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A Li/Mg Double-Salt Strategy Based on Amine Solvent Achieves Bulk Phase-Interface-Electrode multi-scale optimization for Mg Metal Batteries

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ADVANCED FUNCTIONAL MATERIALS 2025年第4期35卷

作者： Wang, Fei Hua, Haiming Zhuang, Yichao Wu, Jiayue Zeng, Jing Zhao, Jinbao Xiamen Univ Coll Chem & Chem Engn Engn Res Ctr Electrochem Technol Collaborat Innovat Ctr Chem Energy MatState Prov Xiamen 361005 Peoples R China

Achieving good compatibility between anodes and cathodes with electrolytes still faces great challenges in Mg metal batteries (MMBs). Recently developed amine-based electrolytes have enabled reversible Mg anodes with conventional Mg salts through solvation structure regulations but still suffer from low conductivity and poor cathode compatibility. Herein, a Li/Mg double-salt strategy is proposed to achieve the bulk phase-interface-electrode multi-scale optimization for the amine-based electrolyte, including ionic conductivity, anode stability, and cathode compatibility. Lithium triflate (LiOTf) serves as a multifunctional additive to compensate for the limitations of single Mg salt electrolyte (MgCl2/3-methoxypropylamine). Li+ ions accelerate the ions transport in the bulk phase and ions insertion at the cathode side, benefitting to the conductivity and the cathode compatibility. Attributed to the high reduction stability of OTf- anions, the stable Mg anode/interface is also retained. Therefore, the Mg//SS cell achieves an ultralong cycling life for 2100 cycles with the coulombic efficiency of 99.8% at 1.0 mA cm-2 and the full cell also exhibits a superior cycling performance for 1000 cycles at 5 C. Additionally, the batteries with the optimized electrolyte are verified as a proof-of-concept for the air-assembled MMBs. This work emphasizes the crucial role of multifunctional additives in enhancing amine-based electrolyte performances. Lithium triflate (LiOTf) serves as a multifunctional additive to compensate for the limitations of the MgCl2/3-methoxypropylamine electrolyte. Li+ ions accelerate the ions transport in the bulk phase and ions insertion at the cathode side, benefitting to the conductivity and the cathode compatibility. Attributed to the high reduction stability of OTf- anions, the stable Mg anode/interface is also retained. image

关键词： compatibility with anode and cathode conductivity Li/Mg double-salt strategy Mg metal batteries multi-scale optimization

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Spatially Adaptive multi-scale optimization for Local Parameter Estimation in Cardiac Electrophysiology

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2017年第9期36卷 1966-1978页

作者： Dhamala, Jwala Arevalo, Hermenegild J. Sapp, John Horacek, Milan Wu, Katherine C. Trayanova, Natalia A. Wang, Linwei Rochester Inst Technol B Thomas Golisano Coll Comp & Informat Sci Rochester NY 14623 USA Johns Hopkins Univ Dept Biomed Engn Baltimore MD 21218 USA Johns Hopkins Univ Inst Computat Med Baltimore MD 21218 USA Dalhousie Univ Dept Med Halifax NS B3H 3A7 Canada Dalhousie Univ Physiol & Biophys Dept Halifax NS B3H 3A7 Canada Johns Hopkins Med Inst Dept Med Div Cardiol Baltimore MD 21218 USA

To obtain a patient-specific cardiac electrophysiological (EP) model, it is important to estimate the 3-D distributed tissue properties of themyocardium. Ideally, the tissue property should be estimated at the resolution of the cardiac mesh. However, such high-dimensional estimation faces major challenges in identifiability and computation. Most existing works reduce this dimension by partitioning the cardiac mesh into a pre-defined set of segments. The resulting low-resolution solutions have a limited ability to represent the underlying heterogeneous tissue properties of varying sizes, locations, and distributions. In this paper, we present a novel framework that, going beyond a uniform low-resolution approach, is able to obtain a higher resolution estimation of tissue properties represented by spatially non-uniform resolution. This is achieved by two central elements: 1) a multi-scale coarse-to-fine optimization that facilitates higher resolution optimization using the lower resolution solution and 2) a spatially adaptive decision criterion that retains lower resolution in homogeneous tissue regions and allows higher resolution in heterogeneous tissue regions. The presented framework is evaluated in estimating the local tissue excitability properties of a cardiac EP model on both synthetic and real data experiments. Its performance is compared with optimization using pre-defined segments. Results demonstrate the feasibility of the presented framework to estimate local parameters and to reveal heterogeneous tissue properties at a higher resolution without using a high number of unknowns.

