Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-...Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design(TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects.However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors(MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.展开更多
This study aimed to investigate the effects of temporal variability on the optimization of the Hydrologiska ByrS.ns Vattenbalansavedlning （HBV） model, as well as the calibration performance using manual optimization...This study aimed to investigate the effects of temporal variability on the optimization of the Hydrologiska ByrS.ns Vattenbalansavedlning （HBV） model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed： short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash- Sutcliffe efficiency coefficient （NSE）, relative error （RE）, root mean square error （RMSE）, and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, -0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temooral calibration in hvdrolouical analysis delivers better simulation in general for water balance analysis.展开更多
Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to...Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.展开更多
The conventional Shear Stress Transport(SST)k–ωturbulence model often exhibits substantial inaccu-racies when applied to the prediction of flow behavior in complex regions within axial flow control valves.To enhance...The conventional Shear Stress Transport(SST)k–ωturbulence model often exhibits substantial inaccu-racies when applied to the prediction of flow behavior in complex regions within axial flow control valves.To enhance its predictive fidelity for internal flow fields,this study introduces a novel calibration framework that integrates an artificial neural network(ANN)surrogate model with a particle swarm optimization(PSO)algorithm.In particular,an optimal Latin hypercube sampling strategy was employed to generate representative sample points across the empirical parameter space.For each sample,numerical simulations using ANSYS Fluent were conducted to evaluate the flow characteristics,with empirical turbulence model parameters as inputs and flow rate as the target output.These data were used to construct the high-fidelity ANN surrogate model.The PSO algorithm was then applied to this surrogate to identify the optimal set of empirical parameters tailored specifically to axial flow control valve configurations.A revealed by the presented results,the calibrated SST k–ωmodel significantly improves prediction accuracy:deviations from large eddy simulation(LES)benchmarks at small valve openings were reduced from 7.6%to under 3%.Furthermore,the refined model maintains the computational efficiency characteristic of Reynolds-averaged Navier-Stokes(RANS)simulations while substantially enhancing the accuracy of both pressure and velocity field predictions.Overall,the proposed methodology effectively reconciles the trade-off between computational cost and predictive accuracy,offering a robust and scalable approach for turbulence model calibration in complex internal flow scenarios.展开更多
Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of build...Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.Building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in EnergyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.展开更多
As a type of nonstructural component, infill walls play a significant role in the seismic behavior of high-rise buildings. However, the stiffness of the infill wall is generally either ignored or considered by simplif...As a type of nonstructural component, infill walls play a significant role in the seismic behavior of high-rise buildings. However, the stiffness of the infill wall is generally either ignored or considered by simplified empirical criteria that lead to a period shortening. The difference can be greatly decreased by using a structural identification methodology. In this study, an ambient vibration test was performed on four on-site reinforced concrete high-rise buildings, and the design results were compared with the PKPM models using corresponding finite element(FE) models. A diagonal strut model was used to simulate the behavior of the infill wall, and the identified modal parameters measured from the on-site test were employed to calibrate the parameters of the diagonal strut in the FE models. The SAP2000 models with calibrated elastic modulus were used to evaluate the seismic response in the elastic state. Based on the load-displacement relationship of the infill wall, nonlinear dynamic analysis models were built in PERFORM-3 D and calibrated using the measured modal periods. The analysis results revealed that the structural performance under small/large earthquake records were both strengthened by infill walls, and the contribution of infill walls should be considered for better accuracy in the design process.展开更多
The paper describes an analysis of thermo-mechanical （TM） processes appearing during the Aspo Pillar Stability Experiment （APSE）. This analysis is based on finite elements with elasticity, plasticity and dam- age ...The paper describes an analysis of thermo-mechanical （TM） processes appearing during the Aspo Pillar Stability Experiment （APSE）. This analysis is based on finite elements with elasticity, plasticity and dam- age mechanics models of rock behaviour and some least squares calibration techniques. The main aim is to examine the capability of continuous mechanics models to predict brittle damage behaviour of gran- ite rocks. The performed simulations use an in-house finite element software GEM and self-developed experimental continuum damage MATLAB code. The main contributions are twofold. First, it is an inverse analysis, which is used for （1） verification of an initial stress measurement by back analysis of conver- gence measurement during construction of the access tunnel and （2） identification of heat transfer rock mass properties by an inverse method based on the known heat sources and temperature measurements. Second, three different hierarchically built models are used to estimate the pillar damage zones, i.e. elas- tic model with Drucker-Prager strength criterion, elasto-plastic model with the same yield limit and a combination of elasto-plasticity with continuum damage mechanics. The damage mechanics model is also used to simulate uniaxial and triaxial compressive strength tests on the ,Aspo granite.展开更多
