Wear Modeling Revisited Using Electrical Analogy

2017 ◽  
Vol 139 (6) ◽  
Author(s):  
M. Hanief ◽  
M. F. Wani
Keyword(s):  
Electrical Circuit ◽  
Operating Conditions ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Model Parameters ◽  
Wear Process ◽  
Wear Modeling ◽  
Proposed Model ◽  
Electrical Analogy ◽  
High Degree

Electrical analogy has been used extensively in modeling various mechanical systems such as thermal, hydraulic, and other dynamic systems. However, wear modeling of a tribosystem using electrical analogy has not been reported so far. In this paper, an equivalent electrical analogous system is proposed to represent the wear process. An analogous circuit is developed by mapping the wear process parameters to that of the electrical parameters. The circuit, thus, developed is solved by conventional electrical circuit theory. The material properties and operating conditions are taken into account by model parameters. Accordingly, a model equation in terms of model parameters is developed to represent the wear rate. It is also demonstrated how this methodology can be used to take various system parameters into account by incorporating the equivalent resistance of the parameters. The nonlinear model parameters are evaluated by Gauss–Newton (GN) algorithm. The proposed model is validated by using experimental data. A comparison of the proposed model with the experimental results, based on statistical methods: coefficient of determination (R2), mean-square-error (MSE) and mean absolute percentage error (MAPE), indicates that the model is competent to predict the wear with a high degree of accuracy.

scholarly journals Influence of Operating Parameters on Running-in Wear of EN-31 Steel

2016 ◽  
Vol 13 (1) ◽  
pp. 1-2
Author(s):  
M. Hanief ◽  
M. F. Wani
Keyword(s):  
Surface Roughness ◽  
Wear Rate ◽  
Least Square Method ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Model Parameters ◽  
Operating Parameters ◽  
Proposed Model ◽  
En 31

Abstract In this paper, effect of operating parameters (temperature, surface roughness and load) was investigated to determine the influence of each parameter on the wear rate. A mathematical model was developed to establish a functional relationship between the running-in wear rate and the operating parameters. The proposed model being non-linear, it was linearized by logarithmic transformation and the optimal values of model parameters were obtained by least square method. It was found that the surface roughness has significant effect on wear rate followed by load and temperature. The adequacy of the model was estimated by statistical methods (coefficient of determination (R2) and mean absolute percentage error (MAPE)) .

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

scholarly journals Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Mohamed Abd El Aziz
Keyword(s):  
Relative Error ◽  
Computing Time ◽  
World Health ◽  
Coefficient Of Determination ◽  
Flower Pollination Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Local Optima ◽  
Inference System ◽  
Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

Energy load forecasting: Bayesian and exponential smoothing hybrid methodology

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eman Khorsheed
Keyword(s):  
Hybrid Approach ◽  
Peak Load ◽  
Exponential Smoothing ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Original Research ◽  
Model Parameters ◽  
Learning Tools ◽  
Content Type ◽  
Hybrid Methodology

Purpose The purpose of this study is to present a hybrid approach to model and predict long-term energy peak load using Bayesian and Holt–Winters (HW) exponential smoothing techniques. Design/methodology/approach Bayesian inference is administered by Markov chain Monte Carlo (MCMC) sampling techniques. Machine learning tools are used to calibrate the values of the HW model parameters. Hybridization is conducted to reduce modeling uncertainty. The technique is applied to real load data. Monthly peak load forecasts are calculated as weighted averages of HW and MCMC estimates. Mean absolute percentage error and the coefficient of determination (R2) indices are used to evaluate forecasts. Findings The developed hybrid methodology offers advantages over both individual combined techniques and reveals more accurate and impressive results with R2 above 0.97. The new technique can be used to assist energy networks in planning and implementing production projects that can ensure access to reliable and modern energy services to meet the sustainable development goal in this sector. Originality/value This is original research.

scholarly journals Simulation of the ice thickness of the Heilongjiang River and application of SD models to a river ice model

2021 ◽  
Author(s):  
Ruofei Xing ◽  
Slobondan P. Simonoviæ ◽  
Qin Ju ◽  
Zhenchun Hao ◽  
Feifei Yuan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Dynamic Models ◽  
Sensitivity Analyses ◽  
Coefficient Of Determination ◽  
River Ice ◽  
Ice Thickness ◽  
Model Parameters ◽  
Proposed Model ◽  
Uncertainty Analyses ◽  
Ice Model

