Increasing the thrust-weight ratio of aeroengines requires development of high-strength and stable high-temperature materials. A data-driven design of Ni-based turbine disc superalloys is performed to improve the yiel...Increasing the thrust-weight ratio of aeroengines requires development of high-strength and stable high-temperature materials. A data-driven design of Ni-based turbine disc superalloys is performed to improve the yield strength to reach the target. Through first-principles calculations determining the design superalloy system, the theoretical models and Calculation of Phase Diagram (CALPHAD) screening compositions, and machine learning extrapolating prediction, 14 compositions are selected from 2,865,039 composition combinations. Ni-17Cr-8Co-1Mo-1W-6Al-3Ti-1Nb-1Ta is selected to verify the design accuracy. Experimental tests prove that the designed alloy has trade-offs of microstructure with satisfying design targets, and then, the yield strength is higher in the designed alloy than in commercial superalloys, reaching 728 MPa at 850 ℃. A scheme for increasing the performance of the designed alloy is proposed by discussing the strengthening mechanisms, machine learning process, and alloying chemistry effect. The cross-scale data-driven design is regarded as an accurate and efficient way to design novel high-strength Ni-based turbine disc superalloys, whose significance is the obvious reduction of trial-and-error tests.展开更多
This paper discusses the data-driven design of linear quadratic regulators,i.e.,to obtain the regulators directly from experimental data without using the models of plants.In particular,we aim to improve an existing d...This paper discusses the data-driven design of linear quadratic regulators,i.e.,to obtain the regulators directly from experimental data without using the models of plants.In particular,we aim to improve an existing design method by reducing the amount of the required experimental data.Reducing the data amount leads to the cost reduction of experiments and computation for the data-driven design.We present a simplified version of the existing method,where parameters yielding the gain of the regulator are estimated from only part of the data required in the existing method.We then show that the data amount required in the presented method is less than half of that in the existing method under certain conditions.In addition,assuming the presence of measurement noise,we analyze the relations between the expectations and variances of the estimated parameters and the noise.As a result,it is shown that using a larger amount of the experimental data might mitigate the effects of the noise on the estimated parameters.These results are verified by numerical examples.展开更多
This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including th...This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.展开更多
The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This...The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.展开更多
The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies m...The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.展开更多
This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive mod...This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes.The method relies on a multi-fidelity data gathering campaign,enabling seamless integration into multidisciplinary aircraft design workflows.A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions.High-fidelity CFD simulations were conducted on both clean and iced geometries,forming a multidimensional aerodynamic database.This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics,following a'quasi-3D'approach.The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes.To demonstrate its effectiveness,optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft.The analysis validates the model's reliability,accuracy,and limitations concerning 3D ice shapes and aerodynamic characteristics.Most notably,the model offers near-zero computational cost compared to high-fidelity simulations,making it a valuable tool for efficient aircraft design.展开更多
A small dataset of~300 datapoints of zinc(Zn)alloys were collected and 125 entries containing alloying elements,extrusion parameters(temperature(ET),speed(ES)and ratio(ER)),and mechanical properties(yield strength(YS)...A small dataset of~300 datapoints of zinc(Zn)alloys were collected and 125 entries containing alloying elements,extrusion parameters(temperature(ET),speed(ES)and ratio(ER)),and mechanical properties(yield strength(YS),ultimate tensile strength(UTS),and final elongation(EL))were selected.Machine learning models were applied to predict mechanical properties,in which random forest(RF)model exhibited the best performance and further validated by a new experimental sample of Zn-0.05Mg-0.5Mn,with the mean absolute percentage error(MAPE)less than 10%.[Correction added on 13 May 2025,after first online publication:In the preceding sentence,the value‘12%’has been changed to‘10%’.].Moreover,an empirical formula was induced by the clustering model(CL).Control over strain softening/hardening behavior was achieved through only process parameter adjustment.Finally,by combining multi-objective genetic algorithm and RF models,the optimization alloy composition and extrusion parameters was carried out,targeting high-strength,strength/plasticity synergy,and high plasticity for biodegradable purpose.A notable optimized scheme for strength/plasticity synergy in Zn-0.20Mg-0.60Mn(wt.%)achieves the YS of 303 MPa,UTS of 354 MPa,and EL of 25.1%with the MAPE less than 10%,and exhibits the strain-hardening response,associated with ER of 16,ET of 170°C,and ES of 3.21 mm/s.[Correction added on 13 May 2025,after first online publication:In the preceding sentence,the value‘345 MPa’has been changed to‘354 MPa’.and the value‘3.33 mm/s’has been changed to‘3.21 mm/s’].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
