Maximum Percentage Error Research Articles

Physical and CFD Simulated Models to Analyze the Contaminant Transport through Porous Media under Hydraulic Structures

In this paper the efforts were made in the laboratory to construct a physical model and a simulated model with the help of computational fluid dynamic (CFD) techniques to provide the solutions of contaminant transport within the saturated porous media under a hydraulic structure. Brinkman equations with Forchheimer correction and species flow through porous media were considered together to describe the problem. Three categories factors were considered to be analyzed in this study. The first one is the hydraulic factors represented in the upstream water head (H). While, the second factor is the geometry of the hydraulic structures represented in the length of the base and the length of the sheet pile. The physical properties and chemical properties were the third investigated factors, in which the physical properties of the porous media include porosity and intrinsic permeability, while the chemical properties represented by the rate of generation and retardation caused by the adsorption. The physical properties have a slight effect on the concentrations because of the low flow velocity through the porous media. On the other hand, a considerable decrement on the concentrations were noticed when the rate of generation and retardation caused by adsorption was increased. Also, it was observed that the diffusion coefficient has no dramatic effect on the concentrations and contaminant moving. The results of the CFD simulated model and that of the physical model were verified with two cases for pressure head and one case for the contaminates transport. For the first case of pressure head, the maximum percentage error at five selected points was about 15% at worst point with average error of 10%. While, for the case two, the maximum percentage error is about 9% at worst one with an average error of 8%. For the simulation of the contaminates transport, reliable statistical indexes error indicted that the CFD simulated model gives a good agreement with all experimental results.

Alternate formula for calculating the Darcy Coefficient in turbulent flow in pipes

The purpose of this research was to determine an alternative formula for calculating the Darcy coefficient in turbulent flow in pipes. The proposed alternate formula is an explicit formula that should be used to replace the Colebrook-White formula for calculating the Darcy coefficient in turbulent flow in pipes since it has higher precision than the explicit formulas that are currently in use. In this investigation, the alternate formula was compared with two explicit formulas commonly used in pipe design, the Swamee-Jain and Pavlov formulas. To determining which formula is better, all of them were compared with the Colebrook-White formula. For this, the average percentage and maximum percentage errors of the Darcy coefficient values calculated with each of the explicit formulas were determined, with the values obtained with the Colebrook - White formula. It was determined that the maximum errors in the calculation of the Darcy coefficient concerning the Colebrook-White formula were: 3,104% for the Swamee-Jain formula, 7,973% for the Pavlov formula and 2,740% for the alternate formula.

A hybrid intelligent model for reservoir production and associated dynamic risks

This research presents a hybrid model to predict oil production and to provide a dynamic risk profile of the production system. The introduced predictive approach combines a multilayer perceptron (MLP)-artificial neural network (ANN) model with a hybrid connectionist strategy (BN-DBN), which comprises a Bayesian network (BN) model and a dynamic Bayesian network (DBN) model. The proposed hybrid methodology (MLP-BN-DBN) is designed to find the correlations between the input and output data to forecast the desired oil production rate. The MLP model captures the variabilities in the fluid and rock properties, model's uncertainties, and the effects of pressure maintenance on the production process. The BN model uses the 3σ mathematical rule to promptly signal the arrival of any production rate change and captures the pressure maintenance impact using the early warning source indexes. The DBN model provides a dynamic risk profile of the production system using the observed evidence and reservoir production hyperbolic decline concept.The proposed methodology offers the field operators better opportunity to obtain real time estimate of the likelihood of any impending production loss at any time during production operations. The model exhibits a high capability of oil production prediction with the minimum, average, and maximum percentage errors of 0.01%, 6.57%, and 15.28%, respectively. The developed hybrid model serves as a risk monitoring system. The model is cost-effective and eases the computational burden of history matching processes and bridges the gaps in the existing systems for oilfield development dynamic risk forecast and production predictions. Hence, the proposed methodology offers a multipurpose tool for dynamic risk assessment and for proper reservoir production optimization.

