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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
Allocation of Peptidoglycan Resources Between the Rod System and Class-A Penicillin-Binding Proteins in Myxococcus xanthus
No full textPeptidoglycan (PG) is a polymer scaffolding surrounding the cell membrane of bacteria and is key to survival as it aids in preserving cell structure and protects against harmful stresses. It is composed of alternating N-acetylglucosamine and N-acetylmuramic acid strands that are crosslinked through peptide chains. The synthesis of PG is a highly dynamic and tightly regulated process, spanning across the cytoplasm, inner membrane, and periplasm in gram-negative bacteria. PG synthesis is vital for cell survival and remodeling of the sacculus is required during cell elongation and division as well as during PG repair. PG synthesis has been heavily studied, however, it is still unknown how the allocation of PG precursors are portioned between the two PG synthase systems of the class-A penicillin-binding proteins and the Rod system. Here we take advantage of the monomer-dimer characteristic of the phospho-MurNAc-pentapeptide translocase protein, MraY, as a proxy for PG precursor usage of PG synthases. Using single particle tracking photoactivatable localization microscopy, we show that the Rod system is the main synthase system active in vegetative growth while aPBP���s are responsible for PG repair. This study aims to further understand the properties of PG synthesis and how the cell divides its resources during vegetative growth and repair
Fracture Strength of 3D Printed Resin Interim Fixed Partial Dentures Varying Connector Sizes
No full textThe aim of this in vitro study is to determine the minimum connector size for 3-unit FPD���s comparing two different types of 3D printed resin to zirconia. A mandibular typodont quadrant was prepared for first premolar and molar all ceramic crowns with 0.5mm chamfer margins and total 6-degree convergence angle. The model was scanned via intraoral scanner (3Shape, Trios 4) and uploaded into CAD software (Exocad). 3-unit FPD with various connector sizes 9mm^2, 12mm^2, and 16mm^2 were designed. 3D printed two resins (OnX and Dentca C&B) with SprintRay Pro55. After the prints are completed, the interim FPDs were removed from the build plate, cleaned, and cured using manufacturer recommendations. FPDs were printed at a 0 degree build angle with 100��m layer thickness. 15 samples were printed per test group (N=90). Control group comparison made with a milled 3Y-zirconia 9mm^2 group (n=15). Fracture load tested with Instron machine with vertical pressure applied through pontic site at 1mm/min. FPDs placed onto typodont abutments and mounted on Instron machine. Failure was defined as the moment when the load dropped by 10% under the maximum value. The force (in Newtons) was recorded at the time of failure. Statistical analysis was completed using SPSS software to analyze the differences between the groups. Results were normally distributed, so one-way ANOVA was performed. Descriptive statistics will be added based on the location of fracture, and type of fracture. OnX had significantly higher maximum force at fracture (1236��135N) than Dentca C&B (884��136N, p<.001) and zirconia (456��87N; p<.001). Connector size had a significant difference for OnX for all sizes (p<.001), while significance for Dentca was only between 9 mm^2 and 16mm^2 (p=.006). The resins shattered upon failure, whereas zirconia simply split. Connector size and material type played significant roles in fracture resistance, with both resins withstanding significantly more force than typically generated during chewing
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
Accelerating Finite Element Analysis Using a Multi-Fidelity Computational Scheme for Nuclear Applications
No full textNext-generation microreactors are currently being designed to be operated terrestrial and ex-traterrestrial for remote surface power production. These systems will provide an alternative source of carbon-free energy that is versatile and can be utilized for various applications. This recent de-sire to design and build next-generation nuclear systems requires high-fidelity analysis to ensure the proposed design can operate safely and as intended. Traditionally, this can be achieved by obtaining a combination of experimental and numerical results, however it has become difficult and expensive to perform integral experiments. Therefore, high-fidelity numerical results have become heavily relied upon to provide the required analysis, specifically finite-element based codes. This reliance on numerical codes has presented its own set of issues as it can take millions of CPU hours to gather the required results for a given design. Therefore, a novel computational scheme is pro-posed to accelerate transient finite element analysis of these next-generation nuclear systems. A discrepancy function between a low and high-fidelity model is approximated and used to actively correct the low-fidelity solution. By exploiting the computational cheap low-fidelity solution and a few snapshots in time of the high-fidelity solution, an approximated discrepancy function can be found to correct the low-fidelity model. This approach aims to produce a solution that is close to the full-order high-fidelity model while requiring a smaller computational cost. The idea was decided to be implemented to work alongside the Abaqus finite element software, and utilized to model a series of transient events for two conceptual reactor designs
