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Predictive Modeling For Multiphase Flow Boiling Heat Transfer By Integrating Computational Fluid Dynamics And Machine Learning Approaches
Get PDFThe heat transfer process in multiphase flow systems becomes essential forindustrial applications and when phase change occurs. The heat transfer coefficients become more efficient during boiling and condensation processes when compared to single-phase fluid flow. The design of thermal systems requires knowledge about these coefficients together with their essential parameters. This study evaluates flow boiling heat transfer coefficients for low-global warming potential refrigerants which include pure ethanol and propane (R-290) and R- 600 and R-600A. The experimental database included more than 25 independent studies that covered various operating conditions and channel sizes and fluid properties. Existing empirical correlations were evaluated, revealing limitations in their predictive accuracy. To address these shortcomings, a dual approach was employed which included running two-dimensional CFD simulations with the Volume of Fluid model through ANSYS Fluent and machine learning with symbolic regression. The 2D simulation results demonstrated strong agreement with experimental data thus validating their reliability and cost-effectiveness. Moreover, symbolic regression, a machine learning technique was uniquely applied in this field to develop 2 new predictive equations which are Ethanol and hydrocarbons-based correlation. These correlations provide 2.4 – 8 % MAE respectively providing interpretable formulas compared to conventional artificial neural networks approaches. The results provide accurate and reliable predictive models for engineers and researchers working with eco-friendly refrigerants and help improving the future design of sustainable thermal system
Computational Prediction Of Hemolysis In Laminar Non-Newtonian Fluid
Get PDFHemolysis, the rupture of red blood cells (RBCs), is a critical concern in blood-circulating devices, requiring accurate predictions to ensure patient safety. This study investigates the sensitivity of hemolysis predictions to variations in viscosity models and hemolysis coefficients under complex flow conditions. Computational Fluid Dynamics (CFD) simulations were conducted using Ansys Fluent and OpenFoam on identical meshes with the FDA benchmark nozzle model. Under laminar flow conditions, CFD frameworks accurately predicted global variables, but variations in derived quantities, such as strain rate and vorticity, emerged due to differences in numerical solvers and gradient evaluation methods. These variations affected predictions of blood damage and non-Newtonian flow behavior. To assess this, blood properties, including flow symmetry indices, vortex characteristics, and hemolysis—were evaluated using Newtonian and four non-Newtonian viscosity models (Casson, Cross, Power Law, and Carreau-Yasuda). The Normalized Index of Hemolysis (NIH) vs. Reynolds number analysis highlighted that hemolysis is significantly influenced by viscosity model choice. At low Reynolds numbers, models with higher viscosity at low shear rates (e.g., Casson, Carreau-Yasuda) predicted elevated NIH values, indicating increased hemolysis risk. At higher Reynolds numbers, model predictions converged, reducing NIH variations. Furthermore, absolute values of NIH were very sensitive to the empirical power law coefficients employed, highlighting the need for updated coefficients for accuracy. These findings emphasize the importance of viscosity model selection and hemolysis power law coefficient accuracy in blood damage predictions. Optimizing these parameters is essential for improving CFD-based hemolysis models and minimizing RBC damage in medical devices
AI-Enhanced Nondestructive Testing For Defect Evaluation And Performance Monitoring In Infrastructure Systems
No full textEnsuring the safety and functionality of infrastructure is crucial for maintaining public welfare and economic stability. Traditional inspection methods are often labor-intensive and time-consuming, leading the engineering field to seek advancements in non-destructive techniques to improve inspection speed, accuracy, and safety. Artificial intelligence (AI) has emerged as a key tool in recent studies for interpreting nondestructive (ND) tests, as manual classification is often infeasible due to the complexity and volume of data.
In this research, AI was integrated with various ND technologies to detect defects, such as carbonation in concrete and steel or autonomous measurement in infrastructure. The first objective of this dissertation combines impact echo (IE) and Ground Penetrating Radar (GPR) with AI, to identify various subsurface defects in reinforced concrete bridge decks. Additionally, a feature-selection-based Support Vector Machine (SVM) model was developed using the physics of IE signals to detect defect areas in slabs, and its results were compared with other deep learning algorithms, including 1D-CNN and 2D-CNN as first objective of my dissertation.
