The analysis of structure and dynamics in chaotic systems has long been a significant research direction in nonlinear science.Constructing a reliable chaotic system with rich dynamical characteristics is essential for...The analysis of structure and dynamics in chaotic systems has long been a significant research direction in nonlinear science.Constructing a reliable chaotic system with rich dynamical characteristics is essential for secure communication applications.Existing memristor-based chaotic maps are typically obtained by incorporating discrete mathematical models of memristors into basic chaotic maps.In this study,a simple memristive nonlinear circuit is first designed,from which a memristive oscillator is derived.Subsequently,a memristive map is developed from this oscillator through a linear transformation of the variables.The reliability of the new map is validated through nonlinear dynamic analysis.The results demonstrate that the map exhibits complex nonlinear dynamics under different parameter settings.This finding is beneficial for the construction of memristor maps and the development of image encryption algorithms.展开更多
Soybean seeds contain approximately 40% protein,making soybeans an important source of plant-based protein.Research on QTN mapping,molecular design breeding and mining of genes related to seed protein formation provid...Soybean seeds contain approximately 40% protein,making soybeans an important source of plant-based protein.Research on QTN mapping,molecular design breeding and mining of genes related to seed protein formation provides guiding significance for the analysis of the underlying genetic mechanisms of seed protein formation and the selection of high-protein varieties.The seed protein contents(SPCs)of 144 lines of a soybean four-way recombinant inbred line(FW-RIL)population were determined in 8 environments.A three-variance component multisite random effects mixed linear model(3VmrMLM)was used to conduct a genome-wide association study on protein content.A single detected QTN explained 0.53%-3.37% of the phenotypic variation.A molecular-assisted selection breeding model containing the18 QTNs explained 51.97% of the phenotypic variation in protein content.Eight biparental and five triparental crosses that produced excellent lines with the greatest protein content-related genotype values that could be generated by phenotypic and molecular-assisted selection were screened.An LD block of 17QTNs(QEIs)was identified,and one key candidate gene related to protein formation was predicted by haplotype analysis.The proportion of Hap 1 varieties in the spring-sowing soybean region in North China was lower than those in the Huang-Huai-Hai soybean region in Central China and the multiripe soybean region in South China.The proportion of Hap 1 varieties among the wild varieties and landraces was greater than that among the improved varieties.The results of this study provide important insights into the genetic basis of soybean protein content and information to aid in molecular design breeding methods to improve protein content.展开更多
Powder bed fusion(PBF)in metallic additive manufacturing offers the ability to produce intricate geometries,high-strength components,and reliable products.However,powder processing before energy-based binding signific...Powder bed fusion(PBF)in metallic additive manufacturing offers the ability to produce intricate geometries,high-strength components,and reliable products.However,powder processing before energy-based binding significantly impacts the final product’s integrity.Processing maps guide efficient process design to minimize defects,but creating them through experimentation alone is challenging due to the wide range of parameters,necessitating a comprehensive computational parametric analysis.In this study,we used the discrete element method to parametrically analyze the powder processing design space in PBF of stainless steel 316L powders.Uniform lattice parameter sweeps are often used for parametric analysis,but are computationally intensive.We find that non-uniform parameter sweep based on the low discrepancy sequence(LDS)algorithm is ten times more efficient at exploring the design space while accurately capturing the relationship between powder flow dynamics and bed packing density.We introduce a multi-layer perceptron(MLP)model to interpolate parametric causalities within the LDS parameter space.With over 99%accuracy,it effectively captures these causalities while requiring fewer simulations.Finally,we generate processing design maps for machine setups and powder selections for efficient process design.We find that recoating speed has the highest impact on powder processing quality,followed by recoating layer thickness,particle size,and inter-particle friction.展开更多
Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon...Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon neutrality goals.The hydrogenation of CO_(2)to methanol not only enables carbon sequestration and recycling,but also provides a route to produce high value-added fuels and basic chemical feedstocks,holding significant environmental and economic potential.However,this conversion process is thermodynamically and kinetically limited,and traditional catalyst systems(e.g.,Cu/ZnO/Al_(2)O_(3))exhibit inadequate activity,selectivity,and stability under mild conditions.Therefore,the development of novel high-performance catalysts with precisely tunable structures and functionalities is imperative.Metal-organic frameworks(MOFs),as crystalline porous materials with high surface area,tunable pore structures,and diverse metal-ligand compositions,have the great potential in CO_(2)hydrogenation catalysis.Their structural design flexibility allows for the construction of well-dispersed active sites,tailored electronic environments,and enhanced metal-support interactions.This review systematically summarizes the recent advances in MOF-based and MOF-derived catalysts for CO_(2)hydrogenation to methanol,focusing on four design strategies:(1)spatial confinement and in situ construction,(2)defect engineering and ion-exchange,(3)bimetallic synergy and hybrid structure design,and(4)MOF-derived nanomaterial synthesis.These approaches significantly improve CO_(2)conversion and methanol selectivity by optimizing metal dispersion,interfacial structures,and reaction pathways.The reaction mechanism is further explored by focusing on the three main reaction pathways:the formate pathway(HCOO*),the RWGS(Reverse Water Gas Shift reaction)+CO*hydrogenation pathway,and the trans-COOH pathway.In situ spectroscopic studies and density functional theory(DFT)calculations elucidate the formation and transformation of key intermediates,as well as the roles of active sites,metal-support interfaces,oxygen vacancies,and promoters.Additionally,representative catalytic performance data for MOFbased systems are compiled and compared,demonstrating their advantages over traditional catalysts in terms of CO_(2)conversion,methanol selectivity,and space-time yield.Future perspectives for MOF-based CO_(2)hydrogenation catalysts will prioritize two main directions:structural design and mechanistic understanding.The precise construction of active sites through multi-metallic synergy,defect engineering,and interfacial electronic modulation should be made to enhance catalyst selectivity and stability.In addition,advanced in situ characterization techniques combined with theoretical modeling are essential to unravel the detailed reaction mechanisms and intermediate behaviors,thereby guiding rational catalyst design.Moreover,to enable industrial application,challenges related to thermal/hydrothermal stability,catalyst recyclability,and cost-effective large-scale synthesis must be addressed.The development of green,scalable preparation methods and the integration of MOF catalysts into practical reaction systems(e.g.,flow reactors)will be crucial for bridging the gap between laboratory research and commercial deployment.Ultimately,multi-scale structure-performance optimization and catalytic system integration will be vital for accelerating the industrialization of MOF-based CO_(2)-to-methanol technologies.展开更多
In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honey...In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.展开更多
Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI ...Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully.Rapid progress has been made in the field of advanced chips recently,such as the development of photonic computing,the advancement of the quantum processors,the boost of the biomimetic chips,and so on.Designs tactics of the advanced chips can be conducted with elaborated consideration of materials,algorithms,models,architectures,and so on.Though a few reviews present the development of the chips from their unique aspects,reviews in the view of the latest design for advanced and AI chips are few.Here,the newest development is systematically reviewed in the field of advanced chips.First,background and mechanisms are summarized,and subsequently most important considerations for co-design of the software and hardware are illustrated.Next,strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration,after which the design thought for the advanced chips in the future is proposed.Finally,some perspectives are put forward.展开更多
To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capabl...To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si...Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.展开更多
Subcritical reactors(SCRs)or subcritical assemblies(SCAs)are the main infrastructure for designing power reactors.These reactors are widely used for training and research because of their high level of inherent safety...Subcritical reactors(SCRs)or subcritical assemblies(SCAs)are the main infrastructure for designing power reactors.These reactors are widely used for training and research because of their high level of inherent safety.The objective of this study is to design a subcritical reactor using a pressurized water reactor(PWR)conventional fuel following two safety points.In the first approach,deeply placed SCR cores with an infinite multiplication factor(k_(∞))of less than unity were identified using the DRAGON lattice code.In the second approach,subcritical reactor cores with an effective multiplication factor(k_(eff))of less than unity were determined by coupling the cell calculations of the DRAGON lattice code and core calculations of the DONJON code.For the deeply subcritical reactor design,it was found that the reactor would remain inherently subcritical while using fuel rods with ^(235)U enrichment of up to 0.9%,regardless of the pitch of the fuel rods.In the second approach,the optimal pitches(1.3 to 2.3 cm)were determined for different fuel enrichment values from 1 to 5%.Subsequently,the k_(eff) was obtained for a fuel rod arrangement of 8×8 to 80×80,and the states in which the reactor would be subcritical were determined for different fuel enrichments at the corresponding optimal pitch.To validate the models used in the DRAGON and DONJON codes,the k_(eff) of the Isfahan Light Water Subcritical Reactor(LWSCR)was experimentally measured and compared with the results of the calculations.Finally,the effects of fuel and moderator temperature changes were investigated to ensure that the designed assemblies remained in the subcritical state at all operational temperatures.展开更多
