This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and s...This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.展开更多
The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft ...The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.展开更多
The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is m...The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.展开更多
The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine b...The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.展开更多
A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.T...A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.展开更多
The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precisio...The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.展开更多
Evaluation of damage evolution effects in biodegradable pure Mg was carried out,using transmission electron microscope as surrogate irradiation for high-energy radionuclide β decay in Brachytherapy.Time-dependent qua...Evaluation of damage evolution effects in biodegradable pure Mg was carried out,using transmission electron microscope as surrogate irradiation for high-energy radionuclide β decay in Brachytherapy.Time-dependent quantitative defect production,evolution dynamics,and evolution statistics were revealed in-situ for two prism foils(z=[1.210],[10.10]),in as-received and heat-treated pure Mg,after 300 keV electron irradiation up to 0.468 dpa at R.T.Preferred nucleation of basal-plane interstitial-type 1/6<20.23>loops was confirmed,in addition to a small portion of prism-plane 1/3<11.20>loops.No cavities were found.A higher yield of point defect concentration and a more evident trend of defect coarsening were identified in[1.210]than in[10.10].Pre-existing dislocations(on the orders of 10^(13)−10^(14) m^(−2))in pure Mg resulted in a delay of the first occurrence of visible defects.Defect migration and elastic interactions governed the microstructural evolution of electron irradiation damage in pure Mg,giving rise to events of loop coalescence,growth,and sometimes rotation of habit plane.The influence of incident electron energy can be correlated to the rates of point defect production,and is quantifiable;however,interfered by defect cluster stability,defect mobility,and defect interactions.This forms an important theoretical basis for the application of Mg subjected to MeV-level β-decay radiation in Brachytherapy.The paper concludes with a brief comparison between Mg and conventional Ti casing,outlines the advantages and challenges,and provides reference points for the validation of Mg/Mg-alloys in Brachytherapy seed application.展开更多
Cobalt phosphide has been successfully used as a catalyst in the production of ammonia from nitric acid.Substituting appropriate atoms is expected to further improve its catalytic performance.Owing to the diversity of...Cobalt phosphide has been successfully used as a catalyst in the production of ammonia from nitric acid.Substituting appropriate atoms is expected to further improve its catalytic performance.Owing to the diversity of substituting elements,substitution sites,adsorption sites,and adsorption configurations,extensive time-consuming simulation calculations are required for the high-throughput screening method.Additionally,multi-objective attributes should be considered simultaneously in catalytic design.To tackle this challenge,this paper suggests a multi-objective cobalt phosphide catalytic material design method based on surrogate models.And the effectiveness of the proposed method was validated through comparative experiments.The proposed method led to the discovery of fifteen promising cobalt phosphide catalyst configurations.This study provides a new avenue for expediting the design of catalyst,with the potential for application in other systems.展开更多
Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the s...Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.展开更多
Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determ...Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.展开更多
This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the...This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.展开更多
This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational mach...This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.展开更多
The production optimization in the closed-loop reservoir management is generally empirical,and challenged by the issues such as low precision,low efficiency,and difficulty in solving constrained optimization problems....The production optimization in the closed-loop reservoir management is generally empirical,and challenged by the issues such as low precision,low efficiency,and difficulty in solving constrained optimization problems.This paper outlines the main principles,advantages and disadvantages of commonly used production optimization methods/models,and then proposes an intelligent integrated production optimization method for waterflooding reservoirs that considers efficiency and precision,real-time and long-term effects,and the interaction and synergy between a variety of optimization models.This method integrates multiple optimization methods/models,such as reservoir performance analysis,reduced-physics models,and reservoir numerical models,with these model results and insights organically coupled to facilitate model construction and matching.This proposed method is elucidated and verified by field examples.The findings indicate that the optimal production optimization model varies depending on the specific application scenario.Reduced-physics models are conducive to short-term real-time optimization,whereas the simulator-based surrogate optimization and streamline-based simulation optimization methods are more suitable for long-term optimization strategy formulation,both of which need to be implemented under reasonable constraints from the perspective of reservoir engineering in order to be of practical value.展开更多
Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfiel...Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.展开更多
The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premis...This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premise that the weight is not changed,so as to play a greater role in aerodynamic control such as gust interference.First,as for structural design,based on NACA0012,a morphing wing model constructed by 3 forms of cell structures is proposed.Then,the aerodynamic characteristics under the interference of FLE and FTE are calculated by the Computational Fluid Dynamic(CFD)method.After the surrogate model is established to predict the lift coefficient of the wing effectively,the sensitivity analysis reveals that the main sensitivity index of FTE deflection angle β is 0.565,which has the greatest influence on the lift coefficient.And the total sensitivity index of FLE deflection angle γ is increased by 78.9%,which reveals a strong coupling relationship between FLE and FTE.Finally,using Finite Element Analysis(FEA)method and experiment,the deformation capability of the model under certain static loads are obtained.The results reveal that the maximum deflection angle of the morphing wing model can be±22°at FLE and±64°at FTE,indicating strong structural stiffness and resistance to bending breakage of the model.The presented results can be useful in the design of the cellular morphing wing with multiple flexible systems.展开更多
Self-propelled robots have attracted significant attention due to their remarkable ability to navigate confined terrains.These robots usually have deformable structures while having discontinuous contact forces with t...Self-propelled robots have attracted significant attention due to their remarkable ability to navigate confined terrains.These robots usually have deformable structures while having discontinuous contact forces with the ground,resulting in a complex nonlinear system.To provide a solid foundation for the locomotion prediction and optimization for the self-propelled robots,it is necessary to conduct dynamic modelling and locomotion analysis of the robot.Motivated by these issues,this paper proposes a vibration-driven surrogate dynamic model for a deformable self-propelled robot and presents a detailed dynamic analysis.The surrogate dynamic model is employed to classify various types of stick-slip locomotion.Subsequently,the corresponding experiment demonstrates that the surrogate dynamic model effectively predicts the locomotion of the robot,particularly three types of stick-slip locomotion induced by discontinuous friction.Finally,a multi-objective coordinated optimization regarding the locomotion velocity,the cost of transport,and the energy conversion rate of the self-propelled robot is conducted,aiming to comprehensively enhance the robot’s locomotion performance.Additionally,suggestions for the selection of actuation parameters are presented.展开更多
Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress te...Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress tensor by measuring strain using an HI strain cell.However,existing analytical solutions for stress calculation based on an HI strain cell in a double-layer medium are not applicable when an HI strain cell is used in a three-layer medium,leading to erroneous stress calculations.To address this issue,this paper presents a method for calculating stress tensors in a three-layer medium using numerical simulations,specifically by obtaining a constitutive matrix that relates strain measurements to stress tensors in a three-layer medium.Furthermore,using Latin hypercube sampling (LHS) and orthogonal experimental design strategies,764 groups of numerical models encompassing various stress measurement scenarios have been established and calculated using FLAC^(3D)software.Finally,a surrogate model based on artificial neural network (ANN) was developed to predict constitutive matrices,achieving a goodness of fit (R^(2)) of 0.999 and a mean squared error (MSE) of 1.254.A software program has been developed from this surrogate model for ease of use in practical engineering applications.The method’s accuracy was verified through numerical simulations,analytical solution and laboratory experiment,demonstrating its effectiveness in calculating stress in a three-layer medium.The surrogate model was applied to calculate mining-induced stress in the roadway roof rock of a coal mine,a typical case for stress measurement in a three-layer medium.Errors in stress calculations arising from the use of existing analytical solutions were corrected.The study also highlights the significant errors associated with using double-layer analytical solutions in a three-layer medium,which could lead to inappropriate engineering design.展开更多
In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However...In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.展开更多
Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-dr...Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-driven surrogate model,AxialViT-ConvLSTM,which integrates AxialAttention Vision Transformer,ConvLSTM,and an enhanced loss function to predict pressure dynamics in CBM reservoirs.The results showed that the model achieves a mean square error of 0.003,a learned perceptual image patch similarity of 0.037,a structural similarity of 0.979,and an R^(2) of 0.982 between predictions and actual pressures,indicating excellent performance.The model also demonstrates strong robustness and accuracy in capturing spatialetemporal pressure features.展开更多
