Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stab...Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.展开更多
A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable ci...A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable cities.This is particularly true in Africa,where there aren’t many studies on the topic.The current study suggests a 90 m^(2) model of a sustainable building in a dry climate that is movable to address the issue of housing in remote areas,ensures comfort in harsh weather conditions,uses solar renewable resources—which are plentiful in Africa—uses biosourced materials,and examines how these materials relate to temperature and humidity control while emitting minimal carbon emissions.In order to solve the topic under consideration,the work is split into two sections:numerical and experimental approaches.Using TRNSYS and Revit,the suggested prototype building is examined numerically to examine the impact of orientation,envelope composition made of bio-sourced materials,and carbon emissions.Through a hygrothermal investigation,experiments are conducted to evaluate this prototype’s effectiveness.Furthermore,an examination of the photovoltaic system’s production,consumption,and several scenarios used tomaximize battery life is included in the paper.Because the biosourcedmaterial achieves a thermal transmittance of 0.15(W.m^(-2).K^(-1)),the results demonstrate an intriguing finding in terms of comfort.This value satisfies the requirements of passive building,energy autonomy of the dwelling,and injection in-network with an annual value of 15,757 kWh.Additionally,compared to the literature,the heating needs ratio is 6.38(kWh/m^(2).an)and the cooling needs ratio is 49(kWh/m^(2).an),both of which are good values.According to international norms,the inside temperature doesn’t go above 26℃,and the humidity level is within a comfortable range.展开更多
The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and materia...The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.展开更多
The design and development of solar dryers are crucial in regions with abundant solar energy,such as Bhopal,India,where seasonal variations significantly impact the efficiency of drying processes.The paper is focused ...The design and development of solar dryers are crucial in regions with abundant solar energy,such as Bhopal,India,where seasonal variations significantly impact the efficiency of drying processes.The paper is focused on employing a comprehensive mathematical model to predict the dryer’s performance in drying the materials such as banana slices.To enhance this model,Hyper Tuned Swarm Optimization with Gradient Tree(HT_SOGT)was utilized to accurately predict and determine the optimal size of the dryer dimensions considering various mathematical calculations for material drying.The predictive model considered the influence of seasonal fluctuations,ensuring an efficient drying process with an objective function to optimize the drying time of an average of 7 hrs throughout the year.Across all recorded ambient temperatures(ranging from 16.985○C to 31.4○C),the outlet temperature of the solar dryer is consistently higher,ranging from 39.085○C to 66.2○C.The results show that the optimized dryer design,based on HT_SOGT modelling,significantly improves drying efficiency of the materials across varying conditions,making it suitable for sustainable applications in agriculture and food processing industries in the Bhopal region.展开更多
With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly r...With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly reducing the energy consumption during the operation of EMUs(Electric Multiple Units).This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on threedimensional Hashin failure criteria and the Cheetah Optimizer(CO)to achieve maximum lightweight efficiency.The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters,which were used as design parameters in the CO.The model calculations and result extraction were implemented in MATLAB,and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor.After 100 iterations,ensuring structural integrity,the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure,achieving significant lightweight benefits.This study provides foundational data for the lightweight design of EMUs.展开更多
An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and sa...An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and samples from the model to obtain the next generation,avoiding the problem of building-blocks destruction caused by crossover and mutation.Neighboring search from artificial bee colony algorithm(ABCA)is introduced to enhance the local optimization ability and improved to raise the speed of convergence.The probability model is modified by boundary correction and loss correction to enhance the robustness of the algorithm.The proposed IEDA is compared with other intelligent algorithms in relevant references.The results show that the proposed IEDA has faster convergence speed and stronger optimization ability,proving the feasibility and effectiveness of the algorithm.展开更多
Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-i...Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.展开更多