Active magnetically suspended control moment gyro is a novel attitude control actuator for satellites.It is mainly composed of rotor,active magnetic bearing(AMB)and motor.As a crucial supporting component of control m...Active magnetically suspended control moment gyro is a novel attitude control actuator for satellites.It is mainly composed of rotor,active magnetic bearing(AMB)and motor.As a crucial supporting component of control moment gyro,the performance of AMB is directly related to the stability of the rotor system and pointing precision of the satellites.Therefore,calibrating the parameters of AMB is essential for the realization of super-quiet satellites.This paper proposed a model calibration method,known as the deep reinforcement learningbased model calibration frame(DRLMC).First,the dynamics of magnetic bearing with damage degradation over its life cycle are modeled.Subsequently,the calibration process is formulated as a Markov Decision Process(MDP),and reinforcement learning(RL)is employed to infer the degradation parameters.In addition,experience replay and target network update mechanism are introduced to guarantee stability.Simulation results demonstrate that the proposed method identi¯es force-current factor of AMB during its degradation process e®ectively.Furthermore,additional experiments con¯rm the robustness of the DRLMC approach.展开更多
Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability.However,it is difficult to implement the procedure because of the...Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability.However,it is difficult to implement the procedure because of the aleatory uncertainty.In this paper,a new method of model calibration based on uncertainty propagation is investigated.The calibration process is described as an optimization problem.A two-stage nested uncertainty propagation method is proposed to resolve this problem.Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty.Optimization method is applied for the outer loop to propagate the epistemic uncertainty.The optimization objective function is the consistency between the result of the inner loop and the experimental data.Thus,different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function.Finally,the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.展开更多
An efficient algorithm is proposed for Bayesian model calibration,which is commonly used to estimate the model parameters of non-linear,computationally expensive models using measurement data.The approach is based on ...An efficient algorithm is proposed for Bayesian model calibration,which is commonly used to estimate the model parameters of non-linear,computationally expensive models using measurement data.The approach is based on Bayesian statistics:using a prior distribution and a likelihood,the posterior distribution is obtained through application of Bayes’law.Our novel algorithm to accurately determine this posterior requires significantly fewer discrete model evaluations than traditional Monte Carlo methods.The key idea is to replace the expensive model by an interpolating surrogate model and to construct the interpolating nodal set maximizing the accuracy of the posterior.To determine such a nodal set an extension to weighted Leja nodes is introduced,based on a new weighting function.We prove that the convergence of the posterior has the same rate as the convergence of the model.If the convergence of the posterior is measured in the Kullback–Leibler divergence,the rate doubles.The algorithm and its theoretical properties are verified in three different test cases:analytical cases that confirm the correctness of the theoretical findings,Burgers’equation to show its applicability in implicit problems,and finally the calibration of the closure parameters of a turbulence model to show the effectiveness for computa-tionally expensive problems.展开更多
Lunar dust is considered to be one of the top challenges for enabling humans to have extended stays on the moon.Human activities such as module landings and launches,walking,rover operation and construction activities...Lunar dust is considered to be one of the top challenges for enabling humans to have extended stays on the moon.Human activities such as module landings and launches,walking,rover operation and construction activities will inevitably produce a significant amount of dust.Therefore,it is important to estimate the potential range and intensity of dust deposition caused by these activities to minimize dust accumulation over time and for maintenance planning and execution.A modular model that correlates the dust deposition distribution with initial mean dust particle velocity,its mean ejected angle and the total amount of ejected mass is developed for an elementary mechanical movement.This modular model is further employed to form a modeling framework to estimate dust deposition of a trajectory based activity of similar repeated movements such as the landing process of a lander,walking and rover operation.The model forms a unified modeling framework for different trajectory-based activities and is shown to predict consistent and physically meaningful ranges and intensities of dust deposition provided reliable data to calibrate the model parameters.展开更多
The objective of this paper is to develop a methodology for calibration of a discrete element grain-based model(GBM)to replicate the hydro-mechanical properties of a brittle rock measured in the laboratory,and to appl...The objective of this paper is to develop a methodology for calibration of a discrete element grain-based model(GBM)to replicate the hydro-mechanical properties of a brittle rock measured in the laboratory,and to apply the calibrated model to simulating the formation of excavation damage zone(EDZ)around underground excavations.Firstly,a new cohesive crack model is implemented into the universal distinct element code(UDEC)to control the fracturing behaviour of materials under various loading modes.Next,a methodology for calibration of the components of the UDEC-Voronoi model is discussed.The role of connectivity of induced microcracks on increasing the permeability of laboratory-scale samples is investigated.The calibrated samples are used to investigate the influence of pore fluid pressure on weakening the drained strength of the laboratory-scale rock.The validity of the Terzaghi’s effective stress law for the drained peak strength of low-porosity rock is tested by performing a series of biaxial compression test simulations.Finally,the evolution of damage and pore pressure around two unsupported circular tunnels in crystalline granitic rock is studied.展开更多
This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclinat...This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.展开更多
