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
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
To address the limitations of existing coupling methods in aero-engine system simulation,which fail to adaptively adjust iterative parameters and coupling relationships,which can result in low efficiency and in⁃stabil...To address the limitations of existing coupling methods in aero-engine system simulation,which fail to adaptively adjust iterative parameters and coupling relationships,which can result in low efficiency and in⁃stability,this study introduces a‘Dynamic Event-Driven Co-Simulation’algorithm integrated with decision tree algorithms.This algorithm separates the overall coupling relationships and the main solver from the primary mod⁃el,utilizing a dynamic event monitoring module to adaptively adjust simulation strategies,including iteration pa⁃rameters,coupling relationships,and convergence criteria.This facilitates efficient adaptive simulations of dy⁃namic events while balancing solution accuracy and computational efficiency.The research focuses on a twinshaft turbofan engine,establishing six system-level models that encompass overall performance and various sub⁃systems based on three coupling methods,along with a multidisciplinary multi-fidelity simulation framework in⁃corporating a 3D CFD nozzle model.The study tests both model exchange and coupled simulation methods under a 14 s transient acceleration and deceleration scenario.In a 100%throttle condition,a high-fidelity nozzle model is used to analyze the sensitivity of different convergence criteria on computational efficiency and accuracy.Re⁃sults indicate that the accuracy and efficiency achieved with this method are comparable to those of PROOSIS soft⁃ware(18 s and 35 s,respectively),while being 71%more efficient than Simulink software(62 s and 120 s,re⁃spectively).Furthermore,appropriately relaxing the convergence criteria for the 0D model(from 10-6 to 10-4)while enhancing those for the 3D model(from 3000 steps to 6000 steps)can effectively balance computational accuracy and efficiency.展开更多
This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platfo...This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.展开更多
For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to captu...For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to capture the correlation between different fidelity data,which may lead to a significant increase in low-fidelity samples.In addition,it is difficult to build accurate surrogate models because current methods do not fully consider the nonlinearity between different fidelity samples.To address these problems,a novel multi-fidelity modeling method with active learning is proposed in this paper.Firstly,a nonlinear autoregressive multi-fidelity Kriging(NAMK)model is used to build a surrogate model.To avoid introducing redundant samples in the process of NAMK model updating,a collective learning function is then developed by a combination of a U-learning function,the correlation between different fidelity samples,and the sampling cost.Furthermore,a residual model is constructed to automatically generate low-fidelity samples when high-fidelity samples are selected.The efficiency and accuracy of the proposed method are demonstrated using three numerical examples and an engineering case.展开更多
Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructe...Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructed based on two independent high and low fidelity samples.Co-Kriging method can use a greater quantity of low-fidelity information to enhance the accuracy of a surrogate of the high-fidelity model by modeling the correlation between high and low fidelity model,thus computational cost of building surrogate model can be greatly reduced.A wing-body problem is taken as an example to compare characteristics of co-Kriging multi-fidelity(CKMF)model with traditional Kriging based multi-fidelity(KMF)model.A sampling convergence of the CKMF model and the KMF model is conducted,and an appropriate sampling design is selected through the sampling convergence analysis.The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples,and converges at less high-fidelity samples.A wing-body drag reduction optimization design using genetic algorithm is implemented.Satisfying design results are obtained,which validate the feasibility of CKMF model in engineering design.展开更多
Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stabili...Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Computational Fluid Dynamics(CFD) simulation. This simulation method proposes a form of Front-VABI characteristic and its matching calculation method between it and the ACE performance model, constructs a coupling method between the(2-D) Front-VABI model and the(0-D) ACE performance model. The result shows, when ACE works in triple bypass mode, the approximate model cannot account for the effect of FrontVABI pressure loss on Core Driven Fan Stage(CDFS) design pressure ratio, and the calculated error of high-pressure turbine inlet total temperature is more than 40 K in mode transition condition(the transition operating condition between triple bypass mode and double bypass mode). In double bypass mode, the approximate model can better simulate the performance of FrontVABI by considering the local loss of area expansion. This method can be applied to the performance-optimized design of Front-VABI and the ACE control law design during mode transition.展开更多
