Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectivenes...Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.展开更多
The hepatitis B virus(HBV)is considered to be a major public health problem worldwide,and a significant number of reports on nosocomial outbreaks of HBV infections have been reported.Prevention of indirect HBV transmi...The hepatitis B virus(HBV)is considered to be a major public health problem worldwide,and a significant number of reports on nosocomial outbreaks of HBV infections have been reported.Prevention of indirect HBV transmission by contaminated objects is only possible through the use of infection-control principles,including the use of chemical biocides,which are proven to render the virus non-infectious.The virucidal activity of biocides against HBV cannot be predicted;therefore,validation of the virucidal action of disinfectants against HBV is essential.However,feasible HBV infectivity assays have not yet been established.Thus,surrogate models have been proposed for testing the efficacy of biocides against HBV.Most of these assays do not correlate with HBV infectivity.Currently,the most promising and feasible assay is the use of the taxonomically related duck hepatitis B virus(DHBV),which belongs to the same Hepadnaviridae virus family.This paper reviews the application of DHBV,which can be propagated in vitro in primary duck embryonic hepatocytes,for the testing of biocides and describes why this model can be used as reliable method to evaluate disinfectants for efficacy against HBV.The susceptibility levels of important biocides,which are often used as ingredients for commercially available disinfectants,are also described.展开更多
Successful embryo implantation requires highly coordinated maternal-embryo interactions.Implantation failure is a major factor contributing to infertility.However,the mechanism underlying implantation failure remains ...Successful embryo implantation requires highly coordinated maternal-embryo interactions.Implantation failure is a major factor contributing to infertility.However,the mechanism underlying implantation failure remains unclear.An improved understanding of the early implantation process not only improves the success rate of assisted reproductive treatments but also helps in studying the pathophysiology of reproductive disorders.Owing to ethical concerns,in vivo studies of human embryo implantation are not feasible.However,the results obtained from animal models cannot be directly applied to humans.Over the years,in vitro implantation models have been developed to investigate implantation mechanisms.In this review,we discuss the use of different models for generating embryo-like surrogates to study early embryo development and implantation in vitro,with a specific focus on stem cell-derived blastocyst-like embryo surrogates.There is no definitive evidence that the recently established embryo-like models re-capitulate all developmental events of human embryos during the peri-implantation stage.Regardless,stem cell-derived embryo surrogates are the most valuable tools for studying the mechanisms of early cell lineage differentiation and developmental failures during implantation.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
Jet fuel is widely used in air transportation,and sometimes for special vehicles in ground transportation.In the latter case,fuel spray auto-ignition behavior is an important index for engine operation reliability.Sur...Jet fuel is widely used in air transportation,and sometimes for special vehicles in ground transportation.In the latter case,fuel spray auto-ignition behavior is an important index for engine operation reliability.Surrogate fuel is usually used for fundamental combustion study due to the complex composition of practical fuels.As for jet fuels,two-component or three-component surrogate is usually selected to emulate practical fuels.The spray auto-ignition characteristics of RP-3 jet fuel and its three surrogates,the 70%mol n-decane/30%mol 1,2,4-trimethylbenzene blend(Surrogate 1),the 51%mol n-decane/49%mol 1,2,4-trimethylbenzene blend(Surrogate 2),and the 49.8%mol n-dodecane/21.6%mol iso-cetane/28.6%mol toluene blend(Surrogate 3)were studied in a heated constant volume combustion chamber.Surrogate 1 and Surrogate 2 possess the same components,but their blending percentages are different,as the two surrogates were designed to capture the H/C ratio(Surrogate 1)and DCN(Surrogate 2)of RP-3 jet fuel,respectively.Surrogate 3 could emulate more physiochemical properties of RP-3 jet fuel,including molecular weight,H/C ratio and DCN.Experimental results indicate that Surrogate 1 overestimates the auto-ignition propensity of RP-3 jet fuel,whereas Surrogates 2 and 3 show quite similar auto-ignition propensity with RP-3 jet fuel.Therefore,to capture the spray auto-ignition behaviors,DCN is the most important parameter to match when designing the surrogate formulation.However,as the ambient temperature changes,the surrogates matching DCN may still show some differences from the RP-3 jet fuel,e.g.,the first-stage heat release influenced by low-temperature chemistry.展开更多
Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine ...Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables.For photonic-device models,we find that this training becomes especially challenging as design regions grow larger than the optical wavelength.We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples.Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve,and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.展开更多
