Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous g...Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.展开更多
Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of...Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.展开更多
Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address t...Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.展开更多
The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimi...The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.展开更多
Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes canno...Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes cannot achieve high active material loading and efficient ion/electron transport simultaneously.By contrast,three-dimensional(3D)structures have attracted increasing interest because of their capacity to enhance active material utilization,shorten ion and electron transport pathways,reduce interfacial impedance,and provide spatial accommodation for volume expansion.Additive manufacturing(AM)technology effectively fabricates energy-storage materials with 3D structures by accurately constructing complex 3D structures via layer-by-layer deposition.Recent studies have employed AM to construct ordered 3D electrodes that can optimize ion/electron transport,regulate electric field distribution,or improve the electrode-electrolyte interface,thereby contributing to enhanced kinetic performance and cycling stability.This review systematically summarizes the applications of several AM technologies in the fabrication of energy storage materials and analyzes their respective advantages and limitations.Subsequently,the advantages of AM technology in the fabrication of energy storage materials and several major optimization strategies are comprehensively discussed.Finally,the major challenges and potential applications of AM technology in energy storage material optimization are discussed.展开更多
Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement ...Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.展开更多
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
Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated therma...Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated thermal conditions,sluggish dehydrogena-tion kinetics,and high thermodynamic stability,limit its practical application.One effective method of addressing these challenges is cata-lyst doping,which effectively boosts the hydrogen storage capability of Mg-based materials.Herein,we review recent advancements in catalyst-doped MgH_(2) composites,with particular focus on multicomponent and high-entropy catalysts.Structure-property relationships and catalytic mechanisms in these doping strategies are also summarized.Finally,based on existing challenges,we discuss future research directions for the development of Mg-based hydrogen storage systems.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
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
Capacitive deionization(CDI),as an emerging desalination technique,has been intensively explored because of its energy-saving,cost-effectiveness and sustainability.Despite the promise,CDI systems still encounter vario...Capacitive deionization(CDI),as an emerging desalination technique,has been intensively explored because of its energy-saving,cost-effectiveness and sustainability.Despite the promise,CDI systems still encounter various challenges involving active sites,mass transfer and stability that severely limit their further application.So far,there is still much-limited review across material,electrodes and devices to cope with the above challenges.Notably,carbon nanotubes(CNTs),have garnered significant attention owing to their exceptional conductivity,high specific surface area(S_(BET)),unique skeleton role and superior mechanical strength.More importantly,CNTs serve multifunctional roles in CDI systems,including active materials,conductive agents,binders,and even current collectors,while also making for the thick electrode framework construction.Specifically,this review first discusses current challenges in CDI system design.Subsequently,it systemic highlights how CNTs address these issues through material innovation,electrode optimization and device integration.Eventually,a conceptual model for CNT composite self-supporting CDI systems is further proposed,aiming to exploit advanced CDI desalination systems.Overall,this review underscores the pivotal role of CNTs in overcoming technical bottlenecks and driving the practical application of CDI for sustainable water treatment.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
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
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
A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic the...A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.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
Auxetic metamaterials have attracted much attention due to their outstanding advantages over traditional materials in terms of shear capacity,fracture resistance,and energy absorption.However,there are lack of design ...Auxetic metamaterials have attracted much attention due to their outstanding advantages over traditional materials in terms of shear capacity,fracture resistance,and energy absorption.However,there are lack of design inspirations for novel auxetic structures.According to the materials databases of atomic lattice,some natural crystals possess negative Poisson’s ratio(NPR).In this paper,the mechanism of auxeticity in microscale Ti crystal is investigated through density functional theory simulation.Then we propose a macroscopic auxetic metamaterial by mimicking the microscopic atomic lattice structure of the bodycentered cubic Ti crystal.The NPR property of the macroscopic metamaterial is verified by theoretical,numerical and experimental methods.The auxeticity keeps effective when scaling up to macroscopic Ti crystal-mimic structure,with the similar deformation mechanism.Furthermore,from the geometric parameter investigation,the geometric parameters have great influence on the Poisson’s ratio and Young’s modulus of the macroscopic metamaterial.Importantly,an optimized structure is obtained,which exhibits 2 times enhancement in auxeticity and 25 times enhancement in normalized Young’s modulus,compared to the original architecture.This work establishes a link between the physical properties at micro-nanoscale and macroscale structures,which provides inspirations for high load-bearing auxetic metamaterials.展开更多
基金supported by the National Natural Science Foundation of China(No.52104018,52274030)China National Petroleum Corporation(CNPC)Innovation Foundation(No.2024DQ02-0303)China National Petroleum Corporation(CNPC)14th Five-Year Plan Major Strategic Scientific and Technological Project for Prospective and Fundamental Research(2024DJ86).