The general strategy and method of constructing universal calibration model for levofioxacin injections by near-infrared spectroscopy have been investigated and discussed. Firstly, a constant-temperature homogeneous l...The general strategy and method of constructing universal calibration model for levofioxacin injections by near-infrared spectroscopy have been investigated and discussed. Firstly, a constant-temperature homogeneous liquid calibration model for levofloxacin hydrochloride injections with the same composition but different active principal ingredient （API） content was established as the basic unit for universal model. Then, samples of levofloxacin hydrochloride injections containing propylene glycol or levofloxacin lactate injections were added to develop a primary constant-temperature liquid universal model. Temperature- amended final universal model was established to apply to samples under different temperatures. The final model was built from 61 calibration samples and 77 validation samples. The value of the root mean square error of cross validation （RMSECV） and coefficient of determination （r2） of leave-one-out cross-validation （LOOCV） were 0.792 and 0.9993, respectively, the root mean square error of prediction （RMSEP） of test set validation （TSV） was 0.87, and the average relative deviation was 1.44%. According to the ICH guidelines, the universal calibration model was evaluated. Based on the experimental statistical results, the recommended number of calibration samples for a constant-temperature homogeneous liquid quantitative model was no less than 15.展开更多
Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SM...Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.展开更多
Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks,provide safe and smooth rides,and reduce the maintenance frequency.In this paper,the elastic performance of a ...Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks,provide safe and smooth rides,and reduce the maintenance frequency.In this paper,the elastic performance of a novel slab trackform for high-speed railways is investigated using three-dimensional finite element modelling in Abaqus.It is then compared to the performance of a ballasted track.First,slab and ballasted track models are developed to replicate the full-scale testing of track sections.Once the models are calibrated with the experimental results,the novel slab model is developed and compared against the calibrated slab track results.The slab and ballasted track models are then extended to create linear dynamic models,considering the track geodynamics,and simulating train passages at various speeds,for which the Ledsgard documented case was used to validate the models.Trains travelling at low and high speeds are analysed to investigate the track deflections and the wave propagation in the soil,considering the issues associated with critical speeds.Various train loading methods are discussed,and the most practical approach is retained and described.Moreover,correlations are made between the geotechnical parameters of modern high-speed rail and conventional standards.It is found that considering the same ground condition,the slab track deflections are considerably smaller than those of the ballasted track at high speeds,while they show similar behaviour at low speeds.展开更多
This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input paramet...This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input parameters.Subsequently,the performances of response surface methodology(RSM),artificial neural networks(ANN),and random forest(RF)models for calibration were compared.The results demonstrated that the random forest model outperformed the two other models,achieving an RMSE of 1.89,an R-squared of 94%,and an MAE of 1.63.The ANN model followed closely,with an RMSE of 3.12,an R-squared of 89%,and an MAE of 2.18,while the RSM model exhibited lower performance with an RMSE of 6.84,an R-squared of 86%,and an MAE of 5.41.This study presents a framework for enhancing the accuracy of DEM simulations.Finally,the robustness and adaptability of the calibration approach were demonstrated by applying calibrated parameters from one particle size to another.展开更多
In this paper,an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge(SOC) map is applied to characterize the voltage ...In this paper,an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge(SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles(EVs).As a result,the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile,without a special characterization experiment.In order to ensure good robustness,extended Kalman filtering is adopted to recursively implement the calibration process.The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form.Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions.A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.展开更多
In practical development of unconventional reservoirs,fracture networks are a highly conductive transport media for subsurface fluid flow.Therefore,it is crucial to clearly determine the fracture properties used in pr...In practical development of unconventional reservoirs,fracture networks are a highly conductive transport media for subsurface fluid flow.Therefore,it is crucial to clearly determine the fracture properties used in production forecast.However,it is different to calibrate the properties of fracture networks because it is an inverse problem with multi-patterns and highcomplexity of fracture distribution and inherent defect of multiplicity of solution.In this paper,in order to solve the problem,the complex fracture model is divided into two sub-systems,namely"Pattern A"and"Pattern B."In addition,the generation method is grouped into two categories.Firstly,we construct each sub-system based on the probability density function of the fracture properties.Secondly,we recombine the sub-systems into an integral complex fracture system.Based on the generation mechanism,the estimation of the complex fracture from dynamic performance and observation data can be solved as an inverse problem.In this study,the Bayesian formulation is used to quantify the uncertainty of fracture properties.To minimize observation data misfit immediately as it occurs,we optimize the updated properties by a simultaneous perturbation stochastic algorithm which requires only two measurements of the loss function.In numerical experiments,we firstly visualize that small-scale fractures significantly contribute to the flow simulation.Then,we demonstrate the suitability and effectiveness of the Bayesian formulation for calibrating the complex fracture model in the following simulation.展开更多
