Based on the existing land-surface schemes and models,an improved Land-surface Process Model(LPM-ZD)has been developed.It has the following major characteristics:(1)The combination of physical equations and empirical ...Based on the existing land-surface schemes and models,an improved Land-surface Process Model(LPM-ZD)has been developed.It has the following major characteristics:(1)The combination of physical equations and empirical analytical formulae are used to construct the governing equations of soil temperature and moisture.Higher resolution of model level and physical equations are adopted for the upper soil layers,and for the lower soil layers,lower resolution of model level is adopted and empirical analytical formulae are used.(2)In land surface hydrological process,the sub-grid distribution of rainfall and its effects are taken into account. (3)A simple snow cover submodel has been used,which includes effects of snow cover on soil thermodynamics and hydrology,as well as albedo. By use of this model and three groups of point observation data,a series of“off-line”tests have been carried out.The simulation results indicate that land-surface process model has good performance and can well simulate diurnal and seasonal variation of land surface processes for many kinds of land surface covers(forest,grass,crops and desert)in different climate zone.The results simulated by the model are consistent with the observations.Later,by use of one group of observation data and the model,a series of sensitivity experiments have been done.It is shown that the model is much sensitive to some parameters,such as initial soil moisture,vegetation physical parameters as well as the proportion of the grid covered with rain.Therefore it is much important for land-surface process model to define these parameters as accurately as possible.展开更多
The Land-surface Process Model(LPM-ZD)has been successfully coupled with the regional climate model RegCM2 of NCAR.Then thus-obtained coupled model(CRegCM)has been applied to simulate the climate characteristics of he...The Land-surface Process Model(LPM-ZD)has been successfully coupled with the regional climate model RegCM2 of NCAR.Then thus-obtained coupled model(CRegCM)has been applied to simulate the climate characteristics of heavy rain in middle and East China for three months from May to July 1991.and compared with model output of NCAR-RegCM2 using BATS as land- surface process scheme,abbreviated as NRegCM.The results show that CRegCM has good ability and performance.CRegCM successfully simulates the extreme precipitation event and the simulations of CRegCM for surface temperature and some physical variables related to land surface process are more reasonable than those of NRegCM.展开更多
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir...Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.展开更多
Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening pa...Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.展开更多
With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process...With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.展开更多
The Tibetan Plateau(TP) has powerful dynamics and thermal effects, which makes the interaction between its land and atmosphere significantly affect climate and environment in the regional or global area. By retrospect...The Tibetan Plateau(TP) has powerful dynamics and thermal effects, which makes the interaction between its land and atmosphere significantly affect climate and environment in the regional or global area. By retrospecting the latest research progress in the simulation of land-surface processes(LSPs) over the past 20 years, this study discusses both the simulation ability of land-surface models(LSMs) and the modification of parameterization schemes from two perspectives, the models' applicability and improved parameterization schemes. Our review suggests that different LSMs can well capture the spatiotemporal variations of the physical quantities of LSPs; but none of them can be fully applied to the plateau, meaning that all need to be revised according to the characteristics specific to the TP. Avoiding the unstable iterative computation and determining the freeze-thaw critical temperature according to the thermodynamic equilibrium equation, the unreasonable freeze-thaw parameterization scheme can be improved. Due to the complex underlying surface of the TP, no parameterization scheme of roughness length can well simulate the various characteristics of the turbulent flux over the TP at different temporal scales. The uniform soil thermodynamic and hydraulic parameterization scheme is unreasonable when it is applied to the plateau, as a result of the strong soil heterogeneity. There is little research on the snow-cover process so far,and the improved scheme has no advantage over the original one due to the lack of some related physical processes. The constant interaction among subprocesses of LSPs makes the improvement of a multiparameterization scheme yield better simulation results. According to the review of existing research, adding high-quality observation stations, developing a parameterization scheme suitable for the special LSPs of the TP, and adjusting the model structures can be helpful to the simulation of LSPs on the TP.展开更多
A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary ...A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.展开更多
