scholarly journals Dynamic Modeling and Parameters Optimization of Large Vibrating Screen with Full Degree of Freedom

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaodong Yang ◽  
Jida Wu ◽  
Haishen Jiang ◽  
Wenqiang Qiu ◽  
Chusheng Liu
Keyword(s):  
Neural Network ◽  
Elastic Deformation ◽  
Parametric Modeling ◽  
Parameters Optimization ◽  
Degree Of Freedom ◽  
Design Parameters ◽  
Response Analysis ◽  
Lateral Plate ◽  
Beam Position ◽  
Vibrating Screen

Dynamic characteristic and reliability of the vibrating screen are important indicators of large vibrating screen. Considering the influence of coupling motion of each degree of freedom, the dynamic model with six degrees of freedom (6 DOFs) of the vibrating screen is established based on the Lagrange method, and modal parameters (natural frequencies and modes of vibration) of the rigid body are obtained. The finite element modal analysis and harmonic response analysis are carried out to analyze the elastic deformation of the structure. By using the parametric modeling method, beam position is defined as a variable, and an orthogonal experiment on design is performed. The BP neural network is used to model the relationship between beam position and maximal elastic deformation of the lateral plate. Further, the genetic algorithm is used to optimize the established neural network model, and the optimal design parameters are obtained.

2D Geometrical Parameters Optimization Design Method of CMC/Metal Dovetail Joint

2018 ◽  
Vol 923 ◽  
pp. 156-163
Author(s):  
Tian Yuan Yang ◽  
Duo Qi Shi ◽  
Zhen Cheng
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Optimization Methods ◽  
Parametric Modeling ◽  
Parameters Optimization ◽  
Design Parameters ◽  
Geometrical Parameters ◽  
Element Analysis ◽  
Whole Process ◽  
Parameter Optimization Design

This paper establishes a 2D geometrical parameter optimization design method of CMC/metal dovetail joint by using ABAQUS and ISIGHT. Firstly, use the ABAQUS script to finish the 2D geometric parametric modeling and the whole process of the finite element analysis of the simplified dovetail joint in the Python language. Then use ISIGHT software to optimize the 2D geometrical parameters. Finally, compare the optimization results of different optimization methods and get the optimal design parameters. This method is really efficient for the preliminary 2D design of the CMC/metal dovetail.

Application of ANN Back-Propagation for Fracture Design Parameters in Extra-Low Cycle Rotating Bending Fatigue

2009 ◽  
Vol 610-613 ◽  
pp. 450-453
Author(s):  
Hong Yan Duan ◽  
You Tang Li ◽  
Jin Zhang ◽  
Gui Ping He
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Back Propagation ◽  
Training Data ◽  
Design Parameters ◽  
Ann Model ◽  
Bending Fatigue ◽  
The Neural Network ◽  
Rotating Bending Fatigue ◽  
Rotating Bending

The fracture problems of ecomaterial (aluminum alloyed cast iron) under extra-low cycle rotating bending fatigue loading were studied using artificial neural networks (ANN) in this paper. The training data were used in the formation of training set of ANN. The ANN model exhibited excellent in results comparison with the experimental results. It was concluded that predicted fracture design parameters by the trained neural network model seem more reasonable compared to approximate methods. It is possible to claim that, ANN is fairly promising prediction technique if properly used. Training ANN model was introduced at first. And then the Training data for the development of the neural network model was obtained from the experiments. The input parameters, notch depth, the presetting deflection and tip radius of the notch, and the output parameters, the cycle times of fracture were used during the network training. The neural network architecture is designed. The ANN model was developed using back propagation architecture with three layers jump connections, where every layer was connected or linked to every previous layer. The number of hidden neurons was determined according to special formula. The performance of system is summarized at last. In order to facilitate the comparisons of predicted values, the error evaluation and mean relative error are obtained. The result show that the training model has good performance, and the experimental data and predicted data from ANN are in good coherence.

Optimizing a Modular Half-Speed Synchronous Generator and Implementing Its Voltage Control System Based on a Neural Network

2021 ◽  
Vol 7 (7) ◽  
pp. 61-70
Author(s):  
Andrey A. TATEVOSYAN ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnets ◽  
Magnetic System ◽  
Voltage Control ◽  
Synchronous Generator ◽  
Voltage Regulation ◽  
Design Parameters ◽  
Design Scheme ◽  
The Neural Network

