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Mechanism for ice growth on the surface of a spherical water droplet

Physical Review B 2025年第7期111卷 075426-075426页

作者： Yang Li Prachi Parashar Iver Brevik Clas Persson O. I. Malyi Mathias Boström School of Physics and Materials Science Nanchang University Nanchang 330031 China Institute of Space Science and Technology Nanchang University Nanchang 330031 China John A. Logan College Carterville Illinois 62918 USA Department of Energy and Process Engineering Norwegian University of Science and Technology NO-7491 Trondheim Norway Department of Materials Science and Engineering KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Qingyuan Innovation Laboratory Quanzhou 362801 China Centre of Excellence ENSEMBLE3 Sp. z o. o. Wolczynska Str. 133 01-919 Warsaw Poland Chemical and Biological Systems Simulation Lab Centre of New Technologies University of Warsaw Banacha 2C 02-097 Warsaw Poland

The formation and growth of ice particles, particularly on the surfaces of spherical water droplets, bear profound implications for localized weather systems and global climate. Herein, we develop a theoretical framework for ice nucleation on minuscule water droplets, establishing that 10–5000nm droplets can considerably increase in volume, making a substantial contribution to ice formation within mist, fog, or even cloud systems. We reveal that the Casimir-Lifshitz (van der Waals) interaction within these systems is robust enough to stimulate both water and ice growth on the surfaces of ice-cold spherical water droplets. The significant impacts and possible detectable phenomena from the curvature are demonstrated.

关键词： Van der Waals forces

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A Multi-objective optimization model for simulation-based acquisition 7

A Multi-objective optimization model for simulation-based ac...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Zhang, Xue Xue, Junjie Zhou, Junhua Shi, Guoqiang Yu, Yan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Simulation Center Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781728170862

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed. © 2020 IEEE.

关键词： Multiobjective optimization

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Multiple-Gpu Accelerated High-Order Gas-Kinetic Scheme for Direct Numerical simulation of Compressible Turbulence

SSRN

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SSRN 2022年

作者： Wang, Yuhang Cao, Guiyu Pan, Liang Laboratory of Mathematics and Complex Systems School of Mathematical Sciences Beijing Normal University Beijing China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen China

High-order gas-kinetic scheme (HGKS) has become a workable tool for the direct numerical simulation (DNS) of turbulence. In this paper, to accelerate the computation, HGKS is implemented with the graphical processing unit (GPU) using the compute unified device architecture (CUDA). Due to the limited available memory size, thecomputational scale is constrained by single GPU. To conduct the much large-scale DNS of turbulence, HGKS also be further upgraded with multiple GPUs using message passing interface (MPI) and CUDA architecture. The benchmark cases for compressible turbulence, including Taylor-Green vortex and turbulent channel flows, are presented to assess the numerical performance of HGKS with Nvidia TITAN RTX and Tesla V100 GPUs. For single-GPU computation, compared with the parallel central processing unit (CPU) code running on theIntel Core i7-9700 with open multi-processing (OpenMP) directives, 7x speedup is achieved by TITAN RTX and 16x speedup is achieved by Tesla V100. For multiple-GPU computation, multiple-GPU accelerated HGKS code scales properly with the increasing number of GPU. The computational time of parallel CPU code running on $1024$ Intel Xeon E5-2692 cores with MPI is approximately $3$ times longer than that of GPU code using $8$ Tesla V100 GPUs with MPI and CUDA. Numerical results confirm the excellent performance of multiple-GPUaccelerated HGKS for large-scale DNS of turbulence. Alongside the footprint in reduction of loading and writing pressure of GPU memory, HGKS in GPU is also compiled with FP32 precision to evaluate the effect of number formats precision. Reasonably, compared to the computation with FP64 precision, the efficiency is improved and the memory cost is reduced with FP32 precision. Meanwhile, the differences in accuracy for statistical turbulent quantities appear. For turbulent channel flows, difference in long-time statistical turbulent quantities is acceptable between FP32 and FP64 precision solutions. While the obvious d