ScrollStats: An Automated Tool for Deciphering Meander Scrolls and Uncovering the Hidden Paths of Meander Migration
No full textScroll bars are arcuate, elongated landforms deposited along the inner banks of meandering rivers. This deposition occurs within zones of flow separation typically during discrete high-flow events. As the channel meanders, scroll bars are deposited successively in its wake and are preserved by stabilizing vegetation, leaving behind a well-preserved, visually intuitive migration history in the resulting ridge and swale topography (RST). Because of this episodic deposition, bar location and orientation can serve as a record for migration history while specific bar morphology can contain information about the hydrologic conditions under which the bar was deposited. ScrollStats is an open-source GIS tool developed to digitize and extract this morphological information from DEMs of the RST of meandering bends. ScrollStats produces two primary data products 1) migration pathways derived from preserved scroll bar location and orientation and 2) a ridge area raster delineating the ridge areas within the DEM. From these data products, ScrollStats calculates a suite of ridge morphometrics (ridge width, amplitude, and spacing) for every intersection of an identified ridge and migration pathway, covering the entirety of the bend's interior. In this paper, we showcase the capabilities of ScrollStats on a compound bend of the Lower Brazos River, Texas, USA. We also demonstrate how ScrollStats compares to other methods previously used to extract morphometrics from RST and how it can be used to expand upon previous findings in the literature. ScrollStats allows researchers to extract floodplain morphometrics previously unavailable, enabling new research into bend-scale migration dynamics and linkages between hydrologic process and the resulting landforms
Multi Agent-Based Model for Residential Households Trading Market of Renewable Energy in Qatar
No full textPeer-to-peer energy trading (P2P) is a new paradigm for operating energy systems. You can generate energy from renewable energy sources (RES) in your home or office and share it locally. RES is crucial for the transition to sustainable development. The integration of distributed energy resources (DER) and energy storage systems (ESS) has several advantages, such as reduced Greenhouse gas (GHG) emissions, loss reduction, and reduced dependency on the grid.
An architectural energy market model was proposed and simulated to represent the design and interoperability aspects of components for P2P energy trading between nano-grids. An equitable marketplace is simulated using agent-based modeling (ABM) and game theory to enable owners of photovoltaic (PV) systems to sell their electricity to neighbors and the grid. The transacting entities include decentralized energy producers, consumers, or both, such as residential entities. Each residential entity is a villa simulated as a nano-grid having a battery and solar PV panels. This model was developed as a case study for Qatar.
Solar energy is traded at a rate local energy producers and consumers determine. The energy price is dynamic and depends on changes in the generation-to-demand ratio throughout the day. The amount of excess energy produced can be purchased by anyone and is listed in the market by all available sellers, along with the generation type, price, and location. Buyers place orders for energy, and the trading market framework matches prospective buyers with sellers. Game theoretical
modeling is used for the decision-making between the agents. A novel method integrating game theory and agent-based modeling to simulate the P2P market is developed and validated.
The market framework was tested using various scenarios, including battery storage, reduction of subsidies for non-renewable energy, introduction of a carbon tax, and increasing PV capacity to study plausible outcomes of solar PV trading. The results suggest that when greater PV capacity is assumed, the benefits of trading increase, and a larger proportion of household demand is met locally without the need to buy energy from the grid. Adding battery storage to the system enhances trading, provides more energy for sale, and reduces the amount of energy purchased from the grid.