Evapotranspiration(ET)is the key to the water cycle process and an important factor for studying near-surface water and heat balance.Accurately estimating ET is significant for hydrology,meteorology,ecology,agricultur...Evapotranspiration(ET)is the key to the water cycle process and an important factor for studying near-surface water and heat balance.Accurately estimating ET is significant for hydrology,meteorology,ecology,agriculture,etc..This paper simulates ET in the Madu River Basin of Three Gorges Reservoir Area of China during 2009-2018 based on the Soil and Water Assessment Tool(SWAT)model,which was calibrated and validated using the MODIS(Moderate-resolution Imaging Spectroradiometer)/Terra Net ET 8-Day L4 Global 500 m SIN Grid(MOD16A2)dataset and measured ET.Two calibration strategies(lumped calibration(LC)and spatially distributed calibration(SDC))were used.The basin was divided into 34 sub-basins,and the coefficient of determination(R^(2))and NashSutcliffe efficiency coefficient(NSE)of each sub-basin were greater than 0.6 in both the calibration and validation periods.The R2 and NSE were higher in the validation period than those in the calibration period.Compared with the measured ET,the accuracy of the model on the daily scale is:R^(2)=0.704 and NSE=0.759(SDC results).The model simulation accuracy of LC and SDC for the sub-basin scale was R^(2)=0.857,R^(2)=0.862(monthly)and R^(2)=0.227,R^(2)=0.404(annually),respectively;for the whole basin scale was R^(2)=0.902,R^(2)=0.900(monthly)and R^(2)=0.507 and R^(2)=0.519(annually),respectively.The model performed acceptably,and SDC performed the best,indicating that remote sensing data can be used for SWAT model calibration.During 2009-2018,ET generally increased in the Madu River Basin(SDC results,7.21 mm/yr),with a multiyear average value of 734.37 mm/yr.The annual ET change rate for the sub-basin was relatively low upstream and downstream.The linear correlation analysis between ET and meteorological factors shows that on the monthly scale,precipitation,solar radiation and daily maximum and minimum temperature were significantly correlated with ET;annually,solar radiation and wind speed had a moderate correlation with ET.The correlation between maximum temperature and ET is best on the monthly scale(Pearson correlation coefficient R=0.945),which may means that the increasing ET originating from increasing temperature(global warming).However,the sub-basins near Shennongjia Nature Reserve that are in upstream have a negative ET change rate,which means that ET decreases in these sub-basins,indicating that the’Evaporation Paradox’exists in these sub-basins.This study explored the potential of remote-sensing-based ET data for hydrological model calibration and provides a decision-making reference for water resource management in the Madu River Basin.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 11690040 and 11690043)。
文摘Machine learning-based surrogate models have significant advantages in terms of computing efficiency. In this paper, we present a pilot study on fast calibration using machine learning techniques. Technology computer-aided design(TCAD) is a powerful simulation tool for electronic devices. This simulation tool has been widely used in the research of radiation effects.However, calibration of TCAD models is time-consuming. In this study, we introduce a fast calibration approach for TCAD model calibration of metal–oxide–semiconductor field-effect transistors(MOSFETs). This approach utilized a machine learning-based surrogate model that was several orders of magnitude faster than the original TCAD simulation. The desired calibration results were obtained within several seconds. In this study, a fundamental model containing 26 parameters is introduced to represent the typical structure of a MOSFET. Classifications were developed to improve the efficiency of the training sample generation. Feature selection techniques were employed to identify important parameters. A surrogate model consisting of a classifier and a regressor was built. A calibration procedure based on the surrogate model was proposed and tested with three calibration goals. Our work demonstrates the feasibility of machine learning-based fast model calibrations for MOSFET. In addition, this study shows that these machine learning techniques learn patterns and correlations from data instead of employing domain expertise. This indicates that machine learning could be an alternative research approach to complement classical physics-based research.
基金supported by the National Natural Science Foundation of China(Grant No.41271040)the Special Fund of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(Grant No.20145028012)
文摘This study aimed to investigate the effects of temporal variability on the optimization of the Hydrologiska ByrS.ns Vattenbalansavedlning （HBV） model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed： short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash- Sutcliffe efficiency coefficient （NSE）, relative error （RE）, root mean square error （RMSE）, and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, -0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temooral calibration in hvdrolouical analysis delivers better simulation in general for water balance analysis.
基金the Science and Technology Project of China South Power Grid Co., Ltd. under Grant No. 036000KK52222044 (GDKJXM20222430).
文摘Wake effects in large-scalewind farms significantly reduce energy capture efficiency.ActiveWakeControl(AWC),particularly through intentional yaw misalignment of upstream turbines,has emerged as a promising strategy to mitigate these losses by redirecting wakes away from downstream turbines.However,the effectiveness of yaw-based AWC is highly dependent on the accuracy of the underlying wake prediction models,which often require site-specific adjustments to reflect local atmospheric conditions and turbine characteristics.This paper presents an integrated,data-driven framework tomaximize wind farmpower output.Themethodology consists of three key stages.First,a practical simulation-assisted matching method is developed to estimate the True North Alignment(TNA)of each turbine using historical Supervisory Control and Data Acquisition(SCADA)data,resolving a common source of operational uncertainty.Second,key wake expansion parameters of the Floris engineering wake model are calibrated using site-specific SCADA power data,tailoring the model to the JibeiWind Farm in China.Finally,using this calibrated model,the derivative-free solver NOMAD is employed to determine the optimal yaw angle settings for an 11-turbine cluster under various wind conditions.Simulation studies,based on real operational scenarios,demonstrate the effectiveness of the proposed framework.The optimized yaw control strategies achieved total power output gains of up to 5.4%compared to the baseline zero-yaw operation under specific wake-inducing conditions.Crucially,the analysis reveals that using the site-specific calibrated model for optimization yields substantially better results than using a model with generic parameters,providing an additional power gain of up to 1.43%in tested scenarios.These findings underscore the critical importance of TNA estimation and site-specific model calibration for developing effective AWC strategies.The proposed integrated approach provides a robust and practical workflow for designing and pre-validating yaw control settings,offering a valuable tool for enhancing the economic performance of wind farms.
