检索结果-内蒙古大学图书馆

Evaluation of the Mechanisms Acting on the Atlantic Meridional Overturning Circulation in CESM2 for the 1pctCO_(2) Experiment

学校读者我要写书评

暂无评论

Journal of Atmospheric Science Research 2024年第1期7卷 40-58页

作者： Lívia Sancho Elisa Passos Marcio Cataldi Luiz Paulo de Freitas Assad Luiz Landau Laboratory of Computational Methods in Engineering Civil Engineering ProgramAlberto Luiz Coimbra Institute for Graduate Studies and Research in EngineeringFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil Civil Engineering Program Alberto Luiz Coimbra Institute for Graduate Studies and Engineering ResearchFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil Physical Oceanography Laboratory Department of Physical OceanographyFaculty of OceanographyRio de Janeiro State UniversityRio de Janeiro20559-900Brazil Climate System Monitoring and Modeling Laboratory Water Resources and Environmental EngineeringFluminense Federal University24020-140Brazil Regional Atmospheric Modeling Group(MAR) Physics of the EarthDepartment of PhysicsRegional Campus of International Excellence(CEIR)“Campus Mare Nostrum”University of MurciaMurcia30100Spain Department of Meteorology Institute of GeosciencesFederal University of Rio de JaneiroRio de Janeiro21941-617Brazil

The Atlantic Meridional Overturning Circulation(AMOC)is a crucial component of the Earth’s climate system due to its fundamental role in heat distribution,carbon and oxygen transport,and the *** climate components,such as the atmosphere and sea ice,influence the *** the physical mechanisms of those interactions is paramount to increasing knowledge about AMOC’s *** this study,the authors used outputs from the Community Earth System Model version 2 and observational data to investigate changes in theAMOC and the associated physical *** DECK experiments were evaluated:piControl and 1pctCO_(2),with an annual increase of 1%of atmospheric CO_(2).The analysis revealed a significant decrease in the AMOC,associated with changes in mixed layer depth and buoyancy in high latitudes of the North Atlantic,resulting in the shutdown of deep convection and potentially affecting the formation of North Atlantic Deep Water and Antarctic Bottom Water.A vital aspect observed in this study is the association between increased runoff and reduced water evaporation,giving rise to a positive feedback ***,the rates of freshwater spreading have intensified during this period,which could lead to an accelerated disruption of the AMOC beyond the projections of existing models.

关键词： AMOC Meridional cell Climate change Deep circulation CESM2 results CMIP6

Discovery of High-Temperature Superconducting Ternary Hydrides via Deep Learning

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Wang, Xiaoyang Zhang, Chengqian Wang, Zhenyu Liu, Hanyu Lv, Jian Wang, Han Weinan, E. Ma, Yanming National Key Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China HEDPS CAPT College of Engineering Peking University Beijing100871 China AI for Science Institute Beijing100080 China Center for Machine Learning Research Peking University Beijing100871 China School of Mathematical Sciences Peking University Beijing100871 China

The discovery of novel high-temperature superconductor materials holds transformative potential for a wide array of technological applications. However, the combinatorially vast chemical and configurational search space poses a significant bottleneck for both experimental and theoretical investigations. In this study, we employ the design of high-temperature ternary superhydride superconductors as a representative case to demonstrate how this challenge can be well addressed through a deep-learning-driven theoretical framework. This framework integrates high-throughput crystal structure exploration, physics-informed screening, and accurate prediction of superconducting critical temperatures. Our approach enabled the exploration of approximately 36 million ternary hydride structures across a chemical space of 29 elements, leading to the identification of 144 potential high-Tc superconductors with predicted Tc > 200 K and superior thermodynamic stability at 200 GPa. Among these, 129 compounds spanning 27 novel structural prototypes are reported for the first time, representing a significant expansion of the known structural landscape for hydride superconductors. This work not only greatly expands the known repertoire of high-Tc hydride superconductors but also establishes a scalable and efficient methodology for navigating the complex landscape of multinary hydrides. © 2025, CC BY.

关键词： High temperature superconductors

A kinetic-based regularization method for data science applications

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Ganguly, Abhisek Gabbana, Alessandro Rao, Vybhav Succi, Sauro Ansumali, Santosh Engineering Mechanics Unit Jawaharlal Nehru Centre for Advanced Scientific Research Jakkur Bengaluru560064 India CCS-2 Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87545 United States Los Alamos National Laboratory Los AlamosNM87545 United States AI and Robotics Technology Park Indian Institute of Science Bengaluru560012 India Fondazione Instituto Italiano di Tecnologia Center for Life Nano-Neuroscience at la Sapienza Viale Regina Elena 291 Roma00161 Italy

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets. © 2025, CC BY.