Biodiversity assessments can often be time- and resource-consuming. Several alternative approaches have been proposed to reduce sampling efforts, including indicator taxa and surrogates. In this study, we examine the ...Biodiversity assessments can often be time- and resource-consuming. Several alternative approaches have been proposed to reduce sampling efforts, including indicator taxa and surrogates. In this study, we examine the reliability of higher taxon surrogates to predict species richness in two experimental rice fields of Fujian Province, southeastern China during 2005 and 2009. Spider samples in transgenic and nontransgenie plots were collected using a suction sampler. Both the genus and family surrogates had significant and positive linear relationships with species richness in the transgenic and nontransgenic rice fields. The rice varieties did not significantly influence the linear relationships. Our findings suggest that higher-taxon surrogacy could be a useful alternative to complete species inventory for risk assessments of transgenic rice.展开更多
Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle(Coleoptera:Carabidae)biodiversity patterns that are related to...Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle(Coleoptera:Carabidae)biodiversity patterns that are related to the forest canopy mosaic.Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data.Methods We used a systematic sampling grid of 198 sites,covering 84 km^(2) of boreal mixedwood forest in northwestern Alberta,Canada.For every site,we determined tree basal area,characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information.We used variation partitioning,ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation.Important Findings Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns.The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models.Compared to field survey data,air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures.This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover.Combination of relative canopy cover from provincial inventory with other georeferenced land variables to produce the ecosystem classification maps improved biodiversity predictive power.The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management.In order to complement conservation efforts established in protected areas,accurate,high resolution,wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest.In development of these strategies,an in-depth understanding of vegetation is key.展开更多
A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier curves.Thermal design process includes iterative high fidelity simul...A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier curves.Thermal design process includes iterative high fidelity simulation which is complex,computationally expensive,and time-consuming.With the advancement in machine learning algorithms as well as Graphics Processing Units(GPUs),parallel processing architecture of GPUs can be used to accelerate thermo-fluid simulation.In this study,Convolutional Neural Networks(CNNs)are used to predict results of Computational Fluid Dynamics(CFD)directly from topologies saved as images.A design space with a single fin as well as multiple morphable fins are studied.A comparison of Xception network and regular CNN is presented for the case with a single fin design.Results show that high accuracy in prediction is observed for single fin design particularly using Xception network.Xception network provides 98 percent accuracy in heat transfer and pressure drop prediction of the single fin design.Increasing the design freedom to multiple fins increases the error in prediction.This error,however,remains within three percent of the ground truth values which is valuable for design purpose.The presented predictive model can be used for innovative BREP-based fin design optimization in compact and high efficiency heat exchangers.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
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
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
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
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 andα)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
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
Objective:A conventional endpoint for locally advanced cervical cancer(LACC)clinical trials is overall survival(OS)with five years of follow-up.The primary hypothesis was that progression-free survival(PFS)with three ...Objective:A conventional endpoint for locally advanced cervical cancer(LACC)clinical trials is overall survival(OS)with five years of follow-up.The primary hypothesis was that progression-free survival(PFS)with three years of follow-up(PFS36)would be an appropriate primary surrogate endpoint.Materials and methods:The primary hypothesis,which was developed from our data,was further investigated using phase III randomized controlled trials and then externally validated using retrospective studies up to 2023.Correlation analysis at the treatment-arm level was performed between 2-,3-,4-,and 5-year PFS rates and 5-year OS.Results:A total of 613 patients with histologically confirmed cervical cancer who underwent radiotherapy or chemoradiation at our institute between January 2010 and December 2013 were eligible.The recurrence rates for years 1 through 5 were 12.9%,7.3%,3%,2.3%,and 1.8%,respectively.Patients who did not achieve PFS36 had a 5-year OS rate of 30.3%.However,patients who achieved PFS36 had a 5-year OS rate of 98.2%.Further data were extracted from 26 randomized phase III trials on LACC.The trials included 55 arms,with a pooled sample size of 7,281 patients.Trial-level surrogacy results revealed that PFS36(r2,0.732)was associated with 5-year OS.The correlation between PFS36 and OS was externally validated using independent retrospective data.Conclusion:A significant positive correlation was found between PFS36 and OS at 5 years of follow-up both within patients and across trials.These results suggest that PFS36 is an appropriate endpoint for LACC clinical trials of radiotherapy-based regimens.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
