scholarly journals Interfacial Friction Prediction in a Vertical Annular Two-Phase Flow Based on Support Vector Regression Machine

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3609
Author(s):  
Qiang Liu ◽  
Xingya Feng ◽  
Junru Chen
Keyword(s):  
Reynolds Number ◽  
Support Vector Regression ◽  
Froude Number ◽  
Friction Factor ◽  
Absolute Error ◽  
Interfacial Friction ◽  
Support Vector ◽  
Optimal Model ◽  
Two Phase ◽  
Friction Prediction

Accurate prediction of interfacial friction factor is critical for calculation of pressure drop and investigation of flow mechanism of vertical annular two-phase flows. Theoretical models of interfacial friction factor based on physical insight have been developed; however, these are inconvenient in engineering practice as too many parameters need to be measured. Although many researchers have proposed various empirical correlations to improve computation efficiency, there is no generally accepted simple formula. In this study, an efficient prediction model based on support vector regression machine (SVR) is proposed. Through sensitivity analysis, five factors are determined as the input parameters to train the SVR model, relative liquid film thickness, liquid Reynolds number, gas Reynolds number, liquid Froude number and gas Froude number. The interfacial friction factor is chosen as the output parameter to check the overall performance of the model. With the help of particle swarm algorithm, the optimization process is accelerated considerably, and the optimal model is obtained through iterations. Compared with other correlations, the optimal model shows the lowest average absolute error (AAE of 0.0004), lowest maximum absolute error (MAE of 0.006), lowest root mean square error (RMSE of 0.00076) and highest correlation factor (r of 0.995). The analysis using various data in the literature demonstrates its accuracy and stability in interfacial friction prediction. In summary, the proposed machine learning model is effective and can be applied to a wider range of conditions for vertical annular two-phase flows.

scholarly journals Improvement of SVR-Based Drought Forecasting Models using Wavelet Pre-Processing Technique

2018 ◽  
Vol 65 ◽  
pp. 07007 ◽  
Author(s):  
Kit Fai Fung ◽  
Yuk Feng Huang ◽  
Chai Hoon Koo
Keyword(s):  
Support Vector Regression ◽  
River Basin ◽  
Absolute Error ◽  
Processing Technique ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Drought Risk ◽  
Drought Forecasting ◽  
Drought Prediction ◽  
Langat River

Drought is a damaging natural hazard due to the lack of precipitation from the expected amount for a period of time. Mitigations are required to reduced its impact. Due to the difficulty in determining the onset and offset of droughts, accurate drought forecasting approaches are required for drought risk management. Given the growing use of machine learning in the field, Wavelet-Boosting Support Vector Regression (W-BS-SVR) was proposed for drought forecasting at Langat River Basin, Malaysia. Monthly rainfall, mean temperature and evapotranspiration for years 1976 - 2015 were used to compute Standardized Precipitation Evapotranspiration Index (SPEI) in this study, producing SPEI-1, SPEI-3 and SPEI-6. The 1-month lead time SPEIs forecasting capability of W-BS-SVR model was compared with the Support Vector Regression (SVR) and Boosting-Support Vector Regression (BS-SVR) models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), coefficient of determination (R2) and Adjusted R2. The results demonstrated that W-BS-SVR provides higher accuracy for drought prediction in Langat River Basin.

scholarly journals On Ripple of Annular Two-Phase Flow : 2. Characteristics of Wave and Interfacial Friction Factor

1978 ◽  
Vol 21 (152) ◽  
pp. 279-286 ◽  
Author(s):  
Kotohiko SEKOGUCHI ◽  
Keiichi HORI ◽  
Masao NAKAZATOMI ◽  
Kaneyasu NISHIKAWA
Keyword(s):  
Friction Factor ◽  
Two Phase Flow ◽  
Interfacial Friction ◽  
Phase Flow ◽  
Two Phase

Prediction of Glass Transition Temperature of Polymer by Support Vector Regression

2012 ◽  
Vol 455-456 ◽  
pp. 436-442
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
B. Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Support Vector Regression ◽  
Density Functional ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Leave One Out

. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

scholarly journals A Two-Fluid Model for High-Viscosity Upward Annular Flow in Vertical Pipes

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3485
Author(s):  
Joseph X. F. Ribeiro ◽  
Ruiquan Liao ◽  
Aliyu M. Aliyu ◽  
Salem K. B. Ahmed ◽  
Yahaya D. Baba ◽  
...  
Keyword(s):  
Pressure Gradient ◽  
Friction Factor ◽  
Fluid Model ◽  
Flow Characteristics ◽  
Interfacial Friction ◽  
Internal Diameter ◽  
Liquid Viscosity ◽  
Two Phase ◽  
Two Fluid Model ◽  
Two Fluid