Applying computer science and machine learning techniques to drone-acquired data supports autonomous defect detection and condition assessments, significantly reducing time and labor. The second objective explores the use of visual data for inspecting ancillary structures and detecting defects, such as cracked or defective bolts. Building on promising results from previous studies, this research proposes comprehensive algorithms to further enhance inspection capabilities. The role of infrared thermography is also discussed, particularly for its effectiveness in enhancing deep learning performance by removing background noise. Combining thermal images with visual data (data fusion) significantly improves the accuracy of models in locating ancillary structures and identifying defects. Data fusion of infrared and visual images shows potential in non-contact methods for detecting issues like loosened or missing bolts.
In the third objective, hyperspectral imaging (HSI) is introduced as a novel non-destructive tool for detecting carbonation defects in concrete structures, eliminating the need for traditional techniques like XRD, SEM, or phenolphthalein solution. This study uses hyperspectral cameras to detect carbonation defects on concrete surfaces, with machine learning techniques and image processing approaches applied to detect damaged areas following chemical exposure.
Finally, this dissertation presents an AI‑driven photogrammetric workflow that leverages UAS imagery to measure stockpile volumes and monitor construction progress. Deep‑learning point‑cloud classification and change‑detection algorithms achieve 5 % volume error across multiple flight altitudes and capture layer‑by‑layer pavement thickness changes in near real time, providing contractors with actionable information for material management and schedule control.
This dissertation advances infrastructure monitoring in the U.S. by integrating artificial intelligence with non-destructive technologies such as Impact Echo, GPR, UAS-based imaging, and Hyperspectral Imaging with using AI. These contributions enable faster, safer, and more accurate inspections, enhancing the reliability and resilience of critical infrastructure systems
Perceptions Of Emotional Abuse As A Partial Function Of The Abuser-Victim Gender Dyad
Get PDFEmotional abuse affects approximately half of people in an intimate relationship, yet it is often not considered intimate partner violence (IPV) on its own (Dokkedahl et al., 2022; Leemis et al., 2022). Despite the prevalence, there is a lack of consistency on the definition of emotional abuse and IPV as well as a lack of clarity of what acts qualify for these terms. Past research has largely focused on IPV, specifically physical or sexual abuse, in heterosexual relationships with a male perpetrator and female victim. Few studies have explored the interactions of the gender of the victim and perpetrator in all of its components. This study aims to add to the current literature by investigating how participants’ perceptions of emotionally abusive acts change depending on the gender of the participant, the gender of the perpetrator, the gender of the victim, and the history of physical abuse in the relationship. Four hypotheses were tested for the main effects of the gender of the respondent, the gender of the victim, the gender of the perpetrator, and the history of physical abuse. Three hypotheses were tested for interaction and covariate effects. The results of the final sample (N = 752) showed that the only consistent main effect was participant gender. Interaction effects were present in each of the four scenarios but were not consistent across the scenarios. This is the first study to examine all possible gender dyads between males and females in one sample and to examine how a history of physical abuse might impact perceptions of emotional abuse. These results should serve as a baseline for future research to continue to examine the relationship between the gender dyads and for additional discussions among psychologists to emerge about how to identify emotional abuse in participants of different gender identities and backgrounds
Impact Of Resin Grinding On Per- And Polyfluoroalkyl Substance Adsorption In Rapid Small-Scale Column Tests
No full textPer- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant health risks. Among the different technologies for PFAS removal, adsorption is preferable due to low-cost, operational simplicity, and use of regenerable adsorbents. While pilot-scale fixed-bed columns are ideal for evaluating adsorption media, they are time- and resource-intensive. Rapid Small-Scale Column Tests (RSSCTs) offer a practical alternative, yet most studies have focused on granular activated carbon, with limited insight into ion exchange (IX) resins. Due to limited literature on the effect resin preparation on the performance of RSSCTs and their prediction accuracies are unexplored. This study investigates the effect of five grinding techniques: freeze drying & blender grinding (FD&BG), blender grinding (BG), mortar and pestle (MP), ball milling (BM), and jet milling (JM) on the adsorption performance of IX resins for PFAS removal using RSSCTs in