Absolute Value Of Relative Error Research Articles

Development of New Models Using Empirical Modeling of Global Solar Radiation and Its Application in Usak City, Turkey

With this study, 12 empirical models in the literature, 2 new models developed within the scope of this study, SARAH and CMSAF satellite-based models, COSMO and ERA5 re-analysis solar radiation data sets in the PVGIS database were compared in order to detect the monthly average global solar radiation coming to the horizontal plane of Usak province. New models developed within the scope of the study; it uses the region's temperature, cloudiness coefficient and sunset hour angle. In comparison of the datas within the scope of the study; coefficient of determination (R²), mean percent error (MPE), deviation error (MBE), root mean square error (RMSE), absolute relative error (ARE) parameters were used. As a result of the evaluations, the method that most successfully predicts the global solar radiation values of Usak province was tried to be determined. According to the monthly evaluation of the models; It was determined that 14 models and satellite-based systems have absolute relative error values below 5% in March-April-May-June, September-October and December. The most accurate estimates were realized for May in 16 of 18 different estimation methods used in the study. The coefficient of determination of empirical models and PVGIS data sets was above 0.97. When the success of the models was evaluated according to the RMSE values, It was determined that the logarithmic based Model 14 (0.90058 RMSE, 0.98327 R2, -1.079894 MPE, -0.05033 MBE, 0.185628 t) which was obtained by using the sunset hour angle and the max-min temperature difference developed within the scope of this study, made the most accurate estimations. COSMO data from spatial data (1.053134 RMSE, 0.979036 R2, -1.196348 MPE, -0.25105 MBE, 0.8141 t) made successful estimations, but the accuracy of the COSMO data was lower than the data estimated by Model 14. It was concluded that used the models and satellite-based systems were generally successful. As a result, In the studies to be carried out for the global solar radiation forecast of Usak province. It has been concluded that Model 14 developed within the scope of the study can be used in precise calculations and COSMO data from PVGIS datas can be used in more superficial or pre-feasibility studies.

A novel mathematical method to estimate rice phenological parameters across spatial scales for the ORYZA model

While crop models are increasingly applied to large-scale areas, inadequate observation data make it difficult to calibrate the model’s phenological parameters at the regional scale. The present study proposed a simple mathematical method for estimating rice phenological parameters across spatial scales for the ORYZA model. The method establishes the cumulative function of the phenological parameters (CFPP). By fitting the CFPP with the so-called growth curve, the 4 phenological parameters of the ORYZA model were transformed into 3 fitted parameters in the equation of CFPP. Functions between the fitted parameters and several meteorological and field management factors were established. These established functions were substituted back into CFPP to construct a modified CFPP. Due to the inter-translational relationship between CFPP and the original phenological parameters, the values of phenological parameters could be estimated by the modified CFPP based on meteorological and field management factors. The newly proposed mathematical method was applied in the Yangtze River Basin (YRB), China. The results indicated that the multi-station average of the absolute value of relative errors for the rice panicle initiation, flowering, and maturity dates within the YRB were 12.3 %, 10.5 %, and 8.7 %, respectively, which were at most 4.8 % larger than that simulated using parameters calibrated using each station’s phenological data. The phenological parameters estimated by the novel mathematical method had close performance to those calibrated directly based on observed data at most stations in terms of rice phenology simulation. The present study provided a new solution for phenological parameter calibration for crop models when applied in a large-scale area.

Quantitative expression of LNAPL pollutant concentrations in capillary zone by coupling multiple environmental factors based on random forest algorithm

The capillary zone plays a crucial role in migration and transformation of pollutants. Light nonaqueous liquids (LNAPLs) have become the main organic pollutant in soil and groundwater environments. However, few studies have focused on the concentration distribution characteristics and quantitative expression of LNAPL pollutants within capillary zone. In this study, we conducted a sandbox-migration experiment using diesel oil as a typical LNAPL pollutant, with the capillary zone of silty sand as the research object. The variation characteristics of LNAPL pollutants (total petroleum hydrocarbon) concentration and environmental factors (moisture content, electrical conductivity, pH, and oxidationreduction potential) were essentially consistent at different locations with the same height. These characteristics differed within range of 10.0–50.0 cm and above 60.0 cm from groundwater. A model for quantitative expression of concentrations was constructed by coupling multiple environmental factors of 968 sets-7744 data via random forest algorithm. The goodness of fit (R2) for both training and test sets was greater than 0.90, and the mean absolute percentage error (MAPE) was less than 16.00 %. The absolute values of relative errors in predicting concentrations at characteristic points were less than 15.00 %. The constructed model can accurately and quantitatively express and predict concentrations in capillary zone.

