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Oriented mesoporous nanofibers show nature's wisdom

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National Science Review 2024年第6期11卷 90-91页

作者： Yonghui deng Department of Chemistry State Key Laboratory of Molecular Engineering of Polymers Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Collaborative Innovation Center of Chemistry for Energy Material (iChEM) Fudan University State Key Lab of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences

Electrodes assembled with conventional nanoparticulate materials in electrochemical energy devices usually have a random and torturous porous system. To meet ever-increasing demands for higher volumetric energy density at packagelevel, packing more active materials in a limited volume becomes increasingly difficult since transportation of reactants(e.g. electrolyte) and charges through high-tortuosity channels/pathways in thick electrodes meets large impedance[1].

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Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 719-729页

作者： Qing Ma Changhong deng the School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity *** existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization *** paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation *** cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage *** DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC *** results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.

关键词： Volt-var control convexity conversion convex deep learning power flow

Non-intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 947-957页

作者： Xiaoyang Ma Diwen Zheng Xiaoyong deng Ying Wang Dawei deng Wei Li the College of Electrical Engineering Sichuan UniversityChengdu 610065China State Grid Chongqing Electric Power Company Chongqing 400014China State Grid Sichuan Electric Power Company Chengdu 610095China State Grid Hubei Electric Power Company Limited Research Institute Wuhan 430077China

Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power *** several studies on the mining of unique load characteristics,few studies have extensively considered the high computational burden and sample *** on lowfrequency sampling data,a non-intrusive load monitoring algorithm utilizing the graph total variation(GTV)is proposed in this *** algorithm can effectively depict the load state without the need for prior ***,the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load *** GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative *** introduction of the difference operator reduces the computing cost and addresses the inaccurate reconstruction of the graph *** low-frequency sampling data,the algorithm only requires a little prior data and no training,thereby reducing the computing *** conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

关键词： Non-intrusive load monitoring graph total variation augmented Lagrangian function smart grid

Scattering of Water Waves by Dual Symmetric Inclined Floating Porous Barriers Using the DBEM

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China Ocean Engineering 2024年第1期38卷 156-168页

作者： WANG Li-xian deng Yan-wen YE Yang-sha deng Zheng-zhi Key Laboratory of High Performance Ship Technology(Wuhan University of Technology) Ministry of EducationWuhan 430063China School of Naval Architecture Ocean and Energy Power EngineeringWuhan University of TechnologyWuhan 430063China Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province Changsha 410114China Institute of Port Coastal and Offshore EngineeringZhejiang UniversityZhoushan 316021China

The scattering of normally incident water waves by two surface-piercing inclined perforated barriers in water with a uniform finite depth is investigated within the framework of linear water wave *** that thin barriers are zero-thickness,a novel numerical method involving the the coupling of the dual boundary element method(DBEM)with damping layers is *** order to effectively damp out the reflected waves,two damping layers,instead of pseudoboundaries are implemented near the two side boundaries of the computational ***,the modified linearized free surface boundary conditions are formulated and used for solving both the ordinary boundary integral equation as well as the hypersingular boundary integral equation for degenerate *** newly developed numerical method is validated against analytical methods using the matched eigenfunction expansion method for the special case of two vertical barriers or the inclined angle to the vertical being *** influence of the length of the two damping layers has been ***,these findings are also validated against previous results for several *** validation,the numerical results for the reflection coefficient,transmission coefficient and dissipation coefficient are obtained by varying the inclination angle and porosity-effect *** effects of both the inclination angle and the porosity on the amplitudes of wave forces acting on both the front and rear barriers are also *** is found that the effect of the inclination angle mainly shifts the location of the extremal values of the reflection and the transmission ***,a moderate value of the porosity-parameter is quite effective at dissipating wave energy and mitigating the wave loads on dual barriers.

