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
Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a disti...Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.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
An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers.By incorporating the governing Helmholtz equation into training,the working mechanism of the proposed o...An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers.By incorporating the governing Helmholtz equation into training,the working mechanism of the proposed operator adheres to the physics essence of fiber analysis.The training of the mode-solving operator adopts a hybrid physics-informed and data-driven approach,providing the advantages of strong physical consistency,enhanced prediction accuracy,and reduced data dependency in comparison with purely datadriven methods.Benefiting from the improvements in network input-output mapping formulation,the proposed operator offers broader applicability to different fiber types and greater flexibility for property optimization.Combined with the particle swarm optimization and refractive index optimization,the operator demonstrates its capacity for the inverse design of multi-step-index fibers(MSIFs)and graded-index fibers(GRIFs).For MSIFs,to ensure a low mode crosstalk for short-distance transmission systems,optimized refractive index profiles(RIPs)of both three-ring and four-ring structures are obtained from large structure parameter search spaces.For GRIFs,to ensure a low receiving complexity for long-haul transmission systems,optimized RIP with low root mean square mode group delay is obtained through point-wise fine-tuning.Moreover,the operator is capable of analyzing the effect of dopant diffusion in manufacturing.展开更多
The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)mode...The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)models have improved predictive accuracy,their limited transparency has hindered their widespread adoption in materials engineering.To overcome this barrier,this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations(SHAP)and Partial Dependence Plots(PDPs)to model and explain the compressive strength behavior of 3DPC mixtures.Unlike conventional“black-box”models,SHAP quantifies each variable’s contribution to predictions based on cooperative game theory,which enables causal interpretability,whereas PDP visualizes nonlinear and interactive effects between features that offer practical mix design insights.A systematically optimized random forest model achieved strong generalization(R2=0.978 for training,0.834 for validation,and 0.868 for testing).The analysis identified curing age,Portland cement,silica fume,and the water-tobinder ratio as dominant predictors,with curing age exerting the highest positive influence on strength development.The integrated SHAP-PDP framework revealed synergistic interactions among binder constituents and curing parameters,which established transparent,data-driven guidelines for performance optimization.Theoretically,the study advances explainable artificial intelligence in cementitious material science by linking microstructural mechanisms to model-based reasoning,thereby enhancing both the interpretability and applicability of ML-driven mix design for next-generation 3DPC systems.展开更多
This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis f...This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.展开更多
To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance dat...To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
Effective completion design in hydraulic fracturing(HF)is crucial for optimizing production in unconventional reservoirs.Traditional geometric designs often fail to account for geological and engineering heterogeneity...Effective completion design in hydraulic fracturing(HF)is crucial for optimizing production in unconventional reservoirs.Traditional geometric designs often fail to account for geological and engineering heterogeneity,leading to suboptimal stimulation.This study introduces a mechanism-guided data-driven model for optimized completion design that covers the entire process from sweet spot evaluation to stage and cluster optimization.For geological sweet spot evaluation,a mechanism-guided weighted K-medoids clustering model was developed by assigning weights to petrophysical parameters based on their correlation with production profiles.Engineering sweet spots were characterized using bottomhole mechanical specific energy(MSEb)and minimum horizontal in-situ stress(Shmin).The completion design optimization employed dynamic programming and a hybrid multi-objective optimization approach(NSGA-II),integrating geological and engineering sweet spots with operational constraints.The study showed a positive correlation between high-quality geological sweet spots and production(average correlation coefficient of 0.34),and a negative correlation between fluid allocation and engineering sweet spots(correlation coefficient of−0.46).Field application in the Jimsar Sag,Xinjiang,demonstrated that the proposed model significantly outperforms traditional geometric designs.Test wells showed an average 186%increase in cumulative production per 100 m over three months compared to conventional wells.The key findings of this work provide a novel technical pathway for optimized completion design of unconventional reservoirs with significant engineering applicability.展开更多
Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a cruc...Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a crucial part of managing any construction project-but particularly important for high-speed railway projects that often have several contractual parties and stakeholders,very long project timelines and huge upfront cost overlays.This paper discusses how various project interfaces were managed during the design and construction of the civil engineering infrastructure for the High Speed Two(HS2)project in the United Kingdom.Design/methodology/approach-The paper uses the case study methodology.Key interfaces on the HS2 project are grouped into various categories and the paper discusses how they were managed within the Area North Integrated Project Team(IPT)of the HS2 project made up of contractor Balfour Beatty VINCI(BBV),the Mott MacDonald SYSTRA Design Joint Venture(DJV)and client HS2 Ltd.3 different case studies drawn from across the IPT are used,each of them highlighting different interfaces and how these interfaces were managed.Findings-The paper shows how innovative technical designs and modern methods of construction were used to address some of the unique and peculiar challenges of designing a brand-new railway in the United Kingdom.Addressing the contrasting and often competing requirements of different stakeholders,coupled with challenging physical constraints of the very limited land available for the project and the use of a rarely used Act of Parliament in the delivery of the project required different approach to interface management.Collaboration and proactive stakeholder engagement are necessary for successful interface management on megaprojects.The authors posit that adopting an integrated approach to engineering and construction management is an essential ingredient for the successful delivery of high-speed railway projects.Originality/value-With many high-speed railway projects around the world coming up in the next few years,understanding the context and challenges for each country will help engineering and design managers adopt appropriate approaches for their projects.The lessons learned on the HS2 project are also transferable to other mega infrastructure projects with complex project interfaces.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
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supported by Project of National and Local Joint Engineering Research Center for Biomass Energy Development and Utilization(Harbin Institute of Technology,No.2021A004).
