scholarly journals Prediction model for aerodynamic coefficients of iced quad bundle conductors based on machine learning method

2021 ◽  
Vol 8 (10) ◽  
Author(s):  
Zheyue Mou ◽  
Bo Yan ◽  
Hanxu Yang ◽  
Daoda Cai ◽  
Guizao Huang
Keyword(s):  
Numerical Simulation ◽  
Prediction Model ◽  
Latin Hypercube Sampling ◽  
Attack Angle ◽  
Ice Thickness ◽  
Ice Accretion ◽  
Torsional Moment ◽  
Aerodynamic Coefficients ◽  
Efficient Prediction ◽  
Input Variables

The lift, drag and torsional moment coefficients, versus wind attack angle of iced quad bundle conductors in the cases of different conductor structure, ice and wind parameters are numerically simulated and investigated. With the Latin hypercube sampling and numerical simulation, sampling points are designed and datasets are created. Set the number of sub-conductors, wind attack angle, bundle spacing, ice accretion angle, ice thickness, wind velocity and diameter of the conductor as the input variables, a prediction model for the lift, drag and moment coefficients of iced quad bundle conductors is created, trained and tested based on the dataset and extra-trees algorithm. The final integrated prediction model is further validated by applying the aerodynamic coefficients from the prediction model and numerical simulation, respectively, to analyse the galloping features. The developed efficient prediction model for the aerodynamic coefficients of iced quad bundle conductors plays an important role in the quick investigation, prediction and early warning of galloping.

scholarly journals Wind Tunnel Tests on Aerodynamic Characteristics of Ice-Coated 4-Bundled Conductors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Li Xin-min ◽  
Nie Xiao-chun ◽  
Zhu Yong-kun ◽  
You Yi ◽  
Yan Zhi-tao
Keyword(s):  
Wind Tunnel ◽  
Great Influence ◽  
Aerodynamic Characteristics ◽  
Coated Conductors ◽  
Ice Thickness ◽  
Ice Accretion ◽  
Aerodynamic Coefficients ◽  
Wind Tunnel Tests ◽  
Moment Coefficient ◽  
The Moment

Wind tunnel tests were carried out to obtain the static aerodynamic characteristics of crescent iced 4-bundled conductors with different ice thicknesses, initial ice accretion angles, bundle spaces, and wind attack angles. The test models were made of the actual conductors and have a real rough surface. Test results show that the influence of wake interference on the drag coefficients of leeward subconductors is obvious. The interference angle range is larger than 20° and the drag coefficient curves of leeward subconductors have a sudden decrease phenomenon at some certain wind attack angles. The absolute value of the lift and moment coefficient increases with the increase of the ice thickness. In addition, the galloping of the iced subconductor may occur at the angle of wind attack near ±20° and the wake increases the moment coefficient. The variation of initial ice accretion angle has a significant influence on the aerodynamic coefficients. The aerodynamic coefficient curves exhibit a “moving” phenomenon at different initial ice accretion angles. The bundle spaces have a great influence on the moment coefficient of leeward thin ice-coated conductors. With the increase of ice thickness, the bundle spaces generally have little influence on the aerodynamic coefficients.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Patch antenna sensor for wireless ice and frost detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Kozak ◽  
Kasra Khorsand ◽  
Telnaz Zarifi ◽  
Kevin Golovin ◽  
Mohammad H. Zarifi
Keyword(s):  
Resonant Frequency ◽  
Patch Antenna ◽  
Wide Band ◽  
Power Lines ◽  
Ice Thickness ◽  
Ice Accretion ◽  
Robust Method ◽  
Frequency Shifts ◽  
Resonant Amplitude ◽  
Recorded Data

AbstractA patch antenna sensor with T-shaped slots operating at 2.378 GHz was developed and investigated for wireless ice and frost detection applications. Detection was performed by monitoring the resonant amplitude and resonant frequency of the transmission coefficient between the antenna sensor and a wide band receiver. This sensor was capable of distinguishing between frost, ice, and water with total shifts in resonant frequency of 32 MHz and 36 MHz in the presence of frost and ice, respectively, when compared to the bare sensor. Additionally, the antenna was sensitive to both ice thickness and the surface area covered in ice displaying resonant frequency shifts of 2 MHz and 8 MHz respectively between 80 and 160 μL of ice. By fitting an exponential function to the recorded data, the freezing rate was also extracted. The analysis within this work distinguishes the antenna sensor as a highly accurate and robust method for wireless ice accretion detection and monitoring. This technology has applications in a variety of industries including the energy sector for detection of ice on wind turbines and power lines.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

scholarly journals Analysis of Composite Structures in Curing Process for Shape Deformations and Shear Stress: Basis for Advanced Optimization

2021 ◽  
Vol 5 (2) ◽  
pp. 63
Author(s):  
Niraj Kumbhare ◽  
Reza Moheimani ◽  
Hamid Dalir
Keyword(s):  
Composite Structures ◽  
Latin Hypercube Sampling ◽  
Vital Role ◽  
Optimization Techniques ◽  
Design Parameters ◽  
Curing Process ◽  
Input Variables ◽  
Transient Thermal ◽  
Prototype Design ◽  
Shape Deformations

Identifying residual stresses and the distortions in composite structures during the curing process plays a vital role in coming up with necessary compensations in the dimensions of mold or prototypes and having precise and optimized parts for the manufacturing and assembly of composite structures. This paper presents an investigation into process-induced shape deformations in composite parts and structures, as well as a comparison of the analysis results to finalize design parameters with a minimum of deformation. A Latin hypercube sampling (LHS) method was used to generate the required random points of the input variables. These variables were then executed with the Ansys Composite Cure Simulation (ACCS) tool, which is an advanced tool used to find stress and distortion values using a three-step analysis, including Ansys Composite PrepPost, transient thermal analysis, and static structural analysis. The deformation results were further utilized to find an optimum design to manufacture a complex composite structure with the compensated dimensions. The simulation results of the ACCS tool are expected to be used by common optimization techniques to finalize a prototype design so that it can reduce common manufacturing errors like warpage, spring-in, and distortion.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Numerical Simulation of Airfoil Ice Accretion Based on Parcel Concept

2007 ◽  
Vol 2 (6) ◽  
pp. 500-507
Author(s):  
Yihua Cao . ◽  
Ke Chen . ◽  
Xing Pan . ◽  
Qian Yang .
Keyword(s):  
Numerical Simulation ◽  
Ice Accretion
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