关键词： Interpolation

Physics-driven Explosions of Stripped High-Mass Stars: Synthetic Light Curves and Spectra of Stripped-Envelope Supernovae with Broad Lightcurves

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lu, Jing Barker, Brandon L. Goldberg, Jared Kerzendorf, Wolfgang E. Modjaz, Maryam Couch, Sean M. Shields, Joshua V. Fullard, Andrew G. Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87545 United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87545 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI48824 United States Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Joint Institute for Nuclear Astrophysics Center for the Evolution of the Elements Michigan State University East LansingMI48824 United States National Superconducting Cyclotron Laboratory Michigan State University East LansingMI48824 United States

Stripped-envelope supernovae (SESNe) represent a significant fraction of core-collapse supernovae, arising from massive stars that have shed their hydrogen and, in some cases, helium envelopes. The origins and explosion mechanisms of SESNe remain a topic of active investigation. In this work, we employ radiative-transfer simulations to model the light curves and spectra of a set of explosions of single, solar-metallicity, massive Wolf-Rayet (WR) stars with ejecta masses ranging from 4 to 11 M☉, that were computed from a turbulence-aided and neutrino-driven explosion mechanism. We analyze these synthetic observables to explore the impact of varying ejecta mass and helium content on observable features. We find that the light curve shape of these progenitors with high ejecta masses is consistent with observed SESNe with broad light curves but not the peak luminosities. The commonly used analytic formula based on rising bolometric light curves overestimates the ejecta mass of these high-initial-mass progenitor explosions by a factor up to 2.6. In contrast, the calibrated method by Haynie et al., which relies on late-time decay tails, reduces uncertainties to an average of 20% within the calibrated ejecta mass range. Spectroscopically, the He I 1.083 µm line remains prominent even in models with as little as 0.02 M☉ of helium. However, the strength of the optical He I lines is not directly proportional to the helium mass but instead depends on a complex interplay of factors such as 56Ni distribution, composition, and radiation field. Thus, producing realistic helium features requires detailed radiative transfer simulations for each new hydrodynamic model. © 2024, CC BY.

关键词： Giant stars

Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dhulipala, Somayajulu L.N. Shields, Michael D. Chakroborty, Promit Jiang, Wen Spencer, Benjamin W. Hales, Jason D. Labouré, Vincent M. Prince, Zachary M. Bolisetti, Chandrakanth Che, Yifeng Computational Mechanics and Materials Idaho National Laboratory Idaho FallsID83402 United States Department of Civil and Systems Engineering Johns Hopkins University BaltimoreMD21218 United States Reactor Physics Methods and Analysis Idaho National Laboratory Idaho FallsID83402 United States Advanced Reactor Technology and Design Idaho National Laboratory Idaho FallsID83402 United States

Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models consistently called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time. © 2022, CC BY-NC-SA.

关键词： Kriging

Efficient quadrature rules for finite element discretizations of nonlocal equations

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Aulisa, Eugenio Capodaglio, Giacomo Chierici, Andrea D’Elia, Marta Department of Mathematics and Statistics Texas Tech University TX United States Computational Physics and Methods Group Los Alamos National Laboratory NM United States Department of Industrial Engineering University of Bologna Bologna Italy Computational Science and Analysis Sandia National Laboratories CA United States

In this paper we design efficient quadrature rules for finite element discretizations of nonlocal diffusion problems with compactly supported kernel functions. Two of the main challenges in nonlocal modeling and simulations are the prohibitive computational cost and the nontrivial implementation of discretization schemes, especially in three-dimensional settings. In this work we circumvent both challenges by introducing a parametrized mollifying function that improves the regularity of the integrand, utilizing an adaptive integration technique, and exploiting parallelization. We first show that the "mollified" solution converges to the exact one as the mollifying parameter vanishes, then we illustrate the consistency and accuracy of the proposed method on several two- and three-dimensional test cases. Furthermore, we demonstrate the good scaling properties of the parallel implementation of the adaptive algorithm and we compare the proposed method with recently developed techniques for efficient finite element assembly. Copyright © 2021, The Authors. All rights reserved.