single solute (deionized (DI)water background) and multi-solute systems (simulated ground water background).Three PFAS compounds, perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA), and perfluorooctanoic acid (PFOA) were tested in DI water, while PFBA and PFOA were also evaluated under simulated groundwater, containing natural organic matter (NOM) and inorganic anions. Different grinding techniques influenced the breakthrough profiles of the PFAS. Characterization revealed that grinding altered key physico-chemical properties: water retention capacity dropped from 46.2% (ungrinded resin) to 6.8% (BM), while total anion exchange capacity increased. XPS analysis showed enhanced accessibility to Cl⁻ and quaternary ammonium groups, and zeta potential became more negative post-grinding. Adsorption breakthough trends varied by PFAS type and grinding method, reflecting differences in removal mechanisms and compared based on half breakthrough BVs (BV₅₀). FD&BG yielded the highest BVs values (3,200 BV₅₀) for PFBA in both DI and simulated groundwater, attributed to enhanced electrostatic interactions and improved exchange site accessibility. For PFHxA, JM IX resin had the highest BVs (5,200 BV₅₀) under single solute system, benefiting from both hydrophilic and hydrophobic interactions. For PFOA, BM achieved the highest BV₅₀ (9,300 BVs) in the DI water background, likely because hydrogen bonding between the resin’s hydroxyl groups and carboxylic head of PFOA enhances its removal alongside the dominant hydrophobic interactions. Whereas MP performed highest BVs (5,600 BV₅₀) in the presence of competing solutes. PFBA was more sensitive to background interference, with BV₅₀ reductions of 37.5–57.5%, compared to 9.7–46.2% for PFOA. The grinding technique also influenced RSSCT predictability. For PFBA, FD&BG correlated best with pilot-scale data under single-solute conditions, while JM was most accurate in multi-solute systems. For PFOA, BG and JM provided the best prediction accuracy up to 70% breakthrough in DI water, whereas MP was most accurate under multi-solute conditions. These results demonstrate that the resin grinding technique is PFAS-specific and significantly affects both adsorption behavior and the prediction accuracy of RSSCTs when scaling up pilot applications
Advancing Novel Membrane Fabrication: Overcoming Critical Bottlenecks For Next-Generation Applications
Get PDFMembrane technologies have emerged as a cornerstone of sustainable process operations, providing efficient alternatives to conventional separation methods. With increasing emphasis on sustainability and environmental concerns, the projected membrane market growth is largely driven by their application in novel fields, replacing traditional processes. However, the widespread adoption of next-generation membranes hinges on overcoming critical fabrication challenges, such as membrane fouling, filler-matrix compatibility, and the inherent permeability-selectivity trade-off. This work explores innovative solutions to these challenges across multiple separation processes, including reverse osmosis (RO), pervaporation, and gas separation. Membrane fouling is a critical issue towards Reverse Osmosis (RO) based sustainable water purification. An eight-month long pilot study was conducted using three antiscalants to evaluate RO performance and fouling patterns. The study revealed an antiscalant dependent fouling deposition with critical manganese scaling with environmentally friendly terpolymer antiscalants. Operational modifications along with surface-modified membranes were proposed to mitigate membrane fouling. Pervaporation, a pharmaceutically attractive liquid-liquid membrane separation process, was studied with mixed matrix membranes. In particular, the filler-matrix interactions were closely studied to propose state-of-the-art permeation modeling based on filler hydrophilicity. Additionally, to address the limitations of conventional membrane materials, the study further includes the fabrication of 2D nanomaterial-based membranes, incorporating magnetic stimuli to create tunable membranes for critical gas separations. These membranes demonstrate magnetic control over selectivity and permeability, offering a solution to the traditional permeability-selectivity trade-off. Finally, the study integrates machine learning assisted material discovery into membrane science by directing pathways to streamline membrane material optimization and accelerate innovation. Collectively, the proposed innovations in membrane fabrication pave the way for next-generation applications, providing efficient and sustainable separation solutions across diverse industries
Public Perceptions Of Fracking And Anti-Fracking Activism: A Theory Of Planned Behavior Approach And Its Implications For Environmental Policy