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
The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this chal...The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.展开更多
Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.A...Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.Adopting a NURBSbased parametric modelling scheme with a small number of design variables,the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method,and demonstrated in this work to give high accuracy and efficiency.Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis,in contrast to the generally complex procedures of typical shape and size sensitivity approaches.展开更多
There are considerable challenges associated with the design of ground support for seismically-active underground mines.It is extremely difficult to establish the demand on ground support as well as the capacity of a ...There are considerable challenges associated with the design of ground support for seismically-active underground mines.It is extremely difficult to establish the demand on ground support as well as the capacity of a ground support system.The resulting dynamic or impact loads caused by mining-induced seismicity are difficult to anticipate and quantify.The performance of a ground support system is defined by the load distribution and interaction between several reinforcement and surface support elements.Consequently,the design of ground support in seismically-active mines tends to evolve,or be modified based on qualitative assessments of perceived performance or response to significant seismic events or rockbursts.This research is motivated by a need to provide quantitative and data-driven design guidelines for ground support systems subjected to dynamic-loading conditions.Rockburst data were collected from three deep and seismically-active underground mines in the Sudbury basin in Canada.The constructed database comprises 209 seismic events that resulted in damage to mine excavations and ground support.These events were associated with damage at 324 locations within the three mines.The developed ground support design strategy,based on these documented case studies,identifies areas where the use of dynamic or enhanced support should be employed.The developed design methodology provides guidelines for the zoning of mine locations in which installation of enhanced support is recommended,the specifications for an optimal ground support system,and the timing or sequence of installation.展开更多
Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be...Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.展开更多
A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably m...A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.展开更多
The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution...The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.展开更多
Computer vision(CV)is widely expected to be the next big thing in emerging applications.So many heterogeneous architectures for computer vision emerge.However,plenty of data need to be transferred between different st...Computer vision(CV)is widely expected to be the next big thing in emerging applications.So many heterogeneous architectures for computer vision emerge.However,plenty of data need to be transferred between different structures for heterogeneous architecture.The long data transfer delay becomes the mainly problem to limit the processing speed for computer vision applications.For reducing data transfer delay and fasting computer vision applications,a clustered data-driven array processor is proposed.A three-level pipelining processing element is designed which supports two-buffer data flow interface and 8 bits,16 bits,32 bits subtext parallel computation.At the same time,for accelerating transcendental function computation,a four-way shared pipelining transcendental function accelerator is designed,which is based on Y-intercept adjusted piecewise linear segment algorithm.A distributed shared memory structure based on unified addressing is also employed.To verify efficiency of architecture,some image processing algorithms are implemented on proposed architecture.Simultaneously the proposed architecture has been implemented on Xilinx ZC 706 development board.The same circuitry has been synthesized using SMIC 130 nm CMOS technology.The circuitry is able to run at 100 MHz.Area is 26.58 mm2.展开更多
Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler ...Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler metals is ever-increasing.It is of great significance to investigate the optimized composition design methods and to establish systematic design guidelines for brazing filler metals.This study elucidated the fundamental rules for the composition design of brazing filler metals from a three-dimensional perspective encompassing the basic properties of applied brazing filler metals,formability and processability,and overall cost.The basic properties of brazing filler metals refer to their mechanical properties,physicochemical properties,electromagnetic properties,corrosion resistance,and the wettability and fluidity during brazing.The formability and processability of brazing filler metals include the processes of smelting and casting,extrusion,rolling,drawing and ring-making,as well as the processes of granulation,powder production,and the molding of amorphous and microcrystalline structures.The cost of brazing filler metals corresponds to the sum of materials value and manufacturing cost.Improving the comprehensive properties of brazing filler metals requires a comprehensive and systematic consideration of design indicators.Highlighting the unique characteristics of brazing filler metals should focus on relevant technical indicators.Binary or ternary eutectic structures can effectively enhance the flow spreading ability of brazing filler metals,and solid solution structures contribute to the formability.By employing the proposed design guidelines,typical Ag based,Cu based,Zn based brazing filler metals,and Sn based solders were designed and successfully applied in major scientific and engineering projects.展开更多
The reconfigurable chip,which integrates the advantages of high performance,high flexibility,high parallelism,low power consumption,and low cost,has achieved rapid development and wide application.Generally,the contro...The reconfigurable chip,which integrates the advantages of high performance,high flexibility,high parallelism,low power consumption,and low cost,has achieved rapid development and wide application.Generally,the control part and the computing part of algorithm is accelerated based on different reconfigurable architectures,but it is difficult to obtain overall performance improvement.For improving efficiency of reconfigurable structure both for the control part and the computing part,a hybrid of instruction-driven and data-driven self-reconfigurable cell array is proposed.On instruction-driven mode,processing element(PE)works like a reduced instruction set computer(RSIC)machine,which is mainly for the control part of algorithm.On data-driven mode,data is calculated by flowing between the preconfigured PEs,which is mainly for the computing of algorithm.For verifying the efficiency of architecture,some high-efficiency video coding(HEVC)video compression algorithms are implemented on the proposed architecture.The proposed architecture has been implemented on Xilinx FPGA Virtex UltraScale VU440 develop board.The same circuitry is able to run at75 MHz.Compared with the architecture that only supports instruction-driven,the proposed architecture has better calculation efficiency.展开更多
基金financially supported by the Beijing Municipal Natural Science Foundation(Grant No.2212042)the National Natural Science Foundation of China(Grant Nos.U2141205,52271019)+3 种基金the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-22-03)the Natural Science Foundation of Hebei Province(Grant No.E2022402004)the Natural Science Foundation of Chongqing(Grant No.cstc2021jcyj-msxmX0899)supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.
文摘Increasing the thrust-weight ratio of aeroengines requires development of high-strength and stable high-temperature materials. A data-driven design of Ni-based turbine disc superalloys is performed to improve the yield strength to reach the target. Through first-principles calculations determining the design superalloy system, the theoretical models and Calculation of Phase Diagram (CALPHAD) screening compositions, and machine learning extrapolating prediction, 14 compositions are selected from 2,865,039 composition combinations. Ni-17Cr-8Co-1Mo-1W-6Al-3Ti-1Nb-1Ta is selected to verify the design accuracy. Experimental tests prove that the designed alloy has trade-offs of microstructure with satisfying design targets, and then, the yield strength is higher in the designed alloy than in commercial superalloys, reaching 728 MPa at 850 ℃. A scheme for increasing the performance of the designed alloy is proposed by discussing the strengthening mechanisms, machine learning process, and alloying chemistry effect. The cross-scale data-driven design is regarded as an accurate and efficient way to design novel high-strength Ni-based turbine disc superalloys, whose significance is the obvious reduction of trial-and-error tests.
文摘This paper discusses the data-driven design of linear quadratic regulators,i.e.,to obtain the regulators directly from experimental data without using the models of plants.In particular,we aim to improve an existing design method by reducing the amount of the required experimental data.Reducing the data amount leads to the cost reduction of experiments and computation for the data-driven design.We present a simplified version of the existing method,where parameters yielding the gain of the regulator are estimated from only part of the data required in the existing method.We then show that the data amount required in the presented method is less than half of that in the existing method under certain conditions.In addition,assuming the presence of measurement noise,we analyze the relations between the expectations and variances of the estimated parameters and the noise.As a result,it is shown that using a larger amount of the experimental data might mitigate the effects of the noise on the estimated parameters.These results are verified by numerical examples.