关键词： Direct numerical simulation

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Multiple-GPU accelerated high-order gas-kinetic scheme for direct numerical simulation of compressible turbulence

arXiv

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arXiv 2022年

High-order gas-kinetic scheme (HGKS) has become a workable tool for the direct numerical simulation (DNS) of turbulence. In this paper, to accelerate the computation, HGKS is implemented with the graphical processing unit (GPU) using the compute unified device architecture (CUDA). Due to the limited available memory size, the computational scale is constrained by single GPU. To conduct the much large-scale DNS of turbulence, HGKS also be further upgraded with multiple GPUs using message passing interface (MPI) and CUDA architecture. The benchmark cases for compressible turbulence, including Taylor-Green vortex and turbulent channel flows, are presented to assess the numerical performance of HGKS with Nvidia TITAN RTX and Tesla V100 GPUs. For single-GPU computation, compared with the parallel central processing unit (CPU) code running on the Intel Core i7-9700 with open multi-processing (OpenMP) directives, 7x speedup is achieved by TITAN RTX and 16x speedup is achieved by Tesla V100. For multiple-GPU computation, multiple-GPU accelerated HGKS code scales properly with the increasing number of GPU. The computational time of parallel CPU code running on 1024 Intel Xeon E5-2692 cores with MPI is approximately 3 times longer than that of GPU code using 8 Tesla V100 GPUs with MPI and CUDA. Numerical results confirm the excellent performance of multiple-GPU accelerated HGKS for large-scale DNS of turbulence. Alongside the footprint in reduction of loading and writing pressure of GPU memory, HGKS in GPU is also compiled with FP32 precision to evaluate the effect of number formats precision. Reasonably, compared to the computation with FP64 precision, the efficiency is improved and the memory cost is reduced with FP32 precision. Meanwhile, the differences in accuracy for statistical turbulent quantities appear. For turbulent channel flows, difference in long-time statistical turbulent quantities is acceptable between FP32 and FP64 precision solutions. While the obvious disc

关键词： Direct numerical simulation

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Ru-Catalyzed Chemoselective Methoxycarbonylation of Methyl Pentenoate: An Assistant Role of Chloride Anions

SSRN

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SSRN 2023年

作者： Zhang, Shu Sun, Rui Guan, Pengxin Wang, Yaofeng Wang, Ling Zhu, Yanli Han, Lijun Xin, Jiayu Beijing Key Laboratory of Ionic Liquids Clean Process CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences Beijing100190 China School of Chemical Engineering University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

Alkoxycarbonylation of alkene has been an important basis in modern chemical industry. As the cheapest noble metal, the potential of ruthenium furnishes significant innovations in the homogeneous catalysis. However, the competitive hydrogenation of alkenes to alkanes represents the most challenge in the alkoxycarbonylation of alkene. Herein, the chloride anions-assisted Ru-catalyzed methoxycarbonylation of methyl pentenoate was *** chemoselectivity of methoxycarbonylation can be improved by controlling the ratio of LiCl/Ru3(CO)*** experiments show that the activation energy of hydrogenation is increased due to the change of the active species by increasing the amount of LiCl, which hinders the hydrogenation of methyl pentenoate. The di-methoxycarbonylation of 1,7-octadiene indicates that this methodology has the potential to develop the Ru-catalyzed di-methoxycarbonylation of diene substrates. © 2023, The Authors. All rights reserved.

关键词： Olefins

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Effect of New Dual-Credit Policy on Electric Vehicle Diffusion in China

SSRN

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SSRN 2025年

作者： Zhao, Dan Wang, Jian Li, Wen-Wu Huang, Shuai Tang, Jin-Huan School of Management Science and Engineering Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China School of Economics and Management Shenyang Aerospace University Shenyang110136 China School of Business Qingdao University Qingdao266071 China

The impact of new dual-credit policy on electric vehicle (EV) diffusion is examined through complex network evolutionary game theory, we find that merely tightening new energy vehicle (NEV) credit rule and corporate average fuel consumption (CAFC) credit rule may not necessarily benefit the growth of auto industry. This paper further discusses the influence of different update strategies on EV diffusion. The results indicate that with the increase of NEV credit accounting coefficient, the upper limit of threshold interval of NEV credit trading price remains unchanged, while lower limit decreases. With the decrease of ratio of standard CAFC to target CAFC or the increase of requirement of NEV credits for fuel vehicle manufacturer, the upper and lower limits of threshold interval of NEV credit price are reduced. With the increasing network scale, the driving effect of Fermi function is gradually superior to EWA algorithm. © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Controlled light scattering of a single nanoparticle by wave-front shaping