关键词： dual boundary element method inclined perforated floating breakwater reflection coefficient transmission coefficient damping layer

Mathematical framework of nonlinear elastic waves propagating in pre-stressed media

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Applied Mathematics and Mechanics(English Edition) 2024年第10期45卷 1705-1716页

作者： Jiangcheng CAI Mingxi deng College of Aerospace Engineering Chongqing UniversityChongqing 400044China

Acoustic nonlinearity holds potential as a method for assessing material *** to the acoustoelastic effect,where the velocity of elastic waves is influenced by third-order elastic constants,the propagation of nonlinear acoustic waves in pre-stressed materials would be influenced by higher-order elastic *** this,there has been a notable absence of research exploring this ***,this paper aims to establish a theoretical framework for governing the propagation of nonlinear acoustic waves in pre-stressed *** delves into the impact of pre-stress on higher-order material parameters,and specifically examines the propagation of one-dimensional acoustic waves within the contexts of the uniaxial stress and the biaxial *** paper establishes a theoretical foundation for exploring the application of nonlinear ultrasonic techniques to measure pre-stress in materials.

关键词： acoustoelastic effect nonlinear elastic wave pre-stress medium higher-order elastic constant

INVARIANTS-PRESERVING DU FORT-FRANKEL SCHEMES AND THEIR ANALYSES FOR NONLINEAR SCHRÖDINGER EQUATIONS WITH WAVE OPERATOR

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Journal of Computational Mathematics 2024年第3期42卷 814-850页

作者： Dingwen deng Zhijun Li College of Mathematics and Information Science Nanchang Hangkong UniversityNanchang 330063China

Du Fort-Frankel finite difference method(FDM)was firstly proposed for linear diffusion equations with periodic boundary conditions by Du Fort and Frankel in *** is an explicit and unconditionally von Neumann stable ***,there has been no research work on numerical solutions of nonlinear Schrödinger equations with wave operator by using Du Fort-Frankel-type finite difference methods(FDMs).In this study,a class of invariants-preserving Du Fort-Frankel-type FDMs are firstly proposed for one-dimensional(1D)and two-dimensional(2D)nonlinear Schrödinger equations with wave *** using the discrete energy method,it is shown that their solutions possess the discrete energy and mass conservative laws,and conditionally converge to exact solutions with an order of for ofο(T^(2)+h_(x)^(2)+(T/h_(x))^(2))1D problem and an order ofο(T^(2)+h_(x)^(2)+h_(Y)^(2)+(T/h_(X))^(2)+(T/h_(y))^(2))for 2D problem in ***,τdenotes time-step size,while,hx and hy represent spatial meshsizes in x-and y-directions,***,by introducing a stabilized term,a type of stabilized invariants-preserving Du Fort-Frankel-type FDMs are *** not only preserve the discrete energies and masses,but also own much better stability than original ***,numerical results demonstrate the theoretical analyses.

关键词： Nonlinear Schrodinger equations with wave operator Du Fort-Frankel finite difference methods Discrete energy and mass conservative laws Numerical convergence

Broadband angular spectrum analog processors based on all-dielectric metasurfaces

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 188-190页

作者： Lin deng Yongmin Liu Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA

Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital ***,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.

关键词： dielectric spectrum hardware

Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme

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Acta Oceanologica Sinica 2024年第5期43卷 121-132页

作者： Fangrui Xiu Zengan deng School of Marine Science and Technology Tianjin UniversityTianjin 300072China

The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ***,the accurate determination of its value remains a pressing scientific *** study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of *** optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 *** resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other *** study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

关键词： Langmuir turbulence physical-informed neural network parameter inference

A new method to solve the Reynolds equation including mass-conserving cavitation by physics informed neural networks(PINNs)with both soft and hard constraints

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Friction 2024年第6期12卷 1165-1175页

作者： Yinhu XI Jinhui deng Yiling LI School of Mechanical Engineering Anhui University of Science and TechnologyHuainan 232001China Ericsson AB Datalinjen 3Linköping 58330Sweden

In this work,a new method to solve the Reynolds equation including mass-conserving cavitation by using the physics informed neural networks(PINNs)is *** complementarity relationship between the pressure and the void fraction is *** are several difficulties in problem solving,and the solutions are ***,the difficulty for considering the pressure inequality constraint by PINNs is solved by transferring it into one equality constraint without introducing *** the void fraction inequality constraint is considered by using the hard constraint with the max-min ***,to avoid the fluctuation of the boundary value problems,the hard constraint method is also utilized to apply the boundary pressure values and the corresponding functions are ***,for avoiding the trivial solution the limitation for the mean value of the void fraction is *** results are validated against existing data,and both the incompressible and compressible lubricant are *** agreement can be found for both the domain and domain boundaries.

关键词： Reynolds equation mass-conserving cavitation physics informed neural networks hard constraints trivial solution