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The implications of constitutive model selection in hyperelastic parameter identification
Published in Bertrand Huneau, Jean-Benoit Le Cam, Yann Marco, Erwan Verron, Constitutive Models for Rubber XI, 2019
S. Connolly, D. Mackenzie, Y. Gorash
Due to the capability of predicting the homogeneous experiments with analytical solutions, these solutions are computed within Microsoft Excel. The optimal parameters are then found using the Solver add-in with a nonlinear generalized reduced gradient (NLGRG) optimization algorithm with 10,000 randomly seeded multi-start parameters. If a global minima is not found, the multi-start population size is increased to 100,000, which further increases the probability of finding the global minima. This method requires the use of upper and lower bounds, which are determined based on any physical or stability constraints from the original publications of the models, or are otherwise defined based on prior experience. If the method does not locate appropriate minima, the objective function is minimized using Simulia’s Isight program and an Adaptive Simulated Annealing (ASA) algorithm with the same upper and lower bounds and the same objective function.
Big Data Computing Using Cloud-Based Technologies
Published in Mahmoud Elkhodr, Qusay F. Hassan, Seyed Shahrestani, Networks of the Future, 2017
Samiya Khan, Kashish A. Shakil, Mansaf Alam
Core fields of studies like physics, biology, and economics involves a lot of quantitative problems. In order to solve these problems, optimization methods are used. Some of these methods that have found wide-ranging use, because of the ease with which they can be parallelized, include the genetic algorithm, simulated annealing, quantum annealing, and adaptive simulated annealing (Sahimi and Hamzehpour 2010).
Robustness optimisation of opening casing wall-strengthened cylindrical pressure hulls based on 6σ
Published in Ships and Offshore Structures, 2023
Feng Liu, Zhen Tian, Jingzheng Yao, Yi Ying
Adaptive simulated annealing (ASA) originated from the traditional simulated annealing algorithm, which provides superior and effective solving capability in global optimisation. Rooted in physics, the annealing concept refers to the heating of solids to extremely high temperatures, which changes their originally ordered structures to disordered random arrangements. During the following slow-cooling treatment, the free-particle state is converted into a lattice state with lower energy. To reach the ideal state, this process requires a sufficiently high heating temperature and a sufficiently slow-cooling rate, ensuring that the thermal equilibrium condition is satisfied throughout the cooling process. The probability of a particular event in a system with energy E is given by Equation 23. where T is the absolute temperature and kB is Boltzmann’s constant.