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Variability-based method for balancing structural optimization and reliability
Published in Hiroshi Yokota, Dan M. Frangopol, Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, 2021
Optimization techniques typically fall into one of three categories – (1) Sizing Optimization, (2) Shape Optimization, or (3) Topology Optimization (Srivastava, 2017). Sizing optimization generally describes the optimization of discrete component sizes (e.g. rod diameter, beam depth) given a fixed structural configuration. Shape optimization identifies nodal coordinates as variables, which are then iterated through any number of algorithmic optimization techniques to identify the optimal nodal coordinates. In this case, the structural sizes are fixed, and the configuration is variable. Topology optimization seeks to identify optimal designs through an iterative approach of treating each meshed area as comprised of either structural material or a void.
Damage detection in structures using particle swarm optimization method
Published in Alka Mahajan, B.A. Modi, Parul Patel, Technology Drivers: Engine for Growth, 2018
Aakash Mohan, Sudhirkumar V. Barai
Particle Swarm Optimization (PSO) is a population-based algorithm and it has been applied to many complex engineering optimization problems because of various advantages including simplicity and convergence speed. PSO has been widely used in engineering, such as for control design, in logic circuit design, for topology optimization, in power systems design, and for shape optimization. Fallahian and Seyedpoor (2010) proposed a two-stage structural damage assessment method using PSO and an adaptive neuro-fuzzy inference system. A hybrid particle swarm optimization–simplex algorithm (PSOS) using frequency domain data was suggested by Begambre and Laier (2009) for structural damage assessment. Perara et al. (2010) implemented PSO and GA for multi-objective inverse damage detection problems. An improved particle swarm optimization algorithm was recommended by Yu and Chen (2010) based on macro-economic strategies.
Optimal shape of underground structure
Published in T. Adachi, K. Tateyama, M. Kimura, Modern Tunneling Science and Technology, 2017
Fig.4 shows the domain with boundary conditions for the shape optimization of a single cavity. A quarter sector is analyzed due to the horizontal and vertical symmetries. A pair of distributed uniform load px and py are applied to the right and top boundaries, respectively. A traction-free curved boundary SX denotes the cavity of which shape is optimized under the sense of the minimum compliance stated before. The area of cavity is unchanged in the course of optimization as the constrained condition. It is expected that the circular shape of cavity is obtained if the isotropic stress condition px = py. Fig.5(a) and (b) shows the initial and final shapes of the cavity. Fig.6 illustrates the close-up view of cavity from which almost complete circular shape is confirmed to be the optimal solution to this fundamental problem.
Multidisciplinary design optimization of a generic b-pillar under package and design constraints
Published in Engineering Optimization, 2021
Yannis Werner, Thomas Vietor, Matthias Weinert, Thomas Erber
Shape-optimization approaches use parametric-modelling techniques. Furthermore, shape-optimization methods are split into node-based (referred to as morphing) and CAD-based shape optimization, see Bletzinger (2018). Node-based approaches are normally limited in their maximum displacement, as the stretching of elements results in poor mesh quality. Low mesh quality causes the numerical quality to be lower. Thus, node-based shape-optimization approaches have their scope on simpler geometric optimization tasks. Conversely, CAD-based parameterization techniques exist in which CAD-models are exported to a pre-processor, which creates the FE-model. This kind of approach is classified as shape optimization. This kind of approach requires the linkage of CAD- and FE-methods, which often leads to errors and labourious processes. For better geometric design and mesh quality, automated FE-meshing is striven for, demonstrated e.g. by a NURBS-based approach in Winter et al. (2019). Hence, another approach to handle this process is the topology-based, implicit CAD structure, described by e.g. Zimmer(2002).
Design and optimization of a bio-inspired hull shape for AUV by surrogate model technology
Published in Engineering Applications of Computational Fluid Mechanics, 2021
Tongshuai Sun, Guangyao Chen, Shaoqiong Yang, Yanhui Wang, Yanzhe Wang, Hua Tan, Lianhong Zhang
On the other hand, in order to reduce the drag of a designed shape, previous scholars have proposed some optimization methods. The traditional shape optimization methods mainly rely on the designer’s experience or the computational fluid dynamics (CFD) software. Alvarez. et al. (2009) proposed a first-order Rankine panel method to optimize the hull shape of an AUV in the operating conditions near a free surface. The CFD method can obtain the hydrodynamic parameters under the design condition without making the physical model of the underwater vehicle, which has the ascendancy of reducing the cost of time and money for each case. It is painstaking for CFD to automatically adjust the main parameters and conduct analysis on each case to obtain an ideal shape. Even there is a very slight change of the shape parameters, the process of modelling, meshing and numerical calculation of the AUV model in CFD software need to be reworked. Hence, the automatic optimization technique has important application value to avoid the drawbacks of a large workload and low efficiency of optimization with CFD.
A three-dimensional topology optimization model for tooth-root morphology
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2018
K.-F. Seitz, J. Grabe, T. Köhne
Nevertheless, the proposed optimization approach offers several potential clinical applications. It would be interesting to apply SKO optimization to jaws with pathological bone loss, where the optimal diameter, length and distribution of dental implants is still a field of debate. Another application in implant therapy would be to simulate the impact of different material parameters such as the bone density, which can vary considerably between lower and upper jaw as well as healthy and diseased bone. Finally, SKO optimization could be based on design domains derived from computer or cone beam tomography scans of individual patients. When shape optimization is taken into account by developing an integrated topology and shape design optimization, the method could result in an individual-based choice of implant form, diameter and length.