Perovskite solar cells(PSCs)have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy sources.However,conventional trial-and-error approaches and the vast compositional parame...Perovskite solar cells(PSCs)have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy sources.However,conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics.The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machinelearning(ML)approaches to swiftly advance in the materials field.This review succinctly outlines the fundamental ML procedures,techniques,and recent breakthroughs,particularly in predicting the physical characteristics of perovskite materials.Moreover,it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs.Additionally,this review highlights recent efforts in using characterization data for ML,exploring their correlations with material properties and device performance,which are actively being researched,but they have yet to receive significant attention.Lastly,we provide future perspectives,such as leveraging Large Language Models(LLMs)and text-mining,to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.展开更多
High-temperature phase change materials(PCMs)have attracted significant attention in the field of thermal energy storage due to their ability to store and release large amounts of heat within a small temperature fluct...High-temperature phase change materials(PCMs)have attracted significant attention in the field of thermal energy storage due to their ability to store and release large amounts of heat within a small temperature fluctuation range.However,their practical application is limited due to problems such as leakage,corrosion,and volume changes at high temperatures.Recent research has shown that macroencapsulation technology holds promise in addressing these issues.This paper focuses on the macroencapsulation technology of high-temperature PCMs,starting with a review of the classification and development history of high-temperature macroencapsulatd PCMs.Four major encapsulation strategies,including electroplating method,solid/liquid filling method,sacrificial material method,and powder compaction into sphere method,are then summarized.The methods for effectively addressing issues such as corrosion,leakage,supercooling,and phase separation in PCMs are analyzed,along with approaches for improving the heat transfer performance,mechanical strength,and thermal cycling stability of macrocapsules.Subsequently,the structure and packing arrangement optimization of macrocapsules in thermal storage systems is discussed in detail.Finally,after comparing the performance of various encapsulation strategies and summarizing existing issues,the current technical challenges,improvement methods,and future development directions are proposed.More attention should be given to utilizing AI technology and reinforcement learning to reveal the multiphysics-coupled heat and mass transfer mechanisms in macrocapsule applications,as well as to optimize material selection and encapsulation parameters,thereby enhancing the overall efficiency of thermal storage systems.展开更多
The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increas...The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.展开更多
Simultaneously,reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic.Considering the law of mass,traditional lightweight acoustic metamaterials make it difficult to c...Simultaneously,reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic.Considering the law of mass,traditional lightweight acoustic metamaterials make it difficult to control noise efficiently in real-life applications.In this study,a novel optimization-driven design scheme is developed to obtain lightweight acoustic metamaterials with a strong sound insulation capability for additive manufacturing.In the proposed design scheme,a topology optimization method for an acoustic metamaterial in the acoustic-solid interaction system is implemented to obtain an initial cross-sectional topology of the acoustic microstructure during the conceptual design phase.Then,in the detailed design phase,the parametric model for a higher-dimensional design is formulated based on the topology optimization result.An adaptive Kriging interpolation approach is proposed to accurately reformulate a much easier surrogate model from the original parameterization formulation to avoid repeating calls for nonlinear analyses in the 3D acoustic-structure interaction system.A surrogate model was used to optimize a ready-to-print acoustic metamaterial with improved noise reduction performance.Experimental verification based on an impedance tube is implemented.Results demonstrate characteristics of the devised metamaterial as well as the proposed method.展开更多
Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structur...Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.展开更多
China has abundant renewable energy resources.With the establishment of carbon peaking and carbon neutrality goals,renewable energy sources such as wind power and photovoltaics have undergone tremendous development.Ho...China has abundant renewable energy resources.With the establishment of carbon peaking and carbon neutrality goals,renewable energy sources such as wind power and photovoltaics have undergone tremendous development.However,because of the randomness and volatility of wind and photovoltaic power,the large-scale development of renewable energy faces challenges with accommodation and transmission.At present,the bundling of wind–photovoltaic–thermal power with ultra-high voltage transmission projects is the main development approach for renewable energy bases in western and northern China.Nonetheless,solving the problems of high carbon dioxide emission,carbon dioxide capture,and the utilization