Abstract The Heilongjiang River is a transboundary river between China and Russia, which often experiences ice dams that can trigger spring floods and significant damages in the region. Owing to insufficient data, no river ice model is applicable for the Heilongjiang River. Therefore, a river ice thickness model based on continuous meteorological data and river ice data at the Mohe Station located in the upper reach of the Heilongjiang River was proposed. Specifically, the proposed model was based on physical river ice processes and the Russian empirical theory. System dynamic models were applied to assess the proposed model. The performance of the river ice model was evaluated using root-mean-square error (RMSE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Subsequently, sensitivity analyses of the model parameters through Latin hypercube sampling and uncertainty analyses of input variables were conducted. Results show that the formation of ice starts 10 days after the air temperature reaches below 0 °C. The maximum ice thickness occurs 10 days after the atmospheric temperature reaches the minimum. Ice starts to melt after the highest temperature is greater than 0 °C. The R2 of ice thickness in the middle of river (ITMR) and ice thickness at the riverside (ITRS) are 0.67 and 0.69, respectively; the RMSEs of ITMR and ITRS are 6.50 and 6.84, respectively; and the NSEs of ITMR and ITRS are 0.72 and 0.70, respectively. Sensitivity analyses show that ice growth and ice melt are sensitive to the air temperature characterizing the thermal state. Uncertainty analyses show temperature has the greatest effect on river ice.

scholarly journals Fatigue life assessment method of in-service mechanical structure

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199652
Author(s):  
Xiqiang Ma ◽  
Fang Yang ◽  
Jishun Li ◽  
Yujun Xue ◽  
Zhiqiang Guan
Keyword(s):  
Elastic Modulus ◽  
Fatigue Life ◽  
Modulus Of Elasticity ◽  
Evaluation Model ◽  
Assessment Method ◽  
Operating Conditions ◽  
Life Assessment ◽  
Model Parameters ◽  
Mechanical Structure ◽  
Proposed Model

The most usual failure mode of any mechanical structure is fatigue, which is characterized by an important feature of the decrease of elastic modulus of the material. In this paper, a fatigue life evaluation model based on equivalent elastic modulus is proposed for in-service mechanical structure. In the proposed model, parameters that represent the operating conditions of the mechanical structure, such as load, vibration, and shaft torque, etc., are used as the generalized load. To replace the fatigue stress, the statistical method is used here, which is also used in the conventional fatigue analysis method. The structural strain is also measured simultaneously. Using the statistical theory, the equivalent modulus of elasticity is formulated based on the relationship of stress, strain, and modulus of elasticity. To validate the proposed model, an online fatigue damage experiment has been conducted. The experimental results have been compared with that of the fatigue life prediction model with good agreement. It is expected that the methodology proposed in this paper will be widely used.

scholarly journals Generalized Behavioral Modelling Methodology of Switch-Diode Cell for Power Loss Prediction in Electromagnetic Transient Simulation

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1500
Author(s):  
Yanming Xu ◽  
Carl Ngai Man Ho ◽  
Avishek Ghosh ◽  
Dharshana Muthumuni
Keyword(s):  
Power Loss ◽  
Process Analysis ◽  
Operating Conditions ◽  
Transient Simulation ◽  
Oxide Semiconductor ◽  
Model Parameters ◽  
Electromagnetic Transient ◽  
Proposed Model ◽  
Wide Range ◽  
Electromagnetic Transient Simulation

Modern wide-bandgap (WBG) devices, such as silicon carbide (SiC) or gallium nitride (GaN) based devices, have emerged and been increasingly used in power electronics (PE) applications due to their superior switching feature. The power losses of these devices become the key of system efficiency improvement, especially for high-frequency applications. In this paper, a generalized behavioral model of a switch-diode cell (SDC) is proposed for power loss estimation in the electromagnetic transient simulation. The proposed model is developed based on the circuit level switching process analysis, which considers the effects of parasitics, the operating temperature, and the interaction of diode and switch. In addition, the transient waveforms of the SDC are simulated by the proposed model using dependent voltage and current sources with passive components. Besides, the approaches of obtaining model parameters from the datasheets are given and the modelling method is applicable to various semiconductors such Si insulated-gate bipolar transistor (IGBT), Si/SiC metal–oxide–semiconductor field-effect transistor (MOSFET), and GaN devices. Further, a multi-dimensional power loss table in a wide range of operating conditions can be obtained with fast speed and reasonable accuracy. The proposed approach is implemented in PSCAD/ Electromagnetic Transients including DC, EMTDC, (v4.6, Winnipeg, MB, Canada) and further verified by the hardware setups including different daughter boards for different devices.

scholarly journals Modelling of Supercapacitors Based on Simplified Equivalent Circuit