The universality of this market framework is confirmed through comprehensive validation exercises involving data from Qatar and Europe (Germany), representing distinct regions. The proposed model exhibits scalability, offering the potential to extend its application to entire neighborhoods or even entire cities in various geographical locations. This adaptable framework can address diverse energy trading needs and preferences across different regions
Cricket Song Classification Using Transformers
No full textAs interest in studying animal sounds for biodiversity monitoring grows, the need for automatic methods to classify species based on their unique songs becomes crucial. This thesis presents an innovative approach to identifying various cricket species and genera by analyzing their audio recordings through advanced pretrained transformer models, specifically utilizing the AST (Audio-Spectrogram Transformer). The dataset includes 592 audio files for Gryllus species and 441 audio files for different genera and is meticulously curated to overcome challenges like uneven class distribution and variations in audio file durations. Customized label mapping and strategies such as undersampling and oversampling are applied to adapt the pretrained model to the specific classification task and balance the dataset respectively. The experimental setup includes three to four distinct datasets, each focusing on different subsets of cricket species and genera each. Training involves varying learning rates, with evaluation metrics encompassing validation and training accuracy, precision, recall, and F1-score. Further analysis is conducted to visualize the distribution of the data points. For this, first, Principal Component Analysis (PCA) is used to reduce the dimensionality of the features. Next, t-SNE visualization is used to provide insights into the spatial relationships between different species in the data space. This work differentiates itself from previous approaches that used CNNs, and it explores the capabilities of transformers in classifying cricket species and genus and aims to understand how these models perform in comparison. The transformer model achieved high accuracy rates of 95.31% for classifying Gryllus species and 94.27% for genus classification. This study has potential applications in education, conservation, agricultural pest management, and other ecological studies
Market and Emissions Impact of All-Electric Aircraft
No full textAviation sector greenhouse gas (GHG) emissions are projected to grow nearly 50% by 2050, motivating exploration and future adoption of reduced-emissions aircraft. While all-electric aircraft (AEA) generate effectively zero in-flight GHG emissions, their prospect for commercial implementation faces critical challenges: most notably the comparatively poor specific energy of batteries relative to aviation fuel. Unanswered questions exist regarding the prospective market and emissions impact of future AEA. This dissertation addresses these knowledge gaps, building on detailed AEA designs available in the literature to quantify AEA market impact and emissions reduction potential across thousands of varying model inputs, market bases, emissions scenarios, and timeframes. This work compares the energy consumption and emissions of conventional aircraft and AEA for every domestic commercial flight in the United States ��� nearly 9.2 million flights in total ��� determining feasibility of electrification, energy requirements, and greenhouse gas emissions for each route and providing novel quantitative estimates of potential AEA impact in the United States.
This dissertation demonstrates clear, albeit conditional, pathways for AEA to reduce aviation sector emissions. Modern-day lithium-ion batteries have insufficient energy for use in the aviation sector: AEA potential hinges on development of batteries with a specific energy three to four times greater than modern batteries. Unlike hydrocarbon-powered aircraft, the emissions footprint for AEA varies by point-of-departure based on power sector emissions generated during battery charging. Assuming historic trends in power sector emissions persist, AEA offer regional potential in the Pacific Northwest, California, and the East Coast, but would have limited national impact reducing aviation sector emissions: at maximum market penetration 1.74% to 7.95% depending on emissions timeframe for a 1000 Wh/kg onboard battery. Achieving greater emissions reduction requires transition to reduced-emissions power generation. Assuming net-zero power sector emissions, AEA could reduce total domestic aviation sector emissions by more than 27% at maximum market penetration while capturing the majority of the aviation market in terms of total flights and passenger count but would require roughly 2% of total US electricity generation (96.6 TWh). These results are highly sensitive to inherent uncertainty in the radiative forcing of non-CO��� emissions and vary by emissions model and timeframe
Estimation of Evapotranspiration Using Remote Sensing Data and Sebal Model in Adana, Turkiye
No full textThe world's population has been steadily increasing in recent decades, resulting in a higher demand for water. This demand, combined with the effects of climate change and the depletion of natural resources, has made it crucial to monitor water resources. Agriculture is the largest user of water, and its impact on water usage is becoming a growing concern. Evapotranspiration, which is the combined loss of water through evaporation and plant transpiration, plays a crucial role in the water budget and energy balance. This study examined the use of the python SEBAL (PySEBAL) model for estimating evapotranspiration in agricultural areas by utilizing Landsat satellite imagery and meteorological data. The research centered on Seyhan Plain, Adana, Turkey, from 2017 to 2019 and analyzed both summer and winter seasons across five different crop types: cotton, wheat, corn, soybean, and citrus. The PySEBAL model generated daily actual evapotranspiration maps at 30m resolution for the study area. Analysis of R-square values across years and crops revealed positive correlations, particularly for cotton (R-square = 0.819) and wheat (R-square = 0.809). Corn and citrus also showed positive correlations (R-square = 0.736 and 0.708, respectively), while soybean2 displayed a weaker association (R-square = 0.481). These findings suggest that the PySEBAL model can accurately predict Penman-Monteith evapotranspiration (PM-ET) for some crops (cotton, wheat) compared to others (soybean-2). The results indicated an overestimation of ET by the model compared to literature values. However, a positive correlation was found between PySEBAL-ET and Penman-Monteith evapotranspiration (PM-ET) estimates across all crops and years, suggesting the potential of SEBAL for agricultural water resource monitoring