scholarly journals Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2814
Author(s):  
Mohammad Hassan Dehghanipour ◽  
Hojat Karami ◽  
Hamidreza Ghazvinian ◽  
Zahra Kalantari ◽  
Amir Hossein Dehghanipour
Keyword(s):  
Genetic Algorithm ◽  
Water Resource Management ◽  
Three Dimensional ◽  
Climatic Conditions ◽  
Pan Evaporation ◽  
Linear Quadratic ◽  
Climate Type ◽  
Method Error ◽  
Basic Relationship ◽  
Quadratic Relationship

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

scholarly journals Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

2021 ◽  
Author(s):  
Mohammad Hassan Dehghanipour ◽  
Hojat Karami ◽  
Hamidreza Ghazvinian ◽  
Zahra Kalantari ◽  
Amir Hossein Dehghanipour
Keyword(s):  
Genetic Algorithm ◽  
Three Dimensional ◽  
Climatic Conditions ◽  
Pan Evaporation ◽  
Algorithm Optimization ◽  
Linear Quadratic ◽  
Climate Type ◽  
Method Error ◽  
Basic Relationship ◽  
Quadratic Relationship

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation was drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

Fully Coupled Three-Dimensional Dynamic Response of a TLP Floating Wind Turbine in Waves and Wind

2012 ◽  
Author(s):  
G. K. V. Ramachandran ◽  
H. Bredmose ◽  
J. N. Sørensen ◽  
J. J. Jensen
Keyword(s):  
Wind Turbine ◽  
Dynamic Model ◽  
Three Dimensional ◽  
Geographic Location ◽  
Climatic Conditions ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Total System ◽  
Wave Loads ◽  
Aerodynamic Loads

A dynamic model for a tension-leg platform (TLP) floating offshore wind turbine is proposed. The model includes three-dimensional wind and wave loads and the associated structural response. The total system is formulated using 17 degrees of freedom (DOF), 6 for the platform motions and 11 for the wind turbine. Three-dimensional hydrodynamic loads have been formulated using a frequency- and direction-dependent spectrum. While wave loads are computed from the wave kinematics using Morison’s equation, aerodynamic loads are modelled by means of unsteady Blade-Element-Momentum (BEM) theory, including Glauert correction for high values of axial induction factor, dynamic stall, dynamic wake and dynamic yaw. The aerodynamic model takes into account the wind shear and turbulence effects. For a representative geographic location, platform responses are obtained for a set of wind and wave climatic conditions. The platform responses show an influence from the aerodynamic loads, most clearly through a quasi-steady mean surge and pitch response associated with the mean wind. Further, the aerodynamic loads show an influence from the platform motion through more fluctuating rotor loads, which is a consequence of the wave-induced rotor dynamics. In the absence of a controller scheme for the wind turbine, the rotor torque fluctuates considerably, which induces a growing roll response especially when the wind turbine is operated nearly at the rated wind speed. This can be eliminated either by appropriately adjusting the controller so as to regulate the torque or by optimizing the floater or tendon dimensions, thereby limiting the roll motion. Loads and coupled responses are predicted for a set of load cases with different wave headings. Based on the results, critical load cases are identified and discussed. As a next step (which is not presented here), the dynamic model for the substructure is therefore being coupled to an advanced aero-elastic code Flex5, Øye (1996), which has a higher number of DOFs and a controller module.

scholarly journals Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm

2012 ◽  
Vol 6 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Seiji Aoyagi ◽  
◽  
Masato Suzuki ◽  
Tomokazu Takahashi ◽  
Jun Fujioka ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Tracking System ◽  
Kinematic Model ◽  
Three Dimensional ◽  
Geometric Parameters ◽  
Geometric Errors ◽  
Robot Arm ◽  
Positioning Accuracy ◽  
Laser Tracking

Offline teaching based on high positioning accuracy of a robot arm is desired to take the place of manual teaching. In offline teaching, joint angles are calculated using a kinematic model of the robot arm. However, a nominal kinematic model does not consider the errors arising in manufacturing or assembly, not to mention the non-geometric errors arising in gear transmission, arm compliance, etc. Therefore, a method of precisely calibrating the parameters in a kinematic model is required. For this purpose, it is necessary to measure the three-dimensional (3-D) absolute position of the tip of a robot arm. In this paper, a laser tracking system is employed as the measurement apparatus. The geometric parameters in the robot kinematic model are calibrated by minimizing errors between the measured positions and the predicted ones based on the model. The residual errors caused by non-geometric parameters are further reduced by using neural networks, realizing high positioning accuracy of sub-millimeter order. To speed up the calibration process, a smaller number of measuring points is preferable. Optimal measuring points, which realize high positioning accuracy while remaining small in number, are selected using Genetic Algorithm (GA).

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