Percent Relative Error Research Articles

A Statistical Approach for Analysis of Trend Pattern of Pigeon Pea in India

Aims: This paper deals with the analysis of trend pattern in area, production, and yield of pigeon pea in India. Study design: Firstly, some well-known statistical models have been fitted to the concerned time series data on pigeon pea, and the trend values have been obtained. Then the accuracy of the concerned fitted models has been tested using various statistical measures. Finally, inferences are made regarding the trend pattern of pigeon pea in India. Place and Duration of Study: The analysis is carried out using secondary time series data on area, production, and yield of pigeon pea in India pertaining to the period 2001-2023. The time series data have been collected from the records of the Directorate of Economics & Statistics, Department of Agriculture, Cooperation, and Farmers Welfare (DAC&FW), Govt. of India. Methodology: The trend values have been computed on fitting well-known statistical models viz., linear model, exponential model, quadratic model, and cubic model. Moreover, the accuracy of the concerned fitted models has been evaluated using various statistical measures viz., coefficient of determination (\(R^2\)), root mean square error (RMSE), and relative mean absolute percentage error (RMAPE). Results: The results of the investigation reveal that the concerned models fitted well for exploring the trend in area, production, and yield of pigeon pea. Moreover, the cubic model is slightly more precise as compared to the other fitted models, and hence it could be utilized for forecasting the scenario of pigeon pea in India. Conclusion: The findings of the study provide useful insights towards policy formulation regarding enhancement of pigeon pea production for meeting global food demand and nutritional security.

Assessment of Trend Pattern of Rice Production in Some Rice Growing States of India

In this paper, the trend pattern of rice production has been examined for some rice growing states of India viz., Punjab, Andhra Pradesh and Uttar Pradesh. The analysis has been carried out by collecting secondary time series data on rice production during the period 2011-2020, and fitting some well-known models viz., linear model, exponential model and cubic model. The trend values have been estimated by fitting the respective models to the concerned time series data. Moreover, the accuracy of the models has been examined using coefficient of determination (), root mean square error (RMSE), and relative mean absolute percentage error (RMAPE). Furthermore, Chi-square test of “goodness of fit” has been applied in the empirical investigation for validation of the concerned fitted models. The results of the investigation reveal the suitability of the models for exploring the scenario of trend patterns of rice production in the concerned states of India.

CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans

BackgroundBody composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.MethodsA retrospective dataset of 205 contrast-enhanced thoraco-abdominal CT examinations was used for training, while 54 scans from a publicly available dataset were used for independent testing. Two radiology residents performed manual segmentation, identifying the centers of the L1 and L3 vertebrae and segmenting the corresponding axial slices. MultiResUNet was used to identify CT slices intersecting the L1 and L3 vertebrae, and its performance was evaluated using the mean absolute error (MAE). Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA’s performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland–Altman analyses.ResultsOn the independent dataset, CompositIA achieved a MAE of about 5 mm in detecting slices intersecting the L1 and L3 vertebrae, with a MAE < 10 mm in at least 85% of cases and a vDSC greater than 0.85 in segmenting axial slices. Regression and Bland–Altman analyses demonstrated a strong linear relationship and good agreement between automated and manual scores (p values < 0.001 for all indices), with mean PREs ranging from 5.13% to 15.18%.ConclusionCompositIA facilitated the automated quantification of body composition scores, achieving high precision in independent testing.Relevance statementCompositIA is an automated, open-source pipeline for quantifying body composition indices from CT scans, simplifying clinical assessments, and expanding their applicability.Key PointsManual body composition assessment from CTs is time-consuming and prone to errors.CompositIA was trained on 205 CT scans and tested on 54 scans.CompositIA demonstrated mean percentage relative errors under 15% compared to manual indices.CompositIA simplifies body composition assessment through an artificial intelligence-driven and open-source pipeline.Graphical

Forecasting the number of rural migrant workers in Chongqing using the optimized grey N_Verhulst model

