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Reinforcement Learning Model to Demystify the Limited Human Motor Learning Efficacy Due to the Sensory Mismatch
No full textVision and proprioception have fundamental sensory mismatches in delivering locational information, and such mismatches are critical factors limiting the efficacy of motor learning. However, it is still not clear how and to what extent this mismatch limits motor learning outcomes. To further the understanding of the effect of sensory mismatch on motor learning outcomes, a reinforcement learning algorithm and the simplified biomechanical elbow joint model were employed to mimic the motor learning process in a computational environment. By applying a reinforcement learning algorithm to the motor learning of elbow joint flexion task, simulation results successfully explained how visual-proprioceptive mismatch limits motor learning outcomes in terms of motor control accuracy and task completion speed. The larger the perceived angular offset between the two sensory modalities, the lower the motor control accuracy. Also, the more similar the peak reward amplitude of the two sensory modalities, the lower the motor control accuracy. In addition, simulation results suggest that insufficient exploration rate limits task completion speed, and excessive exploration rate limits motor control accuracy. Such a speed-accuracy trade-off shows that a moderate exploration rate could serve as another important factor in motor learning
Design & Manufacture of Additive Manufactured Polymer-Metal Interfaces
No full textThis research investigates the design and manufacture of additively manufactured polymer-metal interfaces while correlating their processing, structure, properties, and performance (PSPP). Our primary goal is to tailor interface design and manufacture to effectively withstand tensile, shear, and combined loading, while fulfilling the constraints of avoiding adhesives or fasteners between the components. To achieve this goal, this study employs mechanical interlocking between polymer and metal surfaces (at a specific size-scale) that includes repeated geometry features (unit-cells) additively manufactured onto the metal surface, and the polymer additively ���infilled��� onto them. The geometrical designs were tailored taking into consideration the advantages and limitations of ���PBF-LB��� for the metal printing (316L stainless-steel), and ���MEX-TRB��� for the polymer printing (PCABS). The study includes the evaluation of various factors affecting the interface ���quality���, including unit-cell shape/size/distribution designs and their implications for polymer filling vs. load capability, the effect of manufacturing process parameters on the interface, and their impact on stress distribution and eventually performance under various loading scenarios. FEA simulations were employed to tailor designs and to parametrically predict the stress-based performance of the interfaces; insights into stress distributions, load capacity and critical points were obtained. Finally, candidate interface designs were additively manufactured, and their performance analyzed under various loading conditions. Altogether, this research strives to facilitate the use of polymer-metal interfaces within additive manufactured components
Discovering Global False Negatives on the Fly for Self-supervised Contrastive Learning
No full textIn self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, this approach can result in the creation of negative pairs with similar semantics, referred to as ���false negatives���, leading to their embeddings being falsely pushed apart. To address this issue, we introduce GLOFND, an optimization-based approach that automatically learns on the fly the threshold for each anchor data to identify its false negatives during training. In contrast to previous methods for false negative discovery, our approach globally detects false negatives across the entire dataset rather than locally within the mini-batch. Moreover, the per-iteration computation cost of our approach remains independent of the dataset size. Experimental results on image and image-text data demonstrate the effectiveness of the proposed method
Development of an Active Barium Vapor Notch Filter for Ultraviolet Scattering Based Diagnostics
No full textAtomic and molecular filters are proven tools in laser-based diagnostics. Their use as notch filters has greatly expanded the usefulness of scattering phenomena in both ground testing and remote sensing applications. However, current filtering technology limits researchers to the use of the frequency doubled Nd:YAG signal (532 nm) or more rare and complicated lasers such as Ti:Sapphire and Dye lasers. The visible spectrum presents eye safety issues and lacks the molecular scattering signal strength found in the UV. This work aims to develop a vapor filter functioning at the near UV wavelength of the Nd:YAG third harmonic (355 nm). The frequency required for this filter, which utilizes an excited state transition in atomic barium vapor, falls between the ozone absorption region and the retinal hazard region, provides a stronger backscattered signal than visible light, and can be easily attained with the robust and commonly used high-power Nd:YAG laser. These benefits have significant implications for atmospheric measurements, including the aerosol profiling technique of High Spectral Resolution Lidar (HSRL). Through a combined theoretical and experimental effort, a barium vapor filter has been fabricated and characterized for a variety of filter conditions. The results for low vapor pressures of barium are particularly notable and represent the first reported measurements of the absorption feature of interest in the absence of a neutral buffer gas. The addition of a weak argon buffer gas reduced spatial diffusion, resulting in a more stable and deep absorption feature, capable of implementation in scattering based diagnostics. The AURa (Aggie Ultraviolet Rayleigh) Lidar facility has been developed to serve as a testbed for this and other lidar techniques. Aerosol backscatter and extinction results using the novel filter, which mark the first HSRL measurements at 355 nm with a vapor-based filter, will be presented alongside a discussion of the measurement errors and suggested improvements to this system. Finally, a few other applications of the novel filtering approach will be examined