An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map nav...An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility;this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.展开更多
Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method （FEM） analysis commerce software. M...Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method （FEM） analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output dam. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrpolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system （SSHLPS） is calculated as an instance. A three-layer back-propagation （BP） net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrpulation contrasts with integrity re-analysis. Any layer SSHLPS among 1-8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is appfied into the same topological structure, with reduced distortion and assured precision.展开更多
To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measur...To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measurements,and a data-mining method.The simulation is based on a computational thermal-fluid dynamics(CtFD)model,which can obtain thermal behavior,solidification parameters such as cooling rate,and the dilution of solidified clad.Based on the computed thermal information,dendrite arm spacing and microhardness are estimated using well-tested mechanistic models.Experimental microstructure and microhardness are determined and compared with the simulated values for validation.To visualize process-structure-properties(PSPs)linkages,the simulation and experimental datasets are input to a data-mining model-a self-organizing map(SOM).The design windows of the process parameters under multiple objectives can be obtained from the visualized maps.The proposed approaches can be utilized in AM and other data-intensive processes.Data-driven linkages between process,structure,and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.展开更多
基金supported by the Youth Innovation Team of Shaanxi Universities。
文摘The analysis of structure and dynamics in chaotic systems has long been a significant research direction in nonlinear science.Constructing a reliable chaotic system with rich dynamical characteristics is essential for secure communication applications.Existing memristor-based chaotic maps are typically obtained by incorporating discrete mathematical models of memristors into basic chaotic maps.In this study,a simple memristive nonlinear circuit is first designed,from which a memristive oscillator is derived.Subsequently,a memristive map is developed from this oscillator through a linear transformation of the variables.The reliability of the new map is validated through nonlinear dynamic analysis.The results demonstrate that the map exhibits complex nonlinear dynamics under different parameter settings.This finding is beneficial for the construction of memristor maps and the development of image encryption algorithms.
基金funded by the Opening Competition Mechanism to Select the Best Candidates Project of Heilongjiang Province for Science and Technology Science(2023ZXJ02B02)。
文摘Soybean seeds contain approximately 40% protein,making soybeans an important source of plant-based protein.Research on QTN mapping,molecular design breeding and mining of genes related to seed protein formation provides guiding significance for the analysis of the underlying genetic mechanisms of seed protein formation and the selection of high-protein varieties.The seed protein contents(SPCs)of 144 lines of a soybean four-way recombinant inbred line(FW-RIL)population were determined in 8 environments.A three-variance component multisite random effects mixed linear model(3VmrMLM)was used to conduct a genome-wide association study on protein content.A single detected QTN explained 0.53%-3.37% of the phenotypic variation.A molecular-assisted selection breeding model containing the18 QTNs explained 51.97% of the phenotypic variation in protein content.Eight biparental and five triparental crosses that produced excellent lines with the greatest protein content-related genotype values that could be generated by phenotypic and molecular-assisted selection were screened.An LD block of 17QTNs(QEIs)was identified,and one key candidate gene related to protein formation was predicted by haplotype analysis.The proportion of Hap 1 varieties in the spring-sowing soybean region in North China was lower than those in the Huang-Huai-Hai soybean region in Central China and the multiripe soybean region in South China.The proportion of Hap 1 varieties among the wild varieties and landraces was greater than that among the improved varieties.The results of this study provide important insights into the genetic basis of soybean protein content and information to aid in molecular design breeding methods to improve protein content.
基金supported by the funding provided by Boeing Center for Aviation and Aerospace Safety.