基金funded by Gansu Provincial Department of Education(Industrial Support Plan Project:2025CYZC-048).
文摘The conventional Shear Stress Transport(SST)k–ωturbulence model often exhibits substantial inaccu-racies when applied to the prediction of flow behavior in complex regions within axial flow control valves.To enhance its predictive fidelity for internal flow fields,this study introduces a novel calibration framework that integrates an artificial neural network(ANN)surrogate model with a particle swarm optimization(PSO)algorithm.In particular,an optimal Latin hypercube sampling strategy was employed to generate representative sample points across the empirical parameter space.For each sample,numerical simulations using ANSYS Fluent were conducted to evaluate the flow characteristics,with empirical turbulence model parameters as inputs and flow rate as the target output.These data were used to construct the high-fidelity ANN surrogate model.The PSO algorithm was then applied to this surrogate to identify the optimal set of empirical parameters tailored specifically to axial flow control valve configurations.A revealed by the presented results,the calibrated SST k–ωmodel significantly improves prediction accuracy:deviations from large eddy simulation(LES)benchmarks at small valve openings were reduced from 7.6%to under 3%.Furthermore,the refined model maintains the computational efficiency characteristic of Reynolds-averaged Navier-Stokes(RANS)simulations while substantially enhancing the accuracy of both pressure and velocity field predictions.Overall,the proposed methodology effectively reconciles the trade-off between computational cost and predictive accuracy,offering a robust and scalable approach for turbulence model calibration in complex internal flow scenarios.
基金This research was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy,Office of Building Technologies of the United States Department of Energy,under Contract No.DE-AC02-05CH11231.
文摘Building performance simulation has been adopted to support decision making in the building life cycle.An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations.Building energy model calibration is a technique that takes various types of measured performance data(e.g.,energy use)and tunes key model parameters to match the simulated results with the actual measurements.This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings.A public building dataset that includes high-level building attributes(e.g.,building type,vintage,total floor area,number of stories,zip code)of 111 buildings in San Francisco,California,USA,was used to generate building models in EnergyPlus.Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’monthly electricity and natural gas consumption.The results showed 57 out of 111 buildings were successfully calibrated against actual buildings,while the remaining buildings showed opportunities for future calibration improvements.Enhancements to the pattern-based model calibration method are identified to expand its use for:(1)central heating,ventilation and air conditioning(HVAC)systems with chillers,(2)space heating and hot water heating with electricity sources,(3)mixed-use building types,and(4)partially occupied buildings.
基金Supported by:National Key Research and Development Program of China under Grant Nos.2016YFC0701400 and 2016YFC0701308the Key Research and Development Program of Hunan Province under Grant No.2017SK2220the National Natural Science Foundation of China(NSFC)under Grant No.51878264
文摘As a type of nonstructural component, infill walls play a significant role in the seismic behavior of high-rise buildings. However, the stiffness of the infill wall is generally either ignored or considered by simplified empirical criteria that lead to a period shortening. The difference can be greatly decreased by using a structural identification methodology. In this study, an ambient vibration test was performed on four on-site reinforced concrete high-rise buildings, and the design results were compared with the PKPM models using corresponding finite element(FE) models. A diagonal strut model was used to simulate the behavior of the infill wall, and the identified modal parameters measured from the on-site test were employed to calibrate the parameters of the diagonal strut in the FE models. The SAP2000 models with calibrated elastic modulus were used to evaluate the seismic response in the elastic state. Based on the load-displacement relationship of the infill wall, nonlinear dynamic analysis models were built in PERFORM-3 D and calibrated using the measured modal periods. The analysis results revealed that the structural performance under small/large earthquake records were both strengthened by infill walls, and the contribution of infill walls should be considered for better accuracy in the design process.
基金the context of the international DECOVALEX Project (DEmonstration of COupled models and their VALidation against EXperiments)financed by Radioactive Waste Repository Authority (RAWRA),through Technical University of Liberec (TUL), Czech RepublicSKB through its sp Pillar Stability Experiment project
文摘The paper describes an analysis of thermo-mechanical （TM） processes appearing during the Aspo Pillar Stability Experiment （APSE）. This analysis is based on finite elements with elasticity, plasticity and dam- age mechanics models of rock behaviour and some least squares calibration techniques. The main aim is to examine the capability of continuous mechanics models to predict brittle damage behaviour of gran- ite rocks. The performed simulations use an in-house finite element software GEM and self-developed experimental continuum damage MATLAB code. The main contributions are twofold. First, it is an inverse analysis, which is used for （1） verification of an initial stress measurement by back analysis of conver- gence measurement during construction of the access tunnel and （2） identification of heat transfer rock mass properties by an inverse method based on the known heat sources and temperature measurements. Second, three different hierarchically built models are used to estimate the pillar damage zones, i.e. elas- tic model with Drucker-Prager strength criterion, elasto-plastic model with the same yield limit and a combination of elasto-plasticity with continuum damage mechanics. The damage mechanics model is also used to simulate uniaxial and triaxial compressive strength tests on the ,Aspo granite.
