In this paper a new moving-boundary algorithm is presented for the solution of two-dimensional flows on unstructured grids. Application of this algorithm is investigated by simulating the internal ballistics of solid rocket motors (SRM). Solution domain is discretised to a number of triangular elements using control-volume based finite-element (CVFE) method. Euler equations are integrated over the control volumes, and Roe's flux-difference scheme is used to evaluate flux vectors at the control-volume surfaces. To eliminate numerical errors due to grid movement, geometric conservation laws (GCL) are carefully implemented. The present algorithm recognises locations along the moving boundaries where the stretched elements result in a distorted grid. To avoid this, new elements are inserted through a local re-triangulation of elements in these regions. Internal ballistics of three SRMs with cylindrical grains burning from inside, are numerically simulated. Using a heat transfer model, erosive burning is included in calculation of propellant burning rate. The aftend of third SRM is tapered to demonstrate capability of present algorithm in solving problems with moving boundaries approaching each other. Very good agreement is observed between the time variations of head-end chamber pressure, obtained from the present work and the other methods.

A new method for deriving the components of drag from the CFD solution of an aircraft flowfield, by analysis of its wake, is presented. This method is applied to viscous flow calculations. It is also applied to configurations containing powerplants. The drag values predicted by this method are compared with the values obtained using more traditional methods, and good agreement is demonstrated.

A new methodology is described to identify aircraft dynamics and extract the corresponding aerodynamic coefficients. The proposed approach makes use of fuzzy modelling for the identification process where input/output data are first classified by means of the concept of fuzzy clustering and then the linguistic rules are extracted from the fuzzy clusters. The fuzzy rule-based models are in the form of affine Takagi-Sugeno models, that are able to approximate a large class of nonlinear systems. A comparative study is performed with existing techniques based on the employment of neural networks, showing interesting advantages of the proposed methodology both for the physical insight of the identified model and the simplicity to obtain accurate results with fewer parameters to be properly tuned.