With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design p...With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design phase exceeded expectations.This paper conducted a survey to the relevant participants involved in the design,revealed that a lack of proper process management is a critical issue.The current MBSE methodology does not provide clear guidelines for monitoring,controlling,and managing processes,which are crucial for both efficiency and effectiveness.To address this,the present paper introduced an improved Process Model(PM)within the MBSE framework for civil aircraft design.This improved model incorporates three new Management Blocks(MB):Progress Management Block(PMB),Review Management Block(RMB),and Configuration Management Block(CMB),developed based on the Capability Maturity Model Integration(CMMI).These additions aim to streamline the design process and better align it with engineering practices.The upgraded MBSE method with the improved PM offers a more structured approach to manage complex aircraft design projects,and a case study is conducted to validate its potential to reduce timelines and enhance overall project outcomes.展开更多
Coriolis effects,encompassing the dilative,compressive,and deflective manifestations,constitute pivotal considerations in the centrifugal modelling of high-speed granular run-out processes.Notably,under the deflective...Coriolis effects,encompassing the dilative,compressive,and deflective manifestations,constitute pivotal considerations in the centrifugal modelling of high-speed granular run-out processes.Notably,under the deflective Coriolis condition,the velocity component parallel to the rotational axis exerts no influence on the magnitude of Coriolis acceleration.This circumstance implies a potential mitigation of the Coriolis force's deflective impact.Regrettably,extant investigations predominantly emphasize the dilative and compressive Coriolis effects,largely neglecting the pragmatic import of the deflective Coriolis condition.In pursuit of this gap,a series of discrete element method(DEM)simulations have been conducted to scrutinize the feasibility of centrifugal modelling for dry granular run-out processes under deflective Coriolis conditions.The findings concerning the deflective Coriolis effect reveal a consistent rise in the run-out distance by 2%–16%,a modest increase in bulk flow velocity of under 4%,and a slight elevation in average flow depth by no more than 25%.These alterations display smaller dependence on the specific testing conditions due to the granular flow undergoing dual deflections in opposing directions.This underscores the significance and utility of the deflective Coriolis condition.Notably,the anticipated reduction in error in predicting the final run-out distance is substantial,potentially reaching a 150%improvement compared to predictions made under the dilative and compressive Coriolis conditions.Therefore,the deflective Coriolis condition is advised when the final run-out distance of the granular flow is the main concern.To mitigate the impact of Coriolis acceleration,a greater initial height of the granular column is recommended,with a height/width ratio exceeding 1,as the basal friction of the granular material plays a crucial role in mitigating the deflective Coriolis effect.For more transverse-uniform flow properties,the width of the granular column should be as large as possible.展开更多
Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)mo...Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)model to represent allowable variations of the CSTR-DC-recycle chemical processes.The NOZ is a geometric space containing all safe operating points of the CSTR-DC-recycle chemical processes,so that it is an effective model for process monitoring.The novelty of the proposed method is to establish the NOZ model based on boundary points.The boundary points make it possible to capture the actual geometric space irrespective of the space shape.In contrast,existing methods represent the NOZ of processes by fixed mathematical models such as ellipsoidal and convex-hull models;they are not suitable for the CSTR-DC-recycle chemical processes whose NOZs cannot be exactly defined by fixed mathematical structures.Simulated case studies based on Aspen Hysys software are given to illustrate the proposed method.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving ...This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.展开更多
Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep lear...Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep learning retrieval method for process reuse was proposed,which integrates process design features into the retrieval of casting 3D models.This method leverages the comparative language-image pretraining(CLIP)model to extract shape features from the three views and sectional views of the casting model and combines them with process design features such as modulus,main wall thickness,symmetry,and length-to-height ratio to enhance process reusability.A database of 230 production casting models was established for model validation.Results indicate that incorporating process design features improves model accuracy by 6.09%,reaching 97.82%,and increases process similarity by 30.25%.The reusability of the process was further verified using the casting simulation software EasyCast.The results show that the process retrieved after integrating process design features produces the least shrinkage in the target model,demonstrating this method’s superior ability for process reuse.This approach does not require a large dataset for training and optimization,making it highly applicable to casting process design and related manufacturing processes.展开更多
This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrheni...This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.展开更多