A combinatorial prediction model for the performance of fuel cell air compressor with air bearings considering accelerated decay characteristics

As the core component in air supply system, the air compressor plays a key role in efficient operation of fuel cell system. In this research, accelerated endurance tests for air compressor with air bearings were conducted based on designed operating condition, and the Hybrid Variational Artificial Bee Colony (HyVABC) algorithm was proposed to achieve enhancement and balance of global optimization capability and local feature capture ability. On this basis, combinatorial prediction models were developed to realize accurate performance prediction of the compressor considering accelerated decay characteristics. The results indicate that the degradation degree of pressure ratio is the greatest at the flow rate of 133 g/s and the speed of 100000 rpm, with the degradation rate of 25.9% compared with that before break-in. The HyVABC algorithm obtains optimal values on 16 out of 20 test problems, which highlights its strong optimization performance, fast convergence speed and high robustness. The XGBoost-HyVABC model is chosen to describe mapping relationship between operating and performance parameters of the compressor, and absolute values of relative errors between predicted and test values are below 3%. All these provide efficient heuristic algorithm and accurate combinatorial model for accelerated decay performance prediction of fuel cell air compressors.

Predicting hourly heating load in residential buildings using a hybrid SSA–CNN–SVM approach

This study proposes a hybrid prediction model using sparrow search algorithm (SSA) to optimize the convolutional neural network (CNN) and support vector machine (SVM), in order to perform accurate prediction of secondary supply temperature (Ts2). The historical operation data of Weifang residential building thermal station was adopted and reasonable data preprocessing was performed to suppress the interference of abnormal data. The input variables of the prediction model were screened using the correlation analysis method, taking the influence of the hysteresis effect into consideration. The SSA–CNN–SVM model was then developed for prediction. The performance of the model was evaluated by the root mean square error, mean absolute error, mean absolute percentage error (MAPE), and absolute value of relative error of each time step. The results obtained demonstrated that the SSA–CNN–SVM model has high prediction accuracy. The MAPE values of the two heat exchange stations were between 2.28 % and 2.4 %. The indoor temperature significantly affected the prediction accuracy of Ts2. After the introduction of the indoor temperature, the MAPE of the predicted values of the hybrid model was reduced by 0.35 %. The maximum reduction in MAPE of the SSA–CNN–SVM model was 1.5 % compared with other prediction models.

High-temperature mechanical properties of additively manufactured 420 stainless steel

Martensitic stainless steels are indispensable alloys in various high stress and temperature applications such as plastic injection molds and components in steam generators. Subtractive manufacturing methods used to fabricate these parts, however, limits its functionality and performance due to design constraint of cooling channels. This limitation can be resolved by means of additive manufacturing while ensuring that acceptable high-temperature properties can be achieved. In this work, the mechanical behavior of additively manufactured 420 stainless steel (AM420SS) is explored through material constitutive modeling to determine the mathematical model that best describes its flow stress in extreme conditions. This is accomplished by subjecting the samples to hot compression under the strain rates of 0.1–1.0 s−1, and temperatures between 973–1423 K (700 °C–1150 °C) via Gleeble thermomechanical test. The experimental data were used to generate the predictive flow stress curves of constitutive models which includes Johnson-Cook, Zerilli-Armstrong, Zener-Hollomon, and Hensel-Spittel equations. Results showed that Zener-Hollomon and Hensel-Spittel models are the most accurate material constitutive equations with relatively high R values of 0.986 and 0.976, and low average absolute relative error values of 6.96% and 7.69%, respectively. The material constants derived from these models can be applied in finite element analysis simulations to assess the performance of using AM420SS parts at high temperature and strain conditions.

