Numerical analysis of high‑speed railway slab tracks using calibrated and validated 3D time‑domain modelling

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.

Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

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.

Calibrate complex fracture model for subsurface flow based on Bayesian formulation

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.

A machine learning approach to TCAD model calibration for MOSFET

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.

Application of Spatially Distributed Calibrated Hydrological Model in Evapotranspiration Simulation of Three Gorges Reservoir Area of China:A Case Study in the Madu River Basin

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.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

Structural Model Updating of Jacket Platform by Control Theory Using Vibration Measurement Approach

The identification of variations in the dynamic behavior of structures is an important subject in structural integrity assessment.Improvement and servicing of offshore platforms in the marine environment with constant changing,requires understanding the real behavior of these structures to prevent possible failure.In this work,empirical and numerical models of jacket structure are investigated.A test on experimental modal analysis is accomplished to acquire the response of structure and a mathematical model of the jacket structure is also performed.Then,based on the control theory using developed reduction system,the matrices of the platform model is calibrated and updated.The current methodology can be applied to prepare the finite element model to be more adaptable to the empirical model.Calibrated results with the proposed approach in this paper are very close to those of the actual model and also this technique leads to a reduction in the amount of calculations and expenses.The research clearly confirms that the dynamic behavior of fixed marine structures should be designed and assessed considering the calibrated analytical models for the safety of these structures.

Analysis of sp Pillar Stability Experiment: Continuous thermo-mechanical model development and calibration

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.

Calibration of coupled hydro-mechanical properties of grain-based model for simulating fracture process and associated pore pressure evolution in excavation damage zone around deep tunnels

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.

Application of SWAT Model to Non-point Source Pollution in Xincai River Basin

[Objective]The study aimed to simulate the production and transportation process of surface runoff,sediment and non-point source pollution in Xincai River basin based on SWAT model.[Method]On the basis of analyzing the principles of SWAT model,the correlative parameters of runoff,sediment and water quality were calibrated,then the spatial and temporal distribution of runoff,sediment and non-point source pollutants in Xincai River basin were studied by using SWAT model.[Result]The results of calibration and validation showed that SWAT model was reasonable and available,and it can be used to simulate the non-point source pollution of Xincai River basin.The simulation results revealed that the load of sediment and various pollutants was the highest in the rainy year,followed by the normal year,while it was the minimum in the dry year,indicating that the production of sediment and non-point source pollutants was closely related to annual runoff.[Conclusion]The research could provide scientific references for the prevention of non-point source pollution in a basin.

Effects of temporal variability on HBV model calibration

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.

Field measurements for calibration of simplified models of the stiffening effect of infill masonry walls in high-rise RC framed and shear-wall buildings

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.

Assessment of the Sensitivity of Streamflow Simulations to Changes in Patch Resolution Using GIS Based Hydro-Ecologic Model

Eight different patch configurations were investigated to analyze the effect of patch characterization/formation in streamflow simulation, using the Regional Hydro-Ecologic Simulation Systems (RHESSys) model. It is investigated for eight different patch configurations of a subcatchment of the Turkey Lakes Watershed, Ontario. The model’s hydrological parameters are calibrated for each of these patch configurations and the performance of the simulations is evaluated. Results indicate that both the nature of the flow simulation and the calibrated parameter values are sensitive to patch configuration. The best simulation results were obtained for the patch configuration with the highest spatial variation of climate, stream network and hillslope conditions across the subcatchment. Different patch configurations also lead to markedly different calibrations of the model’s hydrological parameters (54.26 < k < 119.13;and 1.02 < m < 2.28), which has implications for the physical interpretation and transferability of the calibrated parameter values.

Modeling Preparative Chromatographic Separation of Heavy Rare Earth Elements and Optimization of Thulium Purification

Rare Earth Elements are in growing demand globally. This paper presents a case study of applied mathematical modeling and multi objective optimization to optimize the separation of heavy Rare Earth Elements, Terbium-Lutetium, by means of preparative solid phase extraction chromatography, which means that an extraction ligand, HDEHP, is immobilized on a C18 silica phase, and nitric acid is used as an eluent. An ICP-MS was used for online detection of the Rare Earths. A methodology for calibration and optimization is presented, and applied to an industrially relevant mixture. Results show that Thulium is produced at 99% purity, with a productivity of 0.2 - 0.5 kg Tm per m3 stationary phase and second, with Yields from 74% to 99%.