A three-dimensional heat transfer modelling and experimental study on friction stir welding of dissimilar steels

In this research, numerical as well as experimental analysis on friction stir welding (FSW) of dissimilar steels, i.e. DH36 steel and AISI 1008 steel, was performed. A 3D heat transfer FE model based on Abaqus/CAE using Fortran code via DFLUX subroutine was developed to investigate the effect of FSW parameters (i.e. rotational speed and traverse speed) on temperature distribution during the welding. It was observed that decreasing the traverse speed, increasing the rotational speed and shoulder diameter enhanced the peak temperature due to higher heat input. Based on the experimentally obtained sound quality welding parameters, the FE model was successfully validated with a maximum percentage error of 5.57% for peak temperature. Experimental results revealed that the higher heat generation reduced the grain size by increasing the rotational speed and decreasing the traverse speed. The inhomogeneous hardness distribution was observed across the weld cross section due to grain size variation. The higher grain refinement at a rotational speed of 600 rpm with a traverse speed of 70 mm/min produced the maximum value of the hardness and impact toughness. The tensile weld samples were fractured in the base metal zone (i.e. AISI 1008 steel) and experienced the tensile strength within the range of the AISI 1008 steel. The microstructure in the SZ exhibited the Widmanstatten ferrite in AISI 1008 steel and acicular-shaped bainite ferrite in DH36 steel. EDS analysis of the fractured surface of impact specimens confirmed the embedment of tungsten carbide particles in the weld zone due to the tool wear.

Thermal conductivity of ethylene glycol-based nanofluid containing SiO2 nanoadditives: experimental data and modeling through curve fitting

The work focuses on an experimental evaluation of the changes of thermal conductivity (TC) of ethylene glycol-based nanofluid containing SiO2 nanoadditives against volume concentration of nanoadditives (φ) and temperature. The experiments are carried out in the φ range of 0–2.5% and temperature range of 30–55 °C. The dynamic light scattering method is used to obtain the particle size distribution, while the transmission electron microscopy technique is utilized to visualize agglomerated particles in the prepared nanofluid samples. The outcomes revealed that the TC of the nanofluid grows by boosting both the φ and temperature. The percentage enhancement varied in the range of 0.72–26.66%. Furthermore, the curve-fitting method was utilized to model the TC of the nanofluid using experimental data. It was found that the developed model is able to properly forecast the TC of nanofluid with the maximum percentage error of 1.75%.

Comparative Analysis of Young’s Modulus Predictions for Lightweight Aggregate Concrete

Many experimental and numerical works are attempting to predict the elastic properties of Lightweight Aggregate Concrete (LWAC). The purpose of this paper is to estimate the Young’s modulus of Lightweight Aggregate Concrete utilizing two-phase composite models. However, results of experimental data published in the literature were used as a platform, upon which, two-phase composite models had been utilized. The outcomes of this comparative analysis show that neither of the two-phase analytical models could be directly utilized for predicting Young’s modulus of LWAC. The Hashin-Hansen composite model provides a good prediction of experimental Young’s modulus of all LWAC tested with a maximum error percentage equal to 16.94%. This model provides an upper bound whereas the Counto2 model provides the lower bound of experimental Young’s modulus of LWAC.

Fuzzy logic applications for data acquisition systems of practical measurement

In laboratory works, the error in measurement, reading the measurring devices, similarity of experimental data and lack of understanding of practicum materials are often found. These will lead to the inacurracy and invalid in data obtanined. As an alternative solution, application of fuzzy logic to the data acquisition system using a web server can used. This research focuses on the design of data acquisition systems with the target of reducing the error rate in measuring experimental data on the laboratory. Data measurement on laboratory practice module is done by taking the analog data resulted from the measurement. Furthermore, the data are converted into digital data via arduino and stored on the server. To get valid data, the server will process the data by using fuzzy logic method. The valid data are integrated into a web server so that it can be accessed as needed. The results showed that the data acquisition system based on fuzzy logic is able to provide recommendation of measurement result on the lab works based on the degree value of membership and truth value. Fuzzy logic will select the measured data with a maximum error percentage of 5% and select the measurement result which has minimum error rate.

Experimental and Numerical Investigation of Shot-Peening and Solidification Effects on the Endurance Limit of Composite Material

This work studied experimentally and numerically the effect of the shot-peening on the properties of fatigue of composite materials during the solidification period of composite material. Different two type of composite material have been investigated, woven (matt) reinforcement E-fiber glass with matrix epoxy resin, the second type made from random reinforcement E-fiber glass with matrix epoxy resin. Different shot peening times and also different times of solidification have been studied in this work. The experimental work includes calculating the mechanical properties (modulus of elasticity and the ultimate tensile stress) using the tensile test, also calculating the fatigue stress using the fatigue test. The numerical verification was done using ANSIS\\ Workbench.15 to obtain the S-N curves of the composite material. The result shows, the best enhancement in fatigue strength had been 95% and 33.78% for matt reinforcement material and random reinforcement material respectively, both types have the best improvement at 6 minutes shot peening time (SPT) and on two days of solidification. The numerical verification shows an agreement with the experimental results of the S-N curves, in which the maximum percentage error between the experimental and numerical results does not exceed 12 % and 15.12% for the matt reinforced material and for the random reinforced material respectively.