文摘Machine learning(ML)is recognized as a potent tool for the inverse design of environmental functional material,particularly for complex entities like biochar-based catalysts(BCs).Thus,the tailored BCs can have a distinct ability to trigger the nonradical pathway in advance oxidation processes(AOPs),promising a stable,rapid and selective degradation of persistent contaminants.However,due to the inherent“black box”nature and limitations of input features,results and conclusions derived from ML may not always be intuitively understood or comprehensively validated.To tackle this challenge,we linked the front-point interpretable analysis approaches with back-point density functional theory(DFT)calculations to form a chained learning strategy for deeper sight into the intrinsic activation mechanism of BCs in AOPs.At the front point,we conducted an easy-to-interpret meta-analysis to validate two strategies for enhancing nonradical pathways by increasing oxygen content and specific surface area(SSA),and prepared oxidized biochar(OBC500)and SSA-increased biochar(SBC900)by controlling pyrolysis conditions and modification methods.Subsequently,experimental results showed that OBC500 and SBC900 had distinct dominant degradation pathways for 1O2 generation and electron transfer,respectively.Finally,at the end point,DFT calculations revealed their active sites and degradation mechanisms.This chained learning strategy elucidates fundamental principles for BC inverse design and showcases the exceptional capacity to integrate computational techniques to accelerate catalyst inverse design.
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 Natural Science Foundation of China(Grant Nos.U24B20133 and 62522104)the Beijing Nova Program(Grant No.20230484331).
文摘An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers.By incorporating the governing Helmholtz equation into training,the working mechanism of the proposed operator adheres to the physics essence of fiber analysis.The training of the mode-solving operator adopts a hybrid physics-informed and data-driven approach,providing the advantages of strong physical consistency,enhanced prediction accuracy,and reduced data dependency in comparison with purely datadriven methods.Benefiting from the improvements in network input-output mapping formulation,the proposed operator offers broader applicability to different fiber types and greater flexibility for property optimization.Combined with the particle swarm optimization and refractive index optimization,the operator demonstrates its capacity for the inverse design of multi-step-index fibers(MSIFs)and graded-index fibers(GRIFs).For MSIFs,to ensure a low mode crosstalk for short-distance transmission systems,optimized refractive index profiles(RIPs)of both three-ring and four-ring structures are obtained from large structure parameter search spaces.For GRIFs,to ensure a low receiving complexity for long-haul transmission systems,optimized RIP with low root mean square mode group delay is obtained through point-wise fine-tuning.Moreover,the operator is capable of analyzing the effect of dopant diffusion in manufacturing.
基金supported by the Ongoing Research Funding Program(Grant No.ORFFT-2025-025-4)at King Saud University,Riyadh,Saudi Arabia.The grant was awarded to Yassir M.Abbas。
文摘The rapid advancement of three-dimensional printed concrete(3DPC)requires intelligent and interpretable frameworks to optimize mixture design for strength,printability,and sustainability.While machine learning(ML)models have improved predictive accuracy,their limited transparency has hindered their widespread adoption in materials engineering.To overcome this barrier,this study introduces a Random Forests ensemble learning model integrated with SHapley Additive exPlanations(SHAP)and Partial Dependence Plots(PDPs)to model and explain the compressive strength behavior of 3DPC mixtures.Unlike conventional“black-box”models,SHAP quantifies each variable’s contribution to predictions based on cooperative game theory,which enables causal interpretability,whereas PDP visualizes nonlinear and interactive effects between features that offer practical mix design insights.A systematically optimized random forest model achieved strong generalization(R2=0.978 for training,0.834 for validation,and 0.868 for testing).The analysis identified curing age,Portland cement,silica fume,and the water-tobinder ratio as dominant predictors,with curing age exerting the highest positive influence on strength development.The integrated SHAP-PDP framework revealed synergistic interactions among binder constituents and curing parameters,which established transparent,data-driven guidelines for performance optimization.Theoretically,the study advances explainable artificial intelligence in cementitious material science by linking microstructural mechanisms to model-based reasoning,thereby enhancing both the interpretability and applicability of ML-driven mix design for next-generation 3DPC systems.
基金Chengdu City Philosophy and Social Sciences Research Center“artificial intelligence+urban communication”theory and Application Research Center Project“Chengdu real estate vertical market public opinion data visualization research”(Project No.RZCC2025017).