文摘This study investigates the uncertain dynamic characterization of hybrid composite plates by employing advanced machine-assisted finite element methodologies.Hybrid composites,widely used in aerospace,automotive,and structural applications,often face variability in material properties,geometric configurations,and manufacturing processes,leading to uncertainty in their dynamic response.To address this,three surrogate-based machine learning approaches like radial basis function(RBF),multivariate adaptive regression splines(MARS),and polynomial neural networks(PNN)are integrated with a finite element framework to efficiently capture the stochastic behavior of these plates.The research focuses on predicting the first three natural frequencies under material uncertainties,which are critical to ensuring structural reliability.Monte Carlo simulation(MCS)is used as a benchmark for generating probabilistic datasets,including mean values,standard deviations,and probability density functions.The surrogate models are then trained and validated against these datasets,enabling accurate representation of uncertainty with substantially fewer samples compared to conventionalMCS.Among the methods studied,the RBFmodel demonstrates superior performance,closely approximating MCS results with a reduced sample size,thereby achieving significant computational savings.The proposed framework not only reduces computational time and costs but also maintains high predictive accuracy,making it well-suited for complex engineering systems.Beyond free vibration analysis,the methodology can be extended to more sophisticated scenarios,such as forced vibration,damping effects,and nonlinear structural responses.Overall,this work presents a computationally efficient and robust approach for surrogate-based uncertainty quantification,advancing the analysis and design of hybrid composite structures under uncertainty.
基金funded by Vale S.A.company(www.vale.com)and the Institute of Technology Vale(ITV—www.itv.org),grant number SAP 4600048682.
文摘The roller is one of the fundamental elements of ore belt conveyor systems since it supports,guides,and directs material on the belt.This component comprises a body(the external tube)that rotates around a fixed shaft supported by easels.The external tube and shaft of rollers used in ore conveyor belts are mostly made of steel,resulting in high mass,hindering maintenance and replacement.Aiming to achieve mass reduction,we conducted a structural optimization of a roller with a polymeric external tube(hereafter referred to as a polymeric roller),seeking the optimal values for two design parameters:the inner diameter of the external tube and the shaft diameter.The optimization was constrained by admissible values for maximum stress,maximum deflection and misalignment angle between the shaft and bearings.A finite element model was built in Ansys Workbench to obtain the structural response of the system.The roller considered is composed of an external tube made of high-density polyethylene(HDPE),bearing seats of polyamide 6(PA6),and a steel shaft.To characterize the polymeric materials(HDPE and PA6),stress relaxation tests were conducted,and the data on shear modulus variation over time were inserted into the model to calculate Prony series terms to account for viscoelastic effects.The roller optimization was performed using surrogate modeling based on radial basis functions,with the Globalized Bounded Nelder-Mead(GBNM)algorithm as the optimizer.Two optimization cases were conducted.In the first case,concerning the roller’s initial material settings,the designs found violated the constraints and could not reduce mass.In the second case,by using PA6 in both bearing seats and the tube,a design configuration was found that respected all constraints and reduced the roller mass by 15.5%,equivalent to 5.15 kg.This study is among the first to integrate experimentally obtained viscoelastic data into the surrogate-based optimization of polymeric rollers,combining methodological innovation with industrial relevance.
基金supported by the Science and Technology Research Project of Henan Province(242102241055)the Industry-University-Research Collaborative Innovation Base on Automobile Lightweight of“Science and Technology Innovation in Central Plains”(2024KCZY315)the Opening Fund of State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment(GZ2024A03-ZZU).
文摘The moving morphable component(MMC)topology optimization method,as a typical explicit topology optimization method,has been widely concerned.In the MMC topology optimization framework,the surrogate material model is mainly used for finite element analysis at present,and the effectiveness of the surrogate material model has been fully confirmed.However,there are some accuracy problems when dealing with boundary elements using the surrogate material model,which will affect the topology optimization results.In this study,a boundary element reconstruction(BER)model is proposed based on the surrogate material model under the MMC topology optimization framework to improve the accuracy of topology optimization.The proposed BER model can reconstruct the boundary elements by refining the local meshes and obtaining new nodes in boundary elements.Then the density of boundary elements is recalculated using the new node information,which is more accurate than the original model.Based on the new density of boundary elements,the material properties and volume information of the boundary elements are updated.Compared with other finite element analysis methods,the BER model is simple and feasible and can improve computational accuracy.Finally,the effectiveness and superiority of the proposed method are verified by comparing it with the optimization results of the original surrogate material model through several numerical examples.