The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product l...The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product lifecycle,the problem of high energy usage is increasingly notable.Nevertheless,current analyses of carbon emissions in textile manufacturing emphasize the dynamic temporal characteristics while failing to adequately consider critical information such as material flows and energy consumption.A carbon emission analysis method based on a holographic process model(HPM)is proposed to address these issues.First,the system boundary in the textile manufacturing is defined,and the characteristics of carbon emissions are analyzed.Next,an HPM based on the object-centric Petri net(OCPN)is constructed,and simulation experiments are conducted on three different scenarios in the textile manufacturing.Subsequently,the constructed HPM is utilized to achieve a multi-perspective analysis of carbon emissions.Finally,the feasibility of the method is verified by using the production data of pure cotton products from a certain textile manufacturing enterprise.The results indicate that this method can analyze the impact of various factors on the carbon emissions of pure cotton product production,and by applying targeted optimization strategies,carbon emissions have been reduced by nearly 20%.This contributes to propelling the textile manufacturing industry toward sustainable development.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
During the excavation of deep engineering,high in situ stress is one prominent feature that often causes instability in the vicinity of underground openings.The propagation and coalescence of cracks in the surrounding...During the excavation of deep engineering,high in situ stress is one prominent feature that often causes instability in the vicinity of underground openings.The propagation and coalescence of cracks in the surrounding rock are characterized by anisotropy under a true triaxial stress state and play a crucial role in the development of stress-induced engineering disasters.Thus,a three-dimensional anisotropic fracturing model of hard rock is proposed to interpret fracturing activities and evaluate the mechanical property deterioration under complex stress conditions.This anisotropic fracturing model is derived from the evolution of microcracks and attributes the inelastic deformation of hard rock to crack propagation and coalescence.Through analyzing the competitive process of crack propagation in different orientations,the stress-induced anisotropic fracturing characteristics and the post-peak brittle-ductile transition could be revealed.Finally,the accuracy and effectiveness of this model are validated.Results show that this proposed anisotropic fracturing model can elucidate the primary characteristics observed in triaxial compression tests,which offers a fresh perspective on comprehending the failure process of hard rock.展开更多
Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to in...Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
[Objective]The research aimed to carry out numerical simulation on impact of land-surface process in a Meiyu front rainstorm.[Method]Based on the meso-scale atmospheric non-hydrostatic model GRAPES-Meso which coupled ...[Objective]The research aimed to carry out numerical simulation on impact of land-surface process in a Meiyu front rainstorm.[Method]Based on the meso-scale atmospheric non-hydrostatic model GRAPES-Meso which coupled with NOAH land-surface module,a Meiyu front rainstorm in Jianghuai basin during 6-8 July,2005 was simulated.Via sensitivity tests with and without land-surface process,the impact of land-surface process on Meiyu front rainstorm was studied.[Result]GRAPES-Meso which coupled with NOAH land-surface process could simulate daily variation characteristics of Meiyu front precipitation and atmospheric low-level southwest jet.The land-surface process could improve the simulations of precipitation initiation,intensity and distribution.The improvement of precipitation initiation simulation was especially obvious,which solved spin-up problem of model to some extent.When the land-atmosphere interaction wasn’t considered,the precipitation initiation of model was very slow.The intensity evolution of simulated precipitation was different from actual situation.Moreover,it couldn’t simulate daily variation characteristics of precipitation and southwest jet.[Conclusion]The land-surface process had important impact on Meiyu process in Jianghuai basin.展开更多
文摘Based on the existing land-surface schemes and models,an improved Land-surface Process Model(LPM-ZD)has been developed.It has the following major characteristics:(1)The combination of physical equations and empirical analytical formulae are used to construct the governing equations of soil temperature and moisture.Higher resolution of model level and physical equations are adopted for the upper soil layers,and for the lower soil layers,lower resolution of model level is adopted and empirical analytical formulae are used.(2)In land surface hydrological process,the sub-grid distribution of rainfall and its effects are taken into account. (3)A simple snow cover submodel has been used,which includes effects of snow cover on soil thermodynamics and hydrology,as well as albedo. By use of this model and three groups of point observation data,a series of“off-line”tests have been carried out.The simulation results indicate that land-surface process model has good performance and can well simulate diurnal and seasonal variation of land surface processes for many kinds of land surface covers(forest,grass,crops and desert)in different climate zone.The results simulated by the model are consistent with the observations.Later,by use of one group of observation data and the model,a series of sensitivity experiments have been done.It is shown that the model is much sensitive to some parameters,such as initial soil moisture,vegetation physical parameters as well as the proportion of the grid covered with rain.Therefore it is much important for land-surface process model to define these parameters as accurately as possible.