基金supported in part by National Natural Science Foundation of China under Grant no.62122038the Natural Science Foundation of Jiangsu Province under Grant no.BK20211565.
文摘Active magnetically suspended control moment gyro is a novel attitude control actuator for satellites.It is mainly composed of rotor,active magnetic bearing(AMB)and motor.As a crucial supporting component of control moment gyro,the performance of AMB is directly related to the stability of the rotor system and pointing precision of the satellites.Therefore,calibrating the parameters of AMB is essential for the realization of super-quiet satellites.This paper proposed a model calibration method,known as the deep reinforcement learningbased model calibration frame(DRLMC).First,the dynamics of magnetic bearing with damage degradation over its life cycle are modeled.Subsequently,the calibration process is formulated as a Markov Decision Process(MDP),and reinforcement learning(RL)is employed to infer the degradation parameters.In addition,experience replay and target network update mechanism are introduced to guarantee stability.Simulation results demonstrate that the proposed method identi¯es force-current factor of AMB during its degradation process e®ectively.Furthermore,additional experiments con¯rm the robustness of the DRLMC approach.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61403097)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.2015035).
文摘Model calibration is the procedure that adjusts the unknown parameters in order to fit the model to experimental data and improve predictive capability.However,it is difficult to implement the procedure because of the aleatory uncertainty.In this paper,a new method of model calibration based on uncertainty propagation is investigated.The calibration process is described as an optimization problem.A two-stage nested uncertainty propagation method is proposed to resolve this problem.Monte Carlo Simulation method is applied for the inner loop to propagate the aleatory uncertainty.Optimization method is applied for the outer loop to propagate the epistemic uncertainty.The optimization objective function is the consistency between the result of the inner loop and the experimental data.Thus,different consistency measurement methods for unary output and multivariate outputs are proposed as the optimization objective function.Finally,the thermal challenge problem is given to validate the reasonableness and effectiveness of the proposed method.
文摘An efficient algorithm is proposed for Bayesian model calibration,which is commonly used to estimate the model parameters of non-linear,computationally expensive models using measurement data.The approach is based on Bayesian statistics:using a prior distribution and a likelihood,the posterior distribution is obtained through application of Bayes’law.Our novel algorithm to accurately determine this posterior requires significantly fewer discrete model evaluations than traditional Monte Carlo methods.The key idea is to replace the expensive model by an interpolating surrogate model and to construct the interpolating nodal set maximizing the accuracy of the posterior.To determine such a nodal set an extension to weighted Leja nodes is introduced,based on a new weighting function.We prove that the convergence of the posterior has the same rate as the convergence of the model.If the convergence of the posterior is measured in the Kullback–Leibler divergence,the rate doubles.The algorithm and its theoretical properties are verified in three different test cases:analytical cases that confirm the correctness of the theoretical findings,Burgers’equation to show its applicability in implicit problems,and finally the calibration of the closure parameters of a turbulence model to show the effectiveness for computa-tionally expensive problems.
文摘Lunar dust is considered to be one of the top challenges for enabling humans to have extended stays on the moon.Human activities such as module landings and launches,walking,rover operation and construction activities will inevitably produce a significant amount of dust.Therefore,it is important to estimate the potential range and intensity of dust deposition caused by these activities to minimize dust accumulation over time and for maintenance planning and execution.A modular model that correlates the dust deposition distribution with initial mean dust particle velocity,its mean ejected angle and the total amount of ejected mass is developed for an elementary mechanical movement.This modular model is further employed to form a modeling framework to estimate dust deposition of a trajectory based activity of similar repeated movements such as the landing process of a lander,walking and rover operation.The model forms a unified modeling framework for different trajectory-based activities and is shown to predict consistent and physically meaningful ranges and intensities of dust deposition provided reliable data to calibrate the model parameters.
文摘The objective of this paper is to develop a methodology for calibration of a discrete element grain-based model(GBM)to replicate the hydro-mechanical properties of a brittle rock measured in the laboratory,and to apply the calibrated model to simulating the formation of excavation damage zone(EDZ)around underground excavations.Firstly,a new cohesive crack model is implemented into the universal distinct element code(UDEC)to control the fracturing behaviour of materials under various loading modes.Next,a methodology for calibration of the components of the UDEC-Voronoi model is discussed.The role of connectivity of induced microcracks on increasing the permeability of laboratory-scale samples is investigated.The calibrated samples are used to investigate the influence of pore fluid pressure on weakening the drained strength of the laboratory-scale rock.The validity of the Terzaghi’s effective stress law for the drained peak strength of low-porosity rock is tested by performing a series of biaxial compression test simulations.Finally,the evolution of damage and pore pressure around two unsupported circular tunnels in crystalline granitic rock is studied.