基金supported by the National Natural Science Foundation of China(No.12301672)the Shanghai Science and Technology Innovation Action Plan(Yangfan Special Project),China(No.23YF1401300)。
文摘The micro-riblet structures have been demonstrated effective in controlling the Total Pressure Loss(TPL)of aero-engine blades.However,due to the considerable scale gap between micro-texture and an actual aero-engine blade,wind tunnel tests and numerical simulations with massive grids directly describing the global flow field are costly for aerodynamic evaluation.Furthermore,the fine micro surface structure brings unavoidable manufacturing errors,and the probability prediction contributes to gaining the confidence interval of the results.Therefore,a novel relay-based probabilistic model for multi-fidelity scenarios in the TPL prediction of a compressor cascade with micro-riblet surfaces is proposed to trade off accuracy and efficiency.Combined with the low-fidelity flow data generated by an aerodynamic solution strategy using the boundary surrogate model and the high-fidelity flow data from the experiment,the relay-based modeling has been achieved through knowledge transferring,and the confidence interval can be provided by the Gaussian Process Regression(GPR)model.The TPL of compressor cascades with micro-riblet surfaces under different surface structures at March number Ma=0.64,0.74,0.84 have been evaluated using the Relay-Based Probabilistic(RBP)model.The results illustrate that the RBP model could provide higher accuracy than the Single-Fidelity-Data-Driven(SFDD)prediction model,which show the promising potential of multi-fidelity scenarios data fusion in the aerodynamic evaluation of multi-scale configurations.
基金supported by the National Science and Technology Major Project,China(No.2017-II-0006-0019)the National Natural Science Foundation of China(No.52375266)the Shaanxi Science Foundation for Distinguished Young Scholars,China(No.2022JC-36)。
文摘A Hybrid Free-Form Deformation(HFFD)method is developed to improve shape preservation in mesh deformation for perforated surfaces,which traditional Free-Form Deformation(FFD)techniques struggle to handle effectively.The proposed method enables high-fidelity parameterized deformation for both flat and curved perforated surfaces while maintaining mesh quality with minimal geometric distortion.To evaluate its effectiveness,comparative studies between HFFD and conventional FFD methods are conducted,demonstrating superior performance in mesh quality and geometric fidelity.The HFFD-based framework is further applied to the Multidisciplinary Design Optimization(MDO)of a double-wall turbine blade leading edge.Results indicate an 11.6%increase in cooling efficiency and a 16.21%reduction in maximum stress.Additionally,compared to traditional geometry-based parameterization in MDO,the HFFD approach improves model processing efficiency by 84.15%and overall optimization efficiency by20.05%.These findings demonstrate HFFD's potential to significantly improve complex engineering design optimization by achieving precise shape preservation and improving computational efficiency.
基金Supported by the National Key Research and Development Program of China(2022YFB3904803)。
文摘The Infrared Hyperspectral Atmospheric SounderⅡ(HIRAS-Ⅱ)is the key equipment on FengYun-3E(FY-3E)satellite,which can realize vertical atmospheric detection,featuring hyper spectral,high sensitivity and high precision.To ensure its accuracy of detection,it is necessary to correlate their thermal models to in-orbit da⁃ta.In this work,an investigation of intelligent correlation method named Intelligent Correlation Platform for Ther⁃mal Model(ICP-TM)was established,the advanced Kriging surrogate model and efficient adaptive region opti⁃mization algorithm were introduced.After the correlation with this method for FY-3E/HIRAS-Ⅱ,the results indi⁃cate that compared with the data in orbit,the error of the thermal model has decreased from 5 K to within±1 K in cold case(10℃).Then,the correlated model is validated in hot case(20℃),and the correlated model exhibits good universality.This correlation precision is also much superiors to the general ones like 3 K in other similar lit⁃erature.Furthermore,the process is finished in 8 days using ICP-TM,the efficiency is much better than 3 months based on manual.The results show that the proposed approach significantly enhances the accuracy and efficiency of thermal model,this contributes to the precise thermal control of subsequent infrared optical payloads.