Flow around a real-life underwater vehicle often happens at a high Reynolds number with flow structures at different scales from the boundary layer around a blade to that around the hull. This poses a great challenge ...Flow around a real-life underwater vehicle often happens at a high Reynolds number with flow structures at different scales from the boundary layer around a blade to that around the hull. This poses a great challenge for large-eddy simulation of an underwater vehicle aiming at resolving all relevant flow scales. In this work, we propose to model the hull with appendages using the immersed boundary method, and model the propeller using the actuator disk model without resolving the geometry of the blade. The proposed method is then applied to simulate the flow around Defense Advanced Research Projects Agency(DARPA) suboff. An overall acceptable agreement is obtained for the pressure and friction coefficients. Complex flow features are observed in the near wake of suboff. In the far wake, the core region is featured by a jet because of the actuator disk, surrounded by an annular region with velocity deficit due to the body of suboff.展开更多
A directed signature is a type of signature with restricted verification ability.Directed signatures allow only a designated verifier to check the validity of the signature issued to him,and at the time of trouble or ...A directed signature is a type of signature with restricted verification ability.Directed signatures allow only a designated verifier to check the validity of the signature issued to him,and at the time of trouble or if necessary,any third party can verify the signature with the help of the signer or the designated verifier.Directed signature schemes are widely used in situations where the receiver's privacy should be protected.Proxy signatures allow an entity to delegate its signing capability to another entity in such a way that the latter can sign message on behalf of the former when the former is not available.Proxy signature schemes have found numerous practical applications such as distributed systems and mobile agent applications.In this paper,we firstly define the notion of the directed proxy signature by combining the proxy signature and directed signature.Then,we formalize its security model and present a concrete scheme in the standard model.Finally,we use the techniques from provable security to show that the proposed scheme is unforgeable under the gap Diffie-Hellman assumption,and invisible under the decisional Diffie-Hellman assumption.展开更多
Wellbore instability is one of the concerns in the field of drilling engineering.This phenomenon is affected by several factors such as azimuth,inclination angle,in-situ stress,mud weight,and rock strength parameters....Wellbore instability is one of the concerns in the field of drilling engineering.This phenomenon is affected by several factors such as azimuth,inclination angle,in-situ stress,mud weight,and rock strength parameters.Among these factors,azimuth,inclination angle,and mud weight are controllable.The objective of this paper is to introduce a new procedure based on elastoplastic theory in wellbore stability solution to determine the optimum well trajectory and global minimum mud pressure required(GMMPR).Genetic algorithm(GA) was applied as a main optimization engine that employs proportional feedback controller to obtain the minimum mud pressure required(MMPR).The feedback function repeatedly calculated and updated the error between the simulated and set point of normalized yielded zone area(NYZA).To reduce computation expenses,an artificial neural network(ANN) was used as a proxy(surrogate model) to approximate the behavior of the actual wellbore model.The methodology was applied to a directional well in southwestern Iranian oilfield.The results demonstrated that the error between the predicted GMMPR and practical safe mud pressure was 4%for elastoplastic method,and 22%for conventional elastic solution.展开更多
This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization.The optimization problem is posed to maximize the lift and drag coefficient ratio subjec...This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization.The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints.Computational Fluid Dynamic(CFD)and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value.A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed.To validate and further assess the proposed methods,a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied with multi-fidelity simulation,while their numerical performance is investigated.The results obtained show that the proposed technique is the best performer for the demonstrated airfoil shape optimization.According to this study,applying multi-fidelity with multi-objective infill sampling criteria for surrogate-assisted optimization is a powerful design tool.展开更多