of thermal power is still necessary.Based on power-to-hydrogen,powerto-methanol,and oxygen-enriched combustion power generation technologies,this article proposes a power-to-hydrogen-andmethanol model based on the collaborative optimization of energy flow and material flow,which is expected to simultaneously solve the problems of renewable energy accommodation and low-carbon transformation of thermal power.Models with different ways of linking power to hydrogen and methanol are established,and an 8760-hour-time-series operation simulation is incorporated into the planning model.A case study is then conducted on renewable energy bases in the deserts of western and northern China.The results show that the power-to-hydrogen-and-methanol model based on the collaborative optimization of energy flow and material flow can greatly reduce the demand for hydrogen storage and energy storage,reduce the cost of carbon capture,make full use of by-product oxygen and captured carbon dioxide,and produce high-value chemical raw materials,thus exhibiting significant economic advantages.展开更多
Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of p...Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of photocatalysts or membrane materials to improve their catalytic and filtration performance,the complex structures and interface mechanisms,as well as insufficient light utilization,are still often overlooked,limiting the overall performance improvement of photocatalytic membranes.This work provides an overview of enhancement strategies involving restricted area effects,external fields,such as mechanical,magnetic,thermal,and electrical fields,as well as coupling techniques with advanced oxidation processes(e.g.,O_(3),Fenton,and persulfate oxidation)for dual enhancement of photocatalysts and membranes.In addition,the synthesis method of photocatalytic membranes and the influence of factors,such as light source type,frequency,and relative position on photocatalytic membrane performance were also studied.Finally,economic feasibility and pollutant removal performance were further evaluated to determine the promising enhancement strategies,paving the way for more efficient and scalable applications of photocatalytic membranes.展开更多
Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Me...Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.展开更多
This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volu...This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.展开更多
Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is co...Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.展开更多
Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonun...Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonuniform corrosion using different materials.In this study,a reliability-based design optimization(RBDO)procedure is improved for the design of coastal bridge piers using six groups of commonly used materials,i.e.,normal performance concrete(NPC)with black steel(BS)rebar,high strength steel(HSS)rebar,epoxy coated(EC)rebar,and stainless steel(SS)rebar(named NPC-BS,NPC-HSS,NPC-EC,and NPC-SS,respectively),NPC with BS with silane soakage on the pier surface(named NPC-Silane),and high-performance concrete(HPC)with BS rebar(named HPC-BS).First,the RBDO procedure is improved for the design optimization of coastal bridge piers,and a bridge is selected to illustrate the procedure.Then,reliability analysis of the pier designed with each group of materials is carried out to obtain the time-dependent reliability in terms of the ultimate and serviceability performances.Next,the repair time of the pier is predicted based on the time-dependent reliability indices.Finally,the time-dependent LCCs for the pier are obtained for the selection of the optimal design.展开更多
Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high...Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.展开更多
Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue...Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.展开更多
基金partly supported by the National Natural Science Foundation of China(Grant No.52272225).
文摘Na_(3)V_(2)(PO_(4))_(3)(NVP)has garnered great attentions as a prospective cathode material for sodium-ion batteries(SIBs)by virtue of its decent theoretical capacity,superior ion conductivity and high structural stability.However,the inherently poor electronic conductivity and sluggish sodium-ion diffusion kinetics of NVP material give rise to inferior rate performance and unsatisfactory energy density,which strictly confine its further application in SIBs.Thus,it is of significance to boost the sodium storage performance of NVP cathode material.Up to now,many methods have been developed to optimize the electrochemical performance of NVP cathode material.In this review,the latest advances in optimization strategies for improving the electrochemical performance of NVP cathode material are well summarized and discussed,including carbon coating or modification,foreign-ion doping or substitution and nanostructure and morphology design.The foreign-ion doping or substitution is highlighted,involving Na,V,and PO_(4)^(3−)sites,which include single-site doping,multiple-site doping,single-ion doping,multiple-ion doping and so on.Furthermore,the challenges and prospects of high-performance NVP cathode material are also put forward.It is believed that this review can provide a useful reference for designing and developing high-performance NVP cathode material toward the large-scale application in SIBs.
