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Higher-Dimensional Data in Powder-Bed Fusion Additive Manufacturing: A Path to Improved Printability Predictions
No full textThis dissertation explores the optimization of alloy design and process parameters in metal additive manufacturing (AM), specifically focusing on powder bed fusion (PBF) processes like laser powder bed fusion (L-PBF) and electron beam powder bed fusion (EB-PBF). Conventionally, process parameters for well-known alloys were optimized in hopes of achieving properties similar to those obtained using traditional manufacturing methods. However, the potential of AM processes, particularly PBF, demands the development of alloys tailored to exploit these unique benefits. The complexity of PBF systems, with numerous design degrees of freedom, necessitates a strategic approach to alloy design and process parameter optimization.
To address these challenges, this dissertation introduces two frameworks centered around the use of higher-dimensional data. The first framework is a purely data-driven model that efficiently explores composition and process parameter spaces. Drawing from a database collected from the literature and in-house experiments, this screening tool incorporates classification techniques to predict process defects and identify regions conducive to good printability. Alloys with larger printability regions are better suited to be fabricated using PBF processes.
The second framework is a physics-based model for the fine-tuning of metal alloy printability. Overcoming challenges present in current frameworks, this model employs dimensionality reduction techniques and regression methods to predict higher-dimensional spatial thermal field outputs instead of relying on lower-dimensional melt-pool dimensions. This approach allows for predicting melt-pool dimensions at varying layer thicknesses and beam spot sizes without the need for additional experiments. This allows for the exhaustive exploration of the process parameter design space.
Overall, the dissertation���s focus on leveraging higher-dimensional data provides a comprehensive and efficient methodology for advancing alloy design and optimizing process parameters in PBF processes, contributing to the evolution of metal additive manufacturing
Sheep as a Potential Tool for In-Season Cotton Weed Management
No full textIncreased reliance on herbicides in crop production has led to many weed species becoming
resistant to multiple herbicide modes of action. Sheep herbivory may be a viable alternative
weed control method, as sheep have the potential to preferentially graze weeds and be averse to
eating the cotton plant due to the presence and concentration of gossypol. Common weeds such
as Palmer amaranth and field bindweed are major competitors with cotton plants in western
Texas but are also palatable to sheep. Field research on the integration of sheep into cotton
systems was performed at the Texas AgriLife extension and research center in San Angelo,
Texas during the 2022 and 2023 seasons. Treatments included three different cotton growth
stages to initiate grazing (4-leaf, 8-leaf, and mid-bloom) and three different levels of grazing
intensity based on weed removal (approximately 70%, 90%, and 100%) with presumably greater
cotton damage with increasing intensity. Treatment effects were quantified through monitoring
sheep grazing activity, assessments of weed biomass removal, cotton damage, and cotton yield.
During the 4-leaf, 8-leaf, and mid-bloom initiation for both years, sheep spent 87%, 86%, and
93% of feeding time, respectively, grazing on weeds rather than cotton. Final cotton biomass was
not influenced by year, intensity, or timing of treatments. Final weed biomass was affected by
year (P > 0.069) and timing (P > 0.036). The year 2022 had less final weed biomass than 2023
and grazing initiated at the 4-leaf stage resulted in greater weed biomass at the end of the
growing season when compared to grazing initiated at the 8-leaf stage. This trial emphasizes the
challenge of extrapolating small-scale findings to field conditions, where sheep grazing may
occur at different times. While small-plot research is valuable, its limitations highlight the need
for field-scale observations. Integrating sheep grazing into production systems shows promise for
farmers seeking reduced herbicide/organic management, but further refinement and consideration of economic impacts are necessary. Future research may assess grazing preferences relative to sheep age and breed to provide greater insight into integrated crop-livestock management practices
Machine Learning-Based Automated Fault Detection and Diagnostics in Building Systems
No full textAutomated fault detection and diagnostics (AFDD) analysis in commercial building systems using machine learning (ML) can improve the building���s efficiency and conserve energy costs from inefficient equipment operation. Boolean rules-based analysis is standard in current AFDD solutions but limits analysis to the rules defined and calibrated by energy engineers. As part of this dissertation, an automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training in ML. The process can be applied to buildings with a variety of configurations, which reduces time and manual effort required for fault analysis when compared to Boolean rule-based systems. The developed procedure introduces advanced diagnostics with automatically generated metrics to validate the ML model���s predictions and rank detected faults in order of fault severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used.