文摘Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.
基金Natural Science Foundation of Beijing Municipality under Grant L243004the National Natural Science Foundation of China under Grant 62403060.
文摘Protective hardware is essential for mitigating damage caused by unavoidable falls in humanoid robots.Despite notable progress in fall protection hardware,the theoretical foundation for modeling and the feasibility of conducting full-scale fall experiments on robots or their surrogates remain somewhat limited.This paper proposes a method for optimizing the thickness of Expandable Polyethylene(EPE),which is used as back protection for the Chubao humanoid robot,based on small-scale impact test data to predict full-scale behavior.The optimal thickness is defined as a balance between compact design and protective effectiveness.An equivalent impact model characterized by four parameters:contact area S,mass m,fall height h,and cushioning material thickness d is introduced to describe impact conditions.The relationship between the peak impact acceleration ap and material thickness d,which forms the core of the method and gives rise to the name AP-D,is analyzed through their plotted curves.After introducing three characteristic parameters and two correction fac-tors,the relationship among the aforementioned variables is derived.Subsequently,both the optimal thickness do and its corresponding peak impact acceleration aop are predicted via nonlinear and linear regression models.Finally,the accuracy and effectiveness of the theoretically derived optimal thickness are validated on both a dummy and the actual robot.With the cushioning material applied,the peak chest acceleration is reduced to 41.57g for the dummy and 32.08g for the robot.
基金supported by the Fundamental Research Funds for the Provincial Universities (Grant No.2024-KYYWF-0141)the National Natural Science Foundation of China (Grant Nos.52406076,52227813)+1 种基金the National Key Research and Development Program of China (Grant No.2022YFE0133900)the China Postdoctoral Science Foundation (Grant No.2023M740905)。
文摘Visible lighting and energy-saving are dual needs of energy efficiency and occupant comfort in modern buildings.In this study,a smart window based on phase-change material VO_(2) is designed and optimized to address the critical challenges in building energy management.The proposed phase-adaptive radiative(PAR)coating is a multilayer nanostructure consisting of TiO/VO_(2)2/TiO/Ag_(2) and polydimethylsiloxane(PDMS).For different VO_(2) phases,visible transmittance T_(vis)>0.6 and emissivity difference in the atmospheric window Δε_(AW)=0.422 can be achieved,which means the PAR window can transfer interior heat to the outside through thermal radiation for cooling or minimize thermal emission for insulation,while ensuring the transmission of visible light for natural daylighting.Compared to normal glass,the PAR window has an average temperature drop of 14.8℃.The year-round energy-saving calculation for four different cities in China indicates that the PAR window can save 22%-32% of the annual cooling and heating energy consumption by seamlessly transitioning between two phases of VO_(2)modes.The multi-objective optimization of the phase-adaptive radiative smart window provides a potential strategy for energy saving.