How to protect the database, the kernel resources of information warfare, is becoming more and more important since the rapid development of computer and communication technology. As an application-level firewall, dat...How to protect the database, the kernel resources of information warfare, is becoming more and more important since the rapid development of computer and communication technology. As an application-level firewall, database security proxy can successfully repulse attacks originated from outside the network, reduce to zerolevel damage from foreign DBMS products. We enhanced the capability of the COAST' s firewall reference model by adding a transmission unit modification function and an attribute value mapping function,describes the schematic and semantic layer reference model, and finally forms a reference model for DBMS security proxy which greatly helps in the design and implementation of database security proxies. This modeling process can clearly separate the system functionality into three layers, define the possible security functions for each layer, and estimate the computational cost for each layer.展开更多
Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment b...Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.展开更多
Iterative coupled methods are widely used in multi-fidelity simulation of rotating components due to the simple implementation,which iteratively eliminates the errors between the computational fluid dynamics models an...Iterative coupled methods are widely used in multi-fidelity simulation of rotating components due to the simple implementation,which iteratively eliminates the errors between the computational fluid dynamics models and approximate characteristic maps.However,the convergence and accuracy of the iterative coupled method are trapped in characteristic maps.In particular,iterative steps increase sharply as the operation point moves away from the design point.To address these problems,this paper developed an auxiliary iterative coupled method that introduces the static-pressure-auxiliary characteristic maps and modification factor of mass flow into the component-level model.The developed auxiliary method realized the direct transfer of static pressure between the high-fidelity models and the component-level model.Multi-fidelity simulations of the throttle characteristics were carried out using both the auxiliary and traditional iterative coupled methods,and the simulation results were verified using the experimental data.Additionally,the consistency between the auxiliary and traditional iterative coupled methods was confirmed.Subsequently,multi-fidelity simulations of the speed and altitude characteristics were also conducted.The auxiliary and traditional iterative coupled methods were evaluated in terms of convergence speed and accuracy.The evaluation indicated that the auxiliary iterative coupled method significantly reduces iterative steps by approximately 50%at the near-choked state.In general,the auxiliary iterative coupled method is preferred as a development of the traditional iterative coupled method in the near-choked state,and the combined auxiliary-traditional iterative coupled method provides support for successful multi-fidelity simulation in far-off-design conditions.展开更多
Currently available proxies were studied as networks for building reconstruction models of the Atlantic Multidecadal Oscillation (AMO). Only proxies that would double the current record length (backwards in time from ...Currently available proxies were studied as networks for building reconstruction models of the Atlantic Multidecadal Oscillation (AMO). Only proxies that would double the current record length (backwards in time from AD 1564) were included. We present two proxy networks and corresponding reconstruction (transfer) models, one for tree-growth based proxies only and another for multiproxies. Both of them show a useful match in timing as well as amplitude with the AMO. These model structures demonstrated reasonable model performance (overall r<sup>2</sup> = 0.45 - 0.36). The time stability of proxy-AMO relationships was also validated. The new models produced acceptable results in cross-calibration-verification (reduction of error and coefficient of efficiency statistics in 1856-1921 and 1922-1990 vary between 0.41 and 0.21). The spatial distribution of these data series indicate that proxies respond to an AMO-like climatic oscillation over much of the Northern Hemisphere.展开更多
建立了一个中等规模的结构向量自回归模型,系统探讨了货币政策不确定性对宏观经济波动与银行系统金融风险的影响。首先,比较现阶段我国主流货币政策不确定性及经济政策不确定性代理指标,发现Huang&Luk (2020)所测度MPU与EPU并不能...建立了一个中等规模的结构向量自回归模型,系统探讨了货币政策不确定性对宏观经济波动与银行系统金融风险的影响。首先,比较现阶段我国主流货币政策不确定性及经济政策不确定性代理指标,发现Huang&Luk (2020)所测度MPU与EPU并不能对我国宏观经济波动做出较好解释。比较而言,Baker et al.(2016)与Davis et al.(2019)的EPU对我国宏观经济波动影响更符合预期,EPU的影响类似于紧缩性供给冲击,导致产出、固定资产投资以及社会零售消费降低,通货膨胀上升。特别是在EPU_Davis指标存在意外正向冲击时,可能导致银行系统金融风险上升。其次,利用条件波动率思想,重新估计货币政策不确定性(MPU_Volatility),发现无论是简约形式或结构形式估计,所获得MPU_Volatility在波动趋势上大体一致,且MPU_Volatility与EPU_Baker及EPU_Davis对宏观经济作用效果存在较大一致,其中MPU_Volatility与EPU_Davis对银行系统金融风险影响一致。最后,进一步将叙事性EPU_Davis指标与MPU_Volatility结合使用,选用Proxy SVAR对货币政策不确定性结构冲击进行识别,这保证了即使存在测量误差情况下,估计偏误也会大幅降低。实证发现MPU对宏观经济变量造成不利影响,导致银行系统金融风险上升并持续较长时间。脉冲响应结果与EPU_Davis及MPU_Volatility结果相似,这一定程度上支持之前结论的稳健。展开更多
