3487 research outputs found
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Machine Learning Boiling Prediction: From Autonomous Vision of Flow Visualization Data to Performance Parameter Theoretical Modeling
No full textFlow boiling is a highly efficient configuration for meeting the high heat dissipation demands of thermal management systems. However, the complex physics of two-phase flow has hindered its broader application, especially in terms of quantifying visual information. Recent advancements in machine learning vision tools have revolutionized the analysis of phase change phenomena by enabling the digitalization of physically meaningful features such as void fraction, vapor-liquid interfacial behaviors, and liquid-solid wall wetting front areas en masse. In this study, we systematically investigate two-phase models that compute void fractions, heat transfer coefficients, and critical heat flux using live bubble data streams under microgravity. The collected empirical bubble data is used to supplement and validate traditional control-volume-based theoretical modeling approaches. Void fraction data is first validated with analytical frameworks. This is followed by void fractions and wetting front areas being used to improve correlations predicting heat transfer coefficients. This work showcases the potential of using a new machine learning-based strategy to accelerate scientific formula discovery through the extraction of multi-level and physically meaningful features
Diffuse Interface Modeling of Non-Isothermal Stokes-Darcy Flow With Immersed Transmissibility Conditions
No full textThe coupling between free and porous medium flows has received significant attention since it plays an important role in a wide range of problems from fluid-soil interactions to biofluid dynamics. However, modeling this coupled process remains a difficult task as it often involves a domain decomposition algorithm in conjunction with a special treatment at the interface. The problem can become more challenging under non-isothermal conditions because it requires the iterative procedure at every time step to simultaneously meet the transient mass continuity, force equilibrium, and energy balance for the entire system. This article presents a diffuse interface framework for modeling non-isothermal Stokes-Darcy flow and the corresponding finite element formulation that bypasses the need for explicitly splitting the domain into two, which enables the unified treatment for distinct regions with different hydrothermal flow regimes. To achieve this goal, we employ the Allen-Cahn type phase field model to generate the diffuse geometry, where the solution field can be seen as a regularized approximation of the Heaviside indicator function, allowing us to transfer the interface conditions into a set of immersed boundary conditions. Our formulation suggests that the isothermal operator splitting strategy can be adopted without compromising accuracy if the heat and mass transfer processes are decoupled by assuming that the density and viscosity of the phase constituents are independent to the temperature. Numerical examples are also introduced to verify the implementation and to demonstrate the model capacity
When and Why Ecological Systems Respond to the Rate Rather than the Magnitude of Environmental Changes
No full textEcologists and conservation biologists have become quite familiar with the concept of tipping points: abrupt changes in an ecosystem\u27s state that occur after a period of relative stasis. Most of the familiar ecological examples of tipping points occur either because a once-stable state has lost stability, or the system has been subjected to a particularly large perturbation and transitions to an alternative stable state, distinct from the pre-perturbed state. A different class of tipping points, known as rate-induced tipping (or r-tipping) points, are likely present in many ecological communities but remain little known in the field. Rate-induced tipping occurs when an environmental change is too fast for the community to track; even though the original state never loses stability, the ecological response to the change is too slow to remain in that stable state\u27s basin of attraction. R-tipping is part of the broader phenomenon of rate dependence that arises because ecological systems cannot respond instantaneously to external changes. In this article, we provide a non-technical introduction to the theory of rate dependent responses to change, discuss the implications of this theory to conservation problems, and illustrate its application through a series of case studies. When a tipping point is rate dependent, effective management relies not only on the type of intervention used but also the rate at which it is applied. Our work highlights how a mechanistic understanding of different types of tipping points leads to stronger guidance on when, where, and how different interventions can used to achieve conservation goals
Fostering Empathetic Career Development Through Non-Violent Communication Principles
No full textCareer services professionals can embody compassion and essential dialogue skills by embracing Nonviolent Communication (NVC) principles. This framework can aid career services professionals in serving as powerful role models for students entering the job market. Students will feel empowered to navigate the professional world with increased emotional intelligence. Influential leaders like Satya Nadella, the CEO of Microsoft, recognized the significance of NVC. When he became the CEO of Microsoft in 2014, Nadella decided to use the NVC framework to change the company culture (Abadi, 2018)
Promoting AI Literacy in Library Research Guides: Practical and Ethical Considerations
Get PDFLibrary research guides are crucial in enhancing students’ research and information literacy skills. This presentation discusses a study conducted between November 2023 and February 2024, focusing on AI-related library research guides at R1 institutions. Guided by AI Literacy principles and the ACRL Framework for Information Literacy, the study aimed to explore the practical and ethical dimensions of AI Literacy as reflected in these guides through content review and data extraction.
