Accelerated DC loadflow solver for topology optimization

An approach for reliability-based topology optimization of interval parameters structures under dynamic loads is proposed. We modify the equivalent static loads method for non linear static response structural optimization (ESLSO) to solve the dynamic reliability optimization problem. In our modified ESLSO, the equivalent static loads (ESLs) are redefined to consider the uncertainties. The new ESLs including all the uncertainties from geometric dimensions, material properties and loading conditions generate the same interval response field as dynamic loads. Based on the definition of the interval non-probabilistic reliability index, we construct the static reliability topology optimization model using ESLs. The method of moving asymptotes (MMA) is employed as the optimization problem solver. The applicability and validity of the proposed model and numerical techniques are demonstrated with three numerical examples.

A continuous variable optimization method and a topological optimization method are proposed for the vibration control of piezoelectric truss structures by means of the optimal placements of active bars. In this optimization model, a zero-one discrete variable is defined in order to solve the optimal placement of piezoelectric active bars. At the same time, the feedback gains are also optimized as continuous design variables. A two-phase procedure is proposed to solve the optimization problem. The sequential linear programming algorithm is used to solve optimization problem and the sensitivity analysis is carried out for objective and constraint functions to make linear approximations. On the basis of the Newmark time integration of structural transient dynamic responses, a new sensitivity analysis method is developed in this paper for the vibration control problem of piezoelectric truss structures with respect to various kinds of design variables. Numerical examples are given in the paper to demonstrate the effectiveness of the methods.

The present work shows an innovative design framework for fluid Topology Optimization (TO) able to fully exploit the flexibility offered by Additive Manufacturing (AM) in the production of fluid-structure interaction systems. We present a geometry optimization method able to automatically design complex and efficient heat exchangers, adapted to maximizing fluid-structure heat transfer while minimizing turbulent flow pressure drop. The core of the method is the in-house Fluid Topology Optimization solver extended to include conjugate heat transfer problems. The TO method consists in emulating a sedimentation process inside an empty cavity in which a fluid dynamics problem is numerically solved. A design variable, in this case impermeability, is iteratively updated across the fluid dynamics domain. This mechanism leads to the formation of internal solid structures accordingly to a Lagrangian multi-objective optimization approach, adopted to include a multi-objective function. The solution of the optimization routine is the set of solidified structures, shaping the final optimized geometry. In order to match engineering applications, real conditions are implemented: an impermeability dependent thermal conductivity is included and a smoother operator is adopted to bound numerical thermal conductivity gradients across solid and fluid regions. The optimization is performed on a 3-dimensional straight duct: on the walls the temperature is constant and a coolant turbulent flow is simulated (Re 10000) inside the duct. The solver builds structures enhancing the heat transfer level between the walls of the domain and a coolant flow, by generating counter rotating vortices and complex fluid patterns. This is consistent to solution proposed in the open literature, such as v-shaped ribs, even if the geometry generated is more complex and efficient. The solution is validated with a high fidelity numerical simulation on StarCCM+, using a Detached Eddy Simulation (DES). Validation results shows higher heat transfer efficiency compared to the results present in the literature: the average Nusselt number computed on the domain walls is about 20% higher than the value obtained through experimental investigations on v-shape ribbed ducts. It is the first time that this method is applied and validated on real working conditions.

The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller's method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts.

A combinatoric-approximation method for the decomposition and composition of systems, and finite topological spaces, lattices, and optimization

<p>This paper deals with optimization of Gripper engine block manipulator. The Gripper provides the holding to the engine block is mounted on the Subject manipulator. There are two brackets to hold engine block. Analysis is done on one gripper and the results are considered for other one gripper. Analysis is done for only Gripper. On production line first clamp engine block, lift engine block then rotate 180º and keep on same position on assembly line its two degree freedom. The gripper manipulator is a two degrees-offreedom manipulator, with one linear hoist and rotates with help pneumatic motor. FEM Optimization is useful in designing the new shape and size with reduced design time. It also helps to reduction of unnecessary material and thereby weight of the component decreases. To optimized design with the help of hyper mesh, hyper works and optistuct solver for optimization the design. A gripper is used to hold work piece accurately & stable during the engine block handling. Because of increase in global competition we must decrease the cost and at the same time increase the performance and achieve all these with time constraint. Topology optimization tool is used not only designing gripper bracket analysis to get better design with less time but also helps in the reduce of unwanted material. Gripper engine block Structure is optimized to minimize the mass and the stress requirements.</p> <p><a href="https://journalnx.com/journal-article/20150092">https://journalnx.com/journal-article/20150092</a></p>