Searching for homogeneous regions in regional flood frequency analysis for Southeast Australia

Study regionSoutheast Australia Study focusThis study examines identification of homogeneous regions for regional flood frequency analysis (RFFA) using 201 gauged catchments and applying principal component analysis (PCA) and cluster analysis. Quantile regression technique (QRT) is applied to develop prediction equations. Leave-one-out (LOO) validation is used to evaluate the accuracy of the developed prediction equations. New hydrologic insights for the regionFor the regions formed based on PCA and cluster analysis, the Hosking and Wallis’ heterogeneity (H1) statistics are found to vary in the range of 11.55–24.52, and 5.11–26.27, respectively. The assumed regions are highly heterogeneous as H1 values are much higher than 1.00. The median of absolute relative error values associated with the developed prediction equations for the assumed regions are found to vary in the range of 17–62 %, which are comparable to similar RFFA studies. PCA and cluster analysis do not deliver sufficiently homogeneous regions in southeast Australia. The degree of heterogeneity of a region has little effect on the relative accuracy of flood quantile estimates. The findings of this study will assist to update RFFA technique in Australian Rainfall and Runoff (national guideline).

Hot Compression Behavior of 18Cr–11Mn–0.06Ni–0.19N High‐Mn Ultralow Nickel Duplex Stainless Steel under Large Deformation

To enhance the rolling production efficiency of high‐manganese low‐nickel duplex stainless steel (DSS) and optimize hot compression parameters, hot compression with a range of 0.01–10 s−1/850–1150 °C and deformations of 70% is used to investigate the hot deformation behavior of 18Cr–11Mn–0.06Ni–0.19 N DSS, which reveals the differences in the recrystallization mechanism of two phases and their thermoplastic properties. The results show that strain partitioning and softening occur first in the ferrite phase, with the austenite phase undergoing complex and incomplete softening. Most ferrite experiences continuous dynamic recrystallization and develops a <001>//ND texture when deformed at 0.1–10 s−1/950 °C, with higher temperatures facilitating the merging of high‐angle grain boundaries. A deformation‐induced phase transformation from δ to γ occurs in ferrite when deformed at 0.01 s−1/850–950 °C. However, austenite softening is dominated by discontinuous dynamic recrystallization, enhanced by twinning‐induced strain‐induced boundary migration when deformed at 0.1 s−1/950 °C; dynamic recovery controlled by dislocations is enhanced when deformed at 0.1 s−1/1150°C and 10 s−1/950 °C. A strain‐compensated constitutive model with an average absolute relative error value of 7.8% is established to better predict the flow stresses. Experimental DSS displays optimal hot workability in the parameter ranges of 0.1 s−1/950–1050 °C.

Comparison of Methods Predicting Advance Time in Furrow Irrigation

In the design of furrow irrigation, and in general in surface irrigation, the reliable estimation of the advance time at the furrow end (tL) is a key issue for improving the efficiency and uniformity of irrigation. In this study, three methods are used for estimating the tL, and their results are compared with the experimental data of fifteen different furrows from the international literature. These methods are as follows: (a) the Valiantzas equation, (b) the method presented by Walker and Skogerboe, based on solving the volume balance equation by the Newton–Raphson iterative procedure and (c) the method of Philip and Farrell. The first two methods assume that the infiltration is described by the Lewis–Kostiakov equation and the extended Lewis–Kostiakov equation, respectively, while in the case of the Philip and Farrell method, the infiltration is described by the Philip equation and the Lewis-Kostiakov equation. The results showed that in most cases of the first two methods, the absolute relative error value of the predicted time tL was less than 10%. The Philip and Farrell method using the Lewis–Kostiakov infiltration equation underestimates the time tL and fails especially in the case where the volume of the surface water is not negligible compared to the total volume of water entering the system. The Valiantzas method is recommended because it was simpler and easier to use and showed greater prediction accuracy of tL, resulting in better planning of irrigation systems and contributing to water saving, which is currently a big issue.