Design, Development, and Characterization of a 3D Solar Heat Exchanger
No full textThis thesis presents a multifaceted approach to the design, characterization, and development of a 3D Solar heat exchanger, employing state-of-the-art Computational Fluid Dynamics (CFD) and experimental techniques. The Solar Model represents a compact and innovative multi-level heat exchanger, incorporating intricate design elements such as pin-fins, vanes, multiple fluid passages, or channels. The primary objective of this design is to bolster heat transfer efficiency by prolonging the contact duration between the fluid and the heat transfer surfaces. To substantiate the theoretical models and designs, a comprehensive CFD and experimental study of the 3D model was conducted. CFD has proven to be an effective tool in the design and optimization of heat exchangers by considering thermal properties and it has been employed to study different modifications, compare results, and present the best possible combination of variables to ensure optimum performance. The proposed experimental procedure was implemented, with a step-by-step guide for system setup, including the installation of a solar collector, heat exchanger, and fluid circulation system. Measurement devices, comprising of thermocouples and pressure transducers, were placed throughout the system to monitor temperatures, pressure drop, and heat transfer rates under varying experimental conditions.
This comprehensive research endeavor not only explores the theoretical aspects of solar design and optimization using CFD but also validates these models through practical experimentation. The findings emphasize the paramount role of design configurations and parameters, particularly the aspect ratios and how they influence the overall thermal performance of solar systems. This combined approach paves the way for the advancement of efficient solar thermal systems, contributing to sustainable and eco-friendly energy solutions.
Steady state CFD simulations were conducted to study the fluid flow patterns, velocity profiles and temperature distribution of the solar at different aspect ratios. The modified geometry with an aspect ratio of 0.5 led to a more homogenous temperature distribution within the computational domain characterized by well-distributed fluid flow patterns. The modified geometry was then selected for fabrication so a prototype could be characterized experimentally. Experimental results revealed that the overall heat transfer coefficient, U, increased with flow rate. Furthermore, U reached an optimum value between 2.8 and 3.0 l/min, suggesting that the flow behavior inside the solar heat exchanger reached an optimum condition despite depicting higher pressure drop at higher flow rates. In summary, designing, numerically simulating, and experimentally characterizing a solar heat exchanger with features such as long fins and guide vanes proved to be a successful heat exchanger development approach. Such an approach should help the development of heat exchangers for renewable energy applications
Wind Tunnel Data Quality Assessment and Improvement Through Integration of Uncertainty Analysis in Test Design
No full textThe practical application of uncertainty quantification in wind tunnel testing is not consistently or proactively applied. Although there is a solid methodology to quantify uncertainty, the resources required to implement this methodology at the pace of testing while adapting to the unique designs for each test are rarely available. This research combines the use of Monte Carlo simulations for uncertainty quantification with a decision-based integration of uncertainty estimates into the test design process and test execution. This implementation reduces the resources required to routinely quantify uncertainty to a practicable level and aims to proactively affect data quality by incorporating uncertainty estimates into early test design decisions. This methodology is used in the design, execution, and data analysis of a wind tunnel test at the Oran W. Nicks Low-Speed Wind Tunnel (LSWT) at Texas A&M University. The test analyzes the uncertainty in the aerodynamic coefficients and performance parameters of an aircraft test model. After quantifying the uncertainty of the aerodynamic coefficients, this research investigates a potential elemental error source in the measurement of static aerodynamic coefficients due to oscillating nonlinear aerodynamic loads. Notable results from this research include the demonstration of integrating uncertainty analysis with test design in a practical way, reduction of uncertainty intervals in aircraft performance parameters measured in the LSWT by an average of more than 90% through this integration, and experimental evidence of an elemental error source from oscillating nonlinear aerodynamic loads in the measurement of static aerodynamic coefficients
Motion Control Analysis of Hydrofoil-Based Autonomous Surface Vehicle: An Integrated Approach Utilizing Moving-Mass-Actuated Stabilizer and Variable RPM Propeller Modeled with Computational Fluid Dynamics and Auto-Control Algorithm
No full textHydrofoil-based Surface Vehicles (HSVs) have garnered significant attention for their potential to achieve high speeds, low hydrodynamic resistance, and reduced energy consumption. This efficiency is primarily due to the vehicle���s hull being elevated above the waterline, leaving only the hydrofoils and propeller submerged to generate the necessary lift force to counterbalance the vehicle���s weight at operational speeds. This study aims to extend these advantages by developing an autonomous control system, thereby enhancing the operational capabilities of these vehicles.