文摘A clean environment with low carbon emissions is the goal of research on the development of green and sustainable buildings that use bio-sourced materials in conjunction with solar energy to create more sustainable cities.This is particularly true in Africa,where there aren’t many studies on the topic.The current study suggests a 90 m^(2) model of a sustainable building in a dry climate that is movable to address the issue of housing in remote areas,ensures comfort in harsh weather conditions,uses solar renewable resources—which are plentiful in Africa—uses biosourced materials,and examines how these materials relate to temperature and humidity control while emitting minimal carbon emissions.In order to solve the topic under consideration,the work is split into two sections:numerical and experimental approaches.Using TRNSYS and Revit,the suggested prototype building is examined numerically to examine the impact of orientation,envelope composition made of bio-sourced materials,and carbon emissions.Through a hygrothermal investigation,experiments are conducted to evaluate this prototype’s effectiveness.Furthermore,an examination of the photovoltaic system’s production,consumption,and several scenarios used tomaximize battery life is included in the paper.Because the biosourcedmaterial achieves a thermal transmittance of 0.15(W.m^(-2).K^(-1)),the results demonstrate an intriguing finding in terms of comfort.This value satisfies the requirements of passive building,energy autonomy of the dwelling,and injection in-network with an annual value of 15,757 kWh.Additionally,compared to the literature,the heating needs ratio is 6.38(kWh/m^(2).an)and the cooling needs ratio is 49(kWh/m^(2).an),both of which are good values.According to international norms,the inside temperature doesn’t go above 26℃,and the humidity level is within a comfortable range.
基金Supported by Direktorat Riset dan Pengembangan(Directorate of Research and Development)Universitas Indonesia(NKB-690/UN2.RST/HKP.05.00/2022).
文摘The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.
文摘The design and development of solar dryers are crucial in regions with abundant solar energy,such as Bhopal,India,where seasonal variations significantly impact the efficiency of drying processes.The paper is focused on employing a comprehensive mathematical model to predict the dryer’s performance in drying the materials such as banana slices.To enhance this model,Hyper Tuned Swarm Optimization with Gradient Tree(HT_SOGT)was utilized to accurately predict and determine the optimal size of the dryer dimensions considering various mathematical calculations for material drying.The predictive model considered the influence of seasonal fluctuations,ensuring an efficient drying process with an objective function to optimize the drying time of an average of 7 hrs throughout the year.Across all recorded ambient temperatures(ranging from 16.985○C to 31.4○C),the outlet temperature of the solar dryer is consistently higher,ranging from 39.085○C to 66.2○C.The results show that the optimized dryer design,based on HT_SOGT modelling,significantly improves drying efficiency of the materials across varying conditions,making it suitable for sustainable applications in agriculture and food processing industries in the Bhopal region.
文摘With the development of composite materials,their lightweight and high-strength characteristics have caused more widespread use from aerospace applications to automotive and rail transportation sectors,significantly reducing the energy consumption during the operation of EMUs(Electric Multiple Units).This study aims to explore the application of composite materials in the lightweight design of EMU front skirts and proposes a design method based on threedimensional Hashin failure criteria and the Cheetah Optimizer(CO)to achieve maximum lightweight efficiency.The UMAT subroutine was developed based on the three-dimensional Hashin failure criteria to calculate failure parameters,which were used as design parameters in the CO.The model calculations and result extraction were implemented in MATLAB,and the Cheetah Optimizer iteratively determined the optimal laminating angle design that minimized the overall failure factor.After 100 iterations,ensuring structural integrity,the optimized design reduced the weight of the skirt panel by 60% compared to the original aluminum alloy structure,achieving significant lightweight benefits.This study provides foundational data for the lightweight design of EMUs.
