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arXiv 2024年

作者： Li, Lianxia Gruninger, Cole Lee, Jae H. Griffith, Boyce E. Department of Mathematics University of North Carolina Chapel HillNC United States Department of Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina School of Medicine Chapel HillNC United States McAllister Heart Institute University of North Carolina School of Medicine Chapel HillNC United States

In the class of immersed boundary (IB) methods, the choice of the regularized delta function plays a crucial role in transferring information between fluid and solid domains through interpolation and spreading operators. Most prior work using the IB method has used isotropic kernels that do not preserve the divergence-free condition of the velocity field, leading to loss of incompressibility of the solid when interpolating the Eulerian velocity to Lagrangian markers. To address this issue, in simulations involving large deformations of incompressible hyperelastic structures immersed in fluid, researchers often use a volumetric stabilization approach such as adding a volumetric energy term and using modified invariants in the constitutive model of the immersed structure. Composite B-spline (CBS) kernels offer an alternative approach by inherently maintaining the discrete divergence-free property. This work evaluates the performance of CBS kernels in terms of their volume conservation and accuracy, comparing them with several traditional isotropic kernel functions using a construction introduced by Peskin (referred to as IB kernels) and B-spline (BS) kernels. Benchmark tests include pressure-loaded and shear-dominated flows, such as an elastic band under differential pressure loads, a pressurized membrane, a compressed block, Cook’s membrane, a slanted channel flow, and a modified Turek-Hron problem. Additionally, we validate our methodology using a complex fluid-structure interaction model of bioprosthetic heart valve dynamics in a pulse duplicator. Results demonstrate that CBS kernels achieve superior volume conservation compared to conventional isotropic kernels, eliminating the need for additional volumetric stabilization techniques typically required to address instabilities arising from volume conservation errors. Further, CBS kernels achieve convergence on coarser fluid grids, while IB and BS kernels need finer grids for comparable accuracy. Unlike IB and BS ke

关键词： Delta functions

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Artificial neural network approach for turbulence models: A local framework

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Physical Review Fluids 2021年第8期6卷 084612-084612页

作者： Chenyue Xie Xiangming Xiong Jianchun Wang Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by the artificial neural network based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model in the flows over periodic hills. The correlation coefficients of the RANS unclosed terms predicted by the LANN model can be made larger than 0.96 in an a priori analysis, and the relative error of the unclosed terms can be made smaller than 18%. In an a posteriori analysis, detailed comparisons are made on the results of RANS simulations using the LANN, global artificial neural network (GANN), Spalart-Allmaras (SA), and shear stress transport (SST) k−ω models. It is shown that the LANN model performs better than the GANN, SA, and SST k−ω models in the prediction of the average velocity, wall-shear stress, and average pressure, which gives the results that are essentially indistinguishable from the direct numerical simulation data. The LANN model trained at low Reynolds number, Re=2800, can be directly applied to the cases of high Reynolds numbers, Re=5600, 10 595, 19 000, and 37 000, with accurate predictions. Furthermore, the LANN model is verified for flows over periodic hills with varying slopes. These results suggest that the LANN framework has a great potential to be applied to complex turbulent flows with curved walls.

关键词： Compressible boundary layers Turbulence Artificial neural networks

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI 41

Position: Bayesian Deep Learning is Needed in the Age of Lar...

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41st International Conference on Machine Learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Ni-Mo2C/γ-Al2O3 catalysts for syngas production in pyrolysis-dry reforming of plastics - the effect of amine nature on catalyst performance

SSRN

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SSRN 2024年

作者： Pawelczyk, Ewelina Frąckiewicz, Izabela Gil, Antonio Karczewski, Jakub Maciejewski, Michal E. Gębicki, Jacek Department of Process Engineering and Chemical Technology Faculty of Chemistry Gdansk University of Technology Narutowicza 11/12 St. Gdansk80-233 Poland INAMAT^2 Science Department Los Acebos Building Public University of Navarra Campus of Arrosadia PamplonaE-31006 Spain Faculty of Applied Physics and Mathematics Institute of Nanotechnology and Materials Engineering Gdansk University of Technology Narutowicza 11/12 St. Gdansk80-233 Poland Spain