基金Project supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2025-02303676)。
文摘The stress minimization multi-material topology optimization(MMTO)approach has recently attracted significant attention because of its applications in aerospace and mechanical engineering.Nonetheless,the stress minimization MMTO approach may result in stress surpassing the material's tolerance limit,potentially culminating in failure.This research proposes a novel way for imposing stress constraints on each material to regulate their respective stress levels.The fundamental concept is that each material possesses its own interpolation function for the stress model.The maximum von Mises stress for each material can be established with the definition of an upper limit,ensuring that the materials will perform safely and effectively.This aids topological structures in resisting failure and augmenting strength.A multi-physics system including thermoelastic and self-weight loads is concurrently examined alongside stress limitations.The global stress constraint utilizes the p-norm function,and the adjoint method is used to derive sensitivity.This work employs a three-field strategy utilizing density filtering and Heaviside projection functions to mitigate the artificial stress in low density.The technique is assessed through two-dimensional(2D)and three-dimensional(3D)examples,illustrating the influence of stress limits on the compliance minimization under heat and self-weight loads.The optimized results indicate a substantial decrease in the stress levels accompanied by a minor gain in compliance,while maintaining the stress within the specified range for all materials.
基金support of the National Natural Science Foundation of China(No.52574411)Beijing Natural Science Foundation(No.2242043).
文摘Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes cannot achieve high active material loading and efficient ion/electron transport simultaneously.By contrast,three-dimensional(3D)structures have attracted increasing interest because of their capacity to enhance active material utilization,shorten ion and electron transport pathways,reduce interfacial impedance,and provide spatial accommodation for volume expansion.Additive manufacturing(AM)technology effectively fabricates energy-storage materials with 3D structures by accurately constructing complex 3D structures via layer-by-layer deposition.Recent studies have employed AM to construct ordered 3D electrodes that can optimize ion/electron transport,regulate electric field distribution,or improve the electrode-electrolyte interface,thereby contributing to enhanced kinetic performance and cycling stability.This review systematically summarizes the applications of several AM technologies in the fabrication of energy storage materials and analyzes their respective advantages and limitations.Subsequently,the advantages of AM technology in the fabrication of energy storage materials and several major optimization strategies are comprehensively discussed.Finally,the major challenges and potential applications of AM technology in energy storage material optimization are discussed.
文摘Inverse design of advanced materials represents a pivotal challenge in materials science.Leveraging the latent space of Variational Autoencoders(VAEs)for material optimization has emerged as a significant advancement in the field of material inverse design.However,VAEs are inherently prone to generating blurred images,posing challenges for precise inverse design and microstructure manufacturing.While increasing the dimensionality of the VAE latent space can mitigate reconstruction blurriness to some extent,it simultaneously imposes a substantial burden on target optimization due to an excessively high search space.To address these limitations,this study adopts a Variational Autoencoder guided Conditional Diffusion Generative Model(VAE-CDGM)framework integrated with Bayesian optimization to achieve the inverse design of composite materials with targeted mechanical properties.The VAE-CDGM model synergizes the strengths of VAEs and Denoising Diffusion Probabilistic Models(DDPM),enabling the generation of high-quality,sharp images while preserving a manipulable latent space.To accommodate varying dimensional requirements of the latent space,two optimization strategies are proposed.When the latent space dimensionality is excessively high,SHapley Additive exPlanations(SHAP)sensitivity analysis is employed to identify critical latent features for optimization within a reduced subspace.Conversely,direct optimization is performed in the low-dimensional latent space of VAE-CDGM when dimensionality is modest.The results demonstrate that both strategies accurately achieve the targeted design of composite materials while circumventing the blurred reconstruction flaws of VAEs,which offers a novel pathway for the precise design of advanced materials.
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.
