-
Prospective Students
-
Faculty and Staff
-
Current Students
-
Alumni and Friends
Additive manufacturing (AM), particularly laser powder bed fusion (LPBF), is of great interest to the aerospace community as it can be used to manufacture parts with complex internal and external geometries. This is in contrast to conventional manufacturing methods that limit the complexity of part designs. Of particular interest are the manufacture of parts with cooling channels consisting of complex surface topographies designed to improve thermal performance of the channels. While conventional machining can reduce the roughness of external surfaces, most surface treatment processes cannot be applied to internal channels, especially when the dimensions are a millimeter or submillimeter scale. Additive manufacturing offers an alternative that has the potential to overcome this limitation.
For a successful industrial adoption of AM for parts requiring complex cooling channels, an understanding of the relationship between the as-built surface finish and heat transfer is needed. In LPBF, there are numerous build parameters, such as part orientation during the build, that affect the final part surface topography and hence heat transfer. The classic literature on the impact of surface roughness on heat transfer (Moody's diagram) uses a simplified treatment of surface roughness, while powder bed fusion processes generate complex surfaces with strong anisotropic features, spatter, and surface and subsurface defects, all of which may affect heat transfer and fluid flow.
The primary focus of this work is to study the effects of AM roughness characteristics (build orientations, density of spatter deposits and their sizes, amplitudes/wavelengths, etc.) on heat transfer from the corresponding AM surfaces and pressure drop across cooling channels. Both numerical and experimental investigations are carried out for this purpose. Computational Fluid Dynamics (CFD) models for mini-channels using StarCCM+ (a commercial CFD code) were developed by acquiring the roughness data from real AM surfaces with various roughness parameters. To explore the correlation between sand-grain modeled roughness and AM surface roughness with 90° build orientation, CFD simulations for the entire system model (experimental setup) were employed. Further, CFD modeling of mini-channels with different wavy surfaces helped in determining the suitable dimensions for the mini-channel experimental set-up and the range of Reynolds numbers necessary for carrying out relevant experiments. Based on CFD findings, an exchangeable experimental setup was developed and on the basis of roughness characterizations, the AM parts with three critical orientations (0°, 45°, and 90°) were fabricated and then machined to the required shape and size to fit into the set-up. In addition, an Inconel part with a smooth surface has been machined from a forged Inconel-625 circular bar to serve as the baseline control condition.
Both CFD and experimental results were compared for different Reynolds numbers. The experimental results validated the CFD findings. Significant differences in the Nusselt numbers and pressure drops were observed across the different AM surfaces, with the surface with 90° build orientation performing best in terms of heat transfer. Based on these results, further investigation on the effects of 90° weld tracked surfaces in a circular form was also carried out. For this exploration, two aluminum (Al-6061) channels - one with a smooth surface and the other with internal threads serving as artificial waviness, similar to an AM surface with a 90° build orientation to the fluid flow direction - were conventionally manufactured and both CFD and experimental investigations were carried out for different mass flow rates. Both CFD and experimental results show that the artificial waviness (structured surfaces) has a significant impact on heat transfer and leads to a high cooling efficiency with a Nusselt number approximately 3x larger for various flow conditions compared to the smooth channel. However, the intentional structured surface also leads to larger pressure drops and may require extra pumping power, depending upon the application.
Wastewater-based epidemiology (WBE) has drawn significant attention as an early warning tool to detect and predict the trajectory of COVID-19 cases in a community, in conjunction with public health data. This means of monitoring for outbreaks has been used at municipal wastewater treatment centers to analyze COVID-19 trends in entire communities, as well as by universities and other community living environments to monitor COVID-19 spread in buildings. Precise and accurate quantification of viral copies in wastewater is a prerequisite for a successful WBE surveillance project. Accurate quantification of SARS-CoV-2 is dependent on the choice of an effective and reliable virus concentration method. Sample concentration is crucial, especially when viral abundance in raw wastewater is below the threshold of detection by RT-qPCR analysis. The first objective of my dissertation is the performance evaluation of a rapid ultrafiltration-based virus concentration method using InnovaPrep Concentrating Pipette (CP) Select and how it compares with the electronegative membrane filtration (HA) method. The criteria of the evaluation were based on the SARS-CoV-2 detection sensitivity, surrogate virus recovery rate, and sample processing time. Results suggested that the CP Select concentrator was more efficient at concentrating SARS-CoV-2 from wastewater compared to the HA method. About 25% of samples that tested SARS-CoV-2 negative when concentrated with the HA method produced a positive signal with the CP Select protocol. The optimization of the CP Select protocol by adding AVL lysis buffer and sonication increased Bovine Coronavirus (BCoV) recovery by 19%, which seems to compensate for viral loss during centrifugation. Filtration time decreased by approximately 30% when using the CP Select protocol, making this an optimal choice for building surveillance applications where quick turnaround time is necessary.