基金This work is supported by the National Key Research Project 96-908-02-04.
文摘The Land-surface Process Model(LPM-ZD)has been successfully coupled with the regional climate model RegCM2 of NCAR.Then thus-obtained coupled model(CRegCM)has been applied to simulate the climate characteristics of heavy rain in middle and East China for three months from May to July 1991.and compared with model output of NCAR-RegCM2 using BATS as land- surface process scheme,abbreviated as NRegCM.The results show that CRegCM has good ability and performance.CRegCM successfully simulates the extreme precipitation event and the simulations of CRegCM for surface temperature and some physical variables related to land surface process are more reasonable than those of NRegCM.
基金funded by Fundación CajaCanarias and Fundación Bancaria“la Caixa”,grant number 2023DIG11.
文摘Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams.
基金financial support of the National Natural Science Foundation of China(No.52371103)the Fundamental Research Funds for the Central Universities,China(No.2242023K40028)+1 种基金the Open Research Fund of Jiangsu Key Laboratory for Advanced Metallic Materials,China(No.AMM2023B01).financial support of the Research Fund of Shihezi Key Laboratory of AluminumBased Advanced Materials,China(No.2023PT02)financial support of Guangdong Province Science and Technology Major Project,China(No.2021B0301030005)。
文摘Oxide dispersion strengthened(ODS)alloys are extensively used owing to high thermostability and creep strength contributed from uniformly dispersed fine oxides particles.However,the existence of these strengthening particles also deteriorates the processability and it is of great importance to establish accurate processing maps to guide the thermomechanical processes to enhance the formability.In this study,we performed particle swarm optimization-based back propagation artificial neural network model to predict the high temperature flow behavior of 0.25wt%Al2O3 particle-reinforced Cu alloys,and compared the accuracy with that of derived by Arrhenius-type constitutive model and back propagation artificial neural network model.To train these models,we obtained the raw data by fabricating ODS Cu alloys using the internal oxidation and reduction method,and conducting systematic hot compression tests between 400 and800℃with strain rates of 10^(-2)-10 S^(-1).At last,processing maps for ODS Cu alloys were proposed by combining processing parameters,mechanical behavior,microstructure characterization,and the modeling results achieved a coefficient of determination higher than>99%.
基金supported by the National Natural Science Foundation of China(62225302,623B2014,and 62173023).
文摘With the emergence of general foundational models,such as Chat Generative Pre-trained Transformer(ChatGPT),researchers have shown considerable interest in the potential applications of foundation models in the process industry.This paper provides a comprehensive overview of the challenges and opportunities presented by the use of foundation models in the process industry,including the frameworks,core applications,and future prospects.First,this paper proposes a framework for foundation models for the process industry.Second,it summarizes the key capabilities of industrial foundation models and their practical applications.Finally,it highlights future research directions and identifies unresolved open issues related to the use of foundation models in the process industry.
基金funded by the National Natural Science Foundation of China (41571066, 41601077, and 41771068)the Strategic Priority Research Pro gram of the Chinese Academy of Sciences (CAS) (XDA20100102, XDA19070204)+2 种基金the CAS "Light of West China" Programthe Youth Innovation Promo tion Association CAS (2018460)the Program of China Scholarship Council (201804910129)
文摘The Tibetan Plateau(TP) has powerful dynamics and thermal effects, which makes the interaction between its land and atmosphere significantly affect climate and environment in the regional or global area. By retrospecting the latest research progress in the simulation of land-surface processes(LSPs) over the past 20 years, this study discusses both the simulation ability of land-surface models(LSMs) and the modification of parameterization schemes from two perspectives, the models' applicability and improved parameterization schemes. Our review suggests that different LSMs can well capture the spatiotemporal variations of the physical quantities of LSPs; but none of them can be fully applied to the plateau, meaning that all need to be revised according to the characteristics specific to the TP. Avoiding the unstable iterative computation and determining the freeze-thaw critical temperature according to the thermodynamic equilibrium equation, the unreasonable freeze-thaw parameterization scheme can be improved. Due to the complex underlying surface of the TP, no parameterization scheme of roughness length can well simulate the various characteristics of the turbulent flux over the TP at different temporal scales. The uniform soil thermodynamic and hydraulic parameterization scheme is unreasonable when it is applied to the plateau, as a result of the strong soil heterogeneity. There is little research on the snow-cover process so far,and the improved scheme has no advantage over the original one due to the lack of some related physical processes. The constant interaction among subprocesses of LSPs makes the improvement of a multiparameterization scheme yield better simulation results. According to the review of existing research, adding high-quality observation stations, developing a parameterization scheme suitable for the special LSPs of the TP, and adjusting the model structures can be helpful to the simulation of LSPs on the TP.