Scientific prediction of migrant worker numbers provides decision-making references for resolving rural talent supply issues. Based on the evolutionary patterns and data features of Chongqing’s migrant workers, a new grey prediction model is constructed. The new model is constructed by introducing fractional-order operators in the real domain. In this way, the accumulating order of the traditional N_Verhulst model is optimized. It expands from 1 to all real numbers, thus enhancing its capacity to mine approximately saturated S-shaped time-series data. When the new N_Verhulst model is applied to simulate and predict migrant worker numbers, after optimizing the accumulating order, the mean relative simulation percentage error of the N_Verhulst model reduces from 3.66 to 2.93%, the mean relative forecasting percentage error from 8.02 to 2.18%, and the comprehensive mean relative percentage error from 4.53 to 2.78%. This shows that the optimization boosts the simulation and prediction performance of the N_Verhulst model. The prediction results show that the number of migrant workers in Chongqing will experience an orderly growth, rising from 2.41 million in 2023 to 2.85 million in 2028, with an increase of 18.26% and an average annual growth rate of 3.41%.

Generalised Additive Model-Based Regional Flood Frequency Analysis: Parameter Regression Technique Using Generalised Extreme Value Distribution

This study examines the effectiveness of Generalised Additive Models (GAMs) and log-log linear models for estimating the parameters of the generalised extreme value (GEV) distribution, which are then used to estimate flood quantiles in ungauged catchments. This is known as the parameter regression technique (PRT). Using data from 88 gauged catchments in New South Wales, Australia, flood quantiles were estimated for various annual exceedance probabilities (AEPs) of 50%, 20%, 10%, 5%, 2%, and 1%, corresponding to return periods of 2, 5, 10, 20, 50, and 100 years, denoted by Q2, Q5, Q10, Q20, Q50, and Q100, respectively. These flood quantiles were then used as dependent variables, while several catchment characteristics served as independent variables in the regression. GAMs were employed to capture non-linearities in flood generation processes. This study evaluates different GAMs and log-log linear models, identifying the best ones based on significant predictors and various statistical metrics using a leave-one-out (LOO) validation approach. The results indicate that GAMs provide more accurate and reliable predictions of flood quantiles compared to the log-log linear models, demonstrating better performance in capturing observed values across different quantiles. The absolute median relative error percentage (REr%) ranges from 33% to 39% for the GAMs and from 36% to 45% for the log-log models. GAMs demonstrate better performance compared to the log-log linear models for quantiles Q2, Q5, Q10, Q20, and Q50; however, their performances appear to be similar for Q100.

Predicting Bubble Point Pressure and Gas-Oil Ratio PVT Properties Using Bagging Ensemble Techniques

An enhanced accurate predictive model has been developed for the estimation of reservoir oil Pressure-Volume-Temperature (PVT) properties of Bubble Point Pressure (BPP) and Gas-Oil Ratio (GOR) using Bagging Ensemble machine learning. To develop the Bagging ensemble (BE) model, three different estimators, Decision Tree Regressor, Random Forest Regressor, and Extra Trees Regressor, were used as the base estimators. An averaging method to finally predict the model performance was done using a voting regressor to fit the base estimators. Hyper-parameter tunings for optimization were determined using cross-validation grid search and the implementation of Bagging ensemble described. The ensemble methods were compared with those developed using Artificial Neural Network (ANN) and some selected empirical correlations. Their performances were evaluated using Average Absolute Percentage Relative Error (AAPRE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and graphical cross-plot error analysis Correlation Coefficient (R2). Result showed improved performances, notably the R2 for BPP, the BE is 96.6%, ANN is 89.04%, and the best empirical model is 88.1%. For the GOR, the R2 for BE and ANN are 94.1% and 89.0%, respectively while best empirical model is 88.1%.

Modeling and experimental validation of nanophotonics-enhanced solar membrane distillation technology for treating reverse osmosis brine