基金Supported by National Key Research and Development Program of China (Grant No.2018YFB1700704)National Natural Science Foundation of China (Grant No.52075068)。
文摘This paper presents a cloud-based data-driven design optimization system,named DADOS,to help engineers and researchers improve a design or product easily and efficiently.DADOS has nearly 30 key algorithms,including the design of experiments,surrogate models,model validation and selection,prediction,optimization,and sensitivity analysis.Moreover,it also includes an exclusive ensemble surrogate modeling technique,the extended hybrid adaptive function,which can make use of the advantages of each surrogate and eliminate the effort of selecting the appropriate individual surrogate.To improve ease of use,DADOS provides a user-friendly graphical user interface and employed flow-based programming so that users can conduct design optimization just by dragging,dropping,and connecting algorithm blocks into a workflow instead of writing massive code.In addition,DADOS allows users to visualize the results to gain more insights into the design problems,allows multi-person collaborating on a project at the same time,and supports multi-disciplinary optimization.This paper also details the architecture and the user interface of DADOS.Two examples were employed to demonstrate how to use DADOS to conduct data-driven design optimization.Since DADOS is a cloud-based system,anyone can access DADOS at www.dados.com.cn using their web browser without the need for installation or powerful hardware.
基金partially supported by the Construction of Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences(KJCX20240406)the Beijing Natural Science Foundation(JQ24037)+1 种基金the National Natural Science Foundation of China(32330075)the Earmarked Fund for China Agriculture Research System(CARS-02 and CARS-54)。
文摘The security of the seed industry is crucial for ensuring national food security.Currently,developed countries in Europe and America,along with international seed industry giants,have entered the Breeding 4.0 era.This era integrates biotechnology,artificial intelligence(AI),and big data information technology.In contrast,China is still in a transition period between stages 2.0 and 3.0,which primarily relies on conventional selection and molecular breeding.In the context of increasingly complex international situations,accurately identifying core issues in China's seed industry innovation and seizing the frontier of international seed technology are strategically important.These efforts are essential for ensuring food security and revitalizing the seed industry.This paper systematically analyzes the characteristics of crop breeding data from artificial selection to intelligent design breeding.It explores the applications and development trends of AI and big data in modern crop breeding from several key perspectives.These include highthroughput phenotype acquisition and analysis,multiomics big data database and management system construction,AI-based multiomics integrated analysis,and the development of intelligent breeding software tools based on biological big data and AI technology.Based on an in-depth analysis of the current status and challenges of China's seed industry technology development,we propose strategic goals and key tasks for China's new generation of AI and big data-driven intelligent design breeding.These suggestions aim to accelerate the development of an intelligent-driven crop breeding engineering system that features large-scale gene mining,efficient gene manipulation,engineered variety design,and systematized biobreeding.This study provides a theoretical basis and practical guidance for the development of China's seed industry technology.
基金supported by National Natural Science Foundation of China(Grant No.52375380)National Key R&D Program of China(Grant No.2022YFB3402200)the Key Project of National Natural Science Foundation of China(Grant No.12032018).
文摘The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.
基金funding from the Department of Industrial Engineering,University of Naples FedericoⅡ,Italy。
文摘This study presents a data-driven approach to predict tailplane aerodynamics in icing conditions,supporting the ice-tolerant design of aircraft horizontal stabilizers.The core of this work is a low-cost predictive model for analyzing icing effects on swept tailplanes.The method relies on a multi-fidelity data gathering campaign,enabling seamless integration into multidisciplinary aircraft design workflows.A dataset of iced airfoil shapes was generated using 2D inviscid methods across various flight conditions.High-fidelity CFD simulations were conducted on both clean and iced geometries,forming a multidimensional aerodynamic database.This 2D database feeds a nonlinear vortex lattice method to estimate 3D aerodynamic characteristics,following a'quasi-3D'approach.The resulting reduced-order model delivers fast aerodynamic performance estimates of iced tailplanes.To demonstrate its effectiveness,optimal ice-tolerant tailplane designs were selected from a range of feasible shapes based on a reference transport aircraft.The analysis validates the model's reliability,accuracy,and limitations concerning 3D ice shapes and aerodynamic characteristics.Most notably,the model offers near-zero computational cost compared to high-fidelity simulations,making it a valuable tool for efficient aircraft design.
