Simulation of three-dimensional viscous flow in turbomachinery geometries using a solution-adaptive unstructured mesh methodology

This paper presents a numerical method for the simulation of flow in turbomachinery blade rows using a solution-adaptive mesh methodology. The fully three-dimensional, compressible, Reynolds-averaged Navier-Stokes equations with k-ε turbulence modeling (and low Reynolds number damping terms) are solved on an unstructured mesh formed from tetrahedral finite volumes. At stages in the solution, mesh refinement is carried out based on flagging cell faces with either a fractional variation of a chosen variable (like Mach number) greater than a given threshold or with a mean value of the chosen variable within a given range. Several solutions are presented, including that for the highly three-dimensional flow associated with the corner stall and secondary flow in a transonic compressor cascade, to demonstrate the potential of the new method.

RANS-URANS in axial compressor, a design methodology

Turbomachinery flows are inherently unsteady. Until now during the design process, unsteadiness has been neglected, with resort merely to steady numerical simulations. Despite the assumption involved, the results obtained with steady simulations have been used with success. One of the questions arising in recent years is can unsteady simulations be used to improve the design of turbomachines? In this work the numerical simulation of a multi-stage axial compressor is carried out. Comparison of Reynolds averaged Navier-Stokes (RANS) and unsteady Reynolds averaged Navier-Stokes (URANS) calculation shows that the unsteadiness affects pressure losses and the prediction of stall limit. The unsteady inflow due to the wake passing mainly modifies the losses and whirl angle near the endwalls. The computational cost of the fully unsteady compared with a steady simulation is about four times in terms of mesh dimension and two orders of magnitude as number of iterations. A mixed RANS-URANS solution has been proposed to give the designer the possibility to simulate an unsteady stage embedded in a steady-state simulation. This method has been applied to the simulation of a four-stage axial compressor rig. The mixed RANS-URANS approach has been developed using sliding and mixing planes as interface conditions. The rotor-stator interaction has been captured physically while reducing the computational time and mesh size.

A process model based methodology to support conflict management

The product design development has increasingly become a collaborative process. Conflicts often appear in the design process due to multi-actors interactions. Therefore, a critical element of collaborative design would be conflict situations resolution. In this paper, a methodology, based on a process model, is proposed to support conflict management. This methodology deals mainly with the conflict resolution team identification and the solution impact evaluation issues. The proposed process model allows the design process traceability and the data dependencies network identification; which making it be possible to identify the conflict resolution actors as well as to evaluate the selected solution impact. Copyright © 2006 IFAC.

Application of the fixed point method for solution in time stepping finite element analysis using the inverse vector Jiles-Atherton model

An implementation of the inverse vector Jiles-Atherton model for the solution of non-linear hysteretic finite element problems is presented. The implementation applies the fixed point method with differential reluctivity values obtained from the Jiles-Atherton model. Differential reluctivities are usually computed using numerical differentiation, which is ill-posed and amplifies small perturbations causing large sudden increases or decreases of differential reluctivity values, which may cause numerical problems. A rule based algorithm for conditioning differential reluctivity values is presented. Unwanted perturbations on the computed differential reluctivity values are eliminated or reduced with the aim to guarantee convergence. Details of the algorithm are presented together with an evaluation of the algorithm by a numerical example. The algorithm is shown to guarantee convergence, although the rate of convergence depends on the choice of algorithm parameters. © 2011 IEEE.