Optimal capacity reliability design of networks based on genetic algorithm

The cost, capacity and reliability of components vary in different component types, and the optimal component combination is determined by minimizing the total cost under the constraint of network capacity reliability requirement. To solve the problem that the gradient method can only be applied for networks whose capacity and reliability of components monotonically increase with the cost, a general optimization model is presented, and a Genetic Algorithm (GA) method using the minimal path sets to calculate the network capacity reliability is proposed to solve this optimal capacity reliability design problem. The optimal types of both nodes and links can be obtained using our optimization method. Our case study on ARPA network shows that our algorithm is efficient for the problem with good convergence and search performance

3D BEM-based cooling-channel shape optimization for injection moulding processes

International audienceToday, around 30 % of manufactured plastic goods rely on injection moulding. The cooling time can represents more than 70 % of the injection cycle. Moreover, in order to avoid defects in the manufactured plastic parts, the temperature in the mould must be homogeneous. We propose in this paper a practical methodology to optimize both the position and the shape of the cooling channels in 3D injection moulding processes. For the evaluation of the temperature required both by the objective and the constraint functions, we must solve 3D heat-transfer problems via numerical simulation. We solve the heat-transfer problem using Boundary Element Method (BEM). This yields a reduction of the dimension of the computational space from 3D to 2D,avoiding full 3D remeshing: only the surface of the cooling channels needs to be remeshed at each evaluation required by the optimization algorithm. We propose a general optimization model that attempts at minimizing the desired overall low temperature of the plastic-part surface subject to constraints imposing homogeneity of the temperature. Encouraging preliminary results on two semi-industrial plastic parts show that our optimization methodology is viable

On the extrapolation of magneto-hydro-static equilibria on the sun

Modeling the interface region between solar photosphere and corona is challenging, because the relative importance of magnetic and plasma forces change by several orders of magnitude. While the solar corona can be modeled by the force-free assumption, we need to take care about plasma forces (pressure gradient and gravity) in photosphere and chromosphere, here within the magneto-hydro-static (MHS) model. We solve the MHS equations with the help of an optimization principle and use vector magnetogram as boundary condition. Positive pressure and density are ensured by replacing them with two new basic variables. The Lorentz force during optimization is used to update the plasma pressure on the bottom boundary, which makes the new extrapolation works even without pressure measurement on the photosphere. Our code is tested by using a linear MHS model as reference. From the detailed analyses, we find that the newly developed MHS extrapolation recovers the reference model at high accuracy. The MHS extrapolation is, however, numerically more expensive than the nonlinear force-free field (NLFFF) extrapolation and consequently one should limit their application to regions where plasma forces become important, e.g. in a layer of about 2 Mm above the photosphere.Comment: accepted for publication in Ap