Pyrolysis combined with dry reforming (PCDR) is eco-friendly technology for plastic waste management, allowing for sustainable production of syngas. Design of suitable catalysts with high performance and stability is crucial for its industrialization. In this work, Ni-Mo2C/γ-Al2O3 catalysts were synthesized using organic-inorganic precursors. Three types of amines (hexylamine - aliphatic linear, cyclohexylamine - aliphatic cyclic, aniline - aromatic) were used as carbon source in the precursor preparation to investigate their effect on catalyst physicochemical properties and its catalytic performance. Catalytic performance of the obtained catalysts was examined in PCDR of model waste plastic mixture. It was revealed that catalytic activity of catalysts prepared with aliphatic amines was higher compared to aromatic aniline, amine commonly used for carbide synthesis. The differences in catalyst properties, which affected their catalytic activity were characterized using N2-adsorption at -196 ºC, XRD, H2-TPR, SEM, TG/DTG and CO2-adsorption methods. A correlation was observed between amine molecule complexity and Mo2C crystallite size, significantly affecting hydrogen generation. The less complex the amine, the smaller the crystallite and the increased hydrogen production. Moreover, the use of aliphatic amines resulted in the stronger Ni-support interactions, the increased number of more favorable, larger mesopores as well as the presence of the metallic Mo phase and the lack of unbound carbon in contrast to the catalyst obtained using aniline. The catalyst synthesized using hexylamine increased H2 yield by more than tenfold, syngas yield by more than threefold, and the H2/CO ratio by fivefold, which offers an opportunity for future industrialization of PCDR. © 2024, The Authors. All rights reserved.

关键词： Crystallite size

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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The development of simple microcontroller-based QCM with some treatment on the crystal oscillator surface 5

The development of simple microcontroller-based QCM with som...

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5th Padjadjaran International Physics Symposium, PIPS 2022

作者： Faizal, Ferry Maulana, Mochamad Irfan Wuled Lenggoro, I. Panatarani, Camellia Made Joni, I. Functional Nanopowder University Center of Excellence Universitas Padjadjaran Jl. Bandung-Sumedang KM 21 Jatinangor Sumedang45363 Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Padjadjaran Jl. Bandung-Sumedang KM 21 Jatinangor Sumedang45363 Indonesia Departmen of Applied Physics and Chemical Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Koganei Tokyo184-8588 Japan

The low-cost quartz crystal microbalance (QCM) instrument with the microcontroller as the basis of the embedded system of the frequency counter was implemented. The system uses a quartz crystal transducer from the crystal oscillator component. The frequency shifting was observed and analysed in association with some experimental tests. The experiments such as ethanol evaporation, water vapor in the humidity chamber, and coating the surface with TiO2 particles had been conducted. The developed QCM instrument has shown the ability to measure the frequencies in response to the addition or reduction of mass on the surface of the crystal oscillator. These responses are caused by evaporation, humid condition, and deposited particle on the quartz crystal surface. The QCM was varied into two initial frequencies, 5 and 10 MHz. The 5 MHz system represents the proper "in range"frequency counters, inside a counting limit of the microcontroller up to 5.6 MHz. On the other side, the 10 MHz is an experimental development system. Both types utilise a D flip-flop frequency divider that made a microcontroller-based frequency counter and can count the frequency twice the limit. The more stable 10 MHz have shown promising potential application related to the surface interaction with other material and can hold thicker deposition on its surface with a better stability response than the lower frequency counterpart. © Published under licence by IOP Publishing Ltd.

关键词： Quartz crystal microbalances

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Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Films

Outcoupling Hyperbolic Modes from Aligned Carbon Nanotube Fi...

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Jerome, Bryant Prasad, Ciril S. Doumani, Jacques Dewey, Oliver S. Baydin, Andrey Pasquali, Matteo Kono, Junichiro Gao, Weilu Alabastri, Alessandro Naik, Gururaj V. Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemical and Biomolecular Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Chemistry Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering University of Utah Salt Lake CityUT84112 United States

Aligned carbon nanotube films make an excellent hyperbolic material platform in the infrared. Here, we experimentally demonstrate the presence of high-k modes in aligned carbon nanotube films and outcouple them to fre... 详细信息

ISBN: (纸本)9781557528209

关键词： Carbon films

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Amplification of enzymatic and microbial fuel cells using organic electrochemical transistors

Device

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Device 2025年

作者： Saxena, Ravindra Zhang, Xu Cai, Rong Suggs, Olivia Bruggeman, Chase Hickey, David P. Ajo-Franklin, Caroline M. Verduzco, Rafael Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston 77005 TX United States Department of Biosciences Rice University Houston 77005 TX United States Department of Chemical Engineering and Materials Science Michigan State University East Lansing 48824 MI United States Department of Bioengineering Rice University Houston 77005 TX United States Department of Chemical and Biomolecular Engineering Rice University Houston 77005 TX United States Rice Advanced Materials Institute Rice University Houston 77005 TX United States