基金financially supported by the National Key Research and Development Program of China (No. 2021YFB4000604)the National Natural Science Foundation of China (No. 52271220)+2 种基金the 111 Project (No. B12015)the Fundamental Research Funds for the Central UniversitiesHaihe Laboratory of Sustainable Chemical Transformations, Guangxi Collaborative Innovation Centre of Structure and Property for New Energy and Materials, Science Research and Technology Development Project of Guilin (No. 20210102-4)
文摘Novel hydrogen storage materials have propelled progress in hydrogen storage technologies.Magnesium hydride(MgH_(2))is a highly promising candidate.Nevertheless,several drawbacks,including the need for elevated thermal conditions,sluggish dehydrogena-tion kinetics,and high thermodynamic stability,limit its practical application.One effective method of addressing these challenges is cata-lyst doping,which effectively boosts the hydrogen storage capability of Mg-based materials.Herein,we review recent advancements in catalyst-doped MgH_(2) composites,with particular focus on multicomponent and high-entropy catalysts.Structure-property relationships and catalytic mechanisms in these doping strategies are also summarized.Finally,based on existing challenges,we discuss future research directions for the development of Mg-based hydrogen storage systems.
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.
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financial support of the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grants No.163105819)Nanjing Forestry University High-level Talent Introduction Research Foundation(Grants No.163105774)。
文摘Capacitive deionization(CDI),as an emerging desalination technique,has been intensively explored because of its energy-saving,cost-effectiveness and sustainability.Despite the promise,CDI systems still encounter various challenges involving active sites,mass transfer and stability that severely limit their further application.So far,there is still much-limited review across material,electrodes and devices to cope with the above challenges.Notably,carbon nanotubes(CNTs),have garnered significant attention owing to their exceptional conductivity,high specific surface area(S_(BET)),unique skeleton role and superior mechanical strength.More importantly,CNTs serve multifunctional roles in CDI systems,including active materials,conductive agents,binders,and even current collectors,while also making for the thick electrode framework construction.Specifically,this review first discusses current challenges in CDI system design.Subsequently,it systemic highlights how CNTs address these issues through material innovation,electrode optimization and device integration.Eventually,a conceptual model for CNT composite self-supporting CDI systems is further proposed,aiming to exploit advanced CDI desalination systems.Overall,this review underscores the pivotal role of CNTs in overcoming technical bottlenecks and driving the practical application of CDI for sustainable water treatment.
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.
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.
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.
基金supported by the National Natural Science Foundation of China(No.62033014)the Application Projects of Integrated Standardization and New Paradigm for Intelligent Manufacturing from the Ministry of Industry and Information Technology of China in 2016,and the Fundamental Research Funds for the Central Universities of Central South University,China(No.2021zzts0700).
文摘A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints.An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established.Then,optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements.Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size,with root mean square errors of approximately 5%and 3%,respectively.Furthermore,the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31%and keeping temperature by 11.96%.This research provides new insights and methods for the preparation of ternary cathode materials.
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 Key R&D Program of China(Grant No.2021YFA1400300)the National Natural Science Foundation of China(Grant Nos.12172047,12372177,and 12102007)+1 种基金Beijing Natural Science Foundation(Grant Nos.1244057 and Z190011)Beijing Institute of Technology Research Fund Program for Young Scholars.
文摘Auxetic metamaterials have attracted much attention due to their outstanding advantages over traditional materials in terms of shear capacity,fracture resistance,and energy absorption.However,there are lack of design inspirations for novel auxetic structures.According to the materials databases of atomic lattice,some natural crystals possess negative Poisson’s ratio(NPR).In this paper,the mechanism of auxeticity in microscale Ti crystal is investigated through density functional theory simulation.Then we propose a macroscopic auxetic metamaterial by mimicking the microscopic atomic lattice structure of the bodycentered cubic Ti crystal.The NPR property of the macroscopic metamaterial is verified by theoretical,numerical and experimental methods.The auxeticity keeps effective when scaling up to macroscopic Ti crystal-mimic structure,with the similar deformation mechanism.Furthermore,from the geometric parameter investigation,the geometric parameters have great influence on the Poisson’s ratio and Young’s modulus of the macroscopic metamaterial.Importantly,an optimized structure is obtained,which exhibits 2 times enhancement in auxeticity and 25 times enhancement in normalized Young’s modulus,compared to the original architecture.This work establishes a link between the physical properties at micro-nanoscale and macroscale structures,which provides inspirations for high load-bearing auxetic metamaterials.
