Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating du...Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.展开更多
Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on th...Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method(FEM).To reduce the heavy computational burden,a surrogate model of a dome structure was constructed to solve this problem.The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance-and distribution-based methods through the surrogate model.The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure.The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states.Finally,the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed.The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.展开更多
Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to ...Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) t...In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) to provide an efficient dataset for modeling suction response through machine learning. Twocharacteristic parameters representing suction response during wetting processes, i.e. response time andmean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) usingeight selected influential parameters including depth, initial soil suction, vegetation- and atmosphererelated parameters. An error standardebased performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivityanalysis revealed the importance of tree canopy and mean wind speed to estimation of response timeand indicated that initial soil suction and rainfall amount have an important effect on the estimatedsuction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGPmodels describing suction response after rainfall are reliable and robust under uncertain conditions. Indepth analysis of spatial variations in suction response validated the robustness of two obtained MGGPmodels in prediction of suction variation characteristics under natural conditions.展开更多
Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since th...Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.展开更多
A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitu...A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol's method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.展开更多
The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less se...The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.展开更多
High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four typ...High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.展开更多
One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated wit...One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.展开更多
Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the pa...Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.展开更多
The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index...The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.展开更多
Cardiac modeling entails the epistemic uncertainty of the input parameters,such as bundles and chambers geometry,electrical conductivities and cell parameters,thus calling for an uncertainty quantification(UQ)analysis...Cardiac modeling entails the epistemic uncertainty of the input parameters,such as bundles and chambers geometry,electrical conductivities and cell parameters,thus calling for an uncertainty quantification(UQ)analysis.Since the cardiac activation and the subsequent muscular contraction is provided by a complex electrophysiology system made of interconnected conductive media,we focus here on the fast conductivity structures of the atria(internodal pathways)with the aim of identifying which of the uncertain inputs mostly influence the propagation of the depolarization front.Firstly,the distributions of the input parameters are calibrated using data available from the literature taking into account gender differences.The output quantities of interest(Qols)of medical relevance are defined and a set of metamodels(one for each Qol)is then trained according to a polynomial chaos expansion(PCE)in order to run a global sensitivity analysis with non-linear variance-based SoboF indices with confidence intervals evaluated through the bootstrap method.The most sensitive parameters on each Qol are then identified for both genders showing the same order of importance of the model inputs on the electrical activation.Lastly,the probability distributions of the Qols are obtained through a forward sensitivity analysis using the same trained metamodels.It results that several input parameters-including the position of the internodal pathways and the electrical impulse applied at the sinoatrial node一have a little influence on the Qols studied.Vice-versa the electrical activation of the atrial fast conduction system is sensitive on the bundles geometry and electrical conductivities that need to be carefully measured or calibrated in order for the electrophysiology model to be accurate and predictive.展开更多
Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an imp...Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.展开更多
文摘Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.
基金the Key Project of the Natural Science Foundation of Tianjin City(No.19JCZDJC39300)is acknowledged.
文摘Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method(FEM).To reduce the heavy computational burden,a surrogate model of a dome structure was constructed to solve this problem.The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance-and distribution-based methods through the surrogate model.The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure.The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states.Finally,the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed.The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.
文摘Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
基金the financial support funded by the Science and Technology Development Fund of Macao SAR (Grant Nos. 0026/2020/AFJ and SKL-IOTSC(UM)-2021-2023)the Funds for University of Macao (Grant No. MYRG2018-00173-FST)
文摘In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) to provide an efficient dataset for modeling suction response through machine learning. Twocharacteristic parameters representing suction response during wetting processes, i.e. response time andmean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) usingeight selected influential parameters including depth, initial soil suction, vegetation- and atmosphererelated parameters. An error standardebased performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivityanalysis revealed the importance of tree canopy and mean wind speed to estimation of response timeand indicated that initial soil suction and rainfall amount have an important effect on the estimatedsuction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGPmodels describing suction response after rainfall are reliable and robust under uncertain conditions. Indepth analysis of spatial variations in suction response validated the robustness of two obtained MGGPmodels in prediction of suction variation characteristics under natural conditions.
文摘Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.
基金Supported by the National Science Foundation for Post-doctoral Scientists of China (20090460216 )the National Defense Fundamental Research Foundation of China(B222006060)
文摘A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol's method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.
基金The study is supported by the National Numerical Wind tunnel project(No.2019ZT2-A05)the Nature Science Foundation of China(No.11902254).
文摘The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.
基金supported by the National Railway Group Science and Technology Program(Nos.N2020J026 and N2021J028)the Independent Research and Development Project of State Key Laboratory of Traction Power,China(No.2022TPL_Q02)。
文摘High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.
文摘One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.
基金This study was supported by Young Scientists Fund(Grant No.51805028)Postdoctoral Research Foundation of China(Grant No.BX201600017).
文摘Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.
基金support of the National Key Research and Development Plan of China(Grant No.2016YFD020060307)Key Project of Education Department of Liaoning province(LSNZD201605).
文摘The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.
基金This study has been performed with support of the'Fluid dynamics of hearts at risk of failure:towards methods for the prediction of disease progressions’funded by the Italian Ministry of Education and University(Grant 2017A889FP).
文摘Cardiac modeling entails the epistemic uncertainty of the input parameters,such as bundles and chambers geometry,electrical conductivities and cell parameters,thus calling for an uncertainty quantification(UQ)analysis.Since the cardiac activation and the subsequent muscular contraction is provided by a complex electrophysiology system made of interconnected conductive media,we focus here on the fast conductivity structures of the atria(internodal pathways)with the aim of identifying which of the uncertain inputs mostly influence the propagation of the depolarization front.Firstly,the distributions of the input parameters are calibrated using data available from the literature taking into account gender differences.The output quantities of interest(Qols)of medical relevance are defined and a set of metamodels(one for each Qol)is then trained according to a polynomial chaos expansion(PCE)in order to run a global sensitivity analysis with non-linear variance-based SoboF indices with confidence intervals evaluated through the bootstrap method.The most sensitive parameters on each Qol are then identified for both genders showing the same order of importance of the model inputs on the electrical activation.Lastly,the probability distributions of the Qols are obtained through a forward sensitivity analysis using the same trained metamodels.It results that several input parameters-including the position of the internodal pathways and the electrical impulse applied at the sinoatrial node一have a little influence on the Qols studied.Vice-versa the electrical activation of the atrial fast conduction system is sensitive on the bundles geometry and electrical conductivities that need to be carefully measured or calibrated in order for the electrophysiology model to be accurate and predictive.
文摘Due to recent advances in the field of artificial neural networks(ANN)and the global sensitivity analysis(GSA)method,the application of these techniques in structural analysis has become feasible.A connector is an important part of a composite beam,and its shear strength can have a significant impact on structural design.In this paper,the shear performance of perfobond rib shear connectors(PRSCs)is predicted based on the back propagation(BP)ANN model,the Genetic Algorithm(GA)method and GSA method.A database was created using push-out test test and related references,where the input variables were based on different empirical formulas and the output variables were the corresponding shear strengths.The results predicted by the ANN models and empirical equations were compared,and the factors affecting shear strength were examined by the GSA method.The results show that the use of ANN model optimization by GA method has fewer errors compared to the empirical equations.Furthermore,penetrating reinforcement has the greatest sensitivity to shear performance,while the bonding force between steel plate and concrete has the least sensitivity to shear strength.