文摘Powder bed fusion(PBF)in metallic additive manufacturing offers the ability to produce intricate geometries,high-strength components,and reliable products.However,powder processing before energy-based binding significantly impacts the final product’s integrity.Processing maps guide efficient process design to minimize defects,but creating them through experimentation alone is challenging due to the wide range of parameters,necessitating a comprehensive computational parametric analysis.In this study,we used the discrete element method to parametrically analyze the powder processing design space in PBF of stainless steel 316L powders.Uniform lattice parameter sweeps are often used for parametric analysis,but are computationally intensive.We find that non-uniform parameter sweep based on the low discrepancy sequence(LDS)algorithm is ten times more efficient at exploring the design space while accurately capturing the relationship between powder flow dynamics and bed packing density.We introduce a multi-layer perceptron(MLP)model to interpolate parametric causalities within the LDS parameter space.With over 99%accuracy,it effectively captures these causalities while requiring fewer simulations.Finally,we generate processing design maps for machine setups and powder selections for efficient process design.We find that recoating speed has the highest impact on powder processing quality,followed by recoating layer thickness,particle size,and inter-particle friction.
基金Supported by the National Key Research and Development Program of China(2023YFB4104500,2023YFB4104502)the National Natural Science Foundation of China(22138013)the Taishan Scholar Project(ts201712020).
文摘Against the backdrop of escalating global climate change and energy crises,the resource utilization of carbon dioxide(CO_(2)),a major greenhouse gas,has become a crucial pathway for achieving carbon peaking and carbon neutrality goals.The hydrogenation of CO_(2)to methanol not only enables carbon sequestration and recycling,but also provides a route to produce high value-added fuels and basic chemical feedstocks,holding significant environmental and economic potential.However,this conversion process is thermodynamically and kinetically limited,and traditional catalyst systems(e.g.,Cu/ZnO/Al_(2)O_(3))exhibit inadequate activity,selectivity,and stability under mild conditions.Therefore,the development of novel high-performance catalysts with precisely tunable structures and functionalities is imperative.Metal-organic frameworks(MOFs),as crystalline porous materials with high surface area,tunable pore structures,and diverse metal-ligand compositions,have the great potential in CO_(2)hydrogenation catalysis.Their structural design flexibility allows for the construction of well-dispersed active sites,tailored electronic environments,and enhanced metal-support interactions.This review systematically summarizes the recent advances in MOF-based and MOF-derived catalysts for CO_(2)hydrogenation to methanol,focusing on four design strategies:(1)spatial confinement and in situ construction,(2)defect engineering and ion-exchange,(3)bimetallic synergy and hybrid structure design,and(4)MOF-derived nanomaterial synthesis.These approaches significantly improve CO_(2)conversion and methanol selectivity by optimizing metal dispersion,interfacial structures,and reaction pathways.The reaction mechanism is further explored by focusing on the three main reaction pathways:the formate pathway(HCOO*),the RWGS(Reverse Water Gas Shift reaction)+CO*hydrogenation pathway,and the trans-COOH pathway.In situ spectroscopic studies and density functional theory(DFT)calculations elucidate the formation and transformation of key intermediates,as well as the roles of active sites,metal-support interfaces,oxygen vacancies,and promoters.Additionally,representative catalytic performance data for MOFbased systems are compiled and compared,demonstrating their advantages over traditional catalysts in terms of CO_(2)conversion,methanol selectivity,and space-time yield.Future perspectives for MOF-based CO_(2)hydrogenation catalysts will prioritize two main directions:structural design and mechanistic understanding.The precise construction of active sites through multi-metallic synergy,defect engineering,and interfacial electronic modulation should be made to enhance catalyst selectivity and stability.In addition,advanced in situ characterization techniques combined with theoretical modeling are essential to unravel the detailed reaction mechanisms and intermediate behaviors,thereby guiding rational catalyst design.Moreover,to enable industrial application,challenges related to thermal/hydrothermal stability,catalyst recyclability,and cost-effective large-scale synthesis must be addressed.The development of green,scalable preparation methods and the integration of MOF catalysts into practical reaction systems(e.g.,flow reactors)will be crucial for bridging the gap between laboratory research and commercial deployment.Ultimately,multi-scale structure-performance optimization and catalytic system integration will be vital for accelerating the industrialization of MOF-based CO_(2)-to-methanol technologies.