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘An improved estimation of distribution algorithm(IEDA)is proposed in this paper for efficient design of metamaterial absorbers.This algorithm establishes a probability model through the selected dominant groups and samples from the model to obtain the next generation,avoiding the problem of building-blocks destruction caused by crossover and mutation.Neighboring search from artificial bee colony algorithm(ABCA)is introduced to enhance the local optimization ability and improved to raise the speed of convergence.The probability model is modified by boundary correction and loss correction to enhance the robustness of the algorithm.The proposed IEDA is compared with other intelligent algorithms in relevant references.The results show that the proposed IEDA has faster convergence speed and stronger optimization ability,proving the feasibility and effectiveness of the algorithm.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222505,52321002)Shanghai Municipal Natural Science Foundation o China(Grant No.23ZR1415500)。
文摘Biological load-bearing materials,like the nacre in shells,have a unique staggered structure that supports their superior mechanical properties.Engineers have been encouraged to imitate it to create load-bearing bio-inspired materials which have excellent properties not present in conventional composites.To create such materials with desirable mechanical properties,the optimum structural parameters combination must be selected.Moreover,the optimal design of bio-inspired composites needs to take into account the trade-offs between various mechanical properties.In this paper,multi-objective optimization models were developed using structural parameters as design variables and mechanical properties as optimization objectives,including stiffness,strength,toughness,and dynamic damping.Using the NSGA-II optimization algorithm,a set of optimal solutions were solved.Additionally,three different structures in natural nacre were introduced in order to utilize the better structure when design bio-inspired materials.The range of optimal solutions that obtained using results from previous research were examined and explained why this collection of optimal solution ranges is better.Also,optimal solutions were compared with the structural features and mechanical properties of real nacre and artificial biomimetic composites to validate our models.Finally,the optimum design strategies can be obtained for nacre-like composites.Our research methodically proposes an optimization method for achieving load-bearing bio-inspired materials with excellent properties and creates a set of optimal solutions from which designers can select the one that best suits their preferences,allowing the fabricated materials to demonstrate preferred performance.
基金supported by the Ministry of Science and ICT(MSIT)of the Republic of Korea(00302646)supported by the National Research Foundation of Korea grant funded by the Korean Government(MSIT)(NRF-2022R1A4A1019296,1345374646,2022M3J1A1064315).
文摘Perovskite solar cells(PSCs)have developed rapidly,positioning them as potential candidates for nextgeneration renewable energy sources.However,conventional trial-and-error approaches and the vast compositional parameter space continue to pose challenges in the pursuit of exceptional performance and high stability of perovskite-based optoelectronics.The increasing demand for novel materials in optoelectronic devices and establishment of substantial databases has enabled data-driven machinelearning(ML)approaches to swiftly advance in the materials field.This review succinctly outlines the fundamental ML procedures,techniques,and recent breakthroughs,particularly in predicting the physical characteristics of perovskite materials.Moreover,it highlights research endeavors aimed at optimizing and screening materials to enhance the efficiency and stability of PSCs.Additionally,this review highlights recent efforts in using characterization data for ML,exploring their correlations with material properties and device performance,which are actively being researched,but they have yet to receive significant attention.Lastly,we provide future perspectives,such as leveraging Large Language Models(LLMs)and text-mining,to expedite the discovery of novel perovskite materials and expand their utilization across various optoelectronic fields.
基金supported by the National Natural Science Foundation of China(Grant No.51976092)。
文摘High-temperature phase change materials(PCMs)have attracted significant attention in the field of thermal energy storage due to their ability to store and release large amounts of heat within a small temperature fluctuation range.However,their practical application is limited due to problems such as leakage,corrosion,and volume changes at high temperatures.Recent research has shown that macroencapsulation technology holds promise in addressing these issues.This paper focuses on the macroencapsulation technology of high-temperature PCMs,starting with a review of the classification and development history of high-temperature macroencapsulatd PCMs.Four major encapsulation strategies,including electroplating method,solid/liquid filling method,sacrificial material method,and powder compaction into sphere method,are then summarized.The methods for effectively addressing issues such as corrosion,leakage,supercooling,and phase separation in PCMs are analyzed,along with approaches for improving the heat transfer performance,mechanical strength,and thermal cycling stability of macrocapsules.Subsequently,the structure and packing arrangement optimization of macrocapsules in thermal storage systems is discussed in detail.Finally,after comparing the performance of various encapsulation strategies and summarizing existing issues,the current technical challenges,improvement methods,and future development directions are proposed.More attention should be given to utilizing AI technology and reinforcement learning to reveal the multiphysics-coupled heat and mass transfer mechanisms in macrocapsule applications,as well as to optimize material selection and encapsulation parameters,thereby enhancing the overall efficiency of thermal storage systems.
