Shape optimization of braced frames for tall timber buildings: Influence of semi-rigid connections on design and optimization process

2020 ◽  
Vol 216 ◽  
pp. 110692 ◽  
Author(s):  
Koliann Mam ◽  
Cyril Douthe ◽  
Robert Le Roy ◽  
François Consigny
Keyword(s):  
Shape Optimization ◽  
Optimization Process ◽  
Braced Frames ◽  
Design And Optimization

Tooth Root Shape Optimization of Thin-Rimmed Planet Gears

2019 ◽  
Author(s):  
Chen-Xiang Chao ◽  
Dieter Bestle ◽  
David Krüger
Keyword(s):  
Shape Optimization ◽  
High Power ◽  
Load Capacity ◽  
Weight Ratio ◽  
Optimization Process ◽  
Tooth Root ◽  
Turbofan Engines ◽  
To Come

Abstract Planetary gearboxes in highly sophisticated applications such as turbofan engines are required to have a high power-to-weight ratio and excellent reliability. Hence, thin-rimmed gear units need to be designed as compact as possible which, however, is usually limited by the tooth root load capacity. In order to come up with the best design, a tooth root shape optimization process is developed for thin-rimmed planet gears.

A two-stage shape optimization process for cavity preparation

2008 ◽  
Vol 24 (11) ◽  
pp. 1444-1453 ◽  
Author(s):  
Li Shi ◽  
Alex S.L. Fok ◽  
Alison Qualtrough
Keyword(s):  
Shape Optimization ◽  
Optimization Process ◽  
Two Stage ◽  
Cavity Preparation

Parametric and Topological Control in Shape Optimization

2006 ◽  
Author(s):  
Jiaqin Chen ◽  
Vadim Shapiro ◽  
Krishnan Suresh ◽  
Igor Tsukanov
Keyword(s):  
Shape Optimization ◽  
Optimization Techniques ◽  
Free Form ◽  
Large Space ◽  
Design And Optimization ◽  
B Splines ◽  
Novel Approach ◽  
Higher Dimensional ◽  
Special Cases ◽  
R Functions

We propose a novel approach to shape optimization that combines and retains the advantages of the earlier optimization techniques. The shapes in the design space are represented implicitly as level sets of a higher-dimensional function that is constructed using B-splines (to allow free-form deformations), and parameterized primitives combined with R-functions (to support desired parametric changes). Our approach to shape design and optimization offers great flexibility because it provides explicit parametric control of geometry and topology within a large space of freeform shapes. The resulting method is also general in that it subsumes most other types of shape optimization as special cases. We describe an implementation of the proposed technique with attractive numerical properties. The effectiveness of the method is demonstrated by several numerical examples.

Evolutionary Multi-Agent Systems: An Adaptive Approach to Optimization in Dynamic Environments

2008 ◽  
Author(s):  
Lindsay Hanna ◽  
Jonathan Cagan
Keyword(s):  
Organizational Structure ◽  
Engineering Design ◽  
Dynamic Environments ◽  
Optimization Process ◽  
Multi Agent Systems ◽  
Design And Optimization ◽  
Solution Strategies ◽  
Engineering Design Problems ◽  
Potential Applications ◽  
Multi Agent

This paper explores the ability of a team of autonomous software agents to be effective in unknown and changing optimization environments by evolving to use the most successful algorithms at the points in the optimization process where they will be the most effective. We present the core framework and methodology which has potential applications in layout, scheduling, manufacturing, and other engineering design areas. The communal agent team organizational structure employed allows cooperation of agents through the products of their work and creates an ever changing set of individual solutions for the agents to work on. In addition, the organizational structure allows the framework to be adaptive to changes in the design space that occur during the optimization process — making our approach extremely flexible to the kinds of dynamic environments encountered in engineering design problems. An evolutionary approach is used, but evolution occurs at the strategic, rather than solution level — where the strategies of agents in the team (the decisions for picking, altering, and inserting a solution) evolve over time. As an application of this approach, individual solutions are tours in the familiar combinatorial optimization problem of the traveling salesman. With a constantly changing set of these tours, the team, each agent running a different solution strategy, must evolve to apply the solution strategies which are most useful given the set at any point in the process. As a team, the evolutionary agents produce better solutions than any individual algorithm. We discuss the extensions to our preliminary work that will make our framework highly useful to the design and optimization community.