基金the financial supports from National Key R&D Program for Young Scientists of China(Grant No.2022YFC3080900)National Natural Science Foundation of China(Grant No.52374181)+1 种基金BIT Research and Innovation Promoting Project(Grant No.2024YCXZ017)supported by Science and Technology Innovation Program of Beijing institute of technology under Grant No.2022CX01025。
文摘In this study,an inverse design framework was established to find lightweight honeycomb structures(HCSs)with high impact resistance.The hybrid HCS,composed of re-entrant(RE)and elliptical annular re-entrant(EARE)honeycomb cells,was created by constructing arrangement matrices to achieve structural lightweight.The machine learning(ML)framework consisted of a neural network(NN)forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs.The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset,and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space.The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains.By guiding the iteration direction of the optimization algorithm,lightweight designs with high impact resistance were identified.The energy absorption capacity of the optimal design reached 94.98%of the EARE honeycomb,while the initial peak stress and mass decreased by 28.85%and 19.91%,respectively.Furthermore,Shapley Additive Explanations(SHAP)for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance.By reducing the stiffness of the cells at the top boundary of the structure,the initial impact damage sustained by the structure can be significantly improved.Overall,this study proposed a general lightweight design method for array structures under impact loads,which is beneficial for the widespread application of honeycomb-based protective structures.
基金supported by the Hong Kong Polytechnic University(1-WZ1Y,1-W34U,4-YWER).
文摘Recent years have witnessed transformative changes brought about by artificial intelligence(AI)techniques with billions of parameters for the realization of high accuracy,proposing high demand for the advanced and AI chip to solve these AI tasks efficiently and powerfully.Rapid progress has been made in the field of advanced chips recently,such as the development of photonic computing,the advancement of the quantum processors,the boost of the biomimetic chips,and so on.Designs tactics of the advanced chips can be conducted with elaborated consideration of materials,algorithms,models,architectures,and so on.Though a few reviews present the development of the chips from their unique aspects,reviews in the view of the latest design for advanced and AI chips are few.Here,the newest development is systematically reviewed in the field of advanced chips.First,background and mechanisms are summarized,and subsequently most important considerations for co-design of the software and hardware are illustrated.Next,strategies are summed up to obtain advanced and AI chips with high excellent performance by taking the important information processing steps into consideration,after which the design thought for the advanced chips in the future is proposed.Finally,some perspectives are put forward.
基金funded by the Science and Technology Projects of State Grid Corporation of China(Project No.J2024136).
文摘To ensure an uninterrupted power supply,mobile power sources(MPS)are widely deployed in power grids during emergencies.Comprising mobile emergency generators(MEGs)and mobile energy storage systems(MESS),MPS are capable of supplying power to critical loads and serving as backup sources during grid contingencies,offering advantages such as flexibility and high resilience through electricity delivery via transportation networks.This paper proposes a design method for a 400 V–10 kV Dual-Winding Induction Generator(DWIG)intended for MEG applications,employing an improved particle swarmoptimization(PSO)algorithmbased on a back-propagation neural network(BPNN).A parameterized finite element(FE)model of the DWIG is established to derive constraints on its dimensional parameters,thereby simplifying the optimization space.Through sensitivity analysis between temperature rise and electromagnetic loss of the DWIG,the main factors influencing the machine’s temperature are identified,and electromagnetic loss is determined as the optimization objective.To obtain an accurate fitting function between electromagnetic loss and dimensional parameters,the BPNN is employed to predict the nonlinear relationship between the optimization objective and the parameters.The Latin hypercube sampling(LHS)method is used for random sampling in the FE model analysis for training,testing,and validation,which is then applied to compute the cost function in the PSO.Based on the relationships obtained by the BPNN,the PSO algorithm evaluates the fitness and cost functions to determine the optimal design point.The proposed optimization method is validated by comparing simulation results between the initial design and the optimized design.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金financially supported by the National Key Research and Development Program of China (2022YFB3706802)。
文摘Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.