文摘This study integrates multiple sources of data(transaction data,policy text,public opinion data)with visualization techniques(such as heat maps,time-series trend charts,3D building brochures)to construct an analysis framework for the Chengdu real estate market.By using the Adaptive Neuro-Fuzzy Inference System(ANFIS)prediction model,spatial GIS(Geographic Information System analysis)analysis,and interactive dashboards,this study reveals market differentiation,policy impacts,and changes in demand structure,thereby providing decision support for the government,enterprises,and homebuyers.
基金supported in part by the National Natural Science Foundation of China,Grant/Award Number:62003267the Key Research and Development Program of Shaanxi Province,Grant/Award Number:2023-GHZD-33Open Project of the State Key Laboratory of Intelligent Game,Grant/Award Number:ZBKF-23-05。
文摘To address the issue of instability or even imbalance in the orientation and attitude control of quadrotor unmanned aerial vehicles(QUAVs)under random disturbances,this paper proposes a distributed antidisturbance data-driven event-triggered fusion control method,which achieves efficient fault diagnosis while suppressing random disturbances and mitigating communication conflicts within the QUAV swarm.First,the impact of random disturbances on the UAV swarm is analyzed,and a model for orientation and attitude control of QUAVs under stochastic perturbations is established,with the disturbance gain threshold determined.Second,a fault diagnosis system based on a high-gain observer is designed,constructing a fault gain criterion by integrating orientation and attitude information from QUAVs.Subsequently,a model-free dynamic linearization-based data modeling(MFDLDM)framework is developed using model-free adaptive control,which efficiently fits the nonlinear control model of the QUAV swarm while reducing temporal constraints on control data.On this basis,this paper constructs a distributed data-driven event-triggered controller based on the staggered communication mechanism,which consists of an equivalent QUAV controller and an event-triggered controller,and is able to reduce the communication conflicts while suppressing the influence of random interference.Finally,by incorporating random disturbances into the controller,comparative experiments and physical validations are conducted on the QUAV platforms,fully demonstrating the strong adaptability and robustness of the proposed distributed event-triggered fault-tolerant control system.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by the National Natural Science Foundation of China(Grant/Award Nos.:52310001009 and 52122401).
文摘Effective completion design in hydraulic fracturing(HF)is crucial for optimizing production in unconventional reservoirs.Traditional geometric designs often fail to account for geological and engineering heterogeneity,leading to suboptimal stimulation.This study introduces a mechanism-guided data-driven model for optimized completion design that covers the entire process from sweet spot evaluation to stage and cluster optimization.For geological sweet spot evaluation,a mechanism-guided weighted K-medoids clustering model was developed by assigning weights to petrophysical parameters based on their correlation with production profiles.Engineering sweet spots were characterized using bottomhole mechanical specific energy(MSEb)and minimum horizontal in-situ stress(Shmin).The completion design optimization employed dynamic programming and a hybrid multi-objective optimization approach(NSGA-II),integrating geological and engineering sweet spots with operational constraints.The study showed a positive correlation between high-quality geological sweet spots and production(average correlation coefficient of 0.34),and a negative correlation between fluid allocation and engineering sweet spots(correlation coefficient of−0.46).Field application in the Jimsar Sag,Xinjiang,demonstrated that the proposed model significantly outperforms traditional geometric designs.Test wells showed an average 186%increase in cumulative production per 100 m over three months compared to conventional wells.The key findings of this work provide a novel technical pathway for optimized completion design of unconventional reservoirs with significant engineering applicability.
文摘Purpose-Interface management is the process of managing communications,responsibilities and coordination of project parties,phases or physical entities which are dependent on one another.Interface management is a crucial part of managing any construction project-but particularly important for high-speed railway projects that often have several contractual parties and stakeholders,very long project timelines and huge upfront cost overlays.This paper discusses how various project interfaces were managed during the design and construction of the civil engineering infrastructure for the High Speed Two(HS2)project in the United Kingdom.Design/methodology/approach-The paper uses the case study methodology.Key interfaces on the HS2 project are grouped into various categories and the paper discusses how they were managed within the Area North Integrated Project Team(IPT)of the HS2 project made up of contractor Balfour Beatty VINCI(BBV),the Mott MacDonald SYSTRA Design Joint Venture(DJV)and client HS2 Ltd.3 different case studies drawn from across the IPT are used,each of them highlighting different interfaces and how these interfaces were managed.Findings-The paper shows how innovative technical designs and modern methods of construction were used to address some of the unique and peculiar challenges of designing a brand-new railway in the United Kingdom.Addressing the contrasting and often competing requirements of different stakeholders,coupled with challenging physical constraints of the very limited land available for the project and the use of a rarely used Act of Parliament in the delivery of the project required different approach to interface management.Collaboration and proactive stakeholder engagement are necessary for successful interface management on megaprojects.The authors posit that adopting an integrated approach to engineering and construction management is an essential ingredient for the successful delivery of high-speed railway projects.Originality/value-With many high-speed railway projects around the world coming up in the next few years,understanding the context and challenges for each country will help engineering and design managers adopt appropriate approaches for their projects.The lessons learned on the HS2 project are also transferable to other mega infrastructure projects with complex project interfaces.
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.