基金Funded by the Deep Underground National Science&Technology Major Project gram of China(No.2024ZD1003704)the National Natural Science Foundation of China(Nos.51834001 and 52374111)。
文摘The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m^(3)/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO_(2)emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values.
基金supported by the National Key Research and Development Program of China(No.2023YFB4604800)the National Natural Science Foundation of China(No.52075195)the Inelligent Manufacturing Equipment and Technology Open Foundation(No.IMETKF2023016).
文摘Simultaneously,reducing an acoustic metamaterial’s weight and sound pressure level is an important but difficult topic.Considering the law of mass,traditional lightweight acoustic metamaterials make it difficult to control noise efficiently in real-life applications.In this study,a novel optimization-driven design scheme is developed to obtain lightweight acoustic metamaterials with a strong sound insulation capability for additive manufacturing.In the proposed design scheme,a topology optimization method for an acoustic metamaterial in the acoustic-solid interaction system is implemented to obtain an initial cross-sectional topology of the acoustic microstructure during the conceptual design phase.Then,in the detailed design phase,the parametric model for a higher-dimensional design is formulated based on the topology optimization result.An adaptive Kriging interpolation approach is proposed to accurately reformulate a much easier surrogate model from the original parameterization formulation to avoid repeating calls for nonlinear analyses in the 3D acoustic-structure interaction system.A surrogate model was used to optimize a ready-to-print acoustic metamaterial with improved noise reduction performance.Experimental verification based on an impedance tube is implemented.Results demonstrate characteristics of the devised metamaterial as well as the proposed method.
基金supported by the National Natural Science Foundation of China (Grant Nos.12102021,12372105,12172026,and 12225201)the Fundamental Research Funds for the Central Universities and the Academic Excellence Foundation of BUAA for PhD Students.
文摘Advanced programmable metamaterials with heterogeneous microstructures have become increasingly prevalent in scientific and engineering disciplines attributed to their tunable properties.However,exploring the structure-property relationship in these materials,including forward prediction and inverse design,presents substantial challenges.The inhomogeneous microstructures significantly complicate traditional analytical or simulation-based approaches.Here,we establish a novel framework that integrates the machine learning(ML)-encoded multiscale computational method for forward prediction and Bayesian optimization for inverse design.Unlike prior end-to-end ML methods limited to specific problems,our framework is both load-independent and geometry-independent.This means that a single training session for a constitutive model suffices to tackle various problems directly,eliminating the need for repeated data collection or training.We demonstrate the efficacy and efficiency of this framework using metamaterials with designable elliptical holes or lattice honeycombs microstructures.Leveraging accelerated forward prediction,we can precisely customize the stiffness and shape of metamaterials under diverse loading scenarios,and extend this capability to multi-objective customization seamlessly.Moreover,we achieve topology optimization for stress alleviation at the crack tip,resulting in a significant reduction of Mises stress by up to 41.2%and yielding a theoretical interpretable pattern.This framework offers a general,efficient and precise tool for analyzing the structure-property relationships of novel metamaterials.
基金the financial support provided by the Major Program of Xiangjiang Laboratory(No.23XJ01006).
