scholarly journals Optimal Segment Control of Active Twist Rotor for Power Reduction in Forward Flight

2021 ◽  
Vol 11 (3) ◽  
pp. 1041
Author(s):  
Xiaochi Zhang ◽  
Zhiqiang Wan ◽  
De Yan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Numerical Model ◽  
Load Distribution ◽  
Power Reduction ◽  
System Complexity ◽  
Optimization Framework ◽  
Steady Level ◽  
Rotor Disk ◽  
Active Twist

The segment control of active twist rotor is investigated to evaluate the effectiveness in rotor power reduction. A numerical model for predicting the isolated rotor power and loads in steady level flights is deployed and validated. A parametric sweep of the amplitude and phase angle for uniform single-harmonic active twist control is conducted to demonstrate the mechanism of active twist control in rotor power reduction. The optimal control schedules and segment layouts of the segment twist control for power reduction while considering saturation limits are obtained using an optimization framework based on genetic algorithm. Up to 5-seg configuration is considered. The results indicate that the segment twist control reduces the rotor power more than the uniform twist control by applying divergent control schedules to each segment. The load distribution of the rotor disk is harmonized in both circumferential and spanwise directions. The 2-seg and 3-seg control configurations are appropriate, while the configurations with more segments yield limited benefits and they may be penalized with an increase in system complexity.

Genetic algorithm based cooling energy optimization of data centers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jayati Athavale ◽  
Minami Yoda ◽  
Yogendra Joshi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Power Consumption ◽  
Data Center ◽  
Load Distribution ◽  
Data Centers ◽  
Cooling Power ◽  
Temperature Prediction ◽  
Content Type ◽  
Optimization Framework

Purpose This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied. Design/methodology/approach Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots. Findings Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center. Research limitations/implications The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized. Practical implications The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption. Originality/value This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

Air Suspension Optimal Control Based on the Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 1066-1071
Author(s):  
Zhong Xin Li ◽  
Ji Wei Guo ◽  
Ming Hong Gao ◽  
Hong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Dynamic Model ◽  
Control Method ◽  
Control Model ◽  
Weight Matrix ◽  
Object A ◽  
Full Vehicle ◽  
Air Suspension ◽  
Optimal Control Method

Taking the full-vehicle eight-freedom dynamic model of a type of bus as the simulation object , a new optimal control method is introduced. This method is based on the genetic algorithm, and the full-vehicle optimal control model is built in the MatLab. The weight matrix of the optimal control is optimized through the genetic algorithm; then the outcome is compared with the artificially-set optimal control simulation, which shows that the genetic-algorithm based optimal control presents better performance, thereby creating a smoother ride and improving the steering stability of the vehicle.

scholarly journals Influence of foundation settlements in load redistribution on columns in a monitoring construction - Case Study

2010 ◽  
Vol 3 (3) ◽  
pp. 346-356 ◽  
Author(s):  
G. Savaris ◽  
P. H. Hallak ◽  
P. C. A. Maia
Keyword(s):  
Numerical Analysis ◽  
Numerical Model ◽  
Finite Elements ◽  
Load Distribution ◽  
Numerical Models ◽  
Normal Load ◽  
Finite Elements Method ◽  
Load Redistribution

The objective of this article is to present the results obtained in a study on the interaction between the behavior of the structure and the foundation settlements and verify the influence of normal load distribution on the columns. In this mechanism, known as structure soil interaction (SSI), as the building is constructed, a transfer of loads occurs from the columns which tend to settle more to those that tend to settle less. The study was conducted in a building which had its settlements monitored from the beginning of construction. For this purpose, a linear tridimensional numerical model was constructed and numerical analysis was performed, using the finite elements method. In these analyses, numerical models corre- sponding to the execution of each floor were used, considering the settlements measured in each stage of the construction. The results of analy- ses showed that the effect of SSI are significant for calculating the normal efforts on the columns, particularly on those located in the first floors.

Multi-Objective Optimal Load Distribution Based on Sub Goals Multiplication and Division in Power Plants

2014 ◽  
Vol 494-495 ◽  
pp. 1715-1718
Author(s):  
Gui Li Yuan ◽  
Tong Yu ◽  
Juan Du
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Power Plants ◽  
Optimization Problem ◽  
Thermal Power ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Load ◽  
Optimal Load Distribution

The classic multi-objective optimization method of sub goals multiplication and division theory is applied to solve optimal load distribution problem in thermal power plants. A multi-objective optimization model is built which comprehensively reflects the economy, environmental protection and speediness. The proposed model effectively avoids the target normalization and weights determination existing in the process of changing the multi-objective optimization problem into a single objective optimization problem. Since genetic algorithm (GA) has the drawback of falling into local optimum, adaptive immune vaccines algorithm (AIVA) is applied to optimize the constructed model and the results are compared with that optimized by genetic algorithm. Simulation shows this method can complete multi-objective optimal load distribution quickly and efficiently.

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