranges for which we observe VIV in these experiments cover the ranges of reduced velocities that are expected in a full-scale VAWT. Possible VIV of VAWT blades will have a deleterious effect on the fatigue life of the turbine blades. However, in the real world wind turbine blades are considered continuous structures, whereby their response is a function of time and space. To explore this idea, that of a flexible, continuous structure, we first start with a cross section that is well studied, that of a square prism. The square prism is studied for α = 0◦, whereby the flat side of the square sees the flow first as the reduced velocity is increased. After this, the response of a flexible hydrofoil placed at various angles with respect to the incoming flow and subjected to increasing reduced velocities is studied.
  • Publication
    Plasticity and Adhesion of Nano-Structured Polymeric Materials in High-Strain-Rate Additive Manufacturing
    (2024-05) Kim, Ara
    In many applications, such as aerospace and additive manufacturing, polymeric materials with nanoscale structures can be subjected to extensive plastic deformations and present nonlinear dynamic responses under high-strain-rate adiabatic conditions due to nanostructure changes and temperature-dependent material properties. Their rate-dependent characteristics are determined not just by volumetric plastic deformation but also by the resultant thermal effects in the excessively deformed region. Hence, the dynamic nonlinearity of model polymeric systems, microparticles of multiphase block copolymers, is systemically investigated using the Laser-Induced Projectile Impact Tests with perpendicular (α-LIPIT) and non-perpendicular (θ-LIPIT) incidence angles in this study. The polystyrene-block-polydimethylsiloxane (PS-b-PDMS) copolymers are the model materials consisting of mechanically distinctive nanoscale domains of PS (glassy-phase) and PDMS (rubbery-phase), and the visco-plasticity during impact is quantified through mechanical and rheological analysis. The α-LIPIT produces precisely controlled high-strain-rate collision conditions, and the kinetic parameters are used to analyze the mechanical behaviors of the block copolymer microparticles in the forms of the coefficient of restitution and adhesion windows. Furthermore, the newly introduced θ-LIPIT results with a non-perpendicular incidence angle demonstrate the effect of tribological nonlinearity on adhesion mechanisms through the rheological analysis representing the collision-induced thermal condition changes such as thermal softening. The glassy domain controls the rheological transition, while the rubber domain enhances interfacial conditions and is favorable for adhesion. The microparticles’ post-impact shape changes are used to optimize material parameters for a computational model. The nanostructure changes are directly analyzed after cross-sectional milling with a focused ion beam to understand the stress flow and the effective thermal softening region during impact. This study offers a comprehensive understanding of nanostructured block copolymers’ plastic and adhesion mechanisms for use in high-strain-rate additive manufacturing, such as cold spray. The verified correlations between adhesion and compositional and tribological properties are expected to be used to investigate the applicability of feedstock materials and optimize the material parameters for cold spray.
  • Publication
    Floating Offshore Wind Farms: Lifecycle Costs and Levelized Cost of Energy in Developing Regions
    (2024-05) Watson, Joshua Austin
    This research develops a generalizable cost model for a floating offshore wind farm consisting of wind turbines mounted on semi-submersible platforms deployed along coastal and island developing nations. A case study is developed for the semi- submersible-based wind farm model as deployed off the coast of St. Thomas, in the U.S. Virgin Islands. The case study analyzes wind resources, bathymetric data, and environmental concerns to forecast the levelized cost of energy of such a project. The analytical model developed for this work is based on a detailed literature review of cost models that have been developed for floating offshore wind systems, and recent empirical cost data from public sources. This model adapts and synthesizes several methodologies to assess floating offshore wind farm costs, including the use of ORBIT, a National Renewable Laboratory Model balance-of-system model. This work suggests several coastal and island developing nations which have significant offshore wind resources as potential sites to be analyzed with the model. It also found atmospheric and oceanic data for these regions in order to estimate the range of levelized cost of energy for such systems.
  • Publication
    Continuous Future Joint Kinematics Prediction Based on Surface Electromyography Using Neural Networks and Hybrid Approaches for Reduced-Latency Control
    (2024-02) Sitole, Soumitra
    This work focuses on continuous future prediction of human upper limb joint kinematics from muscle excitations measured with surface electromyography (sEMG) using a novel neural network training approach. The approach aims at leveraging the inherent lead in EMG signals over observed limb motions to predict joint kinematics forward in time over a short horizon. The correlation-causation relationship between EMG and motion is studied to decode and improve the relationship map between the variables. Unlike a forecasting problem, the presented approach predicts future joint kinematics at each time-step over the established horizon. The prediction horizon was quantified using temporal alignment techniques between normalized EMG excitation signals and motion data. Two studies involving 7 and 10 participants were performed targeting single and multiple degrees of freedom predictions respectively. The proposed training strategy was compared to the general neural network training approach used in other studies that maps current time EMG inputs to current time kinematic label data using 2 popular neural network architectures: back-propagation neural network (BPNN) and time-delayed neural network (TDNN). Models trained using the presented approach consistently showed better training results. The prediction results also showed an improvement of about 5-10 deg in testing RMSE over the model trained without the phase lead with identical input signals for all subjects over multiple motion types and multiple degrees of freedom. Accuracy and generalization performance improvements were further explored by using state information and hybrid architectures. The state-informed hybrid TDNN architecture (SIEMG) substantially improved shoulder and elbow kinematics prediction accuracy to upto 5 deg with respect to the baseline measurements. A hybrid model that combines neuromusculoskeletal modeling and neural network architectures was also developed for single DOF elbow motion prediction that improved inter-subject robustness performance. Data curation methods were further explored to improve intra-subject robustness performance. Also presented is a forward kinematics model using Denavit-Hartenberg (DH) parameterization of the human arm to convert the predicted joint kinematics to wrist joint center pose as task space input for robot teleoperation. Using the proposed SIEMG TDNN models trained with the presented training strategy, the wrist joint center position was predicted with an accuracy of 2-3 cm upto 250 ms forward in time. A real-time prediction framework using pre-trained offline networks was developed in ROS (Robot Operating System) to translate the offline work to online. Simulation results confirm that the offline accuracy can translate well to real-time implementations. The developed architectures and the proposed training strategy could facilitate reduced latency control using EMG.
