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Combined influences of Soret effect and horizontal magnetic field on dynamics of vertical convection in binary fluids

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Physical Review Fluids 2025年第5期10卷 053503-053503页

作者： Chao-Nan Zhang Lai-yun Zheng Xu-Long Li Juan-Juan Qin Bing-Xin Zhao Don Liu School of Mathematics and Statistics Ningxia University Yinchuan 750021 China School of Mechanical Engineering Ningxia University Yinchuan 750021 China Ningxia Key Laboratory of Interdisciplinary Mechanics and Scientific Computing Yinchuan 750021 China Ningxia Basic Science Research Center of Mathematics Yinchuan 750021 China Department of Mathematics and Mechanical Engineering Louisiana Tech University Ruston Louisiana 71270 USA

The influence of the Soret effect and a uniform magnetic field on the dynamic behavior of vertical convection are investigated using a high-accuracy numerical method. The effects of the Soret number (Sr, which quantifies the direction and magnitude of species migration relative to the temperature gradient; positive Sr denotes migration toward hotter regions, negative toward cooler regions) on flow, heat and mass transfer, and flow transition in the presence of a magnetic field are analyzed. The results reveal that, for a given magnetic field strength, positive and negative Soret effects have significantly different influences on heat and mass transfer as well as on flow structure. For a high Rayleigh number (Ra, vigor of the buoyancy) and a small magnetic field, the heat transfer initially decreases and then increases with increasing Soret number, while the mass transfer consistently decreases. In cases where temperature is dominant, heat transfer for Sr=−1 exceeds that for Sr=0 by 24%, while mass transfer with Sr=−1 exceeds that for Sr=0 by 85%. When concentration is dominant, heat transfer reaches a minimum at Sr=0, whereas mass transfer peaks at Sr=−1, corresponding to an approximate 71% increase over heat transfer at Sr=0, and around a 210% increase in mass transfer. Additionally, the flow exhibits greater complexity when the Soret number is negative than when it is positive. When the magnetic field is weak, the temperature-dominated flow changes from chaotic to periodic with increasing Soret number and eventually returns to a chaotic state. When the magnetic field is strong, the flow transitions from chaotic to periodic and finally to a steady state. The transition route involves two types of periodic flow: a simple periodic flow with an integer multiple of the fundamental frequency, and a complex periodic flow with multiple lower-frequency subharmonic waves. Furthermore, as the strength of the magnetic field increases, the flow in the rectangular enclosure is

关键词： Diffusion in solids

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Removing degeneracy and multimodality in gravitational wave source parameters

arXiv

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arXiv 2022年

作者： Roulet, Javier Olsen, Seth Mushkin, Jonathan Islam, Tousif Venumadhav, Tejaswi Zackay, Barak Zaldarriaga, Matias Kavli Institute for Theoretical Physics University of California at Santa Barbara Santa BarbaraCA93106 United States Department of Physics Princeton University PrincetonNJ08540 United States Department of Particle Physics & Astrophysics Weizmann Institute of Science Rehovot76100 Israel Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Department of Physics University of California at Santa Barbara Santa BarbaraCA93106 United States International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bangalore560089 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States

Quasicircular binary black hole mergers are described by 15 parameters, of which gravitational wave observations can typically constrain only ∼ 10 independent combinations to varying degree. In this work, we devise coordinates that remove correlations, and disentangle well- and poorlymeasured quantities. Additionally, we identify approximate discrete symmetries in the posterior as the primary cause of multimodality, and design a method to tackle this type of multimodality. The resulting posteriors have little structure and can be sampled efficiently and robustly. We provide a Python package for parameter estimation, cogwheel, that implements these methods together with other algorithms for accelerating the inference process. One of the coordinates we introduce is a spin azimuth that is measured remarkably well in several events. We suggest this might be a sensitive indicator of orbital precession, and we anticipate that it will shed light on the occurrence of spin-orbit misalignment in nature. Copyright © 2022, The Authors. All rights reserved.

关键词： Gravity waves

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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Surrogate model for gravitational wave signals from non-spinning, comparable- to large-mass-ratio black hole binaries built on black hole perturbation theory waveforms calibrated to numerical relativity

arXiv

引用

arXiv 2022年

作者： Islam, Tousif Field, Scott E. Hughes, Scott A. Khanna, Gaurav Varma, Vijay Giesler, Matthew Scheel, Mark A. Kidder, Lawrence E. Pfeiffer, Harald P. Department of Physics University of Massachusetts DartmouthMA02747 United States Department of Mathematics University of Massachusetts DartmouthMA02747 United States Center for Scientific Computing and Visualization Research University of Massachusetts DartmouthMA02747 United States Kavli Institute of Theoretical Physics University of California Santa Barbara Santa BarbaraCA93106 United States Department of Physics MIT Kavli Institute Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics University of Rhode Island KingstonRI02881 United States Am Mühlenberg 1 Potsdam14476 Germany Cornell Center for Astrophysics and Planetary Science Cornell University IthacaNY14853 United States Theoretical Astrophysics Walter Burke Institute for Theoretical Physics California Institute of Technology PasadenaCA91125 United States