A novel, cost-efficient Nanophotonic Enhanced Solar Membrane Distillation (NESMD) system, a solar-driven water desalination technology, was studied. The system features a photothermal membrane acting as a solar collector for water distillation, thus eliminating the need for an external condenser. To address the system’s vulnerability to thermal losses, a comprehensive mathematical model was developed and validated against real-world experimental data. This model represents intricately coupled heat and mass transfer within a sweeping-air NESMD system, incorporating heat loss considerations. The modeling strategy involved dividing the NESMD module into sub-cells and implementing a finite difference method for detailed analysis. This led to a series of nonlinear simultaneous equations, which were resolved via computational code using MATLAB software. The developed NESMD model exhibited commendable conformity to experimental data, exhibiting a relative percentage error of less than 10% for average permeate flux and identifying thermal losses as high as 63%. Depending on the operating conditions, heat transferred to the surroundings takes the lead among the heat loss contributors at higher feed rates (up to 25%), whereas heat conduction across the membrane dominates (up to 42%) thermal losses at low feed rates. The study established an exponential correlation between permeate production and solar energy, with a heat transfer coefficient ranging from 9.5 to 30 W m−2 K−1 and a coefficient of determination of 0.96. An integral part of this work includes calculating solar energy utilization and clarifying the system’s performance. Furthermore, this study examines the influence of diverse operational and geometric parameters, providing insights into enhancing production rates. Hence, an increase in feed layer thickness enhances freshwater production by 7%. Due to the intensification of solar irradiance, freshwater production increased ninefold, and specific energy consumption decreased by 134 kW hr m−3. This research underscores the potential of NESMD for sustainable desalination, providing a validated model that lays the groundwork for future advancements in membrane distillation technology.

Physically Based Green–Ampt Model in Polar Coordinate System Predicting Soil Water Transport in Moistube Irrigation: Comparison of Physical, Semi-Physical-Empirical, and Numerical Models Under Varying Working Pressure Heads

Predicting soil–water dynamics in Moistube irrigation (ΜΤΙ) favours understanding ΜΤΙ functioning mechanisms and technical parameter design. This study proposed a physically based infiltration (PH) model extending the Green–Ampt (GA) model to a two-dimensional polar coordinate system. We treated Moistube as a clay and considered the infiltration from internal Moistube to surrounding soils. The performances of the PH model, together with a semi-physical–empirical (PH–EM) model and a numerical simulation (NUM) model, were evaluated based on regulated working pressure head (WPH) experiments. A HYDRUS 2D model was used based on experimental design to reproduce the soil–water dynamics by assigning Moistube and soil two sets of hydraulic parameters. WPH increase or decrease treatments were applied to Moistube. The Moistube discharge rate, infiltration volume, and wetting front (WF) advance were analyzed and predicted by three models. The results showed that cumulative infiltration, Moistube discharge, and effective saturation around Moistube were enhanced or abated under WPH increase or decrease, with WF accelerating or decelerating. The modelled effective saturation varied between 0.45 and 0.70, providing suitable moist conditions for crops. Percentage of bias (PBIAS) and mean absolute percentage relative error (MAPRE) were employed to evaluate model performances. Three models well-predicted infiltration characteristics and WF advance but differed in accuracy. The PH model overestimated and underestimated the Moistube discharge rate in early and later phases. The prediction accuracy in WF varied across infiltration phases and WPH modes. The PH–EM model yielded accurate results due to its empirical attribute. The NUM model produced novel phenomena of infiltration characteristics at WPH adjustment points, i.e., the discharge rate exponentially decreased over time after the WPH increased but presented restraining followed by rebounding trends after the WPH decreased. The NUM model strongly depended on the selection of the Moistube hydraulic parameters. Extending the GA model to a two-dimensional polar coordinate system by treating Moistube as a clay was practicable in modelling soil water dynamics, thereby contributing to designing and optimizing MTI technical indexes.

Assessment of wind energy potential along the Egyptian Mediterranean Coast

BackgroundCurrently, there is no wind park operating along the Egyptian Mediterranean Coast. Therefore, this study aims to find suitable locations for such projects. Wind data from five coastal meteorological stations were used. These are Marsa Matruh (MM), Ras El-Tin (RE), Abu Qir (AQ), Port Said (PS), and Arish (Ar), in that order from west to east. The wind regime dataset, comprising velocity and direction measurements at a 10-m elevation, was collected from January 2007 to December 2022 (16 years), with a complete record of all data points. The Weibull distribution function, along with its different parameters, was used to characterize wind energy along the Egyptian Mediterranean Coast. The coefficient of determination (R2), root mean squared error (RMSE), and relative root mean squared error (RRMSE) for the Weibull parameters, along with the relative percentage errors (RPE) for the wind power density were calculated to assess the concordance between outcomes derived from observed data and those predicted by the Weibull function.ResultsResults revealed that the dominant wind direction along the Egyptian Mediterranean Coast was the NNW to N wind, except at Ar where the dominant wind was S. The wind velocity range of 4–6 m/s dominated RE, AQ, and PS. At MM and Ar, this was reduced to 2–4 m/s. The analysis of wind power density outlined significant insights into the potential for wind energy generation in the region. The overall analysis showed that AQ and PS were potentially the most suitable locations for wind energy projects. However, the high variability at the AQ site required robust system designs to manage the fluctuating wind conditions. PS might be more suitable for projects prioritizing stability and consistency over maximum energy output. Although Arish, characterized by its lower wind power density, may be less conducive for large-scale wind energy projects, it could still be viable for smaller installations or when integrated with other renewable energy sources.ConclusionsThe different statistical indices reflected good model fitting, displaying the reliability of the Weibull distribution as a tool for preliminary wind resource assessment along the Egyptian Mediterranean Coast and facilitating accurate predictions of wind power availability.