基金National Natural Science Foundation of China for funding support via grant No 12175013the Interdisciplinary Research Project for Young Researchers of USTB and the Youth Teacher International Exchange&Growth Program of USTB(Fundamental Research Funds for the Central Universities,China)for funding support via grant No FRF-IDRY-21–018 and QNXM20250033,respectively.
文摘Evaluation of damage evolution effects in biodegradable pure Mg was carried out,using transmission electron microscope as surrogate irradiation for high-energy radionuclide β decay in Brachytherapy.Time-dependent quantitative defect production,evolution dynamics,and evolution statistics were revealed in-situ for two prism foils(z=[1.210],[10.10]),in as-received and heat-treated pure Mg,after 300 keV electron irradiation up to 0.468 dpa at R.T.Preferred nucleation of basal-plane interstitial-type 1/6<20.23>loops was confirmed,in addition to a small portion of prism-plane 1/3<11.20>loops.No cavities were found.A higher yield of point defect concentration and a more evident trend of defect coarsening were identified in[1.210]than in[10.10].Pre-existing dislocations(on the orders of 10^(13)−10^(14) m^(−2))in pure Mg resulted in a delay of the first occurrence of visible defects.Defect migration and elastic interactions governed the microstructural evolution of electron irradiation damage in pure Mg,giving rise to events of loop coalescence,growth,and sometimes rotation of habit plane.The influence of incident electron energy can be correlated to the rates of point defect production,and is quantifiable;however,interfered by defect cluster stability,defect mobility,and defect interactions.This forms an important theoretical basis for the application of Mg subjected to MeV-level β-decay radiation in Brachytherapy.The paper concludes with a brief comparison between Mg and conventional Ti casing,outlines the advantages and challenges,and provides reference points for the validation of Mg/Mg-alloys in Brachytherapy seed application.
基金supported by the Jiangxi Provincial Natural Science Foundation(No.20224BAB212022)Science and Technology Project of Education Department of Jiangxi Province(No.GJJ211435)+3 种基金the National Key Research and Development Program of China(No.2021YFA1400204)the Project of China Postdoctoral Science Foundation(No.2022M712909)the Natural Science Foundation of China(No.21603109)the Henan Joint Fund of the National Natural Science Foundation of China(No.U1404216)。
文摘Cobalt phosphide has been successfully used as a catalyst in the production of ammonia from nitric acid.Substituting appropriate atoms is expected to further improve its catalytic performance.Owing to the diversity of substituting elements,substitution sites,adsorption sites,and adsorption configurations,extensive time-consuming simulation calculations are required for the high-throughput screening method.Additionally,multi-objective attributes should be considered simultaneously in catalytic design.To tackle this challenge,this paper suggests a multi-objective cobalt phosphide catalytic material design method based on surrogate models.And the effectiveness of the proposed method was validated through comparative experiments.The proposed method led to the discovery of fifteen promising cobalt phosphide catalyst configurations.This study provides a new avenue for expediting the design of catalyst,with the potential for application in other systems.
基金supported by the National Natural Science Foundation of China(Nos.51775021,52302511)the Fundamental Research Funds for the Central Universities,China(Nos.YWF-23-JC-01,YWF-23-JC-04,YWF-23-JC-09)。
文摘Stratospheric airships are lighter-than-air vehicles capable of continuous flying for months.The energy balance of the airship is the key to long-duration flights.The stratospheric airship is entirely powered by the solar array.It is necessary to accurately predict the output power of the array for any flight state.Because of the uneven solar radiation received by the solar array,the traditional model based on components has a slow simulation speed.In this study,a data-driven surrogate modeling approach for prediction the output power of the solar array is proposed.The surrogate model is trained using the samples obtained from the high-accuracy simulation model.By using the input parameter preprocessor,the accuracy of the surrogate model in predicting the output power of the solar array is improved to 98.65%.In addition,the predictive speed of the surrogate model is ten million times faster than the traditional simulation model.Finally,the surrogate model is used to predict the energy balance of stratospheric airships flying throughout the year under actual global wind fields.