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Physical Review A 2022年第6期106卷 063502-063502页

作者： Peilong Hong Willem L. Vos School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China The MOE Key Laboratory of Weak-Light Nonlinear Photonics Nankai University Tianjin 300457 China Complex Photonic Systems MESA+ Institute for Nanotechnology Faculty of Science and Technology University of Twente P.O. Box 217 7500 AE Enschede The Netherlands

Controlling light scattering by nanoparticles is fundamentally important for the understanding and the control of light with photonic nanostructures, as well as for nanoparticle scattering itself, including Mie scattering. Here, we theoretically and numerically investigate the possibility to manipulate nanoparticle scattering by wave-front shaping that was initially developed to control light scattered by large numbers of nanoparticles in nanophotonic media. By employing a scattering matrix analysis, we find that even a single nanoparticle supports multiple strongly scattering eigenchannels, suggesting wave-front shaping as a promising tool to manipulate scattered light of a single nanoparticle. By sending in shaped wave fronts, we selectively excite eigenchannels, as is apparent from the distinct field distributions. These scattering eigenchannels are related to different resonant leaky modes of the scatterer, that reveal remarkable localized “hot spots” where the field is substantially enhanced. Moreover, we investigate the backscattered spectra; we send in wave fronts relevant for a particular eigenchannel, and observe that the backscattered spectrum reveals not only the excited channel but also several others. This result points to the existence of short- and long-range spectral correlations for an eigenchannel. Our paper offers a flexible tool to manipulate light scattering of a single nanoparticle, and thus opens possibilities to control field patterns and light-matter interactions in a nanoparticle, as well as to explore new features of nanoparticle scattering such as the spectral correlation and temporal response of light scattered by nanoscatterers, including Mie spheres.

关键词： Coherent control Mie scattering Scattering theory

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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

arXiv

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arXiv 2023年

作者： Qu, Jingxiang Liu, Ryan Wen Zhao, Chenjie Guo, Yu Xu, Sendren Sheng-Dong Zhu, Fenghua Lv, Yisheng The Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology Wuhan430063 China The State Key Laboratory of Maritime Technology and Safety Wuhan430063 China The Automation and Control Center The Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Composite spiral waves in discrete-time systems

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Physical Review E 2023年第4期108卷 044205-044205页

作者： Xin Wang Jian Gao Changgui Gu Daiyong Wu Xinshuang Liu Chuansheng Shen International Joint Research Center of Simulation and Control for Population Ecology of Yangtze River in Anhui Anqing Normal University Anqing 246011 People's Republic of China Key Laboratory of Modeling Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes Anqing Normal University Anqing 246011 People's Republic of China School of Mathematics and Physics Anqing Normal University Anqing 246011 People's Republic of China Business School University of Shanghai for Science and Technology Shanghai 200093 People's Republic of China

Spiral waves are a type of typical pattern in open reaction-diffusion systems far from thermodynamic equilibrium. The study of spiral waves has attracted great interest because of its nonlinear characteristics and extensive applications. However, the study of spiral waves has been confined to continuous-time systems, while spiral waves in discrete-time systems have been rarely reported. In recent years, discrete-time models have been widely studied in ecology because of their appropriateness to systems with nonoverlapping generations and other factors. Therefore, spiral waves in discrete-time systems need to be studied. Here, we investigated a novel type of spiral wave called a composite spiral wave in a discrete-time predator-pest model, and we revealed the formation mechanism. To explain the observed phenomena, we defined and quantified a move state effect of multiperiod states caused by the coupling of adjacent stable multiperiod orbits, which is strictly consistent with the numerical results. The other move state effect is caused by an unstable focus, which is the state of the local points at the spiral center. The combined effect of these two influences can lead to rich dynamical behaviors of spiral waves, and the specific structure of the composite spiral waves is the result of the competition of the two effects in opposite directions. Our findings shed light on the dynamics of spiral waves in discrete-time systems, and they may guide the prediction and control of pests in deciduous forests.

关键词： Pattern formation Spiral & scroll waves

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