Shape Optimization of Potentially Slender Structures

2008 ◽  
Author(s):  
Kavous Jorabchi ◽  
Joshua Danczyk ◽  
Krishnan Suresh
Keyword(s):  
Finite Element ◽  
Shape Optimization ◽  
Dimensional Reduction ◽  
A Priori ◽  
Computational Cost ◽  
Optimization Process ◽  
Element Analysis ◽  
Dimensional Reduction Method ◽  
Modern Engineering ◽  
Geometric Reduction

Shape optimization lies at the heart of modern engineering design. Through shape optimization, computers can, in theory, ‘synthesize’ engineering artifacts in a fully automated fashion. However, a serious limitation today is that the evolving geometry (during optimization) may become slender, i.e., beam or plate-like. Under such circumstances, modern 3-D computational methods, such as finite element analysis (FEA), will fail miserably, and so will the shape optimization process. Indeed, the recommended method for analyzing slender artifacts is to replace them with 1-D beams/ 2-D plates, prior to discretization and computational analysis, a process referred to as geometric dimensional reduction. Unfortunately explicit geometric reduction is impractical and hard to automate during optimization since one cannot predict a priori when an artifact will become slender. In this paper, we develop an implicit dimensional reduction method where the reduction is achieved through an algebraic process. The proposed method of reduction is computationally equivalent to explicit geometric reduction for comparable computational cost. However, the proposed method can be easily automated and integrated within a shape optimization process, and standard off-the-shelf 3-D finite element packages can be used to implement the proposed methodology.

Shape Optimization Using a Variable Number of Conic Patches

1993 ◽  
Author(s):  
James M. Widmann ◽  
Sheri D. Sheppard
Keyword(s):  
Shape Optimization ◽  
Design Space ◽  
Variable Number ◽  
Optimization Process ◽  
Optimized Design ◽  
Two Dimensional ◽  
Geometric Description ◽  
Design Representation ◽  
Design Variables ◽  
Dimensional Shape

Abstract Shape Optimization is a branch of structural optimization in which the boundaries of geometry are varied. The shape of the boundary is determined by optimizing a set of design variables that form the geometric description of the shape. This paper presents a method of two dimensional shape optimization in which the number of design variables is allowed to change during the optimization process. First an initial design representation is chosen and optimized. Next a new mathematical description of the optimized design is created with an increased number of design variables. This new design is subsequently optimized. This allows the optimization process to work within a larger design space that includes a greater variety of shapes. The process of adding design variables is repeated until no additional improvements in the design are made. Several design examples are solved with this procedure and presented.

scholarly journals Small Wind Turbine Blade Design and Optimization

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 18 ◽  
Author(s):  
Hani Muhsen ◽  
Wael Al-Kouz ◽  
Waqar Khan
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Optimization Process ◽  
Power Coefficient ◽  
Speed Ratio ◽  
Horizontal Axis Wind Turbine ◽  
Design And Optimization ◽  
Low Wind Speed ◽  
Low Performance ◽  
Turbine Blade Design

This work aims at designing and optimizing the performance of a small Horizontal-Axis-Wind-Turbine to obtain a power coefficient (CP) higher than 40% at a low wind speed of 5 m/s. Two symmetric in shape airfoils were used to get the final optimized airfoil. The main objective is to optimize the blade parameters that influence the design of the blade since the small turbines are prone to show low performance due to the low Reynolds number as a result of the small size of the rotor and the low wind speed. Therefore, the optimization process will select different airfoils and extract their performance at the design conditions to find the best sections which form the optimal design of the blade. The sections of the blade in the final version mainly consist of two different sections belong to S1210 and S1223 airfoils. The optimization process goes further by investigating the performance of the final design, and it employs the blade element momentum theory to enhance the design. Finally, the rotor-design was obtained, which consists of three blades with a diameter of 4 m, a hub of 20 cm radius, a tip-speed ratio of 6.5 and can obtain about 650 W with a Power coefficient of 0.445 at a wind-speed of 5.5 m/s, reaching a power of 1.18 kW and a power coefficient of 0.40 at a wind-speed of 7 m/s.

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