The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a commercial building. Three case studies which analyze the building���s equipment are presented to show ML���s advantages over rule-based analysis. ML can detect faults in the system caused by degrading components. ML can also detect faults in system components with missing sensors by modeling expected system operation and making comparisons to actual system operation. An example of ML detecting a failure in a building is shown along with a demonstration of the decision boundaries of ML-based FDD in comparison with Boolean rule-based analysis. The results from these examples are used to show the strengths and weaknesses of using ML for AFDD analysis
High-Dimensional Analysis of the Linear-Quadratic Regulator Problem Using First Order Methods on GPU
No full textThere has been a growing desire to bridge the gap between the fields of machine learning and optimal control theory. While optimal control typically operates on known dynamical systems, machine learning uses large data sets based on sampled data. The differences in input data used have made it difficult to adapt optimal control concepts to machine learning applications. For example, a major challenge in utilizing a linear-quadratic regulator (LQR) is how computationally demanding it can be for high-dimensional systems. Solving the algebraic Riccati equation (ARE)
directly is time-consuming and is typically O(n��) complexity. Posing the problem as a Linear Matrix Inequality (LMI) is even worse, this is typically solved in O(n���) time.
This thesis will examine the discrete-time LQR in the context of first order methods. These gradient-based methods provide an advantage in that they can be parallelized and run on GPUs. Multiple gradient-based algorithms will be proposed, and their performance will be compared with the traditional solutions for optimal LQR gains. The convergence rates of these gradients to the global optimum will be discussed and compared as the dimensionality of the problem increases. The ability to solve the LQR problem faster for high-dimensional systems may be useful for future large-scale optimal control problems in the aerospace field. Additionally, framing the LQR in terms of gradient-dominated policies may allow the LQR to be used in broader fields such as reinforcement learning
Impact of Facial Divergence on Post-Treatment Settling in Patients Retained With Clear-Overlay Retainers
No full textVertical growth patterns are well-recognized by the orthodontic community as being impactful on function, form, and subsequent treatment. The hyperdivergent phenotype is characterized by smaller masticatory muscles, lower bite forces, and thinner cortical bone compared to hypodivergent subjects. As occlusion depends on dentition, muscle function, and periodontium, facial divergence plays a role in establishing occlusion. Just as orthodontics aims to improve a patient���s occlusion, a goal of orthodontics is to retain the occlusion after debonding but to allow for the vertical movement of teeth into better intercuspation, known as settling. With the determinants of occlusion being influenced by factors affected by facial divergence, there is the possibility that different facial divergence patterns have different capacities to experience settling.
The purpose of the study is to investigate and compare the changes in areas of contact and near contact in hypodivergent and hyperdivergent patients retained with a clear overlay retainer.
This study is a retrospective cohort study with patients recruited from a single private practice. 7 hyperdivergent and 11 hypodivergent patients were identified. All patients were retained with upper and lower clear overlay retainers and a lower bonded retainer. Initial lateral cephalograms were used to categorize patients as hyperdivergent or hypodivergent. Intraoral scans from debond and first retention check appointments were compared. Changes in areas of contact (AC) and areas of contact and near contact (ACNC) were used to characterize settling.
Hyperdivergent patients had a statistically significantly higher number of AC as compared to hypodivergent patients at the time of debond (T0) but not at first retention check (T1). There was no statistical difference between AC or ACNC at T0 or T2 between the two groups. There were no statistical differences detected within each group for either AC or ACNC when doing pairwise comparisons.
Without adequate power, this study instead serves to justify the benefits of repeating this study with a larger sample size. It indicates the potential for different facial divergence patterns to respond differently with the same retention protocol which could therefore impact the settling that can be achieved
Electric Vehicle-Induced Grid Impact Analysis and Its Minimization
No full textElectric vehicles are a major component of the clean energy transition. With significant technology improvement and government policies, EVs have increased to 10 million globally. A major bottleneck to accommodate the projected EVs is the development of an affordable and convenient charging infrastructure without needing long waiting times or long-distance travel for charging. As EV chargers draw power from the utility grid, adding the EV charging load impacts the utility grid significantly.
This thesis investigates the impact of EV charging load on three vital grid-performance indicators (voltage profile, load demand curve, and harmonic profile) and develops solutions to minimize them. Firstly, the power electronic circuitry and control algorithms are studied to identify a grid-connected EV load���s power/energy requirements and harmonic profile. Moreover, considering the inter-dependency of voltage profile and power demand, these two parameters are investigated together.