基金co-supported by the National Natural Science Foundation of China(Nos.42074013 and 41704006)。
文摘This paper proposes a novel self-calibration method for a large-FoV(Field-of-View)camera using a real star image.First,based on the classic equisolid-angle projection model and polynomial distortion model,the inclination of the optical axis is thoroughly considered with respect to the image plane,and a rigorous imaging model including 8 unknown intrinsic parameters is built.Second,the basic calibration equation based on star vector observations is presented.Third,the partial derivative expressions of all 11 camera parameters for linearizing the calibration equation are deduced in detail,and an iterative solution using the least squares method is given.Furtherly,simulation experiment is designed,results of which shows the new model has a better performance than the old model.At last,three experiments were conducted at night in central China and 671 valid star images were collected.The results indicate that the new method obtains a mean magnitude of reprojection error of 0.251 pixels at a 120°FoV,which improves the calibration accuracy by 38.6%compared with the old calibration model(not considering the inclination of the optical axis).When the FoV drops below 20°,the mean magnitude of the reprojection error decreases to 0.15 pixels for both the new model and the old model.Since stars instead of manual control points are used,the new method can realize self-calibration,which might be significant for the long-duration navigation of vehicles in some unfamiliar or extreme environments,such as those of Mars or Earth’s moon.
基金National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant No. 2010ZX09401-403)
文摘The general strategy and method of constructing universal calibration model for levofioxacin injections by near-infrared spectroscopy have been investigated and discussed. Firstly, a constant-temperature homogeneous liquid calibration model for levofloxacin hydrochloride injections with the same composition but different active principal ingredient （API） content was established as the basic unit for universal model. Then, samples of levofloxacin hydrochloride injections containing propylene glycol or levofloxacin lactate injections were added to develop a primary constant-temperature liquid universal model. Temperature- amended final universal model was established to apply to samples under different temperatures. The final model was built from 61 calibration samples and 77 validation samples. The value of the root mean square error of cross validation （RMSECV） and coefficient of determination （r2） of leave-one-out cross-validation （LOOCV） were 0.792 and 0.9993, respectively, the root mean square error of prediction （RMSEP） of test set validation （TSV） was 0.87, and the average relative deviation was 1.44%. According to the ICH guidelines, the universal calibration model was evaluated. Based on the experimental statistical results, the recommended number of calibration samples for a constant-temperature homogeneous liquid quantitative model was no less than 15.
基金supported by the National Natural Science Foundation of China(No.U2142206).
文摘Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.
基金Engineering and Physical Sciences Research Council (EPSRC) is also acknowledged for funding this work under Grant Number EP/N009207/1.
文摘Concrete slabs are widely used in modern railways to increase the inherent resilient quality of the tracks,provide safe and smooth rides,and reduce the maintenance frequency.In this paper,the elastic performance of a novel slab trackform for high-speed railways is investigated using three-dimensional finite element modelling in Abaqus.It is then compared to the performance of a ballasted track.First,slab and ballasted track models are developed to replicate the full-scale testing of track sections.Once the models are calibrated with the experimental results,the novel slab model is developed and compared against the calibrated slab track results.The slab and ballasted track models are then extended to create linear dynamic models,considering the track geodynamics,and simulating train passages at various speeds,for which the Ledsgard documented case was used to validate the models.Trains travelling at low and high speeds are analysed to investigate the track deflections and the wave propagation in the soil,considering the issues associated with critical speeds.Various train loading methods are discussed,and the most practical approach is retained and described.Moreover,correlations are made between the geotechnical parameters of modern high-speed rail and conventional standards.It is found that considering the same ground condition,the slab track deflections are considerably smaller than those of the ballasted track at high speeds,while they show similar behaviour at low speeds.
基金support of the Natural Sciences and Engineering Research Council of Canada(NSERC)is gratefully acknowledged.
文摘This paper presents a methodology for calibrating discrete element method input parameters for simulating cohesive materials.The Plackett-Burman method was initially employed to identify the sig-nificant input parameters.Subsequently,the performances of response surface methodology(RSM),artificial neural networks(ANN),and random forest(RF)models for calibration were compared.The results demonstrated that the random forest model outperformed the two other models,achieving an RMSE of 1.89,an R-squared of 94%,and an MAE of 1.63.The ANN model followed closely,with an RMSE of 3.12,an R-squared of 89%,and an MAE of 2.18,while the RSM model exhibited lower performance with an RMSE of 6.84,an R-squared of 86%,and an MAE of 5.41.This study presents a framework for enhancing the accuracy of DEM simulations.Finally,the robustness and adaptability of the calibration approach were demonstrated by applying calibrated parameters from one particle size to another.