关键词： Parameter estimation cardiac electrophysiological model multi-scale optimization Gaussian process

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Gradient design for liquid chromatography using multi-scale optimization

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JOURNAL OF CHROMATOGRAPHY A 2018年 1534卷 32-42页

作者： Lopez-Urena, S. Torres-Lapasio, J. R. Donat, R. Garcia-Alvarez-Coque, M. C. Univ Valencia Fac Math Dept Math C Dr Moliner 50 E-46100 Burjassot Spain Univ Valencia Fac Chem Dept Analyt Chem C Dr Moliner 50 E-46100 Burjassot Spain

In reversed phase-liquid chromatography, the usual solution to the "general elution problem" is the application of gradient elution with programmed changes of organic solvent (or other properties). A correct quantification of chromatographic peaks in liquid chromatography requires well resolved signals in a proper analysis time. When the complexity of the sample is high, the gradient program should be accommodated to the local resolution needs of each analyte. This makes the optimization of such situations rather troublesome, since enhancing the resolution for a given analyte may imply a collateral worsening of the resolution of other analytes. The aim of this work is to design multi-linear gradients that maximize the resolution;while fulfilling some restrictions: all peaks should be eluted before a given maximal time, the gradient should be flat or increasing, and sudden changes close to eluting peaks are penalized. Consequently, an equilibrated baseline resolution for all compounds is sought. This goal is achieved by splitting the optimization problem in a multi-scale framework. In each scale kappa, an optimization problem is solved with N-kappa approximate to 2(kappa) variables that are used to build the gradients. The N-kappa variables define cubic splines written in terms of a B-spline basis. This allows expressing gradients as polygonals of M points approximating the splines. The cubic splines are built using subdivision schemes, a technique of fast generation of smooth curves, compatible with the multi-scale framework. Owing to the nature of the problem and the presence of multiple local maxima, the algorithm used in the optimization problem of each scale kappa should be "global", such as the pattern-search algorithm. The multi-scale optimization approach is successfully applied to find the best multi-linear gradient for resolving a mixture of amino acid derivatives. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Liquid chromatography multi-linear gradients Cubic splines Resolution multi-scale optimization

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Genetic programming-assisted multi-scale optimization for multi-objective dynamic performance of laminated composites: the advantage of more elementary-level analyses

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NEURAL COMPUTING & APPLICATIONS 2020年第12期32卷 7969-7993页

作者： Kalita, Kanak Mukhopadhyay, Tanmoy Dey, Partha Haldar, Salil Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Howrah India Indian Inst Technol Kanpur Dept Aerosp Engn Kanpur Uttar Pradesh India Acad Technol Dept Mech Engn Adisaptagram Hooghly India

High-fidelity multi-scale design optimization of many real-life applications in structural engineering still remains largely intractable due to the computationally intensive nature of numerical solvers like finite element method. Thus, in this paper, an alternate route of metamodel-based design optimization methodology is proposed in multi-scale framework based on a symbolic regression implemented using genetic programming (GP) coupled withd-optimal design. This approach drastically cuts the computational costs by replacing the finite element module with appropriately constructed robust and efficient metamodels. Resulting models are compact, have good interpretability and assume a free-form expression capable of capturing the non-linearly, complexity and vastness of the design space. Two robust nature-inspired optimization algorithms, viz. multi-objective genetic algorithm and multi-objective particle swarm optimization, are used to generate Pareto optimal solutions for several test problems with varying complexity. TOPSIS, a multi-criteria decision-making approach, is then applied to choose the best alternative among the Pareto optimal sets. Finally, the applicability of GP in efficiently tackling multi-scale optimization problems of composites is investigated, where a real-life scenario is explored by varying fractions of pertinent engineering materials to bring about property changes in the final composite structure across two different scales. The study reveals that a microscale optimization leads to better optimized solutions, demonstrating the advantage of carrying out a multi-scale optimization without any additional computational burden.

关键词： multi-scale optimization Machine learning-based optimization Genetic programming Symbolic regression d-Optimal design Robust composite structures

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Concurrent multi-scale optimization design of composite frame structures with maximum fundamental frequency 21

Concurrent multi-scale optimization design of composite fram...