This dissertation dedicates to overcoming the inherent stability challenges in the in-house developed hydrofoil-based Autonomous Surface Vehicle (HASV). This battery-powered HASV leverages hydrofoil to lift its superstructure above the water, significantly decreasing drag and improving efficiency at cruising speeds. However, the design, which incorporates a single mast connecting the superstructure to the substructure, introduces notable stability issues. These challenges primarily arise from nonlinear flow loading on the hydrofoil substructure and external environmental factors such as ocean waves and currents. This complexity necessitates the development of an effective control system.
In response to these challenges, the study introduces an innovative control system utilizing the Proportional-Integral-Derivative (PID) algorithm to regulate the HASV���s pitch, roll, and heave stability. It includes a novel moving-mass-actuated (MMA) stabilizer in conjunction with an adjustable revolutions-per-minute (rpm) propeller. The MMA stabilizer enables dynamic adjustment of the HASV���s center of gravity, enhancing control over pitch and roll movements. Simultaneously, the propeller���s rpm is continuously modulated to manage thrust, thereby adjusting the lift force generated by the HASV substructure, which is crucial for controlling heave motion stability.
Previous research on HASV stability control primarily relied on either physical model testing or mathematical modeling. While physical model testing is comprehensive, it is often prohibitively expensive and time-consuming. In contrast, mathematical models, though efficient, require significant simplifications, frequently failing to fully capture complex physical processes, especially those with strong free surface effects. Given the HASV���s limited stability and pronounced free surface effects, there is an urgent need for a more effective and practical approach to investigate and optimize the PID control system. Addressing this need, the study proposes a more accurate Unsteady Reynolds-Averaged Navier-Stokes (URANS) CFD-based control investigation approach. This approach integrates a PID controller into the URANS CFD model, combining the detailed analysis capabilities of CFD with the precision of PID control. This integration ensures that the performance of the proposed control system can be precisely investigated and optimized under different diverse operational scenarios.
This study first starts with the CFD-based hydrodynamic performance analysis of a 2D dual hydrofoils with different configuration and generated a dataset, the dataset is then used to train an Artificial Neural Network (ANN) in order to use the ANN to interpolate within the interesting range and generate a finer resolution results. This analysis helps to have a basic understanding of how the wing and tail can interact with each other and laying the ground for the design of the submerged portion (substructure) of the HASV. Later, this study continues with a scaled-down experimental setup and procedure designed to test the hydrodynamic characteristics of the HASV���s substructure. This experimental study resulted in an understanding of the drag and lift behavior of the substructure, which is be used in the validation of the 3D CFD model. Then, this study continues to use the validated 3D CFD model as a tool to apply the CFD-based control investigation approach, mentioned above, to optimize the performance of the PID controller for regulating the HASV���s roll, pitch and heave motion. Then the effectiveness of the optimized control system on these three DOFs is tested using the CFD-based control investigation approach under different loading scenarios (calm water and waves)
Ultrasonic Spot Welding of FFF Printed Samples as a Means of Improving Interlayer Adhesion
No full textAdditive manufacturing (AM) is a technology that has improved manufacturing capabilities in a huge variety of industries, as well as increasing rapid prototyping capabilities. Fused filament fabrication (FFF) is one of these technologies, that has seen use in both industry and hobbyist settings to print thermoplastics and polymer matrix composites that have a thermoplastic matrix. However, it suffers from a major flaw in that the strength of printed parts is anisotropic and uneven, with tensile strength in the direction perpendicular to the printed layers, the interlayer adhesion, being anywhere from 50-75% lower than in the other orthogonal directions for fully dense parts made from standard glassy thermoplastics. This study attempts to improve this weakness by introducing an ultrasonic