Computational Design Tool for the Synthesis and Optimization of Gel Formulations (SOGeF)

摘要

Gel propulsion systems combine the best characteristics of solid and liquid propellants. The gel system stores like solid propellant, but flows like a liquid when pressurized, enabling throttle and restart capability similar to liquid propellants. In addition, gels have a lower vapor pressure compared to their liquid counterparts. Consequently, the storage containers holding them can be much lighter. An enabling technology for the further advancement of gelled propulsion is the development of tools to render the synthesis of propellant formulations more systematic. In Phase I, various models were developed to support the project objectives. Given the multi-scale scope of the problem, first quantum mechanical and Grand Canonical Monte Carlo (GCMC) simulations were used. The results of these simulations were then fed into Stokesian models to compute stress characteristics. Finally, Brownian and continuum models were used for syneresis studies. In Phase II, Quantitative Structure-Property Relationship (QSPR) techniques, in the form of a feed- forward / back propagation neural network (NN), were employed to join together the molecular-scale, particle-scale, and continuum models begun in Phase I. In addition, a large-scale organic gel database (over 39 different entries) was created to train and validate the NN. To facilitate operator handling, the network and database were wrapped in a convenient and robust Graphical User Interface (GUI), written in the platform independent Python programming language. Preliminary results show that this software (named SOGeF-Synthesis and Optimization of Gel Formulations) offers a reliable and comprehensive design tool for the prediction of gel formulations. SOGeF uses gel constituent properties (i.e. solvent density, boiling point, silica content, etc.) to make predictions of gelled properties. This approach is advantageous over physics- based models in that input parameters are easily identified and can be arbitrarily chosen.