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21st International Conference on Composite Materials, ICCM 2017

作者： Yan, Jun Duan, Zunyi Wang, Jingyuan Yu, Tao Fan, Zhirui Lund, Erik State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics International Research Center for Computational Mechanics Dalian University of Technology Liaoning Dalian116024 China Department of Mechanical and Manufacturing Engineering Aalborg University AalborgDK-9220 Denmark Harbin Electric Power Equipment Company Limited Harbin Electric Corporation Harbin150028 China Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of

Glass or carbon fiber reinforced composite frames are ideal lightweight and high-span structures in the field of aerospace. Fundamental frequency is a key performance indicator in the process of the composite frame structure design. Increasing the value of fundamental frequency of the frames could avoid the phenomenon of structural resonance. Based on the structure and material concurrent optimization concept, the concurrent multi-scale optimization design for the maximum fundamental frequency of fiber reinforced frame structure is proposed. An optimization model based on the maximum structural fundamental frequency and the specified fiber material volume constraint has been established. The objective function sensitivity information about the two geometrical scales design variables are deduced with semi-analytical method. Numerical example in the paper shows that the concurrent multi-scale optimization for the maximum fundamental frequency can further explore the coupling effect between the macro-structure and micro-material to increase the fundamental frequency. The new two geometrical scales optimization model provides a choice for the design of composite frame structure in aerospace and other industries. © 2017 International Committee on Composite Materials. All rights reserved.

关键词： Composite frame Fiber winding angle Fundamental frequency maximization multi-scale optimization Semi-analytical sensitivity analysis

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Concurrent multi-scale optimization of macro- and micro-shapes of laminated porous shell structure (vol 67, 57, 2024)

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STRUCTURAL AND multiDISCIPLINARY optimization 2024年第5期67卷 57-57页

作者： Hikasa, Motomu Shimoda, Masatoshi Al Ali, Musaddiq Toyota Technol Inst Grad Sch Engn Dept Adv Sci & Technol 2-12-1 HisakataTenpaku Ku Nagoya Aichi 4688511 Japan Toyota Technol Inst Dept Adv Sci & Technol 2-12-1 HisakataTenpaku Ku Nagoya Aichi 4688511 Japan

In this study, we propose a multi-scale shape optimization method that concurrently optimizes the shape of micropores and the curvature of the shell structure in a porous laminated shell structure. The homogenization method is used to bridge the macrostructure and the microporous structures. In the microporous structural optimization the pore shape of the unit cell is optimized in each sub-domain layer by layer. The thickness of each layer is assumed to be constant with respect to the shape variation. A squared error norm is minimized for controlling the displacements at arbitrary points of the laminated shell structure to the target values under the total volume constraint. The equilibrium equation of the macrostructure and the homogenization equations of the unit cells are also used as the constraints. The shape optimization problem is formulated as a distributed parameter optimization problem, and the theoretically derived shape gradient functions are applied to the H1 gradient method to optimize the shape of macro and microporous structures. The validity of the proposed multi-scale method is confirmed by several numerical examples for designing the optimal shapes of the unit cells and the overall shape of the laminated shell structure concurrently. Macro–micro and micro–macro-hierarchical shape optimizations are also implemented for comparison. With the proposed method, arbitrary stiff and flexible porous laminated shell structures can be created by defining the target displacements suitably.

关键词： multi-scale optimization Homogenization method Laminated shell structure Control displacement Shape optimization H1 gradient method Periodic microporous structure

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CONCURRENT multi-scale optimization DESIGN OF COMPOSITE FRAME STRUCTURES WITH MAXIMUM FUNDAMENTAL FREQUENCY

CONCURRENT MULTI-SCALE OPTIMIZATION DESIGN OF COMPOSITE FRAM...

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第二十一届国际复合材料大会

作者： Jun Yan Zunyi Duan Jingyuan Wang Tao Yu Zhirui Fan Erik Lund State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering Mechanics International Research Center for Computational Mechanics Dalian University of Technology Department of Mechanical and Manufacturing Engineering Aalborg University

关键词： Composite frame multi-scale optimization Fundamental frequency maximization Fiber winding angle Semi-analytical sensitivity analysis

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Toward Carbon-Neutral Electric Power Systems in the New York State: a Novel multi-scale Bottom-Up optimization Framework Coupled with Machine Learning for Capacity Planning at Hourly Resolution

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2022年第5期10卷 1805-1821页

作者： Zhao, Ning You, Fengqi Cornell Univ Syst Engn Ithaca NY 14853 USA Cornell Univ Robert Frederick Smith Sch Chem & Biomol Engn Ithaca NY 14853 USA