Organic electrochemical transistors (OECTs) are thin-film devices operated in aqueous and biological environments for sensing chemicals and biomolecules. However, most sensor configurations involve introducing the target biomolecule directly in the OECT device. This has drawbacks because it may not be possible to have an electrolyte compatible with the target biomolecule or an environment optimal for the OECT. Here, we demonstrate a general and modular approach to building electrochemical sensors by coupling OECTs electronically with either an enzymatic fuel cell (EFC) or microbial fuel cell (MFC). We demonstrate that this modular approach can amplify currents by three orders of magnitude and enhance the signal-to-noise ratio. We also show that the power generated by the fuel cell can help tune the sensor's response for different applications. This work demonstrates a simple and versatile approach for amplifying currents from MFCs and EFCs useful for the development of bioelectronic sensors. © 2025 The Authors

关键词： amplification biosensors DTI-3: Develop electro-active bacteria enzymatic fuel cells microbial fuel cells organic electrochemical transistors power

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Upcycling nutrients derived from food waste via microalgae cultivation: A review on impacts on cellular compounds, economy and environment analyses for achieving circular bioeconomy

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Biochemical engineering Journal 2024年 211卷

作者： Ramandani, Adityas Agung Sun, Yi-Ming Lan, John Chi-Wei Chen, Wei-Hsin Chang, Jo-Shu Rachmadona, Nova Lim, Jun Wei Khoo, Kuan Shiong Algae Bioseparation Research Laboratory Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Taiwan Biorefinery and Bioprocess Engineering Laboratory Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan Taiwan Department of Aeronautics and Astronautics National Cheng Kung University Tainan701 Taiwan Research Center for Smart Sustainable Circular Economy Tunghai University Taichung407 Taiwan Department of Mechanical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Chemical Engineering National Cheng Kung University Tainan Taiwan Department of Chemical and Materials Engineering Tunghai University Taichung407 Taiwan Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Padjadjaran West Java Indonesia HICoE-Centre for Biofuel and Biochemical Research Institute of Sustainable Energy and Resources Department of Fundamental and Applied Sciences Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Darul Ridzuan Malaysia Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Centre for Herbal Pharmacology and Environmental Sustainability Chettinad Hospital and Research Institute Chettinad Academy of Research and Education Tamil Nadu Kelambakkam603103 India

Food waste generation is an unavoidable issue due to the increase in the human population and economic growth worldwide. Therefore, it is crucial to explore various eco-friendly and sustainable waste management practices to reduce these environmental impacts while creating value-added products derived from these food waste resources. The cultivation of microalgae can contribute to the global carbon neutrality process and help reduce the emission of greenhouse gases into the environment. However, several concerns such as food safety, quality, social acceptability, and the perception of using food waste to cultivate microalgae remain uncertain in the current food waste management. This review provides a comprehensive assessment of the biochemical mechanisms involved in the metabolization process of microalgae, assimilating organic compounds derived from food waste sources and emphasizing the importance of understanding these complex processes. This review also explores the intricate relationships among the variations in food waste composition, hydrolysis processes, and nutrient bio-accessibility during cultivation of microalgae. Furthermore, we conducted a thorough evaluation of techno-economic analyses and life cycle assessments from various literature sources, highlighting several key elements such as the economic feasibility and environmental impacts of producing microalgae biomass from food waste. Finally, this review summarizes the future outlook and way forward in upcycling food waste with microalgae biotechnology by providing several recommendations for improvement. © 2024 Elsevier B.V.

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Preconditioning hyperbolic 5-equation two-phase model in intermittent pipe flow simulations

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AIP Conference Proceedings 2024年第1期3094卷

作者： Farhad Nikfarjam Hamid Reza Anbarlooei Daniel O. A. Cruz Department of Applied Mathematics Institute of Mathematics Federal University of Rio de Janeiro RJ Brazil Mechanical Engineering Program Institute of Mathematics Federal University of Rio de Janeiro RJ Brazil

The 5-equation unconditionally hyperbolic formulation of the two-fluid model solved by Roe’s method is studied. It is well-known that the 5-equation upwind methods experience loss of accuracy at low Mach numbers. To address this issue, a preconditioning technique is developed to control the numerical dissipation at low velocities. The resulting method is evaluated on different test cases, and is observed that the preconditioned first order Roe method limits the numerical dissipation, comparable to the second order method without preconditioning. The method is used to study 1D intermittent two-phase flow in pipelines. Our method successfully captures the slugs and predicts the statistics of the flow accurately, for the cases where other methods could not capture a slug flow at all or have serious shortcomings.

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