基金supported by the National Natural Science Foundation of China(Grant Nos.52425211,52272360,and 52472394)Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0300)。
文摘Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.
基金Project(52108433)supported by the National Natural Science Foundation of ChinaProject(HSR202004)supported by the Open Foundation of National Engineering Research Center of High-Speed Railway Construction Technology(CSU),China+3 种基金Projects(2024RC3170,2021RC4031)supported by the Science and Technology Innovation Program of Hunan Province,ChinaProjects(2024JJ5018,2024JJ5427)supported by the Hunan Provincial Natural Science Foundation,ChinaProject(KQ2402027)supported by the Changsha City Natural Science Foundation,ChinaProjects(2021-Special-08,2022-Special-09)supported by the Science and Technology Research and Development Program Project of China Railway Group Limited。
文摘This paper proposed a RIME-VMD-BiLSTM surrogate model to rapidly and precisely predict the seismic response of a nonlinear vehicle-track-bridge(VTB)system.The surrogate model employs the RIME algorithm to optimize the variational mode decomposition(VMD)parameters(k and α)and the architecture and hyperparameter of the bidirectional long-and short-term memory network(BiLSTM).After comparing different combinations and optimization algorithms,the surrogate model was trained and used to analyze a typical 9-span 32-m high-speed railway simply supported bridge system.A series of numerical examples considering the vehicle speed,bridge damping,seismic intensity,and training strategy on the prediction effect of the surrogate model were conducted on the extended OpenSees platform.The results show that the BiLSTM model performed better than the LSTM model,whereas the prediction effects of the single-LSTM and BiLSTM models were relatively poor.With the introduction of the VMD and RIME optimization techniques,the prediction effect of the proposed RIME-VMD-BiLSTM model was excellent.The abovementioned factors had a significant influence on the seismic response of a VTB system but little impact on the prediction effect of the surrogate model.The proposed surrogate model exhibits notable transferability and robustness for predicting the VTB’s nonlinear seismic response.
基金supported by the National Natural Science Foundation of China under Grant 52325402,52274057,and 52074340the National Key R&D Program of China under Grant 2023YFB4104200+2 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028China Scholarship Council under Grant 202306450108.
文摘This study introduces a novel approach to addressing the challenges of high-dimensional variables and strong nonlinearity in reservoir production and layer configuration optimization.For the first time,relational machine learning models are applied in reservoir development optimization.Traditional regression-based models often struggle in complex scenarios,but the proposed relational and regression-based composite differential evolution(RRCODE)method combines a Gaussian naive Bayes relational model with a radial basis function network regression model.This integration effectively captures complex relationships in the optimization process,improving both accuracy and convergence speed.Experimental tests on a multi-layer multi-channel reservoir model,the Egg reservoir model,and a real-field reservoir model(the S reservoir)demonstrate that RRCODE significantly reduces water injection and production volumes while increasing economic returns and cumulative oil recovery.Moreover,the surrogate models employed in RRCODE exhibit lightweight characteristics with low computational overhead.These results highlight RRCODE's superior performance in the integrated optimization of reservoir production and layer configurations,offering more efficient and economically viable solutions for oilfield development.
基金Supported by the Major Scientific and Technological Special Project of CNPC(2023ZZ04)。
文摘The production optimization in the closed-loop reservoir management is generally empirical,and challenged by the issues such as low precision,low efficiency,and difficulty in solving constrained optimization problems.This paper outlines the main principles,advantages and disadvantages of commonly used production optimization methods/models,and then proposes an intelligent integrated production optimization method for waterflooding reservoirs that considers efficiency and precision,real-time and long-term effects,and the interaction and synergy between a variety of optimization models.This method integrates multiple optimization methods/models,such as reservoir performance analysis,reduced-physics models,and reservoir numerical models,with these model results and insights organically coupled to facilitate model construction and matching.This proposed method is elucidated and verified by field examples.The findings indicate that the optimal production optimization model varies depending on the specific application scenario.Reduced-physics models are conducive to short-term real-time optimization,whereas the simulator-based surrogate optimization and streamline-based simulation optimization methods are more suitable for long-term optimization strategy formulation,both of which need to be implemented under reasonable constraints from the perspective of reservoir engineering in order to be of practical value.
