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Thermal Behavior and Decomposition Kinetics of RDX and RDX/HTPB Composition Using Various Techniques and Methods

Abd-Elghany M., Elbeih A., Hassanein S.

Central European Journal of Energetic Materials

2016

Vol. 13, no. 3

714--735

In this paper, the thermal behavior and decomposition kinetics of trinitrohexahydrotriazine (RDX) and its polymer bonded explosive (PBX) containing a hydroxyl-terminated polybutadiene (HTPB) based polyurethane binder in the ratio 80% RDX/ 20% HTPB were investigated using various experimental techniques and analytical methods. The HTPB polyurethane matrix contains other additives and was cured using hexamethylene diisocyanate (HMDI). Thermogravimetric analysis (TGA), Differential Scanning Calorimetry (DSC), Vacuum Stability Test (VST) and Ignition Delay Techniques were applied both isothermally and non-isothermally. The kinetic parameters were determined using both the isoconversional (model free) and the model-fitting methods. For comparison, Advanced Kinetics and Technology Solution (AKTS) software was also used. It was found that the addition of an HTPB-based polyurethane matrix to pure RDX decreased its decomposition temperature. It was also found that RDX/HTPB has a lower activation energy than pure RDX. The polyurethane matrix had a significant effect on the decomposition mechanism of RDX resulting in different reaction models. It was concluded that the activation energies obtained using the Ozawa, Flynn, and Wall (OFW) and Kissinger-Akahira-Sunose (KAS) methods were very close to the results obtained via the AKTS software lying in the range 218.3-220.2 kJ•mol−1. The VST technique yielded kinetic parameters close to those obtained using TG/DTG. On the other hand, the Ignition Delay Technique yielded different and inconsistent kinetic parameters.

EDFCES: A new example-driven 3D face construction and editing system

Zhang Y., Sung E.

Machine Graphics and Vision

2008

Vol. 17, No. 3

313-346

This paper presents an automatic runtime system for generating varied, realistic face models by synthesizing a global face shape and local facial features according to intuitive, high-level control parameters. Our method takes as examples 3D face scans in order to exploit the parameter-to-geometry correlations present in real faces. In order to establish the correspondences among the scanned models, we use a three-step model fitting approach to conform a generic head mesh onto each scanned model. We transform the obtained data sets of global face shapes and local feature shapes into vector space representations by applying a principal component analysis (PCA). We compute a set of face anthropometric measurements to parameterize the exemplary shapes in the measurement spaces. Using PCA coefficients as a compact shape representation, we approach the shape synthesis problem by forming scattered data interpolation functions designed to generate the desired face shape by taking anthropometric parameters as input. At runtime, the interpolation functions are evaluated for the input parameter values to produce new face geometries at an interactive rate. The correspondence among all exemplary face textures is obtained by parameterizing the 3D generic mesh over a 2D image domain. The new feature texture with the desired attributes is synthesized by interpolating the example textures. The resulting system is intuitive to control and fine-grained. We demonstrate our method by applying different parameters to generate a wide range of face models.