welding process to the normal FFF 3D printing method. Two welding treatments are attempted following different patterns, one where there are overlapping welds to cover as much surface area as possible, and another with no overlapping welds to prevent the risk of damaging or over-welding the part. The samples, as tested by a modified version of ASTM D5528, showed drastically increased maximum fracture load, suggesting a much stronger level of layer adhesion. Nylon samples showed a change from an average of 85.94 N for the control samples and 191.2 N for the best performing welding group average. ABS samples showed an average of 68.3 N for the control group and 102.58 N for the best performing welding group average. More testing will be required to produce a procedure that can be reliably applied to printed parts, but the procedure used in this study proved effective for both ABS and Nylon double cantilever beam samples
A Multiphysics Model for Predicting Spatiotemporal Temperature Profiles in Microwave-Heated CO2 Direct Air Capture Processes
No full textDue to alarming rise in atmospheric CO2 ppm levels, the direct air capture process has been engineered to capture low concentrations of CO2 directly from the atmosphere using chemisorbents. However, the regeneration of chemisorbents is highly energy-intensive and inefficient. To enhance this, microwave is employed for selective and targeted heating of the chemisorbent. Existing measurement techniques struggle to accurately capture the spatiotemporal temperature profile of solvent impregnated polymer (SIP) under microwave heating. Therefore, it becomes crucial to determine the spatiotemporal temperature distribution inside the polymer phase to expedite CO2 desorption, improve energy efficiency, and prevent material degradation. Motivated by these considerations, we propose a 3D-multiphysics model to study the spatiotemporal distribution of temperature inside a hybrid nanoscale multi-functional material. We focus on the microwave heating of a novel heterogeneous system comprising a SIP with a ferromagnetic additive, further solving the heat diffusion and Maxwell���s electromagnetic equations to analyze the temperature variation inside the SIP system. By coupling the physics of electromagnetism and heat transfer, our model allows for a comprehensive analysis of the temperature distribution and heating effects inside the chemisorbent. The proposed model is validated experimentally by the surface temperature findings of the SIP from IR-sensor. Additionally, we conduct sensitivity analysis of spatiotemporal temperature profile on various thermal and dielectric parameters, as well as the size and location of the Fe3O4 layer, to optimize desorption rates and make the regeneration process more energy-efficient
Investigating the Impact of Timing of Basal Leaf Removal and Fruit Thinning on Potassium Accumulation in Red Wine Grapes
No full textIn hot climates such as Texas, high juice/wine pH represents a serious challenge for wineries. The objective of this study was to evaluate the impact of timing of basal leaf removal and cluster thinning on potassium (K+) concentration in ���Tempranillo��� and ���Camminare Noir��� grapes as a possible vineyard management practice to mitigate high pH. This research took place during the 2021 and 2022 growing seasons in two vineyards, one in the Texas Gulf Coast and the other in the North Texas region. Treatments consisted of basal leaf removal (removal of lowest three basal leaves), cluster thinning (thinning to one cluster per shoot), and leaf removal plus cluster thinning conducted at either berry set or veraison. Differences in fruiting zone canopy density were consistently observed between treatments, across years, cultivars, and sites. Leaf removal treatments averaged a 35.83% reduction in canopy density in the fruiting zone, determined by occlusion layer, leading to an increase of 124.17% in cluster exposure flux availability. However, differences in yield (yield per vine, clusters per vine), berry chemical composition (alpha amino nitrogen, ammonium, fructose, malic acid, soluble solids, tartaric acid, titratable acidity, and pH) and tissue nutrient content were only observed in singular instances, with no consistent differences between years, cultivar, or site. Berry K + concentrations and juice pH varied by rootstock, year, and cultivar, but there was no clear impact of leaf removal or cluster thinning on berry K+ or pH. In both years of the study, very low yields were observed as a result of a severe winter event and possibly negated any effects from leaf removal or cluster thinning