基金supported by the Science Foundation of Zhejiang Province of China(Grant No.LY22E080016)the National Natural Science Foundation of China(Grant No.51808499)the Fundamental Research Funds of Zhejiang Sci-Tech University(Grant No.24052126-Y).
文摘Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures.This study establishes a Conditional Generative Adversarial Network(CGAN)combined with randomfieldmodeling for the efficient prediction of stochastic crack patterns and stress-strain responses.Atotal dataset of 500 samples,including crack propagation images and corresponding stress-strain curves,is generated via random Finite Element Method(FEM)simulations.This dataset is then partitioned into 400 training and 100 testing samples.Themodel demonstrates robust performance with Intersection overUnion(IoU)scores of 0.8438 and 0.8155 on training and testing datasets,and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions.By using these results,the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo(MC)Simulations to capture the key statistical characteristics such as crack density and spatial distribution.Compared to conventional FEM-based methods,this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy.The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials,balancing computational efficiency with physical fidelity in capturing material stochasticity.
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
基金co-supported by the National Natural Science Foundation of China(No.52402460)project funded by the China Postdoctoral Science Foundation(No.2024T171113)supported by the Fundamental Research Funds for the Central Universities,China。
文摘This paper aims to design a morphing wing with both Flexible Leading Edge(FLE)and Flexible Trailing Edge(FTE)by using cellular structures,which can help the wing boost the deformation to a greater extent on the premise that the weight is not changed,so as to play a greater role in aerodynamic control such as gust interference.First,as for structural design,based on NACA0012,a morphing wing model constructed by 3 forms of cell structures is proposed.Then,the aerodynamic characteristics under the interference of FLE and FTE are calculated by the Computational Fluid Dynamic(CFD)method.After the surrogate model is established to predict the lift coefficient of the wing effectively,the sensitivity analysis reveals that the main sensitivity index of FTE deflection angle β is 0.565,which has the greatest influence on the lift coefficient.And the total sensitivity index of FLE deflection angle γ is increased by 78.9%,which reveals a strong coupling relationship between FLE and FTE.Finally,using Finite Element Analysis(FEA)method and experiment,the deformation capability of the model under certain static loads are obtained.The results reveal that the maximum deflection angle of the morphing wing model can be±22°at FLE and±64°at FTE,indicating strong structural stiffness and resistance to bending breakage of the model.The presented results can be useful in the design of the cellular morphing wing with multiple flexible systems.
基金supported by the National Natural Science Foundation of China(Grant Nos.11932015,12072237,and 12372022)the Shanghai Gaofeng Project for University Academic Program Development,and the Fundamental Research Funds for the Central Universities(Grant No.22120220590).
文摘Self-propelled robots have attracted significant attention due to their remarkable ability to navigate confined terrains.These robots usually have deformable structures while having discontinuous contact forces with the ground,resulting in a complex nonlinear system.To provide a solid foundation for the locomotion prediction and optimization for the self-propelled robots,it is necessary to conduct dynamic modelling and locomotion analysis of the robot.Motivated by these issues,this paper proposes a vibration-driven surrogate dynamic model for a deformable self-propelled robot and presents a detailed dynamic analysis.The surrogate dynamic model is employed to classify various types of stick-slip locomotion.Subsequently,the corresponding experiment demonstrates that the surrogate dynamic model effectively predicts the locomotion of the robot,particularly three types of stick-slip locomotion induced by discontinuous friction.Finally,a multi-objective coordinated optimization regarding the locomotion velocity,the cost of transport,and the energy conversion rate of the self-propelled robot is conducted,aiming to comprehensively enhance the robot’s locomotion performance.Additionally,suggestions for the selection of actuation parameters are presented.