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
基金supported by the Technology Innovation Program of the Korea Evaluation Institute of Industrial Technology (KEIT)under the Ministry of Trade,Industry and Energy (MOTIE)of Republic of Korea (20012121)by the National Research Foundation of Korea (NRF)grant funded by the Korea government (MSIT) (2022M3J7A106294)。
文摘Polymer electrolyte membrane fuel cells(PEMFCs)are considered a promising alternative to internal combustion engines in the automotive sector.Their commercialization is mainly hindered due to the cost and effectiveness of using platinum(Pt)in them.The cathode catalyst layer(CL)is considered a core component in PEMFCs,and its composition often considerably affects the cell performance(V_(cell))also PEMFC fabrication and production(C_(stack))costs.In this study,a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcelland Cstack.Four essential cathode CL parameters,i.e.,platinum loading(L_(Pt)),weight ratio of ionomer to carbon(wt_(I/C)),weight ratio of Pt to carbon(wt_(Pt/c)),and porosity of cathode CL(ε_(cCL)),are considered as the design variables.The simulation results of a three-dimensional,multi-scale,two-phase comprehensive PEMFC model are used to train and test two famous surrogates:multi-layer perceptron(MLP)and response surface analysis(RSA).Their accuracies are verified using root mean square error and adjusted R^(2).MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithmⅡ.Compared to a typical PEMFC stack,the results of the optimal study show that the single-cell voltage,Vcellis improved by 28 m V for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by$5.86/k W for the same stack performance.
文摘The hepatitis B virus(HBV)is considered to be a major public health problem worldwide,and a significant number of reports on nosocomial outbreaks of HBV infections have been reported.Prevention of indirect HBV transmission by contaminated objects is only possible through the use of infection-control principles,including the use of chemical biocides,which are proven to render the virus non-infectious.The virucidal activity of biocides against HBV cannot be predicted;therefore,validation of the virucidal action of disinfectants against HBV is essential.However,feasible HBV infectivity assays have not yet been established.Thus,surrogate models have been proposed for testing the efficacy of biocides against HBV.Most of these assays do not correlate with HBV infectivity.Currently,the most promising and feasible assay is the use of the taxonomically related duck hepatitis B virus(DHBV),which belongs to the same Hepadnaviridae virus family.This paper reviews the application of DHBV,which can be propagated in vitro in primary duck embryonic hepatocytes,for the testing of biocides and describes why this model can be used as reliable method to evaluate disinfectants for efficacy against HBV.The susceptibility levels of important biocides,which are often used as ingredients for commercially available disinfectants,are also described.
基金supported in part by a General Research Fund(grant number:17111414)Research Grants Council of Hong Kong+3 种基金Health and Medical Research Fund(grant numbers:HMRF 04151546)Food and Health Bureau,Government of the Hong Kong Special Administrative RegionShenzhen Science and Technology Program(KQTD20190929172749226)The University of Hong Kong-Shenzhen Hospital Fund for Shenzhen Key Medical Discipline(SZXK2020089)
文摘Successful embryo implantation requires highly coordinated maternal-embryo interactions.Implantation failure is a major factor contributing to infertility.However,the mechanism underlying implantation failure remains unclear.An improved understanding of the early implantation process not only improves the success rate of assisted reproductive treatments but also helps in studying the pathophysiology of reproductive disorders.Owing to ethical concerns,in vivo studies of human embryo implantation are not feasible.However,the results obtained from animal models cannot be directly applied to humans.Over the years,in vitro implantation models have been developed to investigate implantation mechanisms.In this review,we discuss the use of different models for generating embryo-like surrogates to study early embryo development and implantation in vitro,with a specific focus on stem cell-derived blastocyst-like embryo surrogates.There is no definitive evidence that the recently established embryo-like models re-capitulate all developmental events of human embryos during the peri-implantation stage.Regardless,stem cell-derived embryo surrogates are the most valuable tools for studying the mechanisms of early cell lineage differentiation and developmental failures during implantation.