A novel combined hardening rule for the brass cartridge case using crimping simulation and optimization

A cartridge shell case, a critical component in the defence industry, undergoes a crimping process to form one end for the insertion of a bullet. The shell case is primarily composed of brass. The crimping process is simulated using a novel plasticity model that combines a hardening rule with the Hill48 yield criterion and the associated flow rule. This study focuses primarily on the hardening rule, where a bilinear isotropic hardening (BISO) and Chaboche's nonlinear kinematic hardening (CHAB) rules are combined to develop a novel hardening model. The raw parameters of the models are calculated by regression performed on the data from monotonic tensile test and low-cycle fatigue (LCF) test. A multi-objective genetic algorithm is used to calibrate the raw parameters by the inverse analysis. The performance of the models is evaluated on the diameter of the shell considering the work-hardening. The novelty of this study lies in the acquisition of calibrated hardening rule parameters for the crimping process, which involves a multi-axial deformation pattern, using only data from uniaxial tensile and LCF tests, which are simpler to conduct. The force-moment requirements, springback, and material flow path are also calculated. The results provide a valuable insight into the combined hardening model, its parameters, and their sensitivity for the crimping process. The calibration process results in significant improvements in material properties, particularly yield strength ( YS) and tangent modulus ( TM). For the crimping, YS is observed to increase by 37.82% and TM is observed to decrease by 13.84% while parameters [Formula: see text] increase approximately 9% where Cm is hardening modulus, and [Formula: see text] is decrease rate. The model achives an absolute percent relative error (APE) of 0.18%.

Machine learning-based axial compressive capacity estimation of cold-formed steel build-up sections

To consider the highly nonlinear and complex buckling behaviour of various sections of cold-formed steel (CFS) built-up columns, experimental and finite element (FE) methods are commonly used for calculating their axial compressive capacity, although these methods are time-consuming and costly. This paper proposes machine learning (ML) methods to overcome the issues of traditional methods for predicting the maximum axial load capacity (MALC) of CFS built-up columns. A total of 3839 samples from more than 33 different types of sections were collected from 43 published papers, including 817 experimental data and 3,022 FE simulation data. A total of 15 characteristic parameters, such as the second moment of area, the radius of gyration, and the polar second moment of area, are considered when nine different ML models are trained. The robustness of these models was compared via the Monte Carlo simulation method, and the predicted results were compared with the calculated results from the current codes. The results show that the extreme gradient boosting (XGB) model has higher accuracy and smaller prediction errors in estimating the MALC. The proportion of data with a relative percentage error less than 10% in the prediction results of the AISI-DSM codes is 45.31%, whereas the XGB model achieves a proportion of 87.57%, and the results calculated from current codes tend to be more conservative, with a large deviation, which needs to be further considered.

Investigate the Relationship Between the type of Non-Linear Equation and the Initial Estimation on the Results of the Roots of the Non-Linear Equation using Open Numerical Methods with an Internal Stop Condition

This research focuses on highlighting the impact of initial guessing on finding the roots of a nonlinear equation using open numerical methods with an internal stopping condition. Three examples were taken to understand the relationship between the initial guess and the results of the roots of the nonlinear equation using open numerical methods with an internal stopping condition. Precise values of the roots were calculated using the direct mathematical method. Subsequently, the roots of the nonlinear equation were calculated using open numerical methods, which are: Newton-Raphson method, secant method, and fixed-point method. The relative percentage error for each method was calculated for the three examples, in order to study and compare the results. The findings indicated that the type of equation directly controls the possibility or impossibility of finding the initial guess that meets the convergence condition for the three methods, and thus, it controls the results of the roots of the nonlinear equation using the internal stopping condition. Keywords: Fixed Point Method, Secant Method, Newton Raphson Method.