Proper selection and application of interfacial friction factor correlations has a significant impact on prediction of key flow characteristics in gas–liquid two-phase flows. In this study, experimental investigation of gas–liquid flow in a vertical pipeline with internal diameter of 0.060 m is presented. Air and oil (with viscosities ranging from 100–200 mPa s) were used as gas and liquid phases, respectively. Superficial velocities of air ranging from 22.37 to 59.06 m/s and oil ranging from 0.05 to 0.16 m/s were used as a test matrix during the experimental campaign. The influence of estimates obtained from nine interfacial friction factor models on the accuracy of predicting pressure gradient, film thickness and gas void fraction was investigated by utilising a two-fluid model. Results obtained indicate that at liquid viscosity of 100 mPa s, the interfacial friction factor correlation proposed by Belt et al. (2009) performed best for pressure gradient prediction while the Moeck (1970) correlation provided the best prediction of pressure gradient at the liquid viscosity of 200 mPa s. In general, these results indicate that the two-fluid model can accurately predict the flow characteristics for liquid viscosities used in this study when appropriate interfacial friction factor correlations are implemented.

scholarly journals Path Smoothing With Support Vector Regression

2020 ◽  
Vol 4 (1) ◽  
pp. 142-150
Author(s):  
Donni Richasdy ◽  
Saiful Akbar
Keyword(s):  
Support Vector Regression ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Moving Object ◽  
Percentage Error ◽  
Support Vector ◽  
Average Value ◽  
Path Smoothing ◽  
Aircraft Tracking ◽  
Smoothing Process

One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value.Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE

scholarly journals Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018 ◽  
Vol 10 (10) ◽  
pp. 3434 ◽  
Author(s):  
Omer Azeez ◽  
Biswajeet Pradhan ◽  
Helmi Shafri
Keyword(s):  
Support Vector Regression ◽  
Urban Areas ◽  
Road Traffic ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Support Vector ◽  
Vehicular Emissions ◽  
Support Vector Regression Model ◽  
Promising Tool ◽  
Proposed Model

Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model’s results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

scholarly journals Drag reduction in numerical two-phase Taylor–Couette turbulence using an Euler–Lagrange approach

2016 ◽  
Vol 798 ◽  
pp. 411-435 ◽  
Author(s):  
Vamsi Spandan ◽  
Rodolfo Ostilla-Mónico ◽  
Roberto Verzicco ◽  
Detlef Lohse
Keyword(s):  
Reynolds Number ◽  
Drag Reduction ◽  
Froude Number ◽  
Carrier Phase ◽  
Dispersed Phase ◽  
Gravitational Acceleration ◽  
Momentum Exchange ◽  
Two Phase ◽  
Taylor Couette ◽  
Lagrange Approach

Two-phase turbulent Taylor–Couette (TC) flow is simulated using an Euler–Lagrange approach to study the effects of a secondary phase dispersed into a turbulent carrier phase (here bubbles dispersed into water). The dynamics of the carrier phase is computed using direct numerical simulations (DNS) in an Eulerian framework, while the bubbles are tracked in a Lagrangian manner by modelling the effective drag, lift, added mass and buoyancy force acting on them. Two-way coupling is implemented between the dispersed phase and the carrier phase which allows for momentum exchange among both phases and to study the effect of the dispersed phase on the carrier phase dynamics. The radius ratio of the TC setup is fixed to ${\it\eta}=0.833$, and a maximum inner cylinder Reynolds number of $Re_{i}=8000$ is reached. We vary the Froude number ($Fr$), which is the ratio of the centripetal to the gravitational acceleration of the dispersed phase and study its effect on the net torque required to drive the TC system. For the two-phase TC system, we observe drag reduction, i.e. the torque required to drive the inner cylinder is lower compared with that of the single-phase system. The net drag reduction decreases with increasing Reynolds number $Re_{i}$, which is consistent with previous experimental findings (Murai et al., J. Phys.: Conf. Ser., vol. 14, 2005, pp. 143–156; Phys. Fluids, vol. 20(3), 2008, 034101). The drag reduction is strongly related to the Froude number: for fixed Reynolds number we observe higher drag reduction when $Fr<1$ than for with $Fr>1$. This buoyancy effect is more prominent in low $Re_{i}$ systems and decreases with increasing Reynolds number $Re_{i}$. We trace the drag reduction back to the weakening of the angular momentum carrying Taylor rolls by the rising bubbles. We also investigate how the motion of the dispersed phase depends on $Re_{i}$ and $Fr$, by studying the individual trajectories and mean dispersion of bubbles in the radial and axial directions. Indeed, the less buoyant bubbles (large $Fr$) tend to get trapped by the Taylor rolls, while the more buoyant bubbles (small $Fr$) rise through and weaken them.

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