关键词： Adaptive algorithms

Predictive Scale-Bridging Simulations through Active Learning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Karra, Satish Mehana, Mohamed Lubbers, Nicholas Chen, Yu Diaw, Abdourahmane Santos, Javier E. Pachalieva, Aleksandra Pavel, Robert S. Haack, Jeffrey R. McKerns, Michael Junghans, Christoph Kang, Qinjun Livescu, Daniel Germann, Timothy C. Viswanathan, Hari S. Computational Earth Science Group Earth and Environmental Sciences Division Los Alamos National Laboratory NM87545 United States Environmental Molecular Sciences Laboratory Pacific Northwest National Laboratory RichlandWA99354 United States Information Sciences Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzen518055 China RadiaSoft LLC 6525 Gunpark Dr. Suite 370-411 BoulderCO80301 United States Applied Computer Science Group Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los AlamosNM87545 United States Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87545 United States Physics and Chemistry of Materials Group Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models. © 2022, CC BY.

关键词： Molecular dynamics

Simulations of Interacting Binary Systems - Pathways to Radio Bright GRB Progenitors

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hernandez, Angel Cheng, Roseanne M. Lloyd-Ronning, Nicole M. Fields, C.E. Department of Physics University of Colorado at Boulder BoulderCO80309-0390 United States CCS-2 Computational Physics and Methods Los Alamos National Laboratory Los AlamosNM87544 United States T-3 Fluid Dynamics and Solid Mechanics Los Alamos National Laboratory Los AlamosNM87544 United States Department of Physics and Astronomy University of New Mexico AlbuquerqueNM87106 United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM87544 United States Department of Math Science and Engineering University of New Mexico Los AlamosNM87544 United States Steward Observatory University of Arizona TucsonAZ85721 United States

Although the association of gamma-ray bursts with massive stellar death is on firm footing, the nature of the progenitor system and the key ingredients required for a massive star to produce a gamma-ray burst remain open questions. Here, we investigate the evolution of a massive star with a closely orbiting black hole using the stellar evolution code MESA. In particular, we examine how the companion influences the angular momentum of the massive star as it evolves over its lifetime under a range of orbital periods where the tidal effects are significant. We consider dependence on stellar metallicity, mass ratio, initial stellar spin, as well as accretion and dynamo prescriptions while tracking the mass loss and angular momentum in the simulations. We conclude that massive star-black hole binary systems with low metallicity massive stars under an initial orbital period range of ∼20-50 days may be viable GRB progenitors with post-collapse black hole masses of 5-10 M☉ and a black hole spin parameter ≥ 0.5. © 2024, CC BY-SA.

关键词： Gamma rays

Optimization of a parametric composite wing using engineering sketch pad and NASTRAN 34

学校读者我要写书评

暂无评论

Optimization of a parametric composite wing using engineerin...

34th Technical Conference of the American Society for Composites, ASC 2019

作者： Joe, John Aparicio, Christian Dalir, Hamid Dannenhoffer, John F. Advanced Composite Structures Engineering Laboratory Purdue School of Engineering and Technology IndianapolisIN46202 United States Aerospace Computational Methods Laboratory Syracuse University SyracuseNY13244 United States

ISBN: (纸本)9781605956022

Metallic aircraft wings are usually not optimized to their fullest potential due to shortage of development time. This problem gets compounded when it comes to optimization of composite wings. With roughly $1000 worth of potential fuel savings per pound of weight reduction over the operational life of an aircraft, airlines are trying to minimize the weight of aircraft structures. In this study, a multi-level optimization strategy to optimize the weight of a composite transport aircraft wing is proposed. The loads on the wing are applied from a stick model as is done in the industry. A parametric software called engineering Sketch Pad (ESP) is used for the geometry creation and Nastran is used to optimize the model. The part is assumed to initially have some arbitrary number of composite super plies. Super plies are a concept consisting of a set of plies all arranged in the same direction. The thickness and orientation angles of the super plies are optimized. Then, each ply undergoes topometry optimization to obtain the areas of each super ply taking the least load so that it could be cut and removed. Each of the super plies are then optimized for the thickness and orientation angles of the sub plies. The work presented on this paper is part of a project done for Air Force Research laboratory (AFRL) connecting the parametric geometry modeler (ESP) with the finite element solver (Nastran). © 2019 by DEStech Publications, Inc. and American Society for Composites. All rights reserved.

关键词： Wings