Get PDFHydraulic fracturing, or fracking, has become one of the most contentious energy technologies of the 21st century, drawing support for its economic and energy security benefits and opposition due to its perceived environmental and health risks. This study applies the Theory of Planned Behavior (TPB) to examine the psychological, social, and informational factors that shape public perceptions of fracking and motivate anti-fracking activism. Using a nationally representative sample of U.S. adults (N = 522), this research investigates how attitudes, subjective norms, perceived behavioral control, and domain-specific knowledge predict support or opposition to fracking, as well as intentions to engage in environmental activism. Structural equation modeling (SEM) reveals that subjective norms and perceived behavioral control are the strongest predictors of activism intention, while attitudes toward fracking mediate the influence of risk perception, knowledge, and benefit evaluation. Additionally, political ideology, negative personal experiences with fracking, and distinct forms of fracking-related knowledge (socioeconomic vs. environmental health) significantly influence public opinion. Individuals who perceive higher benefits and possess more economic knowledge tend to support fracking, whereas those with greater environmental health risk knowledge and negative experiences are more likely to oppose it and express activist intentions. The findings highlight the multidimensional nature of fracking attitudes and provide insights for policymakers, risk communicators, and environmental planners. Specifically, they underscore the importance of tailoring policy and outreach strategies to address both informational asymmetries and localized concerns. This study contributes to a deeper understanding of how cognitive, experiential, and social variables interact to shape energy-related attitudes and public participation, offering actionable recommendations for more effective and equitable environmental policy development
Edmore and Webster Coulee Hydrologic Simulation Data
No full textThe authors compiled a hydrologic model for the Edmore at Edmore and Edmore at Webster Coulee Basin using Cold Region Hydrologic Platform. The datasets include observed and simulated streamflow, observed climatic data, land use maps and hydrological representative unit maps (HRU) maps. This study detected a mechanism of hydrologic change to wetting using a cold region hydrologic model during 1991-2024 period
Efficacy of Prediabetes Management Options for Lowering Cardiovascular Risk
No full text• ≈ 86 million people in the United States are diagnosed with prediabetes: a metabolic syndrome characterized by insulin resistance and inflammation. • Inflammatory nature of the disease increases a patient\u27s cardiovascular risk. • Common treatment options include metformin and lifestyle intervention – primarily exercise and diet plans. However, there is no standard medical recommendation for lowering cardiovascular risk. • Purpose: Determine which treatment options, including the combination of lifestyle and intervention, best lower cardiovascular risk and better detail the effect of prediabetes on cardiovascular risk • Current research provides data showing that prediabetes increases the risk of hypertension and mortality from cardiovascular event. • Reversal from prediabetes to normoglycemia lowered risk of cardiovascular event by almost 50%. • Lifestyle interventions was the best method for lowering cardiovascular risk, while metformin was found to lower the effects of lifestyle intervention when studying the combination of the two. • Future research should determine best course of treatment for patients who aren’t able to complete lifestyle intervention goals such as patients who are bedridden or frail. • Gaining a better understanding of the interaction between metformin and lifestyle intervention will serve invaluable for the future of prediabetes treatment. This research should aid in provider treatment recommendations for prediabetic patients and better detail a “standard” of care
Pre-Procedural Anxiolytic Use in Pediatric Laceration Repair
Get PDFPediatric laceration repair is a common, yet anxiety-inducing procedure for those who must undergo them. With limited research analyzing the different types of pre-procedural anxiolytic medications, the purpose of this research and literature review was to explore two distinct but commonly used intranasal (IN) anxiolytics, dexmedetomidine and midazolam, and to compare their efficacy, adverse effects, and overall pediatric anxiety and parental satisfaction. Databases including PubMed, GoogleScholar, and ClinicalKey were searched to find articles investigating clinical trials using pediatric participants. Meta-analyses, animal studies, dental procedures, imaging procedures, full sedation procedures, and ongoing studies were excluded. In terms of effective anxiolysis, intranasal dexmedetomidine (IND) 2 or 3 ug/kg should be considered the preferred IN medication for pediatric laceration repair procedures as it significantly reduced anxiety levels and increased parental satisfaction scores without having any reported significant adverse effects. Counteractively, administration of intranasal midazolam (INM) 0.4 mg/kg 5mg/mL solution revealed a decrease in pediatric anxiety but imposed vomiting adverse effects. Future studies regarding IND’s bioavailability, pharmacokinetics, and optimal doses per age or weight group could be considered for further supportive evidence.https://commons.und.edu/pas-grad-posters/1345/thumbnail.jp