基金funding support from the National Natural Science Foundation of China (Nos. 42477208 and 52079134)the Natural Science Foundation of Hubei Province, China (No. 2024AFA072)+2 种基金the Youth Innovation Promotion Association CAS (No. 2022332)the National Key R&D Program of China (No. 2024YFF0508203)the Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering Safety (Nos. SKLGME-JBGS2402 and SKLGME022022)。
文摘Accurate acquisition of the rock stress is crucial for various rock engineering applications.The hollow inclusion (HI) technique is widely used for measuring in-situ rock stress.This technique calculates the stress tensor by measuring strain using an HI strain cell.However,existing analytical solutions for stress calculation based on an HI strain cell in a double-layer medium are not applicable when an HI strain cell is used in a three-layer medium,leading to erroneous stress calculations.To address this issue,this paper presents a method for calculating stress tensors in a three-layer medium using numerical simulations,specifically by obtaining a constitutive matrix that relates strain measurements to stress tensors in a three-layer medium.Furthermore,using Latin hypercube sampling (LHS) and orthogonal experimental design strategies,764 groups of numerical models encompassing various stress measurement scenarios have been established and calculated using FLAC^(3D)software.Finally,a surrogate model based on artificial neural network (ANN) was developed to predict constitutive matrices,achieving a goodness of fit (R^(2)) of 0.999 and a mean squared error (MSE) of 1.254.A software program has been developed from this surrogate model for ease of use in practical engineering applications.The method’s accuracy was verified through numerical simulations,analytical solution and laboratory experiment,demonstrating its effectiveness in calculating stress in a three-layer medium.The surrogate model was applied to calculate mining-induced stress in the roadway roof rock of a coal mine,a typical case for stress measurement in a three-layer medium.Errors in stress calculations arising from the use of existing analytical solutions were corrected.The study also highlights the significant errors associated with using double-layer analytical solutions in a three-layer medium,which could lead to inappropriate engineering design.
基金funding support from the National Natural Science Foundation of China(No.52204065,No.ZX20230398)supported by a grant from the Human Resources Development Program(No.20216110100070)of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)。
文摘In the realm of subsurface flow simulations,deep-learning-based surrogate models have emerged as a promising alternative to traditional simulation methods,especially in addressing complex optimization problems.However,a significant challenge lies in the necessity of numerous high-fidelity training simulations to construct these deep-learning models,which limits their application to field-scale problems.To overcome this limitation,we introduce a training procedure that leverages transfer learning with multi-fidelity training data to construct surrogate models efficiently.The procedure begins with the pre-training of the surrogate model using a relatively larger amount of data that can be efficiently generated from upscaled coarse-scale models.Subsequently,the model parameters are finetuned with a much smaller set of high-fidelity simulation data.For the cases considered in this study,this method leads to about a 75%reduction in total computational cost,in comparison with the traditional training approach,without any sacrifice of prediction accuracy.In addition,a dedicated well-control embedding model is introduced to the traditional U-Net architecture to improve the surrogate model's prediction accuracy,which is shown to be particularly effective when dealing with large-scale reservoir models under time-varying well control parameters.Comprehensive results and analyses are presented for the prediction of well rates,pressure and saturation states of a 3D synthetic reservoir system.Finally,the proposed procedure is applied to a field-scale production optimization problem.The trained surrogate model is shown to provide excellent generalization capabilities during the optimization process,in which the final optimized net-present-value is much higher than those from the training data ranges.
基金the National Natural Science Foundation of China(No.52474068)the Major Collab-orative Innovation Project of Prospecting Breakthrough Stra-tegic Action in Guizhou Province(No.[2022]ZD001-003).
文摘Coalbed methane(CBM)is a vital unconventional energy resource,and predicting its spatiotemporal pressure dynamics is crucial for efficient development strategies.This paper proposes a novel deep learningebased data-driven surrogate model,AxialViT-ConvLSTM,which integrates AxialAttention Vision Transformer,ConvLSTM,and an enhanced loss function to predict pressure dynamics in CBM reservoirs.The results showed that the model achieves a mean square error of 0.003,a learned perceptual image patch similarity of 0.037,a structural similarity of 0.979,and an R^(2) of 0.982 between predictions and actual pressures,indicating excellent performance.The model also demonstrates strong robustness and accuracy in capturing spatialetemporal pressure features.