Aging trajectory and end-of-life prediction for lithium-ion battery via similar fragment extraction of capacity degradation curves

Considering the limitations on future general deterioration trend description and local capacity regeneration capture of existing methods, this paper designs a novel aging trajectory and end-of-life (EOL) prediction method for lithium-ion battery via similar fragment extraction of capacity degradation curves. The proposed method attempts to make the following three contributions: 1) By fusion of particle filter (PF)-based general deterioration trend description and similar fragment extraction-based local capacity regeneration capture, the developed method can achieve accurate aging trajectory and EOL prediction. 2) Considering the similarity in degradation paths, a novel dynamic time warping-based similar fragment extraction method is proposed to learn the deterioration path from the same type batteries' limited capacity degradation curves. 3) On the basis of the predicted future capacity, the PF-based capacity empiric degradation model can be iteratively updated to accurately reflect battery future general deterioration trend. The validation results based on Beijing Institute of Technology dataset demonstrate that the maximum mean absolute error and root mean squared error of predicted aging trajectory, and the maximum value of absolute relative error between reference and predicted EOL achieved by the developed method with the help of the same type of battery's limited capacity degradation curves are only 1.09%, 1.28% and 3.23%, respectively.

Comparison of semiquantitative methodologies using Raman mapping for corrosion products on iron artifacts

The quantitative determination of corrosion products is essential for evaluating the corrosion state of archeological iron artifacts. This study explored two semiquantitative approaches using Raman mapping technique combined with chemometrics to quantify a binary mixture of magnetite and goethite. The first approach involved establishing quantification models using Raman spectra treated with Principal Components Regression (PCR) and Partial Least Squares (PLS) algorithms. Both the PCR and PLS model showed good predictive ability as indicated by correlation coefficients and root mean square errors (PLS: Rc2=0.9979, Rp2=0.9970, RMSEC=1.93, RMSEP =2.68, RMSECV=3.25). The second approach was based on spectral fitting using non-negative least squares (NNLS) algorithm. This method demonstrated a fair accuracy between the calculated and actual compositions. The absolute value of relative errors was 0.99%∼10.08% for Fe3O4 and 0.81%∼9.71% for α-FeOOH respectively, for compositions greater than 20%. These methods were then applied to quantify the corrosion products on an iron bar excavated from the Nanhai (South China Sea) No. I shipwreck. Compared with XRD quantitative results, Raman results showed that the spectral fitting method was superior to the established PCA and PLS quantification models in both qualitative determination and quantitative accuracy. Compositional maps depicting the distribution of different phases were also generated using spectral fitting method. It was concluded that Raman mapping has significant potential as an accurate quantitative method for the detection of iron corrosion products, and that the spectral fitting method is more suitable for determining iron corrosion compared to the PCR and PLS quantification models in this study.

Identification and verification of PCDD/Fs indicators from four typical large-scale municipal solid waste incinerations with large sample size in China

Monitoring PCDD/Fs emissions from municipal solid waste incinerations (MSWIs) is of paramount importance, yet it can be time-consuming and labor-intensive. Predictive models offer an alternative approach for estimating their levels. However, robust models specific to PCDD/Fs were lacking. In this study, we collected 190 PCDD/Fs samples from 4 large-scale MSWIs in China, with the average PCDD/Fs levels and TEQ levels of 0.987 ng/m3 and 0.030 ng TEQ/m3, respectively. We developed and evaluated predictive models, including traditional statistical methods, e.g., linear regression (LR) as well as machine learning models such as back propagation-artificial neural networks (BP ANN) and random forest (RF). Correlation analysis identified 2,3,4,7,8-PeCDF, 1,2,3,6,7,8-HxCDF, 2,3,4,6,7,8-HxCDF were better indicator congeners for PCDD/Fs estimation (R2 > 0.9, p < 0.001). The predictive results favored the RF model, exhibiting a high R2 value and low root mean square error (RMSE) and mean absolute error (MAE). Additionally, the RF model showed excellent prediction ability during external validation, with low absolute relative error (ARE) of 10.9 %-12.6 % for the three indicator congeners in the normal PCDD/F TEQ levels group (<0.1 ng TEQ/m3) and slightly higher ARE values (13.8 %-17.9 %) for the high PCDD/F TEQ levels group (>0.1 ng TEQ/m3). In conclusion, our findings strongly support the RF model’s effectiveness in predicting PCDD/Fs TEQ emission from MSWIs.