The inherent limitation of most of the current virus concentration methods is capable of processing small volumes of wastewater ranging from 20 – 250 mL. While small volume-based virus concentration methods can be successful for detecting and quantifying SARS-CoV-2 viruses during high community infection, these methods may not be informative, especially during the early stage of community infections. The second objective is to develop a large-volume filtration-based virus concentration method for increased sensitivity of molecular detection of SARS-CoV-2 and application in sequencing techniques. A dead-end hollow fiber ultrafilter (UF) and electronegative membrane filtration (HA) were used as primary and secondary concentration methods for concentrating viruses from wastewater. This study found that a modified UF-HA method, incorporating sonication and centrifugation, showed 100% SARS-CoV-2 positive detection in low COVID-19 infection periods compared to only 9% positive detection with the HA method and 63% with the UF alone. During the high COVID-19 infection period, no significant difference in SARS-CoV-2 detection and quantification was observed among the alternatives. The hollow UF-based primary method showed higher BCoV recovery compared to the combined method and HA method. The combined method (UF-HA_soni) can be used to identify the early stages of COVID-19 infection by detecting SARS-CoV-2 viruses from the low-tittered wastewater which can help prevent future outbreaks. Either the combined method or the UF-based primary method can be used to monitor SARS-CoV-2 viruses during the high COVID-19 infection period.
We also aim to apply digital droplet PCR to track the transmission dynamics of the Omicron variants by assessing the relative proportion of the strains circulating in Charlotte, North Carolina. We applied Digital Droplet Polymerase Chain Reaction (ddPCR) technology to detect and quantify Omicron variants using three different mutation assays targeting the S gene (N764K and N856K). Using these two assays, we first detected the Omicron variants on December 6, 2021, from the wastewater sample of Mecklenburg County which was earlier than the first clinical detection on December 10, 2021. The relative abundance of Omicron VOCs determined by the RT-ddPCR from wastewater was strongly and positively correlated with the clinically reported VOCs (r = 0.98, p = < 0.0001). This surveillance method for the variant analysis can give a near real-time transmission dynamic of the Omicron variants enabling quick administrative intervention such as awareness, preparedness, and control measures.
ABSTRACT
Reexamining Intra Team Conflict: A Dyadic Perspective
Conflict is seen as an emergent process when looking at the construct of the team as a whole. Researchers have investigated the interpersonal dynamics that contribute to intrateam conflict. According to researchers, intrateam conflict may be the result of conflict that arises from interactions between members of the team on a dyadic level. The dyadic perspective works under the assumption that conflicts can arise between individual members of a team. Researchers have focused on the perspective of the team rather than the dyadic perspective. In fact, many previous studies have concentrated on conflict and the causes of conflict within the team. However, more recently, there have been studies that lend support to the idea that dyadic conflict is one of the primary sources of conflict within teams. There is a possibility that various members of the same team will experience varying degrees of conflict with the other members of the team. It is possible that members of the team will perceive an increase in the amount of conflict between one another. This research contributed to the perception of generalized and dyadic reciprocity among the members of the team. The Social Relations Model (SRM) round robin design will be utilized in the execution of this study, which will take place in a real-world environment. This study will address the dyadic relationship between team members by using this design. It will rate the team members individually as well as the team as a whole, and it will report on each individual team member. In addition, the survey will include information on demographics, questions to assess dyadic task and relationship conflict, as well as questions regarding Jehn and Mannix's research on team level conflict. The results will be analyzed using the TripleR package in R statistical analysis software.
Keywords: Dyadic conflict, Conflict, Team level construct, Social Relations Model
In this dissertation we present new results on the classification of limit distributions of random geometric processes. In particular, that develop on the work of Penrose and Wade, who were the first to document the phenomenon with limited initial variables. In this dissertation we put forth not only new results, but a new method of obtaining results through analyzing the sequence of moments produced by random variables. Additionally we have new results in cycle decomposition of the related Dickman-Goncharov distribution. We present a novel proof of the distribution of the three highest order cycles in a random partition of Sn.
This dissertation explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines. The outcomes of causality extraction from Clinical Practice Guidelines on gestational diabetes are presented, marking a first in the field. We also release, the first of its kind, an annotated corpus of causal statements in the Clinical Practice Guidelines.
We address the challenge of classifying causal sentences with a small amount of annotated data at the inter-sentence level by treating it as a cross-domain transfer learning problem. Obtaining these classified sentences is the first step in extracting causality. Furthermore, we delve into the importance of modal verbs and the degree of influence from cause to effect. We show the capability of three models (BERT, DistilBERT, and BioBERT) to identify the degree of influence in the text.
Lastly, we tackle the challenge of sparse annotated data for the causality extraction from Clinical Practice Guidelines by, again, using transfer learning. We investigate the correlation between data similarity and the efficacy of transfer learning. We also investigate a zero-shot and few-shot approach to cross-domain transfer learning and quantify the link between data similarity and success rates. With the cross-domain few-shot transfer learning, we achieve an F1-score of 81%, which suggests transfer learning as a possible solution to address the limited availability of annotated data.