A method for optimizing the parameters of a modular half-speed synchronous generator with permanent magnets (PMSG) and the generator voltage control system with a neural network-based algorithm are proposed. The basic design scheme of the modular half-speed PMSG is considered, which features a compact layout of the generator main parts, thereby ensuring the optimal use of the working volume, smaller sizes of the magnetic system, and smaller mass of the active materials used in manufacturing the machine. Owing to the simple and reliable design of the generator, its output parameters can be varied in a wide range with using standard electrical circuits for voltage stabilization and current rectification along with an additional voltage regulation unit. Owing to this feature, the design scheme of the considered generator has essential advantages over the existing analogs with a common cylindrical magnetic core. In view of these circumstances, the development of a high-efficient modular half-speed PMSG as an autonomous DC power source is of both scientific and practical interest; this generator can be used to supply power to a large range of electricity consumers located in rural areas, low-rise residential areas, military communities, allotments etc. In solving the problem of optimizing the generator’s magnetic system, the main electrical machine analysis equation is obtained. The optimal ratios of the winding wire mass to the mass of permanent magnets and of the PM height to the air gap value for achieving the maximum specific useful power output have been determined. An analytical correlation between the optimal design parameters of a half-speed modular PMSG and its power performance parameters has been established. The expediency to develop a neural network-based control system is shown. The number of load-bearing modules of the half-speed PMSG is determined depending on the wind velocity, load factor and the required output voltage. The neural network was trained on the examples of a training sample using a laboratory test bench. The neural network was implemented in the MatLab 2019b environment by constructing a synchronous generator simulation model in the Simulink software extension. The possibility of using the voltage control system of a half-speed modular PMSG with a microcontroller for operation of the neural network platform of the Arduino family (ArduinoDue) independently of the PC is shown.

scholarly journals Dynamic Response Analysis on the Interaction Between Flexible Bodies of Large-Sized Wind Turbine Under Random Wind Loads

2018 ◽  
Author(s):  
Yilun Li ◽  
Shuangxi Guo ◽  
Min Li ◽  
Weimin Chen ◽  
Yue Kong
Keyword(s):  
Dynamic Response ◽  
Wind Turbine ◽  
Elastic Deformation ◽  
Dynamic Interaction ◽  
Response Analysis ◽  
Flexible Bodies ◽  
Dynamic Response Analysis ◽  
Dynamic Behaviors ◽  
Energy Consuming ◽  
Turbine Model

As the output power of wind turbine increasingly gets larger, the structural flexibility of elastic bodies, such as rotor blades and tower, gets more significant owing to larger structural size. In that case, the dynamic interaction between these flexible bodies become more profound and may significantly impact the dynamic response of the whole wind turbine. In this study, the integrated model of a 5-MW wind turbine is developed based on the finite element simulations so as to carry out dynamic response analysis under random wind load, in terms of both time history and frequency spectrum, considering the interactions between the flexible bodies. And, the load evolution along its transmitting route and mechanical energy distribution during the dynamic response are examined. And, the influence of the stiffness and motion of the supporting tower on the integrated system is discussed. The basic dynamic characteristics and responses of 3 models, i.e. the integrated wind turbine model, a simplified turbine model (blades, hub and nacelle are simplified as lumped masses) and a rigid supported blade, are examined, and their results are compared in both time and frequency domains. Based on our numerical simulations, the dynamic coupling mechanism are explained in terms of the load transmission and energy consumption. It is found that the dynamic interaction between flexible bodies is profound for wind turbine with large structural size, e.g. the load and displacement of the tower top gets around 15% larger mainly due to the elastic deformation and dynamic behaviors (called inertial-elastic effect here) of the flexible blade; On the other hand, the elastic deformation may additionally consume around 10% energy (called energy-consuming effect) coming from external wind load and consequently decreases the displacement of the tower. In other words, there is a competition between the energy-consuming effect and inertial-elastic effect of the flexible blade on the overall dynamic response of the wind turbine. And similarly, the displacement of the blade gets up to 20% larger because the elastic-dynamic behaviors of the tower principally provides a elastic and moving support which can significantly change the natural mode shape of the integrated wind turbine and decrease the natural frequency of the rotor blade.

Prediction of Hydrodynamic Forces and Moments on Submarines Using Neural Networks

2002 ◽  
Author(s):  
Ibrahim Mohamed ◽  
Mahmoud Haddara ◽  
Christopher D. Williams ◽  
Michael Mackay
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hydrodynamic Model ◽  
Parametric Identification ◽  
Hydrodynamic Forces ◽  
Degree Of Freedom ◽  
Experimental Time ◽  
Time Histories ◽  
Trained Network ◽  
Identification Tool

This paper describes a parametric identification tool for predicting the hydrodynamic forces acting on a submarine model using its motion history. The tool uses a neural network to identify the hydrodynamic forces and moments; the network was trained with data obtained from multi-degree-of-freedom captive maneuvering tests. The characteristics of the trained network are demonstrated through reconstruction of the force and moment time histories. This technique has the potential to reduce experimental time and cost by enabling a full hydrodynamic model of the vehicle to be obtained from a relatively limited number of test maneuvers.

Sign in / Sign up

Export Citation Format

Share Document