Stress Measurements of GIS Epoxy Composite with Transverse Waves Based on the Ultrasonic Pulse-Echo Method

The mechanical reliability of the gas-insulated metal-enclosed switchgear (GIS) is critical to the safe and stable operation of a power system. The internal stress induced in GIS insulators, which typically lead to cracking, can degrade the mechanical properties and compromise the reliability of the GIS. In this work, ultrasonic nondestructive measurements of the internal stress in a GIS 126 kV epoxy composite with the transverse waves are demonstrated for the first time. Our results show that the velocities of ultrasonic transverse waves increase linearly with the compressive stress and decrease linearly with the tensile stress. The acoustoelastic coefficients are −1.18 × 10−4/MPa and −1.96 × 10−4/MPa for measurements with transverse waves polarized vertically and parallelly to stress, respectively. Based on these acoustoelastic coefficients, the internal stresses are calculated, and the maximum absolute values of relative errors are 35.4% and 26.3% for measurements with the transverse wave polarized vertically and parallelly to stress, respectively.

Modeling interfacial tension of surfactant–hydrocarbon systems using robust tree-based machine learning algorithms

Interfacial tension (IFT) between surfactants and hydrocarbon is one of the important parameters in petroleum engineering to have a successful enhanced oil recovery (EOR) operation. Measuring IFT in the laboratory is time-consuming and costly. Since, the accurate estimation of IFT is of paramount significance, modeling with advanced intelligent techniques has been used as a proper alternative in recent years. In this study, the IFT values between surfactants and hydrocarbon were predicted using tree-based machine learning algorithms. Decision tree (DT), extra trees (ET), and gradient boosted regression trees (GBRT) were used to predict this parameter. For this purpose, 390 experimental data collected from previous studies were used to implement intelligent models. Temperature, normal alkane molecular weight, surfactant concentration, hydrophilic–lipophilic balance (HLB), and phase inversion temperature (PIT) were selected as inputs of models and independent variables. Also, the IFT between the surfactant solution and normal alkanes was selected as the output of the models and the dependent variable. Moreover, the implemented models were evaluated using statistical analyses and applied graphical methods. The results showed that DT, ET, and GBRT could predict the data with average absolute relative error values of 4.12%, 3.52%, and 2.71%, respectively. The R-squared of all implementation models is higher than 0.98, and for the best model, GBRT, it is 0.9939. Furthermore, sensitivity analysis using the Pearson approach was utilized to detect correlation coefficients of the input parameters. Based on this technique, the results of sensitivity analysis demonstrated that PIT, surfactant concentration, and HLB had the greatest effect on IFT, respectively. Finally, GBRT was statistically credited by the Leverage approach.

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

The five machine learning models (MLM) of ridge regression, gradient boosting regression (GBR), support vector regression, random forest regression (RFR), convolutional neural network, and a metallurgical mechanism model (MMM) are compared in predicting the end‐point P content in the basic oxygen furnace steelmaking process. The prediction accuracy of MMM is much lower than those of five MLM. The GBR and RFR models have the best performance, with the correlation coefficient values of 0.599 and 0.608, respectively. The smallest mean absolute relative error value of 0.155 and the root mean square error value of 0.00319 are obtained with GBR and RFR, respectively. The values of correlation coefficient after data distribution optimization for all MLM are increased two times higher than before. The second blowing time, lime weight, and oxygen consumption amount are evaluated to have the greatest impacts on the end‐point P content. The end‐point P content decreases with decreasing the second blowing time and with increasing the lime weight and the oxygen consumption amount. The GBR and RFR models are optimized by removing the variables with little impacts on the end‐point P content. The highest prediction accuracy is obtained when 14 variables are remained.