基金supported by the National Key R&D Program of China(No.2021YFB3400900)the National Natural Science Foundation of China(Nos.52175373,52205435)+1 种基金Natural Science Foundation of Hunan Province,China(No.2022JJ40621)the Innovation Fund of National Commercial Aircraft Manufacturing Engineering Technology Center,China(No.COMACSFGS-2022-1875)。
文摘A new unified constitutive model was developed to predict the two-stage creep-aging(TSCA)behavior of Al-Zn-Mg-Cu alloys.The particular bimodal precipitation feature was analyzed and modeled by considering the primary micro-variables evolution at different temperatures and their interaction.The dislocation density was incorporated into the model to capture the effect of creep deformation on precipitation.Quantitative transmission electron microscopy and experimental data obtained from a previous study were used to calibrate the model.Subsequently,the developed constitutive model was implemented in the finite element(FE)software ABAQUS via the user subroutines for TSCA process simulation and the springback prediction of an integral panel.A TSCA test was performed.The result shows that the maximum radius deviation between the formed plate and the simulation results is less than 0.4 mm,thus validating the effectiveness of the developed constitutive model and FE model.
基金supported by the National Natural Science Foundation of China(No.62073267)。
文摘With the raising complexity of modern civil aircraft,both academy and industry have shown strong interests on MBSE(Model-Based System Engineering).However,following the application of MBSE,the duration of the design phase exceeded expectations.This paper conducted a survey to the relevant participants involved in the design,revealed that a lack of proper process management is a critical issue.The current MBSE methodology does not provide clear guidelines for monitoring,controlling,and managing processes,which are crucial for both efficiency and effectiveness.To address this,the present paper introduced an improved Process Model(PM)within the MBSE framework for civil aircraft design.This improved model incorporates three new Management Blocks(MB):Progress Management Block(PMB),Review Management Block(RMB),and Configuration Management Block(CMB),developed based on the Capability Maturity Model Integration(CMMI).These additions aim to streamline the design process and better align it with engineering practices.The upgraded MBSE method with the improved PM offers a more structured approach to manage complex aircraft design projects,and a case study is conducted to validate its potential to reduce timelines and enhance overall project outcomes.
基金supported by the National Natural Science Foundation of China(Grant Nos.42120104008 and 42307214)the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230620).
文摘Coriolis effects,encompassing the dilative,compressive,and deflective manifestations,constitute pivotal considerations in the centrifugal modelling of high-speed granular run-out processes.Notably,under the deflective Coriolis condition,the velocity component parallel to the rotational axis exerts no influence on the magnitude of Coriolis acceleration.This circumstance implies a potential mitigation of the Coriolis force's deflective impact.Regrettably,extant investigations predominantly emphasize the dilative and compressive Coriolis effects,largely neglecting the pragmatic import of the deflective Coriolis condition.In pursuit of this gap,a series of discrete element method(DEM)simulations have been conducted to scrutinize the feasibility of centrifugal modelling for dry granular run-out processes under deflective Coriolis conditions.The findings concerning the deflective Coriolis effect reveal a consistent rise in the run-out distance by 2%–16%,a modest increase in bulk flow velocity of under 4%,and a slight elevation in average flow depth by no more than 25%.These alterations display smaller dependence on the specific testing conditions due to the granular flow undergoing dual deflections in opposing directions.This underscores the significance and utility of the deflective Coriolis condition.Notably,the anticipated reduction in error in predicting the final run-out distance is substantial,potentially reaching a 150%improvement compared to predictions made under the dilative and compressive Coriolis conditions.Therefore,the deflective Coriolis condition is advised when the final run-out distance of the granular flow is the main concern.To mitigate the impact of Coriolis acceleration,a greater initial height of the granular column is recommended,with a height/width ratio exceeding 1,as the basal friction of the granular material plays a crucial role in mitigating the deflective Coriolis effect.For more transverse-uniform flow properties,the width of the granular column should be as large as possible.
