scholarly journals Fast Yaw Optimization for Wind Plant Wake Steering Using Boolean Yaw Angles

2021 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Christopher Bay ◽  
Rafael Mudafort ◽  
Paul Fleming
Keyword(s):  
Greedy Algorithm ◽  
Traditional Method ◽  
Mathematical Optimization ◽  
Optimization Methods ◽  
Optimization Method ◽  
New Method ◽  
Computationally Efficient ◽  
Gradient Based ◽  
Boolean Optimization ◽  
Plant Power

Abstract. In wind plants, turbines can be yawed into the wind to steer their wakes away from downstream turbines and achieve an overall increase in plant power. Mathematical optimization is typically used to determine the best yaw angles at which to operate the turbines in a plant. In this paper, we present a new method to rapidly determine the yaw angles in a wind plant. In this method, we define the turbine yaw angles as Boolean—either yawed at a predefined angle or nonyawed—as opposed to the typical methods of formulating yaw angles as continuous or with fine discretizations. We then optimize which turbines should be yawed with a greedy algorithm that sweeps through the turbines from the most upstream to the most downstream. We demonstrate that our new Boolean optimization method can find turbine yaw angles that perform well compared to a traditionally used gradient-based optimizer where the yaw angles are defined as continuous. There is less than 0.6 % difference in the optimized power between the two optimization methods for randomly placed turbine layouts. Additionally, we show that our new method is much more computationally efficient than the traditional method. For plants with nonzero optimal yaw angles, our new method is generally able to solve for the turbine yaw angles 50–150 times faster, and in some extreme cases up to 500 times faster, than the traditional method.

Non-Probabilistic Based Topology Optimization Under External Load Uncertainty With Eigenvalue-Superposition of Convex Models

2013 ◽  
Author(s):  
Xike Zhao ◽  
Hae Chang Gea ◽  
Wei Song
Keyword(s):  
Topology Optimization ◽  
External Load ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Convex Model ◽  
Structure Response ◽  
Gradient Based ◽  
Bounded Convex ◽  
Topology Optimization Method

In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.

Metamodel-Based Optimum Design Examples of Structures

2014 ◽  
pp. 667-676
Author(s):  
Kwon-Hee Lee ◽  
Ji-In Heo
Keyword(s):  
Weight Reduction ◽  
Performance Enhancement ◽  
Fuel Efficiency ◽  
Optimization Methods ◽  
Optimization Method ◽  
The Other ◽  
True Response ◽  
Design Variables ◽  
Weight One ◽  
Gradient Based

In order to achieve greater fuel efficiency and energy conservation, the reduction of weight and enhancement of the performance of structures has been sought. In general, there are two approaches to reducing structural weight. One of which is to use materials that are lighter than steel and the other is to redesign the structure. However, conventional structural optimization methods using gradient-based algorithm directly have difficulties in defining complex shape design variables and preventing mesh distortions. To overcome these difficulties a metamodel-based optimization method is introduced in order to replace the true response by an approximate one. This research presents four case studies of structural design using a metamodel-based approximation model for weight reduction or performance enhancement.

A Density Gradient Approach to Topology Optimization Under Design-Dependent Boundary Loading

2018 ◽  
Author(s):  
Cunfu Wang ◽  
Xiaoping Qian
Keyword(s):  
Topology Optimization ◽  
Density Gradient ◽  
Optimization Method ◽  
Spatial Gradient ◽  
Computationally Efficient ◽  
Physical Density ◽  
Gradient Based ◽  
Design Dependent Loads ◽  
Previous Iteration ◽  
Gradient Approach

The paper proposes a density gradient based approach to topology optimization under design-dependent boundary loading. In the density-based topology optimization method, we impose the design dependent loads through spatial gradient of the density. We transform design-dependent boundary loads into a volume form through volume integral of density gradient. In many applications where loadings only need to be exerted on partial boundary, we introduce an auxiliary loading density to keep track of the loading boundary. During the optimization, the loading density is updated by tracking the changes of the physical density in the vicinity of the loading boundary at previous iteration. The proposed approach is easy to implement and computationally efficient. In addition, by adding more auxiliary density fields, the proposed approach is applicable to multiple design-dependent loads. To prevent the intersection of different loading boundaries, a Heaviside projection based integral constraint is developed. Both heat conduction problems under convection loading and elastic problems under hydrostatic pressure loading are presented to illustrate the effectiveness and efficiency of the method.

scholarly journals Problems of Choosing Optimization Method for Next-Generation Aviation Radio Control Systems

2019 ◽  
Vol 18 (3) ◽  
pp. 535-557 ◽  
Author(s):  
Vladimir Verba ◽  
Vladimir Merkulov
Keyword(s):  
Control Systems ◽  
Dynamic Properties ◽  
Optimization Methods ◽  
Optimization Method ◽  
Information Support ◽  
Computationally Efficient ◽  
Maneuvering Targets ◽  
Control Signals ◽  
High Dynamics ◽  
And Control