A module based modeling framework for estimating lunar dust deposition due to human 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.

Analysis of Protein Content in Panicum miliaceum L. Using Near Infrared Transmittance Spectroscopy

[Objective] To explore a method for the rapid determination of protein con- tent in grains of Panicum miliaceum L. [Method] The near infrared transmittance spec- troscopy （NITS） was used to build the mathematical models for the quantitative analy- sis of protein content in the grains. Four combinations of treatment that first derivative and second derivative were respectively combined with partial least squares （PLS） and modified partial least squares （MPLS） were set to compare their effects on the original transmission spectrum. [Result] The predicting effects of the 4 combinations were similar. The optimal combination was first derivative with MPLS, in which the average determination coefficient of validation （RSQ） was 0.880 6, correlation coeffi- cient of cross validation （1-VR） was 0.857 0, standard error of calibration （SEC） was 0.342 4, standard error of cross validation （SECV） was 0.375 1, and the standard er- ror of prediction （SEP） was 0.454. [Conclusion] The constructed NITS model is a rapid way for the determination of protein content in grains of P. miliaceum.

Calibration Method Based on RBF Neural Networks for Soil Moisture Content Sensor

Temporal and spatial variation of soil moisture content is significant for crop growth,climate change and the other fields.In order to overcome shortage of non-linear output voltage of TDR3 soil moisture content sensor and increase soil moisture content data collection and computational efficiency,this paper presents a RBF neural network calibration method of soil moisture content based on TDR3 soil moisture sensor and wireless sensor networks.Experiment results show that the calibration method is effective...

Rapid and nondestructive detection of watercore and sugar content in Asian pear by near infrared spectroscopy for commercial trade

Watercore and sugar content are internal qualities which are impossible for exterior determi-nation.Therefore the aims of this study were to develop models for nondestructive detection of watercore and predicting sugar content in pear using Near Infrared Spectrosoopy(NIR)tech-nique.A total of 93 samples of Asian pear variety“SH-078”were used.For sugar content,spectrum of each fruit was measured in the short wavelength region(700-1100nm)in the reflection mode and the first derivative of spectra were then correlated with the sugar content in juice determined by digital refractometer.Prediction equation was performed by multiple linear regression.The result showed Standard Error of Prediction(SEP)=0.58°Bx,and Bias=0.11.The result from t-test showed that sugar content predicted by NIR was not signifcantly different from the value analyzed by refractometer at 95%confidence.For watercore disorder,NIR measurement was performed over the short wavelength range(700-850 nm)in the transmission mode.The first derivative spectra were correlated with intemnal qualities.Then principle com-ponent analysis(PCA)and partial least squares discriminant analysis(PLSDA)were used to perform discrimination models.The accuracy of the PCA model was greater than the PLSDA one.The scores from PC1 were separated into two boundaries,one predicted rejected pears with 100%classification accuracy,and the other was accepted pears with 92%accuracy.The high accuracy of sugar content determining and watercore detecting by NIR reveal the high efficiency of NIR technique for detecting other internal qualities of fruit in the future.

Prediction of Crude Protein in Ramie by Near-Infrared Spectroscopy(NIR)

[Objective] This study was conducted to evaluate the feasibility of deter- mining crude protein in ramie using near-infrared （NIR） spectrometer. [Method] Par- tial least square regression （PLSR） was performed to establish a calibration model based on 50 samples for predicting the crude protein content in ramie, and the model was validated with data in the validation set consisting of 10 samples. [Result] The correlation coefficient of the model was 0.98. There was a good correla- tion between the predicted values by the near-infrared prediction model and the measured values by chemical analysis, and the relative error was 3.54% on aver- age between the predicted and the measured values. [Conclusion] The results showed that it is feasible to determine crude protein content in ramie using NIR spectroscopy-based prediction model.

Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system

Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.