In this work, we propose a novel multi-scale bottom-up optimization framework for the carbon-neutral transition planning of the electric power sector, which incorporates hourly time scale and electricity storage to address the reliability and energy balance issues of the future deep-decarbonized power systems. In addition to the technology and capacity information for each facility, the proposed framework also accounts for facility ages, which are usually omitted in the literature, without significantly increasing the computational demand. To reduce the computational requirement of simultaneously optimizing capacity planning and hourly systems operations over the next few decades, a reduced model is developed based on representative days, using a novel approach that integrates multiple machine learning techniques. Based on the optimal transition pathways, hourly operational simulations are conducted for every year within the planning horizon to obtain detailed optimization results. To illustrate the applicability of the proposed framework, a case study for the New York State is presented through two cases, with and without electricity storage capacity expansion. The proposed approach using principal component analysis coupled with K-means outcompetes multiple conventional approaches of using clustering techniques directly. The transition planning results show that the total generation capacity for the case with electricity capacity expansion is 39% higher than the other case, while the latter case has 200% more generation capacity from non-intermittent sources. Detailed hourly operational simulation results indicate that offshore wind, hydro, and utility solar are the primary power sources by 2040 for the case with electricity storage capacity expansion, while hydro, offshore wind, and nuclear are the main electricity sources for the other case.

关键词： decarbonization renewable electricity transition multi-scale optimization renewable generation bottom-up model

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multi-scale water network optimization considering simultaneous intra- and inter-plant integration in steel industry

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JOURNAL OF CLEANER PRODUCTION 2018年 176卷 663-675页

作者： Zhang, Kaili Zhao, Yuehong Cao, Hongbin Wen, Hao Chinese Acad Sci Inst Proc Engn Beijing Engn Res Ctr Proc Pollut Control 12nd North St Beijing 100190 Peoples R China Chinese Acad Sci Inst Proc Engn State Key Lab Multiphase Complex Syst 12nd North St Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Chinese steel industry faces increasing pressure to reduce water consumption and wastewater impact. To perform the optimization of steel production process from the perspective of water use, this work describes a new superstructure-based water network optimization model using typical steel park as our study use. The water systems at different scales (e.g. unit-scale, plant-scale and park-scale) in the water network and their interactions are modeled for optimization. To construct the optimization model: 1) All types of unit-scale typical water systems in the park are identified, and short-cut unit models are developed as the basic elements for network superstructure;2) Intra- and inter-plant superstructures are further established to describe potential configurations of the water network at plant-scale and park scale respectively;3) Built up on the element models and superstructures, a Mixed Integer Non-Linear Programming model using total annual cost as objective is set up to investigate potentials for water network optimization in a steel park. To illustrate its applicability and effectiveness, an industrial case study is carried out, in which two schemes are illustrated, one scheme (termed as scheme A) considering only indirect integration between plants, the other (termed as scheme B) considering both direct and indirect integration strategy (mixed integration). In scheme A, due to the higher water efficiency and optimal configuration in the case study, the freshwater consumption and total annual cost are reduced by 22% and 21% respectively. Scheme B shows 23% savings in total annual cost, while the reduction of freshwater consumption keeps unchanged. Besides, the information about the integration strategies of all scales (e.g. unit-, plant- and park-scale) can all be obtained, which is valuable for decision-making. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Steel industry Water network Typical water system Superstructure multi-scale optimization Direct and indirect integration

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Pyramid Matting: A Resource-Adaptive multi-scale Pixel Pair optimization Framework for Image Matting

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IEEE ACCESS 2020年 8卷 93487-93498页

作者： Liang Yihui Feng Fujian Cai Zhaoquan Univ Elect Sci & Technol China Sch Comp Sci Zhongshan Inst Zhongshan 528400 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China Huizhou Univ Res Dept Huizhou 516007 Peoples R China Shanwei Polytech Shanwei 516600 Peoples R China

Image matting is an important problem in computer vision with significant theoretical interest and diverse practical applications, including image/video editing, target tracking, and object recognition. Pixel-pair-optimization-based image matting approaches have been shown very successful in estimating the opacity of the foreground by searching for the best pair of foreground and background pixels for each unknown pixel. However, extant approaches encounter difficulties in adapting to the changes of available computing resources, which limits the application of image matting. This drawback has motivated the present study, as a part of which a multi-scale evolutionary pixel pair optimization framework named pyramid matting framework (PMF) was developed. In this framework, the large-scale pixel pair optimization problem is transformed to multiple pixel pair optimization problems of different scales using image pyramid. The resulting problems are solved level by level, starting from the problem at the small scale. Pixel pair heuristic information obtained from solving low-scale problems are iteratively propagated to the spatially-related pixel pairs in the larger-scale problem. PMF can adapt to changes in available computing resources due to its capability of transforming a small-scale problem solution to the large-scale problem solution through the heuristic information propagation. Experimental results show that the PMF-based image matting approach not only provides high-quality alpha mattes with sufficient computing resources, but also works well when computing resources are scarce.

关键词： optimization Image color analysis Task analysis Evolutionary computation Target tracking Object recognition Licenses Image matting pixel pair optimization multi-scale optimization evolutionary optimization

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