Concordance Between Actual and Estimated Blood Loss: A Bleeding Calculation Simulation Exercise

Introduction: Historically, visual estimation of blood loss during and after delivery has been the primary method for determining obstetric blood loss. This method is quick, easy, and cost-free, and it allows for the prompt identification of suspected postpartum hemorrhage. However, the majority of clinical studies concur that this method is imprecise and may underestimate the extent of bleeding produced during vaginal delivery. Objectives: The objective of this study was to compare the concordance between the estimated blood loss observed visually by physicians and residents of Gynecology and Obstetrics and the actual bleeding observed in a simulation exercise, with the aim of calculating the bleeding. terial and methods: A quasi-experimental study was conducted through the use of a simulation model at the General Hospital of Zone 20 of the IMSS in Puebla. A visual estimation was conducted between physicians and residents using surgical material (compresses and gauze) with a certain amount of blood. The evaluation of the estimate was carried out in three stations with different bleeding levels. The estimation of total bleeding was counted in milliliters using the visual method. A comparison of means was conducted using the ANOVA statistical test to evaluate the bleeding calculation. The relative error was calculated in order to determine the extent of agreement between the estimated and actual blood values for each station. Results: A total of 94 physicians, including residents in training in the specialty of Gynecology and Obstetrics, enrolled in the study. The discrepancy between the actual and estimated blood loss in a simulation exercise among affiliated obstetrician-gynecologists was 65.3%, while in the group of resident physicians it was 145.6%. It was determined that there was an 84% overestimation of bleeding. The relative error percentage for "Scenario A" was 141%, "Scenario C" yielded a result of 85%, and "Scenario B" produced a result of 74%. Conclusion: The agreement between the actual and estimated blood loss performed by obstetrician-gynecologists assigned to a bleeding calculation simulation exercise is slightly superior to that obtained by resident physicians. The lowest percentages of relative error were obtained by fourth- and third-year residents.

Modeling drilling fluid density at high-pressure high-temperature conditions using advanced machine-learning techniques

The management of oil and gas wells under high-pressure high-temperature (HPHT) conditions is a significant challenge in the oil industry, leading to increased operational and maintenance costs. One method for calculating the density of drilling fluids involves the use of sensors placed at the bottom of the well, which measure the fluid density in real time. Despite their accuracy, these sensors are costly and perform suboptimal under HPHT conditions. In the present study, seven robust machine-learning models, namely generalized regression neural networks (GRNN), wavelet neural networks (WNN), and cascade forward neural networks (CFNN) combined with two different training algorithms containing Levenberg-Marquardt and Bayesian regression (CFNN-LM and CFNN-BR), respectively, and support vector regression (SVR) models combined with three optimization algorithms containing farmland fertility algorithm (FFA) grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO) were utilized to predict mud density at HPHT conditions. Moreover, mathematical models were developed using a group method of data handling algorithm (GMDH) to predict this parameter. WNN, SVR model combined with (PSO, GOA, FFA), CFNN, and GRNN algorithms have better adaptability than other artificial intelligence models. These models can improve performance by changing the data and different situations. Also, these models can better resistance against noisy data, which means that they can show good performance compared to uneven and discontinuous data. To this aim, using 986 experimental data of drilling fluid at HPHT, including 117 water-base fluid data, 440 colloidal gas aphron fluid data, 219 oil-base fluid data, and 210 synthetic fluid data the drilling fluid density was modeled. Input parameters including temperature, pressure, initial mud density, and volume fraction of solid content were used for modeling. The results showed that the volume fraction of the solid content as an input variable improves the accuracy of intelligent models in this work. Based on average absolute percent relative error (AAPRE) the best outcomes for predicting the mud density without the input parameter of solid fraction were observed by the PSO-SVR model with an AAPRE of 0.5569%. Following, the GOA-SVR, WNN, and FFA-SVR delivered the next best results with AAPRE of 0.5860%, 0.5786%, and 0.6148%, respectively. Moreover, with the input parameter of solid fraction, the WNN model demonstrated the highest accuracy among all models, achieving an AAPRE of 0.2475%. Also, the correlation developed using the GMDH method based on input parameters of initial mud density, temperature, and pressure yielded satisfactory outcomes and can provide precise and swift estimates. Eventually, Sensitivity analysis revealed that initial mud density had the highest impact on mud density. Lastly, the leverage analysis indicates that most data points are trustworthy based on experimentation, with only a few falling outside the PSO-SVR and WNN models' scope.