An IEEE 33-bus system is considered, and the actual load data of Qatar���s utility grid is used to define it. A novel two-step EV distribution algorithm has been developed, which helps estimate the 24-hour EV hosting capacity of the network without any intermediate line sections. Furthermore, the impact of the unavailability of DC fast chargers and level-2 chargers (located in parking lots) on EV hosting capacity is investigated to observe whether domestic chargers can address this shortfall.
Renewable-based distributed generators (DG) are optimally placed in the grid using an optimization algorithm to improve the voltage profile and EV hosting capacity. The constraints to this optimization problem reflect the real-world challenges and discourage any transformer or line feeder upgrade. This strategy, known as the non-wire alternative approach, reflects the modular and active solution-based approach of extending the grid performance. The results are assessed and compared with the pre-DG results regarding voltage profile, EV hosting capacity improvement, and peak-shifting phenomenon.
As both the EV charging current and DG injected current contain harmonics, the grid voltage contaminates and thereby deteriorates the power quality of the network. The impact of this deterioration on the grid must be quantified and compared with the actual distribution network. To analyze the overall impact, EVs and DGs are modeled as harmonic sources and added to the utility grid. This modifies the existing harmonic profile of the grid (due to original harmonic loads). Conventional methods of load-side filtering will be ineffective when the penetration levels of these components increase. To address this concern, a novel distributed filtering algorithm is developed, which analyzes the harmonic profile of the entire grid to determine the optimal location of active filters and their power rating. Post-filter placement, the distribution network becomes IEEE 519-2014 compliant
A Numerical Study on the Multiline Ring Anchor in Sand
No full textThe Multiline Ring Anchor is an anchor solution designed to meet the growing demands of the offshore renewable energy sector in the country. It has been presented as an alternative to conventional anchor systems with its enhanced engineering efficiency and financial sustainability. The present doctoral work offers an assessment of the geotechnical performance of the anchor, to aid its transition from theory to practice.
A numerical study was undertaken to achieve a comprehensive understanding of the behavior of the anchor as a support system for arrays of offshore floating wind turbines. Its response under monotonic load conditions from different mooring systems is simulated using finite element analysis. The study was primarily divided into two phases ��� the first focused on the purely vertical capacity of the MRA in drained sand, with an emphasis on its end bearing behavior; and the second studied the response of the MRA when subjected to an inclined tensile pull while exploring the effects of the combination of loading conditions on it. Additionally, as a preliminary step in the analysis of the anchor under partial drainage conditions, a hypoplastic constitutive law is explored as an alternative to the Mohr Coulomb model.
The study finally presents a simple empirical model to estimate the vertical and the inclined ultimate capacities of the MRA in drained sands, along with a means to determine the optimum loading conditions, as functions of the padeye location and load inclination angle
Structure-Guided Strategies to Combat Antibiotic Resistance
No full textThe PhD research focuses on structure-guided strategies to combat antibiotic resistance. It includes three sub-projects: 1). SEQ-9 overcomes Mtb ribosome methylation and inhibits ribosomal activities. Antibiotics are implemented to cue tuberculosis caused by Mycobacterium to inhibit Mtb ribosomes and prevent downstream cellular activities. However, Mtb cells evolve to escape antibiotic pressure. One strategy Mtb implemented is methylation in certain adenosines, which helps Mtb with antibiotic resistance. Our studies found that a naturally derived molecule, SEQ-9, effectively inhibits the methylated Mtb ribosome. By determining the structures of SEQ-9-bound ribosomes, we concluded that SEQ-9 would undergo conformational changes to accommodate the methylation and still be able to inhibit the ribosome. Our results were part of research supported by multiple labs and a pharmaceutical company, Sanofi R&D, and are published in Cell. 2). An antibody derived from AP205 against Acinetobacter genomospecies 16 cells. This research aims to study the organizational pattern of AP205 and the phage-host relationship. Using Cryo-EM, we obtained high-resolution structures of AP205, the host acceptor, and the AP205-host receptor complex. We designed an antibody-like protein based on the structures, and the protein successfully targeted the host receptor. A collaborator is working on the antibody to illustrate its effects against the host. 3). An ongoing project studying Mtb ClpXP protease complex. Mtb ClpXP protease complex plays an essential role in cellular proteohomeostasis. The complex recognizes unfolded/misfolded proteins and degrades them to prevent abnormal activities. Dysregulation of ClpXP functions can cause detrimental effects on cells. To understand the function of the ClpXP complex, we performed structural analysis on the ClpXP complex and yielded a high-resolution structure. We also observed a structure that has yet to be discovered
Accuracy of Three Digital Impression Techniques for Implant-Fixed Complete Dentures
No full textEvidence comparing accuracy of implant-fixed complete denture (IFCD) impression techniques is unclear. The purpose of this in vitro study was to compare the accuracy of three digital impression techniques for IFCD.