  • Publication
    COMPUTATIONAL FLUID DYNAMICS SIMULATIONS AND REDUCED ORDER MODELING OF MULTI-PHYSICS BIOLOGICAL SYSTEMS
    (2024-02) Han, Suyue
    In biological systems, there are numerous instances when structures interact with or are exposed to fluid flow, such as swimming microorganisms, blood flow in arteries and veins, airflow in the respiratory system and more. Detailed Computational Fluid Dynamics (CFD) simulations that capture the intricacies of fluid dynamics or Fluid-Structure Interactions (FSI) provide valuable insights for comprehending the functionality and dynamics of these biological systems, and can assist in the design of medical devices and biomimetic robots or aid in surgical treatment planning. However, because of the complexity of biological systems, simulating them often requires significant computational resources and a profound understanding of their physical and physiological properties. Despite the development of numerous numerical and computational methods, computational simulation of such biological systems still presents several challenges, including high computational cost, a lack of experimental validations and complexities in modeling Fluid-Structure Interactions. This work offers an exploration to these challenges. We first explore the method of building a computationally efficient Reduced Order Model (ROM) based on snapshot Proper Orthogonal Decomposition (POD) method for flow inside a patient-specific aneurysm model generated from a patient’s brain CT scan. The developed ROM is capable of generating accurate simulation results rapidly, which makes the hemodynamics parametric studies over a wide range of boundary conditions feasible. We then explore the method to visualize the flow inside a bone-like scaffold using Phase Contrast Magnetic Resonance Imaging (PC-MRI), in order to validate computational simulations that have been used for studying the flow behavior inside bone-like scaffold models. Finally, we explore the method of building a partitioned parallel implicit FSI framework that could handle flexible structures that are strongly coupled with the fluid environment and undergo large deformations. The developed FSI framework is first utilized to study the possibility of controlling the response of a flexible slender beam in the wake of a cylinder by forcing the cylinder to rotate periodically. This FSI framework is then utilized to study the instantaneous response of a highly flexible half sphere (emulating biological systems) that interacts with the incoming flow and oscillates with large amplitudes.
  • Publication
    Soft Magnetic Sensing on a Compliant Surface and Contact Mechanics Approximations at the Interface
    (2024-02) Aparicio, Julio
    Pressure Injuries (PIs), commonly known as pressure ulcers or bedsores, affect about 2.5 million individuals yearly in the United States [1]. Pressure, shear, and micro-climate are the top external factors concerning PI formation [2]. Most commercial interface pressure sensing systems have several limitations, including cost [3], lack of micro-climate and shear measurements, and possible inaccuracies due to calibration [4–8]. These limitations signal the need for a new generation of sensors. The two aims of this dissertation are to determine the feasibility of magnetic sensing in the context of PI research and to probe the possibility of predicting pressures and displacements at the interface using an analytical model. The first objective entailed manufacturing, calibrating, and characterizing a soft magnetic sensor via % Full-Scale Output (% FSO) errors. Then, the soft sensor’s performance was compared with a commercial alternative via a protocol inspired by the ANSI/RESNA Support Surfaces Standard (RESNA SS-1:2019). These comparisons were quantified using 4 experimental scenarios via bootstrapped confidence intervals. The second objective involved the calculation of surface pressures and displacements based on contact mechanics equations and comparing them to two experimental scenarios. This work produced three characterizations, four comparisons to evaluate the soft sensor performance, and two calculations to evaluate the feasibility of analytical predictions. The soft sensor characterization involved two incline conditions (0° and 30°) with either random or sequential loading. The 0° incline at random loading yielded a 7 %FSO and 1 %FSO on average for compression and shear, respectively. At a 0° incline with sequential loading, the average results were 3 %FSO and 1 %FSO for compression and shear with 2 % hysteresis in compression. The 30° incline at random loading yielded 1 %FSO and 2 %FSO for compression and shear on average. The four comparisons were quantified via the bootstrapped difference of means with 95% confidence intervals. For the flat punch at 0 degrees in compression, the difference of means estimate was 2.1 mmHg (1.7 mmHg, 2.6 mmHg) and shear 0.7 mmHg (0.5 mmHg, 1.0 mmHg). For the STDI at 0 degrees in compression, 1.0 mmHg (-0.1 mmHg, 2.2 mmHg) and shear 0.2 mmHg (0.0 mmHg, 0.4 mmHg). For the STDI at 30 degrees in compression, 10.4 mmHg (9.5 mmHg, 11.2 mmHg) and shear 0.0 mmHg (-0.3 mmHg, 0.2 mmHg). In the last comparison, the flat punch at 30 degrees, in compression, the values are -1.9 mmHg (-2.8 mmHg, -1.2 mmHg), and shear 6.2 mmHg (5.4 mmHg, 6.9 mmHg). Lastly, the analytical solution produced for average interface pressure in flat punch prediction with a relative error of approximately 4%; however, the displacement prediction for the STDI case produced a relative error above 60% for the STDI best case. Thus, only the flat punch prediction might be suitable. The results show potential for this soft sensing modality to be implemented in PI applications, although future works are needed in calibration and testing for generalization and robustness. In addition, future work is needed to estimate foam material parameters such as the elastic modulus and Poisson’s ratio with greater accuracy.