Design and testing of a low-cost mobile platform for contactless building surveying

After the completion of building construction, evaluating the accuracy of the distance between the lower edge of electrical switches and the one-meter line of the building is a crucial metric for assessing construction quality. Currently, the traditional measurement method basically uses manual measurement and is inefficient. To overcome this problem, we present a low-cost, non-contact mobile platform for building measurement, based on a 2D laser rangefinder and RGB camera. The system mainly consists of six key components: a mobile chassis, dual-axis gimbal, laser rangefinder, RGB camera, industrial control computer, and an automatic one-meter line calibration device, which can achieve high-precision distance measurement. Firstly, a sensorless control method for brushless DC motors (BLDCM) based on the Extended Kalman Filter (EKF) algorithm is proposed, which effectively improves the accuracy and robustness of rotor position estimation. Secondly, an automatic calibration device is developed to ensure the laser line consistently remains at a height of one meter from the ground. Finally, by combining image processing technology and 2D laser range data, the distance between the lower edge of the electrical switch and the building’s one-meter line is precisely measured using a triangulation algorithm. Experimental results show that the system’s measurements, compared to manual measurements, have a relative error percentage of 0.57 %, while the automatic calibration device has a maximum error of only 0.2 centimeter. Therefore, the system shows great potential for widespread application in the field of building measurement.

Dynamic Viscosity of Polyethylene Glycol (PEG): Data Assessment, Sensitivity Analysis and Robust Modeling via Artificial Intelligence Methods

ABSTRACTAccurate determination of physio‐chemical properties of polyethylene glycol as a green non‐toxic solvent is vital for any chemical and energy process involving polyethylene glycol. Machine learning algorithms are known as robust methods for prediction purposes. This study put forward least squares support vector machine scheme optimized with either particle swarm optimization, genetic algorithm and coupled simulated annealing optimization methods to precisely carry out the prediction task of polyethylene glycol viscosity in terms of its influencing parameters of pressure, temperature and polyethylene glycol molecular weight using a dataset containing experimental pertinent values. An outlier detection algorithm is made use of to confirm the data reliability for model development. In addition, sensitivity study is done to ascertain the relative impacts of each input factor on polyethylene glycol viscosity. The results showed that least squares support vector machine optimized with coupled simulated annealing is the most accurate model for the PEG viscosity prediction task with coefficient of determination of 0.997, average absolute relative error percent of 0.653 and mean square error of 8.005 for all datapoints. In addition, it was found that unlike temperature, we observe an indirect correlation of pressure and polyethylene glycol molecular weight with the pertinent viscosity data. The robustness of the developed least squares support vector machine optimized with coupled simulated annealing is further approved as it outperforms a renowned correlation that is often used for general viscosity prediction.

EXPERIMENTAL AND OPTIMIZATION-BASED ANALYSIS TO MAXIMIZE MECHANICAL-RELATED ATTRIBUTES OF FRICTION STIR WELDED CDA101 CU ALLOY JOINTS

This paper deals with the identification of optimized process parameters for FSW of 6[Formula: see text]mm thick CDA 101 Cu alloy plates. Three distinctive parameters, the tool’s traverse speed, rotational speed, axial load, have been considered as self-reliant variables for formulating a numerical model based on nonlinear regression-type approach, with the objective of anticipating mechanical attributes of joints. From the surface- and contour-type plots, it was revealed that the axial load in combination with rotational speed has played a dominant role in influencing the tensile and yield strength of the CDA101 Cu alloy joints. At the same time, the traverse speed of the tool has played a dominant role in enhancing the ductility of the CDA101 joints through the generation of ample volumes of frictional heat, which in turn have facilitated both coarsening of grain structures and softening of materials in an ideal manner. Employing tool rotational speed, traverse speed and axial load in the ranges of 1750–1900[Formula: see text]rpm, 23–27[Formula: see text]mm/min and 5.75–6.5[Formula: see text]kN, respectively, was found to enhance the tensile strength of the CDA101 Cu alloy joints. FSW process parameters attained through the numerical meta-model of RSM have led to the attainment of the highest value of tensile strength of 209.5[Formula: see text]MPa together with a 141.4[Formula: see text]MPa yield strength with very minimal fitting errors of 0.28% and 0.52%, respectively. A collection of additional experimental data has been employed to verify the formulated empirical-based formula and additionally, an entirely different optimization technique namely PSO (i.e. particle swarm optimization) was implemented for comparison and validation purposes. The formulated meta-model of RSM generated the largest value of % of elongation of 14.23%, though the relative percent error was around 1.988%, which was slightly larger than that of the value (i.e. 14.11%) generated by PSO, with a relative error of 0.58%.