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This research work was supported by the National Natural Science Foundation of China(Grant Nos.51776124 and 51861135303)the Belt and Road International Collaboration Program by Shanghai Science and Technology Committee(Grant No.19160745400).
文摘Jet fuel is widely used in air transportation,and sometimes for special vehicles in ground transportation.In the latter case,fuel spray auto-ignition behavior is an important index for engine operation reliability.Surrogate fuel is usually used for fundamental combustion study due to the complex composition of practical fuels.As for jet fuels,two-component or three-component surrogate is usually selected to emulate practical fuels.The spray auto-ignition characteristics of RP-3 jet fuel and its three surrogates,the 70%mol n-decane/30%mol 1,2,4-trimethylbenzene blend(Surrogate 1),the 51%mol n-decane/49%mol 1,2,4-trimethylbenzene blend(Surrogate 2),and the 49.8%mol n-dodecane/21.6%mol iso-cetane/28.6%mol toluene blend(Surrogate 3)were studied in a heated constant volume combustion chamber.Surrogate 1 and Surrogate 2 possess the same components,but their blending percentages are different,as the two surrogates were designed to capture the H/C ratio(Surrogate 1)and DCN(Surrogate 2)of RP-3 jet fuel,respectively.Surrogate 3 could emulate more physiochemical properties of RP-3 jet fuel,including molecular weight,H/C ratio and DCN.Experimental results indicate that Surrogate 1 overestimates the auto-ignition propensity of RP-3 jet fuel,whereas Surrogates 2 and 3 show quite similar auto-ignition propensity with RP-3 jet fuel.Therefore,to capture the spray auto-ignition behaviors,DCN is the most important parameter to match when designing the surrogate formulation.However,as the ambient temperature changes,the surrogates matching DCN may still show some differences from the RP-3 jet fuel,e.g.,the first-stage heat release influenced by low-temperature chemistry.
基金This work was supported in part by IBM Research,the MIT-IBM Watson AI Laboratory,the U.S.Army Research Office through the Institute for Soldier Nanotechnologies(under award W911NF-13-D-0001)by the PAPPA program of DARPA MTO(under award HR0011-20-90016).
文摘Surrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components.However,the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables.For photonic-device models,we find that this training becomes especially challenging as design regions grow larger than the optical wavelength.We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples.Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve,and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.
文摘Biodiversity assessments can often be time- and resource-consuming. Several alternative approaches have been proposed to reduce sampling efforts, including indicator taxa and surrogates. In this study, we examine the reliability of higher taxon surrogates to predict species richness in two experimental rice fields of Fujian Province, southeastern China during 2005 and 2009. Spider samples in transgenic and nontransgenie plots were collected using a suction sampler. Both the genus and family surrogates had significant and positive linear relationships with species richness in the transgenic and nontransgenic rice fields. The rice varieties did not significantly influence the linear relationships. Our findings suggest that higher-taxon surrogacy could be a useful alternative to complete species inventory for risk assessments of transgenic rice.