文摘Subcritical reactors(SCRs)or subcritical assemblies(SCAs)are the main infrastructure for designing power reactors.These reactors are widely used for training and research because of their high level of inherent safety.The objective of this study is to design a subcritical reactor using a pressurized water reactor(PWR)conventional fuel following two safety points.In the first approach,deeply placed SCR cores with an infinite multiplication factor(k_(∞))of less than unity were identified using the DRAGON lattice code.In the second approach,subcritical reactor cores with an effective multiplication factor(k_(eff))of less than unity were determined by coupling the cell calculations of the DRAGON lattice code and core calculations of the DONJON code.For the deeply subcritical reactor design,it was found that the reactor would remain inherently subcritical while using fuel rods with ^(235)U enrichment of up to 0.9%,regardless of the pitch of the fuel rods.In the second approach,the optimal pitches(1.3 to 2.3 cm)were determined for different fuel enrichment values from 1 to 5%.Subsequently,the k_(eff) was obtained for a fuel rod arrangement of 8×8 to 80×80,and the states in which the reactor would be subcritical were determined for different fuel enrichments at the corresponding optimal pitch.To validate the models used in the DRAGON and DONJON codes,the k_(eff) of the Isfahan Light Water Subcritical Reactor(LWSCR)was experimentally measured and compared with the results of the calculations.Finally,the effects of fuel and moderator temperature changes were investigated to ensure that the designed assemblies remained in the subcritical state at all operational temperatures.
基金the National Key Research and Development Program of China (2018YFB0105000)the National Natural Science Foundation of China (61773234 and U1864203)+2 种基金the Project of Tsinghua University and Toyota Joint Research Center for AI Technology of Automated Vehicle (TT2018-02)the International Science and Technology Cooperation Program of China (2016YFE0102200)the software developed in the Beijing Municipal Science and Technology Program (D171100005117001 and Z181100005918001).
文摘An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility;this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.
基金This project is supported by Provincial Natural Science Foundation of Shanxi, China (No. 20041074)Provincial Natural Science Youth Foundation of Shanxi, China (No. 20051030)Provincial Education Office Key Subject of Shanxi, China (No. 20045027-20045028)
文摘Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method （FEM） analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output dam. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrpolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system （SSHLPS） is calculated as an instance. A three-layer back-propagation （BP） net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrpulation contrasts with integrity re-analysis. Any layer SSHLPS among 1-8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is appfied into the same topological structure, with reduced distortion and assured precision.
基金Jian Cao,Gregory J.Wagner,and Wing K.Liu acknowledge support from the National Science Foundation(NSF)Cyber-Physical Systems(CPS)(CPS/CMMI-1646592)Hengyang Li acknowledges support from the Northwestern Data Science Initiative(DSI+6 种基金171474500210043324)Jian Cao,Gregory J.Wagner,Wing K.Liu,Jennifer L.Bennett,and Sarah J.Wolff acknowledge support from the Digital Manufacturing and Design Innovation Institute(DMDII15-07)Jian Cao,Wing K.Liu,Zhengtao Gan,and Jennifer L.Bennett acknowledge support from the Center for Hierarchical Materials Design(CHiMaD70NANB14H012)This work made use of facilities at DMG MORI and Northwestern UniversityIt also made use of the MatCI Facility,which receives support from the MRSEC Program(NSF DMR-168 1720139)of the Materials Research Center at Northwestern University.
文摘To design microstructure and microhardness in the additive manufacturing(AM)of nickel(Ni)-based superalloys,the present work develops a novel data-driven approach that combines physics-based models,experimental measurements,and a data-mining method.The simulation is based on a computational thermal-fluid dynamics(CtFD)model,which can obtain thermal behavior,solidification parameters such as cooling rate,and the dilution of solidified clad.Based on the computed thermal information,dendrite arm spacing and microhardness are estimated using well-tested mechanistic models.Experimental microstructure and microhardness are determined and compared with the simulated values for validation.To visualize process-structure-properties(PSPs)linkages,the simulation and experimental datasets are input to a data-mining model-a self-organizing map(SOM).The design windows of the process parameters under multiple objectives can be obtained from the visualized maps.The proposed approaches can be utilized in AM and other data-intensive processes.Data-driven linkages between process,structure,and properties have the potential to benefit online process monitoring control in order to derive an ideal microstructure and mechanical properties.
基金Supported by the National Natural Science Foundation of China(Nos.11172197,11332008 and 11572215)the Natural Science Foundation of Tianjin through a key-project Grant(12JCZDJC30400)the UC MEXUS-CONACy T through the project Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-objective Optimization Algorithms
