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Simultaneous Design of Structural Topology and Control
Published in Norman M. Wereley, Inderjit Chopra, Darryll J. Pines, Twelfth International Conference on Adaptive Structures and Technologies, 2017
Zhu Ye, Jinhao Qiu, Du Hejun, Junji Tani, Masanori Murai
where, f1, f2, f13, and f6 denote the closed-loop H2 norm, volume ratio of the whole structure V, perimeter L, and volume ratio of actuator bonding area Vact, respectively. While f4 and f5 are the two static displacement outputs y1 and y2 caused by the static external disturbance inputs w1 and w2, respectively. The constants F1, F2, ‖, and F6, are listed in Table II. From the above definition and the table, it can be seen that firstly, the maximum volume ratio is set to be 0.5, i.e., in the final topology the number of solid elements will not exceed 50% of the total element number. Secondly, to assure the actuator bonding quality, the minimum volume ratio of actuator bonding area is set to be 0.99, i.e., the bonding areas are forced to be almost solid. Thirdly, the perimeter, static displacement 1 and 2 are limited to be not greater than F3, F4 and F5. Finally, upper and lower bounds are imposed on the actuator locations ax and ay and the topology variable x.
Graph-Theoretic Algorithms for Energy Saving in IP Networks
Published in F. Richard Yu, Xi Zhang, Victor C. M. Leung, Green Communications and Networking, 2016
Francesca Cuomo, Antonio Cianfrani, Marco Polverini
The first two steps are described in Algorithm 4. As in the ESACON case the input of the algorithm is the network topology G and the output is an ordered list of the links composing the network, denoted as ℒ. The key difference with respect to ESACON is that the criterion used by ESTOP to order the network links is ℬ. Links are arranged in increasing order of ℬ, since the aim is to put into sleep mode those links that are less used in the paths between each pair of nodes. The algorithm computes, by using the Dijkstra algorithm, the shortest paths for every pair of nodes, and for each path it finds the links belonging to it and increases ℬ associated with them (lines 6-10 of Algorithm 4). After that, on the basis of the ℬ values, the ordered list ℒ is created containing every bidirectional link of the network. Links at the top of the list are those less used in the shortest paths and their switch off entails a small number of paths to be recalculated. As for the second step, the input is the ordered list ℒ and the output is a final topology Gfin, defined as in (6.5), and the set of links that can be put into sleep mode still keeping the connectivity over a given threshold. In order to evaluate the loss of connectivity produced by this switching off, we use also in this case the expression in (6.3) where 𝒜(G′) is the algebraic connectivity of a reduced graph G′, after the deletion of some links from G. Also for ESTOP the connectivity condition is the one in (6.4).
Cloud-Native Design
Published in Haishi Bai, Zen of Cloud, 2019
In this lab, you'll create a simple website that is deployed to two of the Azure global regions. Then you'll send up a Traffic Manager profile as the entry point to your global website. Users use the traffic manager endpoint—globalsite.trafficmanager.net—to access the site, and they are routed to either mysite-uswest.azurewebsites.net or mysite-japaneast.azurewebsites.net based on a policy of your choice, such as by performance, by assigned weights, or by priorities. The final topology of the site is illustrated in Figure 2.8 (note you need to choose a different set of DNS names in your experiment because DNS names have to be globally unique).
Conceptual design of efficient heat conductors using multi-material topology optimization
Published in Engineering Optimization, 2019
Jaejong Park, Tam H. Nguyen, Jami J. Shah, Alok Sutradhar
Topology optimization is another suitable computational approach that can be employed to derive the geometry of the objective-optimized structures. It iteratively optimizes the layout of a certain amount of material in a given design domain subjected to prescribed loading and boundary conditions such that the final topology satisfies the design objectives and desired performance. This leads topology optimization to have greater design flexibilities than traditional size or shape optimization, and it has attracted tremendous interest since the seminal work of Bendsoe and Kikuchi (1988). Since its inception, topology optimization has been primarily used in structural mechanics problems, but recent research efforts have expanded to many other engineering problems, e.g. fluid flow (Borrvall and Petersson 2003; Guest and Prevost 2006; Zhou and Li 2008; Challis and Guest 2009), buoyant structures (Picelli et al.2017), propagation of waves and acoustics (Silva and Kikuchi 1999; Rupp et al.2007; Dahl, Jensen, and Sigmund 2008), bi-modulus continuum structures (Cai, Gao, and Shi 2014), structural bifurcation via multiobjective optimization (de Kruijf et al.2007), multi-physics considerations (Li, Steven, and Xie 2001; Yin and Ananthasuresh 2002; Torquato, Hyun, and Donev 2002; Lee, Dede, and Nomura 2011; Haertel and Nellis 2017), and biomedical problems (Sutradhar et al.2014, 2016; Park et al.2018) to name but a few. A more detailed overview of topology optimization and recent advances can be found in Sigmund and Maute (2013).
Conceptual Design of the Pedestrian Bridge
Published in Structural Engineering International, 2022
Pengzhen Lu, Yutao Zhou, Qun Lu, Jiahao Wang, Qingtian Shi, Dengguo Li
TO is divided into continuum topology optimization and discrete topology optimization. In either field, it depends on the finite element method. Continuum topology optimization is the discretization of materials in the optimized space into finite units (shell units or volume units). Discrete structure topology optimization establishes a base structure consisting of a finite number of beam elements in the design space, and then determines the removal or retention of elements in the design space according to the algorithm. The remaining elements constitute the final topology scheme, so as to achieve topology optimization.