文摘China has abundant renewable energy resources.With the establishment of carbon peaking and carbon neutrality goals,renewable energy sources such as wind power and photovoltaics have undergone tremendous development.However,because of the randomness and volatility of wind and photovoltaic power,the large-scale development of renewable energy faces challenges with accommodation and transmission.At present,the bundling of wind–photovoltaic–thermal power with ultra-high voltage transmission projects is the main development approach for renewable energy bases in western and northern China.Nonetheless,solving the problems of high carbon dioxide emission,carbon dioxide capture,and the utilization of thermal power is still necessary.Based on power-to-hydrogen,powerto-methanol,and oxygen-enriched combustion power generation technologies,this article proposes a power-to-hydrogen-andmethanol model based on the collaborative optimization of energy flow and material flow,which is expected to simultaneously solve the problems of renewable energy accommodation and low-carbon transformation of thermal power.Models with different ways of linking power to hydrogen and methanol are established,and an 8760-hour-time-series operation simulation is incorporated into the planning model.A case study is then conducted on renewable energy bases in the deserts of western and northern China.The results show that the power-to-hydrogen-and-methanol model based on the collaborative optimization of energy flow and material flow can greatly reduce the demand for hydrogen storage and energy storage,reduce the cost of carbon capture,make full use of by-product oxygen and captured carbon dioxide,and produce high-value chemical raw materials,thus exhibiting significant economic advantages.
基金supported by the BRICS STI Framework Programme(No.52261145703)the Higher Education Discipline Innovation Project(National 111 Project,No.B16016)the Guangxi Key Research and Development Plan Project(AB24010117).
文摘Photocatalytic membranes hold significant potential for promoting pollutant degradation and reducing membrane fouling in filtration systems.Although extensive research has been conducted on the independent design of photocatalysts or membrane materials to improve their catalytic and filtration performance,the complex structures and interface mechanisms,as well as insufficient light utilization,are still often overlooked,limiting the overall performance improvement of photocatalytic membranes.This work provides an overview of enhancement strategies involving restricted area effects,external fields,such as mechanical,magnetic,thermal,and electrical fields,as well as coupling techniques with advanced oxidation processes(e.g.,O_(3),Fenton,and persulfate oxidation)for dual enhancement of photocatalysts and membranes.In addition,the synthesis method of photocatalytic membranes and the influence of factors,such as light source type,frequency,and relative position on photocatalytic membrane performance were also studied.Finally,economic feasibility and pollutant removal performance were further evaluated to determine the promising enhancement strategies,paving the way for more efficient and scalable applications of photocatalytic membranes.
基金sponsored by the National Key Research and Development Program of China(No.2023YFB4604800,2021YFA1202300)the Natural and Science Foundation of China(Grant Nos.52201041,52275331,52205358)+1 种基金the Key Research and Development Program of Hubei Province(Nos.2024BCB091,2022CFA031)the Hong Kong Scholars Program(No.XJ2022014)。
文摘Metal 3D printing holds great promise for future digitalized manufacturing.However,the intricate interplay between laser and metal powders poses a significant challenge for conventional trial-and-error optimization.Meanwhile,the“optimized”yet fixed parameters largely limit possible extensions to new designs and materials.Herein,we report a high throughput design coupled with machine learning(ML)guidance to eliminate the notorious cracks and porosities in metal 3D printing for improved corrosion resistance and overall performance.The high throughput methodologies are mostly on obtaining the printed samples and their structural and physical properties,while ML is used for data analysis by model building for prediction(optimization),and understanding.For 316L stainless steel,we concurrently printed 54 samples with different parameters and subjected them to parallel tests to generate an extensive dataset for ML analysis.An ensemble learning model outperformed the other five single learners while Bayesian active learning recommended optimal parameters that could reduce porosity from 0.57%to below 0.1%.Accordingly,the ML-recommended samples showed higher tensile strength(609.28 MPa)and elongation(50.67%),superior anti-corrosion(I_(corr)=4.17×10^(-8) A·cm^(-2)),and stable alkaline oxygen evolution for>100 hours(at 500 mA·cm^(-2)).Remarkably,through the correlation analysis of printing parameters and targeted properties,we find that the influence of hardness on corrosion resistance is second only to porosity.We then expedited optimization in AlSi7Mg using the learned knowledge and feed hardness and relative density,thus demonstrating the method’s general extensibility and efficiency.Our strategy can significantly accelerate the optimization of metal 3D printing and facilitate adaptable design to accommodate diverse materials and requirements.