Analytical Method for Determining the Viscosity Index of Engine Lubricating Oils

This paper proposes a simple analytical method for determining the viscosity index (VI) that effectively aligns with results obtained from the applicable standards. This method simply needs the kinematic viscosity of the tested oil at 40 and 100 °C as input parameters and does not need to use tables that are an integral part of the mentioned standards. This work presents a method and evaluates the accuracy of determining auxiliary parameters in the form of kinematic viscosity values at 40 °C for two hypothetical oils. These oils have a kinematic viscosity at 100 °C equal to that of the oil under testing and have VI = 0 and 100. The relative fitting error percentage and the coefficient of determination are found for the specified auxiliary indicators. The method is validated using data obtained from previous studies in the form of the kinematic viscosity of lubricating oil–diesel mixtures. The mixtures of viscosity grades SAE 30 and SAE 40 lubricating oil with diesel oil at concentrations of 0, 1, 2, 5, 10, 20, and 50% w/w are tested. The viscosity index for each mixture is determined using a standard-based manual calculation using the Anton Paar viscosity index calculator and the proposed method. The results obtained from the proposed analytical method are compared with those from two other methods. The maximum percentage relative fitting error (δmax ≈ 1%) and the coefficient of determination (R2 &gt; 0.999) are determined. The obtained results demonstrate a very good fit and, thus, confirm the usefulness of the proposed approach.

Enhancing hydrological predictions: optimised decision tree modelling for improved monthly inflow forecasting

ABSTRACT The utilisation of modelling tools in hydrology has been effective in predicting future floods by analysing historical rainfall and inflow data, due to the association between climate change and flood frequency. This study utilised a historical dataset of monthly inflow and rainfall for the Terengganu River in Malaysia, and it is renowned for its hydrological patterns that exhibit a high level of unpredictability. The evaluation of the predictive precision and effectiveness of the Optimised Decision Tree ODT model, along with the RF and GBT models, in this study involved analysing several indicators. These indicators included the correlation coefficient, mean absolute error, percentage of relative error, root mean square error, Nash-Sutcliffe efficiency, and accuracy rate. The research results indicated that the ODT and RF models performed better than the GBT model in predicting monthly inflows. The ODT model, as well as the RF and GBT models, showed validation results with average accuracies of 94%, 91%, and 92%, respectively. The R² values were 90.2%, 84.8%, and 96.0%, respectively, and the NES values ranged from 0.92 to 0.94. The results of this research have greater implications, extending beyond the forecasting of monthly inflow rates to encompass other hydro-meteorological variables that depend exclusively on historical input data.

An Accurate Critical Total Drawdown Prediction Model for Sand Production: Adaptive Neuro-fuzzy Inference System (ANFIS) Technique

AbstractSand production causes many problems in the petroleum industry. The sand production is predicted to control it in the early stages. Therefore, accurate prediction of sand production has been considered substantial in achieving successful sand control. Critical total drawdown (CTD) can indicate the sand production. The main drawback of the previous studies in predicting CTD is their lack of accuracy. Thus, this study aims to develop an accurate CTD estimation prediction model employing a trend analysis and adaptive neuro-fuzzy inference system (ANFIS). The method is chosen because of its higher performance; the model is built based on 23 published datasets from the Adriatic Sea. The developed ANFIS model is evaluated using various methods, namely, trend analyses. Trend analyses are conducted to show the effects of the features on the CTD to present the physical behavior. The model’s performance was also evaluated using statistical error analyses. In addition, the ANFIS and previously published models were assessed. The trend analyses show the correct relationship between all features and the CTD. In addition, the trend analyses for the previous models are discussed. The results show that the proposed ANFIS method outperforms published methods with an R of 0.9984 and an absolute average percentage relative error (AAPRE) of 4.293%.