Analysis of the trends of military-technical confrontation in the aerospace sector allows us to identify a number of areas that directly affect the information and control side of the operation of aviation radio control systems, including: group use of means of attack and defence; the qualitative complexity of the laws of the mutual spatial placement of the aircraft; high dynamics, nonstationarity of environment; use of control modes and information support on the verge of buckling, which is characteristic of super-maneuverable aircraft and intensively maneuvering targets tracking systems; the discrepancy of the dynamic properties of airborne targets and interceptors; growing complexity of information support algorithms. Mathematical apparatus used for synthesis of aircraft control systems must provide: effective guidance on targets maneuvering under complex laws, including the change of signs of derivatives; guaranteed withdrawal from the boundaries of stable (dangerous) work, including collision prevention in groups; accounting for the discrepancy between the dynamic properties; redistribution of control priorities in the guidance process; universality of the formation of guidance methods and feasibility of information support algorithms. Analysis of the possibilities of classical optimization methods based on minimization of quadratic quality functionals showed that they are not able to meet the totality of these requirements and thus new approaches are required. As such, it is proposed to use the synthesis of control signals that are optimal for a minimum of quadratic-biquadrate quality functional. The application of this approach in the framework of computationally efficient local optimization is considered. An example of the synthesis of a method of guidance, illustrating the possibility of the formation of control signals, providing guidance of inertial aircraft to intensively maneuvering targets accounting for both linear and nonlinear dependences on the operation errors and the mismatch of the dynamic characteristics of the target and interceptor.

scholarly journals Architectural Models Enabled Dynamic Optimization for System-of-Systems Evolution

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zhemei Fang ◽  
Xiaozhou Zhou ◽  
Ani Song
Keyword(s):  
Dynamic Optimization ◽  
Systems Engineering ◽  
Conceptual Models ◽  
Mathematical Optimization ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Optimization Method ◽  
System Of Systems ◽  
Architectural Representation ◽  
Architecture Framework

System of Systems (SoS) is designed to deliver value to participant stakeholders in a dynamic and uncertain environment where new systems are added and current systems are removed continuously and on their own volition. This requires effective evolution management at the SoS architectural level with adequate support of process, methods, and tools. This paper follows the principle of Model-Based Systems Engineering (MBSE) and develops a holistic framework integrating MBSE conceptual representations and approximate dynamic programming (ADP) to support the SoS evolution. The conceptual models provide a common architectural representation to improve communication between various decision makers while the dynamic optimization method suggests evolution planning decisions from the analytical perspective. The Department of Defense Architecture Framework (DoDAF) models using Systems Modeling Language (SysML) are used as MBSE artifacts to connect with ADP modeling elements through DoDAF metamodels to increase information traceability and reduce unnecessary information loss. Using a surface warfare SoS as an example, this paper demonstrates and explains the procedures of developing DoDAF models, mapping DoDAF models to ADP elements, formulating ADP formulation, and generating evolutionary decisions. The effectiveness of using ADP in supporting evolution to achieve a near-optimal solution that can maximize the SoS capability over time is illustrated by comparing ADP solution to other alternative solutions. The entire framework also sheds light on bridging the DoDAF-based conceptual models and other mathematical optimization methods.

Synthesis of Gear-Linkage Mechanisms by Topology Optimization

2018 ◽  
Author(s):  
Neung Hwan Yim ◽  
Seok Won Kang ◽  
Yoon Young Kim
Keyword(s):  
Topology Optimization ◽  
Design Space ◽  
Synthesis Method ◽  
Variable Stiffness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Planar Mechanisms ◽  
Gear Design ◽  
Linkage Design ◽  
Gradient Based

This work is concerned with a new mechanism synthesis method for the simultaneous determination of the type, number and dimension of mechanisms by topology optimization. Earlier topology optimization methods can synthesize linkage mechanisms that consist only of links and joints. The proposed synthesis method is a gradient-based topology optimization method useful for the synthesis of planar mechanisms consisting of linkages and gears. To formulate the topology optimization based method, we propose two superposed design spaces as a ground structure: the linkage and gear design spaces. The gear design space is discretized by newly proposed gear blocks while the linkage design space by rigid blocks. The zero-length springs with variable stiffness are used to control the connectivity of blocks, which in turns determines the configuration of the synthesized mechanism. After the proposed topology-optimization-based synthesis formulation is presented, its effectiveness and validity are checked with various synthesis examples.

scholarly journals ANALYSIS OF MODERN TECHNIQUES FOR SOFTWARE OPTIMIZATION

2021 ◽  
Vol 10 (7) ◽  
pp. 46-55
Author(s):  
Okardi, Biobele Ojekudo ◽  
Nathaniel Akofure
Keyword(s):  
Genetic Algorithm ◽  
Traditional Method ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
High Quality ◽  
Artificial Intelligent ◽  
Intelligent Technique ◽  
Artificial Intelligent Technique ◽  
Modern Optimization

Traditional Methods of optimization have failed to meet up the rapid changing world in the demand of high quality and accuracy in solution delivery. Optimization literally means looking for the best possible or most desired solution to a problem. Optimization techniques are basically classified into three groups, namely; the Traditional Method, Artificial Intelligent Method, and Hybrid Artificial Intelligent technique. In this paper, an attempt is made to review literatures on different modern optimization techniques for application in various disciplines. A general review was made on some of the modern optimization methods such as Genetic Algorithm, Ant colony method, Honey Bee optimization method, and Simulated Annealing optimization.

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

scholarly journals Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System

2021 ◽  
Vol 13 (4) ◽  
pp. 707
Author(s):  
Yu’e Shao ◽  
Hui Ma ◽  
Shenghua Zhou ◽  
Xue Wang ◽  
Michail Antoniou ◽  
...  
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Satellite System ◽  
Radar System ◽  
Target Localization ◽  
Computational Load ◽  
Matrix Fusion ◽  
Global Navigation Satellite ◽  
Multistatic Radar ◽  
Combined Algorithm

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time.

Sign in / Sign up

Export Citation Format

Share Document