Low pressure ratio fans of modern civil turbofans suffer from reduced stall margin in the take-off operating line and at part-speed,requiring variable geometry devices.Variable area nozzles(VAN)are one of the investig...Low pressure ratio fans of modern civil turbofans suffer from reduced stall margin in the take-off operating line and at part-speed,requiring variable geometry devices.Variable area nozzles(VAN)are one of the investigated solutions to control engine operating conditions throughout the mission.In this paper,we present a multi-fidelity modelling approach for an ultra-high bypass ratio turbofan engine with a VAN,combining a zero-dimensional thermody-namic cycle simulator using a realistic fan map with two-and three-dimensional detailed computational fluid dynamics(CFD)simulations for internal/external flow coupling.By adopting a novel algorithm to match the cycle conditions to the CFD solutions,the propulsive performance of the turbofan is analysed in a reference aircraft mission.The numerical method captures the effect on thrust generation and nacelle drag,providing a more reliable estimation of the impact of VAN on engine operation and efficiency.Low-speed mission points are confirmed to be those that benefit the most from an enlarged fan nozzle area,with a possible improvement of 3%in terms of thrust and specific fuel consumption at take-off and approach using a 10%larger area,similarly predicted by both 2D and 3D models.A preliminary acous-tic evaluation based on semi-empirical noise models indicates a modest effect on noise emis-sions,with up to 1 dB reduction in microphone signature at the sideline for a nozzle area increased by 10%.展开更多
To support withdrawing and storing money from all levels of the bank for the customers in the real world, in this paper, we propose a proxy blind signature scheme and an off-line e-cash scheme based on the new proxy b...To support withdrawing and storing money from all levels of the bank for the customers in the real world, in this paper, we propose a proxy blind signature scheme and an off-line e-cash scheme based on the new proxy blind signature scheme. The pro- posed proxy blind signature is proven secure in the random oracle model under chosen-target computational Diffie-Hellman assump- tions, and the e-cash scheme can satisfy the security requirements of unforgeability, anonymity, and traceability.展开更多
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.
文摘To address the limitations of existing coupling methods in aero-engine system simulation,which fail to adaptively adjust iterative parameters and coupling relationships,which can result in low efficiency and in⁃stability,this study introduces a‘Dynamic Event-Driven Co-Simulation’algorithm integrated with decision tree algorithms.This algorithm separates the overall coupling relationships and the main solver from the primary mod⁃el,utilizing a dynamic event monitoring module to adaptively adjust simulation strategies,including iteration pa⁃rameters,coupling relationships,and convergence criteria.This facilitates efficient adaptive simulations of dy⁃namic events while balancing solution accuracy and computational efficiency.The research focuses on a twinshaft turbofan engine,establishing six system-level models that encompass overall performance and various sub⁃systems based on three coupling methods,along with a multidisciplinary multi-fidelity simulation framework in⁃corporating a 3D CFD nozzle model.The study tests both model exchange and coupled simulation methods under a 14 s transient acceleration and deceleration scenario.In a 100%throttle condition,a high-fidelity nozzle model is used to analyze the sensitivity of different convergence criteria on computational efficiency and accuracy.Re⁃sults indicate that the accuracy and efficiency achieved with this method are comparable to those of PROOSIS soft⁃ware(18 s and 35 s,respectively),while being 71%more efficient than Simulink software(62 s and 120 s,re⁃spectively).Furthermore,appropriately relaxing the convergence criteria for the 0D model(from 10-6 to 10-4)while enhancing those for the 3D model(from 3000 steps to 6000 steps)can effectively balance computational accuracy and efficiency.
基金financially supported by the National Natural Science Foundation of China(Grant No.52371261)the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).
文摘This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.
基金supported by the Major Projects of Zhejiang Provincial Natural Science Foundation of China(No.LD22E050009)the National Natural Science Foundation of China(No.51475425)the College Student’s Science and Technology Innovation Project of Zhejiang Province(No.2022R403B060),China.