We present a reduced-order surrogate model of gravitational waveforms from non-spinning binary black hole systems with comparable to large mass-ratio configurations. This surrogate model, BHPTNRSur1dq1e4, is trained on waveform data generated by point-particle black hole perturbation theory (ppBHPT) with mass ratios varying from 2.5 to 10,000. BHPTNRSur1dq1e4 extends an earlier waveform model, EMRISur1dq1e4, by using an updated transition-to-plunge model, covering longer durations up to 30,500 m1 (where m1 is the mass of the primary black hole), includes several more spherical harmonic modes up to = 10, and calibrates subdominant modes to numerical relativity (NR) data. In the comparable mass-ratio regime, including mass ratios as low as 2.5, the gravitational waveforms generated through ppBHPT agree surprisingly well with those from NR after this simple calibration step. We also compare our model to recent SXS and RIT NR simulations at mass ratios ranging from 15 to 32, and find the dominant quadrupolar modes agree to better than ≈ 10−3. We expect our model to be useful to study intermediate-mass-ratio binary systems in current and future gravitational-wave detectors. Copyright © 2022, The Authors. All rights reserved.

关键词： Waveform analysis

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Collective Neutrino Oscillations in Three Flavors on Qubit and Qutrit Processors

arXiv

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arXiv 2025年

作者： Spagnoli, Luca Goss, Noah Roggero, Alessandro Rrapaj, Ermal Cervia, Michael J. Patwardhan, Amol V. Naik, Ravi K. Balantekin, A. Baha Younis, Ed Santiago, David I. Siddiqi, Irfan Aldaihan, Sheakha Physics Department University of Trento Via Sommarive 14 TrentoI-38123 Italy INFN-TIFPA Trento Institute of Fundamental Physics and Applications Trento Italy Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics University of California BerkeleyCA94720 United States RIKEN iTHEMS Saitama Wako351-0198 Japan Department of Physics University of Washington SeattleWA98195 United States School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics New York Institute of Technology New YorkNY10023 United States Department of Physics University of Wisconsin MadisonWI53706 United States Department of Physics Gettysburg College GettysburgPA17325 United States

Collective neutrino flavor oscillations are of primary importance in understanding the dynamic evolution of core-collapse supernovae and subsequent terrestrial detection, but also among the most challenging aspects of numerical simulations. This situation is complicated by the quantum many-body nature of the problem due to neutrino-neutrino interactions which demands a quantum treatment. An additional complication is the presence of three flavors, which often is approximated by the electron flavor and a heavy lepton flavor. In this work, we provide both qubit and qutrit encodings for all three flavors, and develop optimized quantum circuits for the time evolution and analyze the Trotter error. We conclude our study with a hardware experiment of a system of two neutrinos with superconducting hardware: the IBM Torino device for qubits and AQT device for qutrits. We find that error mitigation greatly helps in obtaining a signal consistent with simulations. While hardware results are comparable at this stage, we expect the qutrit setup to be more convenient for large-scale simulations since it does not suffer from probability leakage into nonphsycial qubit space, unlike the qubit setup. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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Fourth-Order Conservative Non-Splitting Semi-Lagrangian Hermite Weno Schemes for Kinetic and Fluid Simulations

SSRN

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SSRN 2022年

作者： Zheng, Nanyi Cai, Xiaofeng Qiu, Jing-Mei Qiu, Jianxian School of Mathematical Sciences Xiamen University Fujian Xiamen361005 China Research Center for Mathematics Beijing Normal University Zhuhai519087 China BNU-HKBU United International College Zhuhai519087 China Department of Mathematical Sciences University of Delaware NewarkDE19716 United States School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China

We present fourth-order conservative non-splitting semi-Lagrangian (SL) Hermite essentially non-oscillatory (HWENO) schemes for linear transport equations with applications for nonlinear problems including the Vlasov-Poisson system, the guiding center Vlasov model, and the incompressible Euler equations in the vorticity-stream function formulation. The proposed SL HWENO schemes combine a weak formulation of the characteristic Galerkin method with two newly constructed HWENO reconstruction methods. Fourth-order accuracy is accomplished in both space and time under a non-splitting setting. Mass conservation naturally holds due to the weak formulation of the characteristic Galerkin method and the design of the HWENO reconstructions. We apply a positive-preserving limiter to maintain the positivity of numerical solutions when needed. Although the proposed SL framework allows us to take large time steps for improving computational efficiency, it also brings challenges to the spatial reconstruction technique;we construct two kind of novel HWENO reconstructions to fit the need for the proposed SL framework. Abundant benchmark tests are performed to verify the effectiveness of the proposed SL HWENO schemes. © 2022, The Authors. All rights reserved.