Optical Phase Detection Method for Measurements and Calibration of Pockels Cell-Based Sensors

Nowadays, the most successful optical technique developed for measuring high voltages in 50-/60-Hz electric power lines utilizes the bulk lithium niobate Pockels cell, constituting the so-called optical voltage sensor (OVS). However, unlike the area of vibrometry, where there are ISO standards based on reliable and simple interferometric methods for calibrating the sensors, there is still no standardized procedure for measuring Vπ (half-wave voltage) of Pockels cells or for calibrating OVSs. As the Pockels cell-based OVSs can be considered as a polarimetric interferometer, and inspired by the ISO 16063-41 standard (for the calibration of vibration and shock transducers), specifically the technique called signal coincidence method (SCM), this work presents a new digital and real-time method for optical phase detection tailored for the measurement of Vπ in OVSs. Measurements were made in order to demonstrate the effectiveness of the new technique by applying a voltage signal to the OVS, composed by the superposition of a 60-Hz sinusoidal voltage, with amplitude equal to the reference value of Vπ (4.068 kV in this case) and a dc voltage high enough to provide a 90° bias static phase shift, as specified by SCM, proving that the method can recover the value of Vπ in accordance with the estimated value. However, it is well known that the adjustment of the bias phase in a polarimetric interferometer can undergo undesired variations from time to time, due to drifts in ambient temperature and other external disturbances, taking the OVS out of its optimal operating point and thus not attending the ISO standard. As an advantage, experiments have shown that the new method is tolerant to variations in the 90° bias static phase (ranging from 60° to 120°), as well as to variations in the amplitude of the voltage applied to the OVS, varying ±25% in relation to the 4.068-kV reference voltage. The Vπ values were accurately detected, with a maximum percentage error of 0.2% and, therefore, satisfying the specification of the ISO 16063 standard.

Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models.

The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

Computational Forecasting Methodology for Acute Respiratory Infectious Disease Dynamics.

The study of infectious disease behavior has been a scientific concern for many years as early identification of outbreaks provides great advantages including timely implementation of public health measures to limit the spread of an epidemic. We propose a methodology that merges the predictions of (i) a computational model with machine learning, (ii) a projection model, and (iii) a proposed smoothed endemic channel calculation. The predictions are made on weekly acute respiratory infection (ARI) data obtained from epidemiological reports in Mexico, along with the usage of key terms in the Google search engine. The results obtained with this methodology were compared with state-of-the-art techniques resulting in reduced root mean squared percentage error (RMPSE) and maximum absolute percent error (MAPE) metrics, achieving a MAPE of 21.7%. This methodology could be extended to detect and raise alerts on possible outbreaks on ARI as well as for other seasonal infectious diseases.

A simple approach for evaluating the stress concentration factor of a corroded surface using the fast Fourier transform

In this study, a numerical approach for evaluating the SCF of corroded steel plates with irregular surface was investigated. Complex surfaces were dissembled into sinusoidal waves by FFT and the SCF was calculated using the shape parameters of the derived waves. 3-dimensional surface data was divided into 2-dimensional data in direction of load and the width of pitting corrosion was assumed to be the same in all directions. Compared to results of FEA, the SCF has a percentage error of 0.8% and 10 largest SCFs were detected at the same location with the maximum percentage error of 2.2%.

Newly Developed Correlations to Predict the Rheological Parameters of High-Bentonite Drilling Fluid Using Neural Networks.

High-bentonite mud (HBM) is a water-based drilling fluid characterized by its remarkable improvement in cutting removal and hole cleaning efficiency. Periodic monitoring of the rheological properties of HBM is mandatory for optimizing the drilling operation. The objective of this study is to develop new sets of correlations using artificial neural network (ANN) to predict the rheological parameters of HBM while drilling using the frequent measurements, every 15 to 20 min, of mud density (MD) and Marsh funnel viscosity (FV). The ANN models were developed using 200 field data points. The dataset was divided into 70:30 ratios for training and testing the ANN models respectively. The optimized ANN models showed a significant match between the predicted and the measured rheological properties with a high correlation coefficient (R) higher than 0.90 and a maximum average absolute percentage error (AAPE) of 6%. New empirical correlations were extracted from the ANN models to estimate plastic viscosity (PV), yield point (YP), and apparent viscosity (AV) directly without running the models for easier and practical application. The results obtained from AV empirical correlation outperformed the previously published correlations in terms of R and AAPE.