文摘For complex engineering problems,multi-fidelity modeling has been used to achieve efficient reliability analysis by leveraging multiple information sources.However,most methods require nested training samples to capture the correlation between different fidelity data,which may lead to a significant increase in low-fidelity samples.In addition,it is difficult to build accurate surrogate models because current methods do not fully consider the nonlinearity between different fidelity samples.To address these problems,a novel multi-fidelity modeling method with active learning is proposed in this paper.Firstly,a nonlinear autoregressive multi-fidelity Kriging(NAMK)model is used to build a surrogate model.To avoid introducing redundant samples in the process of NAMK model updating,a collective learning function is then developed by a combination of a U-learning function,the correlation between different fidelity samples,and the sampling cost.Furthermore,a residual model is constructed to automatically generate low-fidelity samples when high-fidelity samples are selected.The efficiency and accuracy of the proposed method are demonstrated using three numerical examples and an engineering case.
基金supported by the Seventh Framework Programme of China-EU Collaborative Projects
文摘Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multi-fidelity surrogate model is constructed based on two independent high and low fidelity samples.Co-Kriging method can use a greater quantity of low-fidelity information to enhance the accuracy of a surrogate of the high-fidelity model by modeling the correlation between high and low fidelity model,thus computational cost of building surrogate model can be greatly reduced.A wing-body problem is taken as an example to compare characteristics of co-Kriging multi-fidelity(CKMF)model with traditional Kriging based multi-fidelity(KMF)model.A sampling convergence of the CKMF model and the KMF model is conducted,and an appropriate sampling design is selected through the sampling convergence analysis.The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples,and converges at less high-fidelity samples.A wing-body drag reduction optimization design using genetic algorithm is implemented.Satisfying design results are obtained,which validate the feasibility of CKMF model in engineering design.
基金funded by National Natural Science Foundation of China(Nos.51776010 and 91860205)National Science and Technology Major Project,China(No.2017-I0001-0001)。
文摘Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Computational Fluid Dynamics(CFD) simulation. This simulation method proposes a form of Front-VABI characteristic and its matching calculation method between it and the ACE performance model, constructs a coupling method between the(2-D) Front-VABI model and the(0-D) ACE performance model. The result shows, when ACE works in triple bypass mode, the approximate model cannot account for the effect of FrontVABI pressure loss on Core Driven Fan Stage(CDFS) design pressure ratio, and the calculated error of high-pressure turbine inlet total temperature is more than 40 K in mode transition condition(the transition operating condition between triple bypass mode and double bypass mode). In double bypass mode, the approximate model can better simulate the performance of FrontVABI by considering the local loss of area expansion. This method can be applied to the performance-optimized design of Front-VABI and the ACE control law design during mode transition.
基金supported by the National Natural Science Foundation of China(NSFC)Basic Science Center Program for“Multiscale Problems in Nonlinear Mechanics”(No.11988102)NSFC(No.12002345)China Postdoctoral Science Foundation(No.2020M680027)。
文摘Flow around a real-life underwater vehicle often happens at a high Reynolds number with flow structures at different scales from the boundary layer around a blade to that around the hull. This poses a great challenge for large-eddy simulation of an underwater vehicle aiming at resolving all relevant flow scales. In this work, we propose to model the hull with appendages using the immersed boundary method, and model the propeller using the actuator disk model without resolving the geometry of the blade. The proposed method is then applied to simulate the flow around Defense Advanced Research Projects Agency(DARPA) suboff. An overall acceptable agreement is obtained for the pressure and friction coefficients. Complex flow features are observed in the near wake of suboff. In the far wake, the core region is featured by a jet because of the actuator disk, surrounded by an annular region with velocity deficit due to the body of suboff.
基金the Natural Science Foundation of Shaanxi Province (No.2010JQ8017)the China Postdoctoral Science Foundation (No.2011M501427)the Special Found for Basic Scientific Research of Central Colleges of Chang’an University(No.CHD2012JC047)
文摘A directed signature is a type of signature with restricted verification ability.Directed signatures allow only a designated verifier to check the validity of the signature issued to him,and at the time of trouble or if necessary,any third party can verify the signature with the help of the signer or the designated verifier.Directed signature schemes are widely used in situations where the receiver's privacy should be protected.Proxy signatures allow an entity to delegate its signing capability to another entity in such a way that the latter can sign message on behalf of the former when the former is not available.Proxy signature schemes have found numerous practical applications such as distributed systems and mobile agent applications.In this paper,we firstly define the notion of the directed proxy signature by combining the proxy signature and directed signature.Then,we formalize its security model and present a concrete scheme in the standard model.Finally,we use the techniques from provable security to show that the proposed scheme is unforgeable under the gap Diffie-Hellman assumption,and invisible under the decisional Diffie-Hellman assumption.