关键词： Computational efficiency

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Fourth-order conservative non-splitting semi-Lagrangian Hermite WENO schemes for kinetic and fluid simulations

arXiv

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arXiv 2022年

关键词： Computational efficiency

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cuFINUFFT: A load-balanced GPU library for general-purpose nonuniform FFTs

arXiv

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arXiv 2021年

作者： Shih, Yu-Hsuan Wright, Garrett Andén, Joakim Blaschke, Johannes Barnett, Alex H. Courant Institute of Mathematical Sciences New York University New YorkNY United States PACM Princeton University PrincetonNJ United States Department of Mathematics KTH Royal Institute of Technology Stockholm Sweden National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States Center for Computational Mathematics Flatiron Institute New YorkNY United States

Nonuniform fast Fourier transforms dominate the computational cost in many applications including image reconstruction and signal processing. We thus present a general-purpose GPU-based CUDA library for type 1 (nonuniform to uniform) and type 2 (uniform to nonuniform) transforms in dimensions 2 and 3, in single or double precision. It achieves high performance for a given user-requested accuracy, regardless of the distribution of nonuniform points, via cache-aware point reordering, and load-balanced blocked spreading in shared memory. At low accuracies, this gives on-GPU throughputs around 109 nonuniform points per second, and (even including host-device transfer) is typically 4-10× faster than the latest parallel CPU code FINUFFT (at 28 threads). It is competitive with two established GPU codes, being up to 90× faster at high accuracy and/or type 1 clustered point distributions. Finally we demonstrate a 5-12× speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10−12 accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphics processing unit

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Corrigendum to “Molecular subtypes classification of breast cancer in DCE-MRI using deep features” [Expert Syst. Appl. 236 (2024) 121371]

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Expert Systems with Applications 2025年 270卷

作者： Ali M. Hasan Noor K.N. Al-Waely Hadeel K. Aljobouri Hamid A. Jalab Rabha W. Ibrahim Farid Meziane College of Medicine Al-Nahrain University Baghdad Iraq Biomedical Engineering Department College of Engineering Al-Nahrain University Iraq Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Nile Street 64001 Thi-Qar Iraq Department of Computer Science and Mathematics Lebanese American University Beirut 1102 2801 Lebanon Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia/Mersin 10 Turkey Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

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Bridging HPC Communities through the Julia Programming Language

arXiv

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arXiv 2022年

作者： Churavy, Valentin Godoy, William F. Bauer, Carsten Ranocha, Hendrik Schlottke-Lakemper, Michael Räss, Ludovic Blaschke, Johannes Giordano, Mosè Schnetter, Erik Omlin, Samuel Vetter, Jeffrey S. Edelman, Alan Massachussetts Institute of Technology United States Oak Ridge National Laboratory United States Paderborn Center for Parallel Computing Paderborn University Germany Department of Mathematics University of Hamburg Germany Applied and Computational Mathematics RWTH Aachen University Germany University of Stuttgart Germany ETH Zurich Switzerland Birmensdorf Switzerland National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Centre for Advanced Research Computing University College London Gower Street LondonWC1E 6BT United Kingdom Perimeter Institute 31 Caroline St. N. WaterlooONN2L 2Y5 Canada Department of Physics and Astronomy University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States ETH Zurich Switzerland

The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.g., Python, R, MATLAB) are used for data analysis and interactive computing. Julia's rapid growth in language capabilities, package ecosystem, and community make it a promising universal language for HPC. This paper presents the views of a multidisciplinary group of researchers from academia, government, and industry that advocate for an HPC software development paradigm that emphasizes developer productivity, workflow portability, and low barriers for entry. We believe that the Julia programming language, its ecosystem, and its community provide modern and powerful capabilities that enable this group's objectives. Crucially, we believe that Julia can provide a feasible and less costly approach to programming scientific applications and workflows that target HPC facilities. In this work, we examine the current practice and role of Julia as a common, end-to-end programming model to address major challenges in scientific reproducibility, data-driven AI/machine learning, co-design and workflows, scalability and performance portability in heterogeneous computing, network communication, data management, and community education. As a result, the diversification of current investments to fulfill the needs of the upcoming decade is crucial as more supercomputing centers prepare for the exascale era. © 2022, CC BY.

关键词： Productivity

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