Blocking Mechanism and Evaluation Model of Insensitive Effect Under Multi-Frequency Electromagnetic Radiation

Due to the insensitive blocking effect on frequency equipment, the current effect evaluation model based on the third-order power series expansion is disabled to the insensitive blocking effect evaluation under multi-frequency electromagnetic radiation. We adopt the fifth-order power series expansion to analyze the insensitive blocking interference effect. In addition to revealing the mechanism of the insensitive blocking effect under multi-frequency electromagnetic radiation, we also build an improved effect evaluation model in this research. It defines the effect evaluation index. Subsequently, a ranging radar and a communication radio station are employed to verify the effectiveness of the proposed effect evaluation model separately. The results show that the proposed model can be applied to the electromagnetic radiation effect evaluation, regardless of whether the multi-frequency insensitive jamming effect exists on tested equipment or not. All effect evaluation indexes of the new model are smaller than that of the previous model based on the third-order power series expansion method. The absolute value of relative errors could be less than 1 dB. Not only is the theoretical analysis result of the multi-frequency insensitive blocking effect mechanism verified, but also the problem of poor universality of the current electromagnetic radiation effect evaluation model is figured out.

Experiment of Canopy Leaf Area Density Estimation Method Based on Ultrasonic Echo Signal

Variable-rate spray systems with canopy leaf area density information detection are an important approach to reducing pesticide usage in orchard management. In order to estimate the canopy leaf area density using ultrasonic sensors, this article proposed three parameter model equations based on ultrasonic echo peaks for canopy leaf area density estimation and verified the accuracy of the three parameter model equations using laboratory-simulated canopy and outdoor tree experiments. The orthogonal regression statistics results from the laboratory-simulated canopy experiment indicated that parameter Vc is more suitable for canopy leaf area density estimation compared to parameter Va and Vb when the density ranges from 0.54 to 5.4 m2m−3. The model equation from parameter Vc has minor systematic errors, and the predicted and observed values of parameter Vc have good agreement with the experimental conditions. The laboratory-simulated canopy and outdoor tree canopy leaf area density verification experiments of parameter Vc were carried out, and the results indicated that the absolute value of the mean relative error is 5.37% in the laboratory-simulated canopy and 2.84% in outdoor tree experiments. The maximum absolute value of the relative error is 8.61% in the laboratory-simulated canopy and 14.71% in the outdoor tree experiments, and the minimum absolute value of the relative error is 3.21% in the laboratory-simulated canopy and 0.56% in the outdoor tree experiments. The laboratory-simulated canopy leaf area density verification results showed that the mean relative errors under canopy leaf area density 0.98 and 4.92 m2m−3 conditions are 6.29% and 5.82%, respectively, which is larger than the mean relative error under 2.95 m2m−3; nevertheless, these results proved that this model equation is applicable for canopy information detection and advanced pesticide application development in future.

Modeling the solubility of light hydrocarbon gases and their mixture in brine with machine learning and equations of state

Knowledge of the solubilities of hydrocarbon components of natural gas in pure water and aqueous electrolyte solutions is important in terms of engineering designs and environmental aspects. In the current work, six machine-learning algorithms, namely Random Forest, Extra Tree, adaptive boosting support vector regression (AdaBoost-SVR), Decision Tree, group method of data handling (GMDH), and genetic programming (GP) were proposed for estimating the solubility of pure and mixture of methane, ethane, propane, and n-butane gases in pure water and aqueous electrolyte systems. To this end, a huge database of hydrocarbon gases solubility (1836 experimental data points) was prepared over extensive ranges of operating temperature (273–637 K) and pressure (0.051–113.27 MPa). Two different approaches including eight and five inputs were adopted for modeling. Moreover, three famous equations of state (EOSs), namely Peng-Robinson (PR), Valderrama modification of the Patel–Teja (VPT), and Soave–Redlich–Kwong (SRK) were used in comparison with machine-learning models. The AdaBoost-SVR models developed with eight and five inputs outperform the other models proposed in this study, EOSs, and available intelligence models in predicting the solubility of mixtures or/and pure hydrocarbon gases in pure water and aqueous electrolyte systems up to high-pressure and high-temperature conditions having average absolute relative error values of 10.65% and 12.02%, respectively, along with determination coefficient of 0.9999. Among the EOSs, VPT, SRK, and PR were ranked in terms of good predictions, respectively. Also, the two mathematical correlations developed with GP and GMDH had satisfactory results and can provide accurate and quick estimates. According to sensitivity analysis, the temperature and pressure had the greatest effect on hydrocarbon gases’ solubility. Additionally, increasing the ionic strength of the solution and the pseudo-critical temperature of the gas mixture decreases the solubilities of hydrocarbon gases in aqueous electrolyte systems. Eventually, the Leverage approach has revealed the validity of the hydrocarbon solubility databank and the high credit of the AdaBoost-SVR models in estimating the solubilities of hydrocarbon gases in aqueous solutions.