文摘Wellbore instability is one of the concerns in the field of drilling engineering.This phenomenon is affected by several factors such as azimuth,inclination angle,in-situ stress,mud weight,and rock strength parameters.Among these factors,azimuth,inclination angle,and mud weight are controllable.The objective of this paper is to introduce a new procedure based on elastoplastic theory in wellbore stability solution to determine the optimum well trajectory and global minimum mud pressure required(GMMPR).Genetic algorithm(GA) was applied as a main optimization engine that employs proportional feedback controller to obtain the minimum mud pressure required(MMPR).The feedback function repeatedly calculated and updated the error between the simulated and set point of normalized yielded zone area(NYZA).To reduce computation expenses,an artificial neural network(ANN) was used as a proxy(surrogate model) to approximate the behavior of the actual wellbore model.The methodology was applied to a directional well in southwestern Iranian oilfield.The results demonstrated that the error between the predicted GMMPR and practical safe mud pressure was 4%for elastoplastic method,and 22%for conventional elastic solution.
基金The authors are grateful for the support from Khon Kaen University Scholarship for ASEAN and GMS Countries’Personnel of Academic Year and the National Research Council of Thailand(N42A650549).
文摘This work presents multi-fidelity multi-objective infill-sampling surrogate-assisted optimization for airfoil shape optimization.The optimization problem is posed to maximize the lift and drag coefficient ratio subject to airfoil geometry constraints.Computational Fluid Dynamic(CFD)and XFoil tools are used for high and low-fidelity simulations of the airfoil to find the real objective function value.A special multi-objective sub-optimization problem is proposed for multiple points infill sampling exploration to improve the surrogate model constructed.To validate and further assess the proposed methods,a conventional surrogate-assisted optimization method and an infill sampling surrogate-assisted optimization criterion are applied with multi-fidelity simulation,while their numerical performance is investigated.The results obtained show that the proposed technique is the best performer for the demonstrated airfoil shape optimization.According to this study,applying multi-fidelity with multi-objective infill sampling criteria for surrogate-assisted optimization is a powerful design tool.
文摘How to protect the database, the kernel resources of information warfare, is becoming more and more important since the rapid development of computer and communication technology. As an application-level firewall, database security proxy can successfully repulse attacks originated from outside the network, reduce to zerolevel damage from foreign DBMS products. We enhanced the capability of the COAST' s firewall reference model by adding a transmission unit modification function and an attribute value mapping function,describes the schematic and semantic layer reference model, and finally forms a reference model for DBMS security proxy which greatly helps in the design and implementation of database security proxies. This modeling process can clearly separate the system functionality into three layers, define the possible security functions for each layer, and estimate the computational cost for each layer.
文摘Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.
基金funded by the Science and Technology Innovation Committee Foundation of Shenzhen,China(Nos.JCYJ20200109141403840 and ZDSYS20220527171405012)the National Natural Science Foundation of China(No.52106045)the Pearl River Talent Recruitment Program,China(No.2019CX01Z084)。
文摘Iterative coupled methods are widely used in multi-fidelity simulation of rotating components due to the simple implementation,which iteratively eliminates the errors between the computational fluid dynamics models and approximate characteristic maps.However,the convergence and accuracy of the iterative coupled method are trapped in characteristic maps.In particular,iterative steps increase sharply as the operation point moves away from the design point.To address these problems,this paper developed an auxiliary iterative coupled method that introduces the static-pressure-auxiliary characteristic maps and modification factor of mass flow into the component-level model.The developed auxiliary method realized the direct transfer of static pressure between the high-fidelity models and the component-level model.Multi-fidelity simulations of the throttle characteristics were carried out using both the auxiliary and traditional iterative coupled methods,and the simulation results were verified using the experimental data.Additionally,the consistency between the auxiliary and traditional iterative coupled methods was confirmed.Subsequently,multi-fidelity simulations of the speed and altitude characteristics were also conducted.The auxiliary and traditional iterative coupled methods were evaluated in terms of convergence speed and accuracy.The evaluation indicated that the auxiliary iterative coupled method significantly reduces iterative steps by approximately 50%at the near-choked state.In general,the auxiliary iterative coupled method is preferred as a development of the traditional iterative coupled method in the near-choked state,and the combined auxiliary-traditional iterative coupled method provides support for successful multi-fidelity simulation in far-off-design conditions.