Oxidative roasting experimentation and optimum predictive model development for copper and iron recovery from a copper smelter dust

The success of a treatment option depends on selection of suitable process variables at which response attains optimum. Hence, this study aimed at investigating the effects of time, temperature and time-temperature on changes in mineralogy (copper sulphate (CuSO4), copper oxide (CuO), ferrous oxide (Fe2O3) and ferric oxide (Fe3O4)) of a copper smelter dust (CSD) during oxidative roasting. This aim was achieved by 2-way analysis of variance and mathematical model development of data obtained. Results showed time as most influential factor with an optimum time of 2 ​hrs for the production of high yield roast products. Furthermore, it was observed that a maximum of 23.31% proportion of CuSO4 was recovered from an input with 7.86% proportion of CuSO4 at a temperature of 680 ​°C within a time frame of 2 ​hrs and for a furnace door opening of 25 ​mm. Similarly, a maximum of 18.37% of CuO was recovered from an input with 23.91% CuO at a temperature of 800 ​°C, for a duration of 3 ​hrs and 25 ​mm opening of the furnace door. Whereas, an optimum 0.44 ratio value of Fe2O3:Fe3O4 was recovered at a temperature 680 ​°C, for a duration of 2 ​hrs and 25 ​mm furnace door opening. These maximum outputs and associated experimental conditions depict optimum operating conditions of experiment. Furthermore, predicted output proportions obtained from developed constraint interpolant models were well aligned with experimental outputs. A maximum percentage error of 0.07% was recorded in the predictive output for both water and acid soluble mineral fractions.

Emperical Modeling of Growth Parameters in Cellulosimicrobium cellulans during Heavy Metal Tolerance

In the present work we have studied the growth kinetics of Cellulosimicrobium cellulans under different metal stress. The strain used was Gram positive and non-pathogenic. Metal studied were Fe, Zn, Pb, Cu, Cd, and Ni. Growth curve of Cellulosimicrobium cellulans was studied under different concentrations of these metals. In the second part of our investigation we tried to established growth equation which can define the growth curve of Cellulosimicrobium cellulans. The three parameters μm, λ, Xm majorly contribute to growth equations. The changes in these parameters affect the location and duration of exponential or log phase t. Thus, we have chosen these factors primarily to model our system. In each case we have observed little deviation from the predicted equation at the maximum error percentage below 10% (α< = 10) R2<0.8. Next we have tried to model the cumulative effect of these metals simultaneously but our model has failed to reach an appreciable correlation with the experimental data set. In this case we may assume that more factors like mole fraction or partial molar volume of metal solution may have contributed to the growth profile.

Optimized Erosion Prediction with MAGA Algorithm Based on BP Neural Network for Submerged Low-Pressure Water Jet

In order to accurately predict the erosion effect of underwater cleaning with an angle nozzle under different working conditions, this paper uses refractory bricks to simulate marine fouling as the erosion target, and studies the optimized erosion prediction model by erosion test based on the submerged low-pressure water jet. The erosion test is conducted by orthogonal experimental design, and experimental data are used for the prediction model. By combining with statistical range and variance analysis methods, the jet pressure, impact time and jet angle are determined as three inputs of the prediction model, and erosion depth is the output index of the prediction model. A virtual data generation method is used to increase the amount of input data for the prediction model. This paper also proposes a Mind-evolved Advanced Genetic Algorithm (MAGA), which has a reliable optimization effect in the verification of four stand test functions. Then, the improved back-propagating (BP) neural network prediction models are established by respectively using Genetic Algorithm (GA) and MAGA optimization algorithms to optimize the initial thresholds and weights of the BP neural network. Compared to the prediction results of the BP and GA-BP models, the R2 of the MAGA-BP model is the highest, reaching 0.9954; the total error is reduced by 47.31% and 35.01%; the root mean square error decreases by 51.05% and 31.80%; and the maximum absolute percentage error decreases by 65.79% and 64.01%, respectively. The average prediction accuracy of the MAGA-BP model is controlled within 3%, which has been significantly improved. The results show that the prediction accuracy of the MAGA-BP prediction model is higher and more reliable, and the MAGA algorithm has a good optimization effect. This optimized erosion prediction method is feasible.