基金partially funded by the National Natural Science Foundation of China(62273215)。
文摘Integrated continuous stirred-tank reactors and distillation columns with recycle(CSTR-DC-recycle)are essential components in chemical processes.This paper proposes a method to establish a normal operating zone(NOZ)model to represent allowable variations of the CSTR-DC-recycle chemical processes.The NOZ is a geometric space containing all safe operating points of the CSTR-DC-recycle chemical processes,so that it is an effective model for process monitoring.The novelty of the proposed method is to establish the NOZ model based on boundary points.The boundary points make it possible to capture the actual geometric space irrespective of the space shape.In contrast,existing methods represent the NOZ of processes by fixed mathematical models such as ellipsoidal and convex-hull models;they are not suitable for the CSTR-DC-recycle chemical processes whose NOZs cannot be exactly defined by fixed mathematical structures.Simulated case studies based on Aspen Hysys software are given to illustrate the proposed method.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金financially by the National Research Council of Thailand(NRCT)under Contract No.N42A670894.
文摘This study presents an innovative development of the exponentially weighted moving average(EWMA)control chart,explicitly adapted for the examination of time series data distinguished by seasonal autoregressive moving average behavior—SARMA(1,1)L under exponential white noise.Unlike previous works that rely on simplified models such as AR(1)or assume independence,this research derives for the first time an exact two-sided Average Run Length(ARL)formula for theModified EWMAchart under SARMA(1,1)L conditions,using a mathematically rigorous Fredholm integral approach.The derived formulas are validated against numerical integral equation(NIE)solutions,showing strong agreement and significantly reduced computational burden.Additionally,a performance comparison index(PCI)is introduced to assess the chart’s detection capability.Results demonstrate that the proposed method exhibits superior sensitivity to mean shifts in autocorrelated environments,outperforming existing approaches.The findings offer a new,efficient framework for real-time quality control in complex seasonal processes,with potential applications in environmental monitoring and intelligent manufacturing systems.
基金supported by the National Natural Science Foundation of China(Nos.52074246,52275390,52375394)the National Defense Basic Scientific Research Program of China(No.JCKY2020408B002)the Key R&D Program of Shanxi Province(No.202102050201011).
文摘Accurate retrieval of casting 3D models is crucial for process reuse.Current methods primarily focus on shape similarity,neglecting process design features,which compromises reusability.In this study,a novel deep learning retrieval method for process reuse was proposed,which integrates process design features into the retrieval of casting 3D models.This method leverages the comparative language-image pretraining(CLIP)model to extract shape features from the three views and sectional views of the casting model and combines them with process design features such as modulus,main wall thickness,symmetry,and length-to-height ratio to enhance process reusability.A database of 230 production casting models was established for model validation.Results indicate that incorporating process design features improves model accuracy by 6.09%,reaching 97.82%,and increases process similarity by 30.25%.The reusability of the process was further verified using the casting simulation software EasyCast.The results show that the process retrieved after integrating process design features produces the least shrinkage in the target model,demonstrating this method’s superior ability for process reuse.This approach does not require a large dataset for training and optimization,making it highly applicable to casting process design and related manufacturing processes.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.51901078)the Central Guidance for Local Scientific and Technological Development Funding Project(Grant No.236Z1003G)+3 种基金the Science and Technology Plan Project of Tangshan City(Grant No.24130207C)the Natural Science Foundation of Hebei Province(Grant No.E2022209070)the High-level Talent Project of Hebei(Grant No.E2019100007)the Open Project Program of Key Laboratory of Ministry of Education for Modern Metallurgy Technology(Grant No.2024YJKF02).
文摘This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.