A significant change that employees in the United States have experienced because of the COVID-19 pandemic is the rise of remote work. There are many benefits of remote work, including increased performance and productivity. However, many remote workers struggle to separate work and life, leading to increased work-life conflict. Parents working remotely during the pandemic were the most likely to report challenges keeping work and life separate. The goal of this study was to better understand how parents’ work-life boundaries have changed during the pandemic. Based on interviews with 16 mothers and 16 fathers, this study examined how parents’ preferences for keeping work and life separate have adjusted and what factors affect their work and life. This research also looked at how mothers and fathers differ in their strategies to navigate between work and life when working remotely. This research uncovered six themes on how parents managed their work-life boundaries: two mindset shifts and four work-life separation strategies. Parents adapted their mindsets by redefining their priorities and setting realistic work and family expectations. The four boundary management strategies were turning off technology, sticking to a schedule, designating a home office space, and using a door-closed policy.
Peak load forecasting is crucial for reliable and effective grid operation. The day-to-day operation of the power grid requires load scheduling and dispatches of different energy resources including Energy Storage System and Demand Side Management programs. An effective implementation of these peak-shaving strategies relies heavily on when the peak demand occurs. Hence, forecasting the timing of peak load is as important as forecasting its magnitude.
A review of relevant literature indicates that there is no inclusive study on the topic of peak timing forecasting. This research aims to bridge the gap between industry requirements and academic research by addressing some key questions. First, the study defines the different forms of peak timing problems that arise in grid operation. Next, we investigate the problem of how we measure the peak timing forecast errors. The research critically reviews error measures used in the literature for peak timing forecasting. Based on the findings five new application-specific error measures are proposed. The research then focuses on one of the manifestations of the peak timing problem, that is, forecasting daily peak hours.
We analyzed the accuracy of peak hour forecasts from a state-of-the-art hourly load forecasting model and set it as the benchmark. The model selection process using different peak timing errors and load shape errors is investigated. Furthermore, two different frameworks for peak hour forecasting have been developed. The effectiveness of the proposed frameworks is empirically demonstrated in two case studies. The first case study is from a medium-sized Utility in the U.S. and the second one is from ISO New England. The proposed models demonstrate improved forecast results on the benchmark model by 12-16% in the test years of the two case studies. Additionally, when the models are only evaluated on the critical days with very high demands, they outperform the benchmark by 25-53%. Findings from this study emphasize the importance of developing explicit models for peak hour forecasting by analyzing the key determinants that vary with geographical location and regional factors.
This dissertation analyzes performance distribution, the financial impact of star performers, and how the researcher's discipline moderates the relationship between individual performance and the value of external funding at a private R1 institution. While publications have traditionally served as a metric for faculty success at research institutions, there needs to be more knowledge regarding the role of star performers in securing external funding. Data drawn from the institution's internal application system encompassed a sample size of 7,213 proposals submitted by faculty members over five years. Utilizing the Dpit package from the Comprehensive R Archive Network (CRAN), results indicate that a power law distribution offers a better fit than a normal distribution for modeling star performance, with a significant portion of generated value concentrated among a select group of star performers. Furthermore, the research demonstrates that an organization's strategic core competence moderates the individual performance of researchers and the overall value derived from research initiatives.
There is a growing mental health concern among Black Ratchet queer womxn in educational and criminal justice realms and the Covid-19 pandemic has left the educational climate in a state of high stress and anxiety. Consequently, Black Womxn in education are quitting from burnout. According to research, teacher burnout has been a concern for more
than 30 years, but currently there is a crisis. According to research “This is a five-alarm crisis. We are facing an exodus as more than half of our nation’s teachers and other school staff are now indicating they will be leaving education sooner than planned.: (Jotkoff, 2022).
Covid-19 has caused an increase in depression, anxiety, suicide ideation, and other health concerns in this population. External hindrances including racism, sexism, homophobia, and other ideologies rooted in America; negatively influence their mental well-being. Black queer womxn and girls are restricted from obtaining proper access to mental health services that take into consideration how identities are critical factors in mental well-being. This analysis will provide a rationale for utilizing culturally relevant mindfulness practices for Black queer womxn in educational sectors.
This dissertation embarks on a comprehensive exploration of the intricate dynamics surrounding access to and appreciation of natural environments within urban parks among the Latine population in Mecklenburg County. The central goal is to explore how Latine communities engage with urban natural spaces, affecting their perceived well-being. Through rigorous research, it investigates how human-environmental interactions in urban green spaces influence perceptions of well-being and their potential to mitigate disparities in access to such spaces and health outcomes. This study contributes significantly to the understanding of the interplay between urban green spaces, cultural perspectives, and well-being within the Latine community, addressing broader issues of environmental equity and health disparities. It enhances the geography literature by examining human-environmental interactions in urban parks within ascending Latine communities in the southern region. By integrating Michel Foucault's power and biopolitics theories and Landscape Theory. The research sheds light on the mechanisms of social control, access, and the cultural dimensions of landscapes. This research has implications for policy development, urban planning, and environmental management, aiming to promote equitable access to urban green spaces such as parks and improve health outcomes for marginalized communities.