基金the National Key Research and Development Program of China(2023YFB3812901)and(2023YFB3812902)for funding the present research.
文摘A small dataset of~300 datapoints of zinc(Zn)alloys were collected and 125 entries containing alloying elements,extrusion parameters(temperature(ET),speed(ES)and ratio(ER)),and mechanical properties(yield strength(YS),ultimate tensile strength(UTS),and final elongation(EL))were selected.Machine learning models were applied to predict mechanical properties,in which random forest(RF)model exhibited the best performance and further validated by a new experimental sample of Zn-0.05Mg-0.5Mn,with the mean absolute percentage error(MAPE)less than 10%.[Correction added on 13 May 2025,after first online publication:In the preceding sentence,the value‘12%’has been changed to‘10%’.].Moreover,an empirical formula was induced by the clustering model(CL).Control over strain softening/hardening behavior was achieved through only process parameter adjustment.Finally,by combining multi-objective genetic algorithm and RF models,the optimization alloy composition and extrusion parameters was carried out,targeting high-strength,strength/plasticity synergy,and high plasticity for biodegradable purpose.A notable optimized scheme for strength/plasticity synergy in Zn-0.20Mg-0.60Mn(wt.%)achieves the YS of 303 MPa,UTS of 354 MPa,and EL of 25.1%with the MAPE less than 10%,and exhibits the strain-hardening response,associated with ER of 16,ET of 170°C,and ES of 3.21 mm/s.[Correction added on 13 May 2025,after first online publication:In the preceding sentence,the value‘345 MPa’has been changed to‘354 MPa’.and the value‘3.33 mm/s’has been changed to‘3.21 mm/s’].
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.
文摘The world’s increasing population requires the process industry to produce food,fuels,chemicals,and consumer products in a more efficient and sustainable way.Functional process materials lie at the heart of this challenge.Traditionally,new advanced materials are found empirically or through trial-and-error approaches.As theoretical methods and associated tools are being continuously improved and computer power has reached a high level,it is now efficient and popular to use computational methods to guide material selection and design.Due to the strong interaction between material selection and the operation of the process in which the material is used,it is essential to perform material and process design simultaneously.Despite this significant connection,the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required.Hybrid modeling provides a promising option to tackle such complex design problems.In hybrid modeling,the material properties,which are computationally expensive to obtain,are described by data-driven models,while the well-known process-related principles are represented by mechanistic models.This article highlights the significance of hybrid modeling in multiscale material and process design.The generic design methodology is first introduced.Six important application areas are then selected:four from the chemical engineering field and two from the energy systems engineering domain.For each selected area,state-ofthe-art work using hybrid modeling for multiscale material and process design is discussed.Concluding remarks are provided at the end,and current limitations and future opportunities are pointed out.
基金National Natural Science Foundation of China(Grant Nos.51705158 and 51805174)the Fundamental Research Funds for the Central Universities(Grant Nos.2018MS45 and 2019MS059)。
文摘Focusing on the structural optimization of auxetic materials using data-driven methods,a back-propagation neural network(BPNN)based design framework is developed for petal-shaped auxetics using isogeometric analysis.Adopting a NURBSbased parametric modelling scheme with a small number of design variables,the highly nonlinear relation between the input geometry variables and the effective material properties is obtained using BPNN-based fitting method,and demonstrated in this work to give high accuracy and efficiency.Such BPNN-based fitting functions also enable an easy analytical sensitivity analysis,in contrast to the generally complex procedures of typical shape and size sensitivity approaches.