基金Project (No. 61004092) supported by the National Natural ScienceFoundation of China
文摘In this paper,an efficient model structure composed of a second-order resistance-capacitance network and a simply analytical open circuit voltage versus state of charge(SOC) map is applied to characterize the voltage behavior of a lithium iron phosphate battery for electric vehicles(EVs).As a result,the overpotentials of the battery can be depicted using a second-order circuit network and the model parameterization can be realized under any battery loading profile,without a special characterization experiment.In order to ensure good robustness,extended Kalman filtering is adopted to recursively implement the calibration process.The linearization involved in the calibration algorithm is realized through recurrent derivatives in a recursive form.Validation results show that the recursively calibrated battery model can accurately delineate the battery voltage behavior under two different transient power operating conditions.A comparison with a first-order model indicates that the recursively calibrated second-order model has a comparable accuracy in a major part of the battery SOC range and a better performance when the SOC is relatively low.
基金supported by the National Natural Science Foundation of China(Grant Nos.51722406,61573018 and 51874335)the Shandong Provincial Natural Science Foundation(Grant JQ201808)+1 种基金the Fundamental Research Funds for the Central Universities(Grant 18CX02097A)the National Science and Technology Major Project of China(Grant 2016ZX05025001-006)
文摘In practical development of unconventional reservoirs,fracture networks are a highly conductive transport media for subsurface fluid flow.Therefore,it is crucial to clearly determine the fracture properties used in production forecast.However,it is different to calibrate the properties of fracture networks because it is an inverse problem with multi-patterns and highcomplexity of fracture distribution and inherent defect of multiplicity of solution.In this paper,in order to solve the problem,the complex fracture model is divided into two sub-systems,namely"Pattern A"and"Pattern B."In addition,the generation method is grouped into two categories.Firstly,we construct each sub-system based on the probability density function of the fracture properties.Secondly,we recombine the sub-systems into an integral complex fracture system.Based on the generation mechanism,the estimation of the complex fracture from dynamic performance and observation data can be solved as an inverse problem.In this study,the Bayesian formulation is used to quantify the uncertainty of fracture properties.To minimize observation data misfit immediately as it occurs,we optimize the updated properties by a simultaneous perturbation stochastic algorithm which requires only two measurements of the loss function.In numerical experiments,we firstly visualize that small-scale fractures significantly contribute to the flow simulation.Then,we demonstrate the suitability and effectiveness of the Bayesian formulation for calibrating the complex fracture model in the following simulation.
基金Under the auspices of National Natural Science Foundation of China(No.42271167)Open Fund of Hubei Key Laboratory of Critical Zone Evolution(No.CZE2022F03)。
文摘Evapotranspiration(ET)is the key to the water cycle process and an important factor for studying near-surface water and heat balance.Accurately estimating ET is significant for hydrology,meteorology,ecology,agriculture,etc..This paper simulates ET in the Madu River Basin of Three Gorges Reservoir Area of China during 2009-2018 based on the Soil and Water Assessment Tool(SWAT)model,which was calibrated and validated using the MODIS(Moderate-resolution Imaging Spectroradiometer)/Terra Net ET 8-Day L4 Global 500 m SIN Grid(MOD16A2)dataset and measured ET.Two calibration strategies(lumped calibration(LC)and spatially distributed calibration(SDC))were used.The basin was divided into 34 sub-basins,and the coefficient of determination(R^(2))and NashSutcliffe efficiency coefficient(NSE)of each sub-basin were greater than 0.6 in both the calibration and validation periods.The R2 and NSE were higher in the validation period than those in the calibration period.Compared with the measured ET,the accuracy of the model on the daily scale is:R^(2)=0.704 and NSE=0.759(SDC results).The model simulation accuracy of LC and SDC for the sub-basin scale was R^(2)=0.857,R^(2)=0.862(monthly)and R^(2)=0.227,R^(2)=0.404(annually),respectively;for the whole basin scale was R^(2)=0.902,R^(2)=0.900(monthly)and R^(2)=0.507 and R^(2)=0.519(annually),respectively.The model performed acceptably,and SDC performed the best,indicating that remote sensing data can be used for SWAT model calibration.During 2009-2018,ET generally increased in the Madu River Basin(SDC results,7.21 mm/yr),with a multiyear average value of 734.37 mm/yr.The annual ET change rate for the sub-basin was relatively low upstream and downstream.The linear correlation analysis between ET and meteorological factors shows that on the monthly scale,precipitation,solar radiation and daily maximum and minimum temperature were significantly correlated with ET;annually,solar radiation and wind speed had a moderate correlation with ET.The correlation between maximum temperature and ET is best on the monthly scale(Pearson correlation coefficient R=0.945),which may means that the increasing ET originating from increasing temperature(global warming).However,the sub-basins near Shennongjia Nature Reserve that are in upstream have a negative ET change rate,which means that ET decreases in these sub-basins,indicating that the’Evaporation Paradox’exists in these sub-basins.This study explored the potential of remote-sensing-based ET data for hydrological model calibration and provides a decision-making reference for water resource management in the Madu River Basin.