基金The work was supported financially by our industrial forestry partners,Canadian Forest Products,Ltd.,Daishowa-Marubeni International,Ltd.Manning Diversified Forest Products,Ltd.+3 种基金Alberta Sustainable Resource Developmentthe Sustainable Forest Management Networkthe Canadian Forest Servicethe Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘Aims We compare performance of ecosystem classification maps and provincial forest inventory data derived from air photography in reflecting ground beetle(Coleoptera:Carabidae)biodiversity patterns that are related to the forest canopy mosaic.Our biodiversity surrogacy model based on remotely sensed tree canopy cover is validated against field-collected ground data.Methods We used a systematic sampling grid of 198 sites,covering 84 km^(2) of boreal mixedwood forest in northwestern Alberta,Canada.For every site,we determined tree basal area,characterized the ground beetle assemblage and obtained corresponding provincial forest inventory and ecosystem classification information.We used variation partitioning,ordination and misclassification matrices to compare beetle biodiversity patterns explained by alternative databases and to determine model biases originating from air photo-interpretation.Important Findings Ecosystem classification data performed better than canopy cover derived from forest inventory maps in describing ground beetle biodiversity patterns.The biodiversity surrogacy models based on provincial forest inventory maps and field survey generally detected similar patterns but inaccuracies in air photo-interpretation of relative canopy cover led to differences between the two models.Compared to field survey data,air photo-interpretation tended to confuse two Picea species and two Populus species present and homogenize stand mixtures.This generated divergence in models of ecological association used to predict the relationship between ground beetle assemblages and tree canopy cover.Combination of relative canopy cover from provincial inventory with other georeferenced land variables to produce the ecosystem classification maps improved biodiversity predictive power.The association observed between uncommon surrogates and uncommon ground beetle species emphasizes the benefits of detecting these surrogates as a part of landscape management.In order to complement conservation efforts established in protected areas,accurate,high resolution,wide ranging and spatially explicit knowledge of landscapes under management is primordial in order to apply effective biodiversity conservation strategies at the stand level as required in the extensively harvested portion of the boreal forest.In development of these strategies,an in-depth understanding of vegetation is key.
文摘A deep learning approach is presented for heat transfer and pressure drop prediction of complex fin geometries generated using composite Bézier curves.Thermal design process includes iterative high fidelity simulation which is complex,computationally expensive,and time-consuming.With the advancement in machine learning algorithms as well as Graphics Processing Units(GPUs),parallel processing architecture of GPUs can be used to accelerate thermo-fluid simulation.In this study,Convolutional Neural Networks(CNNs)are used to predict results of Computational Fluid Dynamics(CFD)directly from topologies saved as images.A design space with a single fin as well as multiple morphable fins are studied.A comparison of Xception network and regular CNN is presented for the case with a single fin design.Results show that high accuracy in prediction is observed for single fin design particularly using Xception network.Xception network provides 98 percent accuracy in heat transfer and pressure drop prediction of the single fin design.Increasing the design freedom to multiple fins increases the error in prediction.This error,however,remains within three percent of the ground truth values which is valuable for design purpose.The presented predictive model can be used for innovative BREP-based fin design optimization in compact and high efficiency heat exchangers.
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 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 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 andα)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.
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funded by the CAMS Innovation Fund for Medical Sciences(CIFMS)(grant number:2024-I2M-C&T-B-071)the Beijing Hope Run Special Fund of Cancer Foundation of China(grant number:LC2018B04).
文摘Objective:A conventional endpoint for locally advanced cervical cancer(LACC)clinical trials is overall survival(OS)with five years of follow-up.The primary hypothesis was that progression-free survival(PFS)with three years of follow-up(PFS36)would be an appropriate primary surrogate endpoint.Materials and methods:The primary hypothesis,which was developed from our data,was further investigated using phase III randomized controlled trials and then externally validated using retrospective studies up to 2023.Correlation analysis at the treatment-arm level was performed between 2-,3-,4-,and 5-year PFS rates and 5-year OS.Results:A total of 613 patients with histologically confirmed cervical cancer who underwent radiotherapy or chemoradiation at our institute between January 2010 and December 2013 were eligible.The recurrence rates for years 1 through 5 were 12.9%,7.3%,3%,2.3%,and 1.8%,respectively.Patients who did not achieve PFS36 had a 5-year OS rate of 30.3%.However,patients who achieved PFS36 had a 5-year OS rate of 98.2%.Further data were extracted from 26 randomized phase III trials on LACC.The trials included 55 arms,with a pooled sample size of 7,281 patients.Trial-level surrogacy results revealed that PFS36(r2,0.732)was associated with 5-year OS.The correlation between PFS36 and OS was externally validated using independent retrospective data.Conclusion:A significant positive correlation was found between PFS36 and OS at 5 years of follow-up both within patients and across trials.These results suggest that PFS36 is an appropriate endpoint for LACC clinical trials of radiotherapy-based regimens.
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.