文摘There are considerable challenges associated with the design of ground support for seismically-active underground mines.It is extremely difficult to establish the demand on ground support as well as the capacity of a ground support system.The resulting dynamic or impact loads caused by mining-induced seismicity are difficult to anticipate and quantify.The performance of a ground support system is defined by the load distribution and interaction between several reinforcement and surface support elements.Consequently,the design of ground support in seismically-active mines tends to evolve,or be modified based on qualitative assessments of perceived performance or response to significant seismic events or rockbursts.This research is motivated by a need to provide quantitative and data-driven design guidelines for ground support systems subjected to dynamic-loading conditions.Rockburst data were collected from three deep and seismically-active underground mines in the Sudbury basin in Canada.The constructed database comprises 209 seismic events that resulted in damage to mine excavations and ground support.These events were associated with damage at 324 locations within the three mines.The developed ground support design strategy,based on these documented case studies,identifies areas where the use of dynamic or enhanced support should be employed.The developed design methodology provides guidelines for the zoning of mine locations in which installation of enhanced support is recommended,the specifications for an optimal ground support system,and the timing or sequence of installation.
基金supported by the National Natural Science Foundation of China(Grant No.51809279)the Major National Science and Technology Program(Grant No.2016ZX05028-001-05)+1 种基金Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT14R58)the Fundamental Research Funds for the Central Universities,that is,the Opening Fund of National Engineering Laboratory of Offshore Geophysical and Exploration Equipment(Grant No.20CX02302A).
文摘Vortex induced vibration(VIV)is a challenge in ocean engineering.Several devices including fairings have been designed to suppress VIV.However,how to optimize the design of suppression devices is still a problem to be solved.In this paper,an optimization design methodology is presented based on data-driven models and genetic algorithm(GA).Data-driven models are introduced to substitute complex physics-based equations.GA is used to rapidly search for the optimal suppression device from all possible solutions.Taking fairings as example,VIV response database for different fairings is established based on parameterized models in which model sections of fairings are controlled by several control points and Bezier curves.Then a data-driven model,which can predict the VIV response of fairings with different sections accurately and efficiently,is trained through BP neural network.Finally,a comprehensive optimization method and process is proposed based on GA and the data-driven model.The proposed method is demonstrated by its application to a case.It turns out that the proposed method can perform the optimization design of fairings effectively.VIV can be reduced obviously through the optimization design.
文摘A successful mechanical property data-driven prediction model is the core of the optimal design of hot rolling process for hot-rolled strips. However, the original industrial data, usually unbalanced, are inevitably mixed with fluctuant and abnormal values. Models established on the basis of the data without data processing can cause misleading results, which cannot be used for the optimal design of hot rolling process. Thus, a method of industrial data processing of C-Mn steel was proposed based on the data analysis. The Bayesian neural network was employed to establish the reliable mechanical property prediction models for the optimal design of hot rolling process. By using the multi-objective optimization algorithm and considering the individual requirements of costumers and the constraints of the equipment, the optimal design of hot rolling process was successfully applied to the rolling process design for Q345B steel with 0.017% Nb and 0.046% Ti content removed. The optimal process design results were in good agreement with the industrial trials results, which verify the effectiveness of the optimal design of hot rolling process.
基金supported by the National Natural Science Foundation of China(61836001,62122014,62173036,62102022)。
文摘The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems.Cloud computing is concerned as a powerful solution to handle complex large-scale control missions by using sufficient computing resources.However,the computing ability enables more complex devices and more data to be involved and most of the data have not been fully utilized.Meanwhile,it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms.Therefore,motivated by the above reasons,we propose a data-driven predictive cloud control system.To achieve the proposed system,a practical data-driven predictive cloud control testbed is established and together a cloud-edge communication scheme is developed.Finally,the simulations and experiments demonstrate the effectiveness of the proposed system.