基金National Key R&D Program of China(No.2019YFB1706300)。
文摘The textile industry,while creating material wealth,also exerts a significant impact on the environment.Particularly in the textile manufacturing phase,which is the most energy-intensive phase throughout the product lifecycle,the problem of high energy usage is increasingly notable.Nevertheless,current analyses of carbon emissions in textile manufacturing emphasize the dynamic temporal characteristics while failing to adequately consider critical information such as material flows and energy consumption.A carbon emission analysis method based on a holographic process model(HPM)is proposed to address these issues.First,the system boundary in the textile manufacturing is defined,and the characteristics of carbon emissions are analyzed.Next,an HPM based on the object-centric Petri net(OCPN)is constructed,and simulation experiments are conducted on three different scenarios in the textile manufacturing.Subsequently,the constructed HPM is utilized to achieve a multi-perspective analysis of carbon emissions.Finally,the feasibility of the method is verified by using the production data of pure cotton products from a certain textile manufacturing enterprise.The results indicate that this method can analyze the impact of various factors on the carbon emissions of pure cotton product production,and by applying targeted optimization strategies,carbon emissions have been reduced by nearly 20%.This contributes to propelling the textile manufacturing industry toward sustainable development.
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金support from the National Natural Science Foundation of China(Grant No.52209125).
文摘During the excavation of deep engineering,high in situ stress is one prominent feature that often causes instability in the vicinity of underground openings.The propagation and coalescence of cracks in the surrounding rock are characterized by anisotropy under a true triaxial stress state and play a crucial role in the development of stress-induced engineering disasters.Thus,a three-dimensional anisotropic fracturing model of hard rock is proposed to interpret fracturing activities and evaluate the mechanical property deterioration under complex stress conditions.This anisotropic fracturing model is derived from the evolution of microcracks and attributes the inelastic deformation of hard rock to crack propagation and coalescence.Through analyzing the competitive process of crack propagation in different orientations,the stress-induced anisotropic fracturing characteristics and the post-peak brittle-ductile transition could be revealed.Finally,the accuracy and effectiveness of this model are validated.Results show that this proposed anisotropic fracturing model can elucidate the primary characteristics observed in triaxial compression tests,which offers a fresh perspective on comprehending the failure process of hard rock.
基金supported by the National Key Research and Development Program of China(2021YFB3301200)the National Natural Science Foundation of China(NSFC)(U21A20483,62373040,62203042).
文摘Due to abrupt changes in the intrinsic degradation mechanism or shock from external environmental pressure,degradations of some equipment are characterized by multi-phase and jumps.Meanwhile,equipment is subject to inherent fluctuations,limited data and imperfect measurements resulting in aleatory,epistemic and measurement uncertainties of the degradation process.This paper proposes a degradation model and remaining useful life(RUL)prediction method under triple uncertainties for a category of complex equipment with multi-phase degradation and jumps.First,a multi-phase degradation model with random jumps and measurement errors is constructed based on uncertain random processes.Afterward,the analytic expression of RUL prediction considering the heterogeneity is derived by modeling the uncertainty of degradation states at change points under the concept of first hitting time.A stochastic uncertain approach is utilized for the proposed multi-phase degradation model to identify model parameters based on historical data.Furthermore,the implied degradation features are adaptively updated in online stage using similarity-based weighted stochastic uncertain maximum likelihood estimation and Kalman filtering.Finally,the effectiveness of the method is verified by simulation example and practical case.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
基金Supported by National Natural Science Fund Item,China(41005002,40875029)
文摘[Objective]The research aimed to carry out numerical simulation on impact of land-surface process in a Meiyu front rainstorm.[Method]Based on the meso-scale atmospheric non-hydrostatic model GRAPES-Meso which coupled with NOAH land-surface module,a Meiyu front rainstorm in Jianghuai basin during 6-8 July,2005 was simulated.Via sensitivity tests with and without land-surface process,the impact of land-surface process on Meiyu front rainstorm was studied.[Result]GRAPES-Meso which coupled with NOAH land-surface process could simulate daily variation characteristics of Meiyu front precipitation and atmospheric low-level southwest jet.The land-surface process could improve the simulations of precipitation initiation,intensity and distribution.The improvement of precipitation initiation simulation was especially obvious,which solved spin-up problem of model to some extent.When the land-atmosphere interaction wasn’t considered,the precipitation initiation of model was very slow.The intensity evolution of simulated precipitation was different from actual situation.Moreover,it couldn’t simulate daily variation characteristics of precipitation and southwest jet.[Conclusion]The land-surface process had important impact on Meiyu process in Jianghuai basin.