Improvement and testing of ORYZA model water balance modules for alternate wetting and drying irrigation

While the ORYZA crop model has been widely used in the simulation of rice growth, its computation ability is limited in terms of simulating the water balance components (WBCs) of paddy fields under water-saving irrigation conditions, such as alternate wetting and drying (AWD) regimes. In this study, a new water balance simulation framework (WBSF) reflecting the characteristics of AWD is integrated into the original ORYZA2000 (v2.13) model to simulate the WBC of paddy fields. In addition, algorithms for dynamically simulating the root length growing rate are added to the original ORYZA. Algorithms quantifying water stress are also modified. The improved model is further applied to paddy fields in Yongkang and Tuanlin, which are two typical rice growing areas in China. The results show that the improved model performs well at simulating WBCs. The Nash-Sutcliffe efficiency (NSE) of daily ponded water depth ranged from 0.82 to 0.94 across experiments under continuous flooding and AWD at the two sites, showing an increase in NSE of at least 0.23 compared to ORYZA (v3) levels. The absolute value of relative errors (A RE) between simulated and measured values for total irrigation and drainage amount was mostly no more than 10% and showed a reduction of at least 11%. The improved model also showed better performance in terms of evapotranspiration, percolation and root length simulation compared to ORYZA (v3). The yield predictive accuracy of the improved model is slightly better than that of ORYZA2000 (v2.13) but worse than that of ORYZA (v3), which might be due to the poor performance of nitrogen balance modules in the improved model. In general, given the results achieved, it is possible to state that the WBSF of the improved ORYZA2000 showed an enhanced capability to simulate WBCs, which are recommended for use in water balance simulations of paddy fields.

Hot Deformation Behavior of the 25CrMo4 Steel Using a Modified Arrhenius Model.

25CrMo4 steel is widely used in the manufacturing of high-speed train axles due to its excellent mechanical properties. The purpose of this study is to develop an accurate modified constitutive model to describe the hot deformation behavior of the steel. Isothermal compression experiments were performed at different strain rates (0.01, 0.1, 0.5, and 1 s−1) and different temperatures (950, 1000, 1050, and 1100 °C) using a Gleeble-3800 thermal simulator. The microstructure after hot deformation was observed by the electron backscatter diffraction (EBSD), and the effects of temperature and strain rate were analyzed. The results showed that the coupling effect of temperature and strain rate on the dislocation density led to the change in the shape of the true stress–strain curve and that dynamic recovery (DRV) and dynamic recrystallization (DRX) caused the macroscopic softening phenomenon, with DRX being the main mechanism. Based on the true stress–strain curves, the strain-compensated Arrhenius constitutive model was calibrated. To improve prediction ability, a modified Arrhenius constitutive model was proposed, in which the temperature and strain rate coupling correction functions were incorporated. The original, modified Arrhenius models were evaluated according to the absolute relative error (ARE), the average absolute relative error (AARE), and the correlation coefficient (R2). Compared with the original model, the modified Arrhenius model has a higher prediction accuracy, with the ARE value mostly below 4%, the AARE value of 1.91%, and the R2 value of 0.9958.