文摘Currently available proxies were studied as networks for building reconstruction models of the Atlantic Multidecadal Oscillation (AMO). Only proxies that would double the current record length (backwards in time from AD 1564) were included. We present two proxy networks and corresponding reconstruction (transfer) models, one for tree-growth based proxies only and another for multiproxies. Both of them show a useful match in timing as well as amplitude with the AMO. These model structures demonstrated reasonable model performance (overall r<sup>2</sup> = 0.45 - 0.36). The time stability of proxy-AMO relationships was also validated. The new models produced acceptable results in cross-calibration-verification (reduction of error and coefficient of efficiency statistics in 1856-1921 and 1922-1990 vary between 0.41 and 0.21). The spatial distribution of these data series indicate that proxies respond to an AMO-like climatic oscillation over much of the Northern Hemisphere.
文摘建立了一个中等规模的结构向量自回归模型,系统探讨了货币政策不确定性对宏观经济波动与银行系统金融风险的影响。首先,比较现阶段我国主流货币政策不确定性及经济政策不确定性代理指标,发现Huang&Luk (2020)所测度MPU与EPU并不能对我国宏观经济波动做出较好解释。比较而言,Baker et al.(2016)与Davis et al.(2019)的EPU对我国宏观经济波动影响更符合预期,EPU的影响类似于紧缩性供给冲击,导致产出、固定资产投资以及社会零售消费降低,通货膨胀上升。特别是在EPU_Davis指标存在意外正向冲击时,可能导致银行系统金融风险上升。其次,利用条件波动率思想,重新估计货币政策不确定性(MPU_Volatility),发现无论是简约形式或结构形式估计,所获得MPU_Volatility在波动趋势上大体一致,且MPU_Volatility与EPU_Baker及EPU_Davis对宏观经济作用效果存在较大一致,其中MPU_Volatility与EPU_Davis对银行系统金融风险影响一致。最后,进一步将叙事性EPU_Davis指标与MPU_Volatility结合使用,选用Proxy SVAR对货币政策不确定性结构冲击进行识别,这保证了即使存在测量误差情况下,估计偏误也会大幅降低。实证发现MPU对宏观经济变量造成不利影响,导致银行系统金融风险上升并持续较长时间。脉冲响应结果与EPU_Davis及MPU_Volatility结果相似,这一定程度上支持之前结论的稳健。
文摘Low pressure ratio fans of modern civil turbofans suffer from reduced stall margin in the take-off operating line and at part-speed,requiring variable geometry devices.Variable area nozzles(VAN)are one of the investigated solutions to control engine operating conditions throughout the mission.In this paper,we present a multi-fidelity modelling approach for an ultra-high bypass ratio turbofan engine with a VAN,combining a zero-dimensional thermody-namic cycle simulator using a realistic fan map with two-and three-dimensional detailed computational fluid dynamics(CFD)simulations for internal/external flow coupling.By adopting a novel algorithm to match the cycle conditions to the CFD solutions,the propulsive performance of the turbofan is analysed in a reference aircraft mission.The numerical method captures the effect on thrust generation and nacelle drag,providing a more reliable estimation of the impact of VAN on engine operation and efficiency.Low-speed mission points are confirmed to be those that benefit the most from an enlarged fan nozzle area,with a possible improvement of 3%in terms of thrust and specific fuel consumption at take-off and approach using a 10%larger area,similarly predicted by both 2D and 3D models.A preliminary acous-tic evaluation based on semi-empirical noise models indicates a modest effect on noise emis-sions,with up to 1 dB reduction in microphone signature at the sideline for a nozzle area increased by 10%.
基金Supported by the National Natural Science Foundation of China(61272501)the National Key Basic Research Program(973Program)(2012CB315905)the Specialized Research Fund for the Doctoral Program of Higher Education(20091102110004)
文摘To support withdrawing and storing money from all levels of the bank for the customers in the real world, in this paper, we propose a proxy blind signature scheme and an off-line e-cash scheme based on the new proxy blind signature scheme. The pro- posed proxy blind signature is proven secure in the random oracle model under chosen-target computational Diffie-Hellman assump- tions, and the e-cash scheme can satisfy the security requirements of unforgeability, anonymity, and traceability.