We examine the presence or absence of instructions on creating prompts to interact with language models such as ChatGPT and explore how these guides address human biases (e.g., racial bias) and machine-based biases (e.g., algorithmic transparency). Our presentation showcases empirical findings and lays the groundwork for crafting library guides that critically engage with Generative AI tools and promote information literacy principles.
Library guides have the potential to empower students to grasp the broader societal implications of AI and gain a deeper understanding of its multifaceted role in academia today
The Chronicles of Narnia and His Dark Materials: The Bible and Paradise Lost of Children’s Literature
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The Efficacy of High-Fidelity Simulation on Knowledge and Performance in Undergraduate Nursing Students: An Umbrella Review of Systematic Reviews and Meta-Analysis
Get PDFObjectives: This umbrella review aimed to consolidate the evidence base on the impact of high-fidelity simulation on knowledge and performance among undergraduate nursing students. Design: Umbrella review with meta-analyses of pooled effect sizes, followed by an additional meta-analysis of primary studies from the included systematic reviews, excluding overlapping results. Data sources: Systematic searches were performed up to August 2023 in PubMed, Embase, and Cochrane Library. We included reviews that compared high-fidelity simulation against other learning strategies. Review methods: The risk of bias was assessed for each included systematic review (ROBIS tool) and primary study (RoB 2 or ROBINS-I as appropriate). Random-effect meta-analyses of meta-analyses were performed to estimate the pooled effects of high-fidelity simulation on knowledge and performance. Further random-effect meta-analyses of primary studies were conducted, with overlapping studies excluded (12 %). Subgroup analyses were performed to provide a more comprehensive analysis of the findings. Trim-and-fill analyses were conducted to adjust for potential publication bias. Results: Six systematic reviews were included and encompassed 133 primary studies (2767 and 3231 participants concerning performance and knowledge, respectively). The adjusted pooled effects for knowledge (SMD = 0.877, 95 % CI: 0.182 to 1.572) and performance (SMD = 0.738, 95 % CI: 0.466 to 1.010) closely aligned with those obtained from meta-analyzing the primary studies for knowledge (SMD = 0.980) and performance (SMD = 0.540), both showing high statistical heterogeneity. Traditional lectures represented the more common comparison. The subgroup analysis revealed significant differences in effect sizes across geographic locations, topics, types of control, and how interventions were reported. Conclusions: The results provide robust evidence supporting the integration of high-fidelity simulation into undergraduate nursing programs to enhance students\u27 knowledge and performance. The high reported heterogeneity may be attributed to variations in study contexts or methodologies. Future research should explore the optimal use of high-fidelity simulation in different educational and cultural contexts
Extreme Value Statistics Analysis of Process Defects in Additive Manufacturing Materials
Get PDFFatigue and fracture studies focused on process defects that occur in Additive Manufacturing (AM) materials have shown that defect populations possess features which are better measured with extreme value statistics (EVS). In AM alloys, defect occurrences increase with material volume. This situation facilitates the need to model process defects in the path of fatigue crack growth with suitable statistical tools, such as EVS, which is more cost-effective when compared to destructive experiments. The application of EVS on defect space features helps determine the difference in defects present on fracture surfaces. As the fatigue quality of any material depends on its extreme value flaws, we use the Block Maxima and Peak over Threshold methodologies to study the distribution of the features of the defects in AM Ti-6Al-4V and make recommendations for the distributions of best fit based on different scenarios with different ranges of complexity of different defect types
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