A polyurethane edentulous mandible with four implant analogs served as the master model. A reference scan was made using a laboratory scanner. Test scans (n=10 per group) were made for the three groups: splinted IOS (Group S), non-splinted IOS (Group NS), and photogrammetry (Group PG). All scans were exported in standard tessellation language (STL) format and superimposed to compare linear, angular, and RMS deviations using a three-dimensional metrology software. Statistical analysis was performed using Kruskal-Wallis test for non-normally distributed data (a = 0.05).
No significant difference in overall accuracy was seen between the three groups. No significant difference in accuracy was seen when splinting ISBs. Significant differences in accuracy were seen within each group depending on position in the arch; higher angular deviation was seen at position RM1 in Group PG and Group S (p<.001).
Digital impressions for IFCD using either IOS or PG yielded similar results. Splinting ISBs did not seem to have a beneficial effect on accuracy. All three methods produced clinically acceptable results
Characterizing Effects That Influence Measured Range Hood Capture Efficiency
No full textKitchen range hoods remove harmful contaminants released by cooking, and they are essential for maintaining healthy indoor air quality. Standardized range hood capture efficiency (RHCE) experiments and tests were performed, and the resulting data were used to calculate RHCE, which is a parameter that provides a measure of how contaminants are removed. The goal of the studies reported herein is to improve the repeatability and reproducibility of RHCE tests performed in accordance with ASTM E3087.18. Changes to the standard aligning with the results of these reported studies will contribute to a reduction in error between measurements made at different testing laboratories, as well as a reduction in test variability. Achieving these goals is an essential step in ensuring further widespread adoption of the testing standard and RHCE as a metric, which in turn will lead to improved indoor air quality and human health, along with less energy consumed and greenhouse gas released.
A detailed experimental study was performed to analyze the effect of tracer gas injection rate, emitter assembly surface temperature, and chamber volume on measured RHCE. One study herein found that when the chamber volume was reduced from 36.6 m3 to 21.0m3 then a sample produced average results 3.6%CE lower at its higher operating speed and 7.8%CE lower at its lower operating speed. In contrast, in another study herein, a more extreme volume reduction led to a decrease in RHCE for another sample���s high-speed setting and an increase on its low-speed setting, suggesting that while chamber volume change has a definite effect on measured RHCE, this effect is not always consistent between samples or operating conditions.
Two analytical models were derived to predict chamber and exhaust concentrations during an RHCE test. The second model, which is the more useful model, assumes that there are two bodies of chamber air with limited interaction. It was validated by comparing its predicted results against experimentally measured ones. While the model predicted chamber CO2 concentration within 1.8% for a high-RHCE case, its accuracy was just 14.8% for a low-RHCE case. Further analysis found the model to have a consistent decrease in accuracy with decreasing nominal RHCE. The models can be used to predict performance and trends if one keeps in mind the limitations of the model with regards to its accuracy in specific flow ranges.
Another experimental study was performed herein to characterize the distribution of tracer gas throughout the test chamber. Results of this study confirmed that the concentration of tracer gas decreases as distance from the emitters increases, but that there is also a local zone of relatively low tracer gas concentration along the centerline of the room.
A reference range hood frame without a blower was constructed and tested for comparison against conventional range hoods. This test series aimed to determine whether the fan blower and motor selection impacts low-CE results. Using the exhaust as a prime mover, the reference box achieved capture efficiencies of 96.5% at 250 CFM, 93.3% at 160 CFM, and 81.3% at 100 CFM, outperforming other range hoods while showing lower test variance, with the result that fan and motor installation have a negative effect on RHCE. While design refinement is needed to better satisfy the initial goal of replicating low-RHCE results, the box���s low variance between tests suggests only minor modification of the design should be needed to do so.
A chamber inlet modification was performed and validated to ensure it did not severely impact the quality of measured RHCE. Three units were tested before and after installing the inlet modification. The results show that the inlet modification slightly reduced variance without noticeably affecting mean RHCE in four of five tested cases, but in one case it led to an increase in mean RHCE from 75.6%CE to 87.2%CE accompanied by an increase in variance from 0.94%CE to 3.06%CE. The results of this study found that the inlet change does not noticeably interfere with RHCE measurements