基金This study is financially supported by StateKey Laboratory of Alternate Electrical Power System with Renewable Energy Sources(Grant No.LAPS22012).
文摘This paper aims to propose a topology optimization method on generating porous structures comprising multiple materials.The mathematical optimization formulation is established under the constraints of individual volume fraction of constituent phase or total mass,as well as the local volume fraction of all phases.The original optimization problem with numerous constraints is converted into a box-constrained optimization problem by incorporating all constraints to the augmented Lagrangian function,avoiding the parameter dependence in the conventional aggregation process.Furthermore,the local volume percentage can be precisely satisfied.The effects including the globalmass bound,the influence radius and local volume percentage on final designs are exploited through numerical examples.The numerical results also reveal that porous structures keep a balance between the bulk design and periodic design in terms of the resulting compliance.All results,including those for irregular structures andmultiple volume fraction constraints,demonstrate that the proposedmethod can provide an efficient solution for multiple material infill structures.
基金financially supported by the Technology Development Fund of China Academy of Machinery Science and Technology(No.170221ZY01)。
文摘Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.
基金National Natural Science Foundation of China under Grant Nos.51921006 and 51725801Fundamental Research Funds for the Central Universities under Grant No.FRFCU5710093320Heilongjiang Touyan Innovation Team Program。
文摘Reinforcement corrosion is the main cause of performance deterioration of reinforced concrete(RC)structures.Limited research has been performed to investigate the life-cycle cost(LCC)of coastal bridge piers with nonuniform corrosion using different materials.In this study,a reliability-based design optimization(RBDO)procedure is improved for the design of coastal bridge piers using six groups of commonly used materials,i.e.,normal performance concrete(NPC)with black steel(BS)rebar,high strength steel(HSS)rebar,epoxy coated(EC)rebar,and stainless steel(SS)rebar(named NPC-BS,NPC-HSS,NPC-EC,and NPC-SS,respectively),NPC with BS with silane soakage on the pier surface(named NPC-Silane),and high-performance concrete(HPC)with BS rebar(named HPC-BS).First,the RBDO procedure is improved for the design optimization of coastal bridge piers,and a bridge is selected to illustrate the procedure.Then,reliability analysis of the pier designed with each group of materials is carried out to obtain the time-dependent reliability in terms of the ultimate and serviceability performances.Next,the repair time of the pier is predicted based on the time-dependent reliability indices.Finally,the time-dependent LCCs for the pier are obtained for the selection of the optimal design.
基金the National Natural Science Foundation of China(21962008)Yunnan Province Excellent Youth Fund Project(202001AW070005)+1 种基金Candidate Talents Training Fund of Yunnan Province(2017PY269SQ,2018HB007)Yunnan Ten Thousand Talents Plan Young&Elite Talents Project(YNWR-QNBJ-2018-346).
文摘Electrochemical water splitting has long been considered an effective energy conversion technology for trans-ferring intermittent renewable electricity into hydrogen fuel,and the exploration of cost-effective and high-performance electrocatalysts is crucial in making electrolyzed water technology commercially viable.Cobalt phosphide(Co-P)has emerged as a catalyst of high potential owing to its high catalytic activity and durability in water splitting.This paper systematically reviews the latest advances in the development of Co-P-based materials for use in water splitting.The essential effects of P in enhancing the catalytic performance of the hydrogen evolution reaction and oxygen evolution reaction are first outlined.Then,versatile synthesis techniques for Co-P electrocatalysts are summarized,followed by advanced strategies to enhance the electrocatalytic performance of Co-P materials,including heteroatom doping,composite construction,integration with well-conductive sub-strates,and structure control from the viewpoint of experiment.Along with these optimization strategies,the understanding of the inherent mechanism of enhanced catalytic performance is also discussed.Finally,some existing challenges in the development of highly active and stable Co-P-based materials are clarified,and pro-spective directions for prompting the wide commercialization of water electrolysis technology are proposed.
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