An ultrasonic-based multiphase flow composition meter

Abstract Accurate multiphase flow metering using non radio-active sensors is highly required in oil fields. This paper suggests a new concept of multiphase flow metering of flow composition with experimental validation in a large lab-scale multiphase flow loop. The device uses an online flow conditioner which comprises a swirl cage to accelerate the liquid-gas centrifugal forces and generate an annular flow with the liquid phase as outer phase. This recommends the usage of liquid-type clamp-on ultrasonic array probe downstream the separator to measure simultaneously and in real-time both the gas-void fraction (GVF) and the water-cut within the liquid phase. The ultrasonic array features high resonance frequency (i.e. 5 MHz in this paper) and small pitch distance (i.e. 0.5 mm pitch between sensors elements), which helps improving the measurement accuracy. Extensive experiments were conducted using an in-house built multiphase flow loop. The corresponding results indicate that for water-cut ranging from 0 to 95% and GVF ranging from 10 to 60% the meter achieves a maximum percentage error of 2% and 3.5% for water-cut and GVF measurement respectively. Exploring further characteristics of ultrasonic waves such as the amplitude of the received signal can help improving the accuracy even further for higher GVF, which however can’t reach 100% since the probe requires a liquid film to operate.

Design and characterization of a buckling-resistant perforated MEMS membrane under residual stress

Micro Electro Mechanical Systems (MEMS) membranes are utilized as both transmitter and receiver in acoustic and ultrasound applications. Their operating frequency ranges are determined by their resonance frequencies. Thus, the resonance frequency estimation is one of the most critical parts of the membrane design. In this study, two perforated circular MEMS membranes are designed, microfabricated and characterized to get proper operation under residual stress since this stress might cause buckling of the membrane. Finite element methods (FEM) are applied and optical measurements are taken to extract the resonance frequencies. For the fundamental mode, the FEM results deviate 5.5% and 6.7% from the experimental work. 33.4% stress relaxation is achieved for the proposed perforated membranes. Furthermore, eight different circular membranes are simulated and compared with the analytical solutions. The minimum and maximum average percentage errors are acquired as 0.5% and 3.3% for the fundamental mode. The electrical characterizations are carried out with the impedance analyzer and results are supported by FEM. Stress was successfully managed with the help of the perforation as verified by the experimental work and simulations.

Delay models and design guidelines for MCML gates with resistor or PMOS load

In this paper we present propagation delay models for MCML gates with resistor- or triode-PMOS-based output I–V conversion. The dependence of the parasitic capacitance of triode PMOS devices is accurately evaluated for the first time in the literature. The proposed models are able to accurately predict the propagation delay as a function of the bias current ISS in different design scenarios which require different tradeoffs between speed, area and power efficiency. The proposed models are validated against transistor level simulations referring to a 28 ​nm CMOS process showing a maximum percentage error lower than 6.5%. Based on these models, a comparative analysis is carried out and useful guidelines for the design of MCML gates are proposed.

A sensor-less stroke detection technique for linear refrigeration compressors using artificial neural network

Abstract Linear compressors are very attractive for domestic refrigeration due to elimination of crank mechanism, high efficiency and compactness compared with conventional compressors. The significance of stroke control in a linear compressor not only lies in avoiding the piston collision into the cylinder head but also enabling cooling capacity modulation. Predicting piston stroke without a displacement sensor reduces the cost and facilitates the stroke control especially in miniature linear compressor where there is very limited space for installing sensors. This paper reports an artificial neural network (ANN) based stroke detection approach that can be used in linear compressors and any other linear (free-piston) machines. Experimental tests were conducted in a novel linear compressor driven refrigeration system to sample and record voltage, current and displacement. Fast Fourier transform (FFT) analysis was performed on current and voltage signals to extract harmonic terms as inputs of the neural network model to predict the stroke. Six cases with different numbers of harmonic term for current and voltage were compared. Both the mean squared errors and correlation coefficients are significantly improved with the increase of harmonic terms from one to three. However, small difference is indicated between the cases with three and six terms. Best percentage error distribution was achieved in the case with six harmonic terms with the majority of percentage errors falling within ±0.7% and a maximum percentage error of 3.5%. It can be concluded that the present ANN based stroke prediction approach can be effectively adopted for linear compressors without expensive displacement sensors. This is a key step towards the commercialization of linear refrigeration compressor technologies.