2021 ◽  
Vol 6 (1) ◽  
pp. 31-39
Author(s):  
Mustafa Şahin ◽  
Keyword(s):  
Energy Storage ◽  
Equivalent Circuit ◽  
Electrical Circuit ◽  
Circuit Model ◽  
Model Parameters ◽  
Equivalent Series Resistance ◽  
Energy Storage Devices ◽  
Proposed Model ◽  
Electrical Circuit Model ◽  
Simulation Results

The need for energy storage devices especially in renewable energy applications has increased the use of supercapacitors. Accordingly, several supercapacitor models have been proposed in previous researches. Nevertheless, most of them require an intensive test to obtain the model parameters. These may not be suitable for an initial simulation study, where a simple model based on the datasheet is required to evaluate the system performance before building the hardware prototype. A simplified electrical circuit model for a supercapacitor (SC) based on the voltage-current equation is proposed in this paper to address this issue. This model doesn’t need an intensive test for accuracy. The structural simplicity and decent modelling accuracy make the equivalent electrical circuit model very suitable for power electronic applications and real-time energy management simulations. The parameters of the proposed model can be obtained from the datasheets value with a minimum test requirement. The experimental method to provide the parameters of the supercapacitor equivalent circuit is described. Based on the proposed method, the supercapacitor model is built in Matlab/Simulink, and the characteristics of equivalent series resistance (ESR) measurement and cycle life are compared with datasheets. The simulation results have verified that the proposed model can be applied to simulate the behaviour of the supercapacitor in most energy and power applications for a short time of energy storage. A supercapacitor test circuit is given to test the charge and discharge of supercapacitor modules. The experimental results are suitable for simulation results.

scholarly journals Modeling and prediction of wear rate of aluminum alloy (Al 7075) using power law and ANN

10.30544/544 ◽  
2020 ◽  
Author(s):  
M. Hanief ◽  
Shafi M. Charoo
Keyword(s):  
Neural Network ◽  
Wear Rate ◽  
Power Law ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Operating Parameters ◽  
Ann Model ◽  
Wear Process ◽  
Artificial Neural Network Ann ◽  
Sliding Distance

The wear process significantly influences machine parts during their useful life. The wear process is complex, and therefore, it is very difficult to develop a comprehensive model involving all the operating parameters. In the present study, wear rate is measured during the wear process at different operating parameters such as force (load), sliding distance, and velocity. Power law and Artificial neural network (ANN) approaches are used to model the wear rate of Al7075 alloy. Power law and neural network-based models are compared using statistical methods with a coefficient of determination (R2), mean absolute percentage error (MAPE), and means square error (MSE). It is seen that the proposed models are competent to predict the wear rate of Al7075 alloy. The ANN model estimates the wear rate with high accuracy compared to that of the power law model. The models developed for wear rate were found to be consistent with the experimental data. ANOVA analysis revealed that the load has a significant effect on the wear rate than the sliding speed and sliding distance.

Arterial Traffic Flow Estimation Based on Vehicle-to-Cloud Vehicle Trajectory Data Considering Multi-Intersection Interaction and Coordination

2019 ◽  
Vol 2673 (6) ◽  
pp. 68-83 ◽  
Author(s):  
Xia Luo ◽  
Bo Liu ◽  
Peter J. Jin ◽  
Yang Cao ◽  
Wansgu Hu
Keyword(s):  
Traffic Flow ◽  
Detection Methods ◽  
Percentage Error ◽  
Model Parameters ◽  
Trajectory Data ◽  
Flow Estimation ◽  
Microwave Radar ◽  
Proposed Model ◽  
Vehicle Trajectory ◽  
Traffic Flow Estimation

Conventional detection methods for intersection traffic flow heavily rely on fixed-location inductive loop, video image processing, infared, and microwave radar detectors. The emerging connected vehicles (CV) technologies can potentially reduce such dependencies on conventional vehicle detectors with the vehicle-to-cloud (V2C) CV data. This paper proposes an analytical method for traffic flow estimation in urban arterial corridors based on CV trajectories collected through V2C communication. Different from the existing single-intersection models, the proposed model considers traffic states and the traffic signal coordination among adjacent intersections, therefore, can capture the delay and queuing dynamics in arterial corridors. The queue spillback phenomenon is explicitly considered by applying the shockwave theory. The proposed model is evaluated based on real-world vehicle trajectory data from the DiDi platform collected on an arterial network in Chengdu, China with a penetration rate of less than 10% of the overall traffic. The flow estimation results are compared with traffic counts collected from video detectors. The model parameters are calibrated with more than 300,000 GPS points during a typical workday and tested on a different workday. The evaluation results show a mean absolute percentage error within the range of 4–7% among all intersections, outperforming the results generated by the existing single-intersection model. The results indicate the promising potential of using the proposed methods to evaluate intersection performance without heavy investment in on-site detectors.

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