基金the National Natural Science Foundation of China(No.61802304,61834005,61772417,61634004,61602377)Shaanxi Provincial Co-ordination Innovation Project of Science and Technology(No.2016KTZDGY02-04-02)+1 种基金Shaanxi Provincial Key R&D Plan(No.2017GY-060)Shaanxi International Science and Technology Cooperation Program(No.2018KW-006).
文摘Computer vision(CV)is widely expected to be the next big thing in emerging applications.So many heterogeneous architectures for computer vision emerge.However,plenty of data need to be transferred between different structures for heterogeneous architecture.The long data transfer delay becomes the mainly problem to limit the processing speed for computer vision applications.For reducing data transfer delay and fasting computer vision applications,a clustered data-driven array processor is proposed.A three-level pipelining processing element is designed which supports two-buffer data flow interface and 8 bits,16 bits,32 bits subtext parallel computation.At the same time,for accelerating transcendental function computation,a four-way shared pipelining transcendental function accelerator is designed,which is based on Y-intercept adjusted piecewise linear segment algorithm.A distributed shared memory structure based on unified addressing is also employed.To verify efficiency of architecture,some image processing algorithms are implemented on proposed architecture.Simultaneously the proposed architecture has been implemented on Xilinx ZC 706 development board.The same circuitry has been synthesized using SMIC 130 nm CMOS technology.The circuitry is able to run at 100 MHz.Area is 26.58 mm2.
基金National Natural Science Foundation of China(U22A20191)。
文摘Brazing filler metals are widely applied,which serve as an industrial adhesive in the joining of dissimilar structures.With the continuous emergence of new structures and materials,the demand for novel brazing filler metals is ever-increasing.It is of great significance to investigate the optimized composition design methods and to establish systematic design guidelines for brazing filler metals.This study elucidated the fundamental rules for the composition design of brazing filler metals from a three-dimensional perspective encompassing the basic properties of applied brazing filler metals,formability and processability,and overall cost.The basic properties of brazing filler metals refer to their mechanical properties,physicochemical properties,electromagnetic properties,corrosion resistance,and the wettability and fluidity during brazing.The formability and processability of brazing filler metals include the processes of smelting and casting,extrusion,rolling,drawing and ring-making,as well as the processes of granulation,powder production,and the molding of amorphous and microcrystalline structures.The cost of brazing filler metals corresponds to the sum of materials value and manufacturing cost.Improving the comprehensive properties of brazing filler metals requires a comprehensive and systematic consideration of design indicators.Highlighting the unique characteristics of brazing filler metals should focus on relevant technical indicators.Binary or ternary eutectic structures can effectively enhance the flow spreading ability of brazing filler metals,and solid solution structures contribute to the formability.By employing the proposed design guidelines,typical Ag based,Cu based,Zn based brazing filler metals,and Sn based solders were designed and successfully applied in major scientific and engineering projects.
基金Supported by the National Natural Science Foundation of China(No.61802304,61834005,61772417,61634004)the Shaanxi Province Key R&D Plan(No.2021GY-029).
文摘The reconfigurable chip,which integrates the advantages of high performance,high flexibility,high parallelism,low power consumption,and low cost,has achieved rapid development and wide application.Generally,the control part and the computing part of algorithm is accelerated based on different reconfigurable architectures,but it is difficult to obtain overall performance improvement.For improving efficiency of reconfigurable structure both for the control part and the computing part,a hybrid of instruction-driven and data-driven self-reconfigurable cell array is proposed.On instruction-driven mode,processing element(PE)works like a reduced instruction set computer(RSIC)machine,which is mainly for the control part of algorithm.On data-driven mode,data is calculated by flowing between the preconfigured PEs,which is mainly for the computing of algorithm.For verifying the efficiency of architecture,some high-efficiency video coding(HEVC)video compression algorithms are implemented on the proposed architecture.The proposed architecture has been implemented on Xilinx FPGA Virtex UltraScale VU440 develop board.The same circuitry is able to run at75 MHz.Compared with the architecture that only supports instruction-driven,the proposed architecture has better calculation efficiency.
