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New Paradigms in High Pressure Combustion Dynamics Prediction and Control


OBJECTIVE: Develop new paradigms in high pressure combustion dynamics that can render conventional approaches obsolete. Explore innovative applications of emerging research in methods to extract key models and information from large data sets. DESCRIPTION: Advanced combustion systems are becoming increasingly dependent on factors which are controlled by the dynamics of the system. The combustion system dynamics are, in turn, controlled by interacting physiochemical processes such as chemical kinetics, turbulence, multiphase flows, and acoustic motions. In addition, combustion systems are designed to operate at the highest possible pressure in order to maximize thermodynamic efficiency. High pressures increase the energy release density and are known to exacerbate the problem. Modern computational and experimental capabilities are now making it possible to explore increasingly complex combustion dynamic behavior, but come at the expense of swamping analytical systems with vast amounts of data. The sheer amount of data can make it extremely challenging to extract the root causes of the behavior required to solve problems. The increasing geometrical complexity of advanced combustion systems contributes significantly to the problem. Rapid advances are being made in computational mathematics in recent times, however, in extracting key information from large data sets and in building efficient reduced numerical models which maintain the physical fidelity of the complete system to a high degree. Some examples of these innovative methods include the extraction of dynamically relevant modal information (Schmid, 2010), construction of"certifiable"reduced basis models for parameterized systems (see the review by Quarteroni et al, 2011) and the implementation of such techniques as the parameter space becomes very large (Bui-Thanh et al, 2008). To a large extent, integration of advances in these other fields into large scale simulations or experimental data sets has not been explored. There is therefore a broad opportunity for significant innovations leading to new paradigms that could render conventional approaches used today obsolete. Innovations are solicited here in the particular area of high pressure combustion dynamics. Significant interest exists in developing reduced order models (ROM) of complex physical phenomena through systematic model reduction. Interest also exists but is not limited to applying these techniques under conditions of geometric complexity, where component-wise analysis might be required leading to systems of ROMs or systems of ROMs that interact with high fidelity simulations or experiments. Reduced order models that are limited only to chemical kinetics schemes are excluded from this solicitation. PHASE I: Identify and demonstrate the feasibility of innovative applications of emerging research to extract key models and information from large data sets to large scale simulations and experimental data related to high pressure combustion dynamics. PHASE II: Develop the innovation or innovations identified in phase I into a workable framework and demonstrate the approach on a variety of cases. PHASE III: Combustion dynamics controls key factors affecting the performance of a large variety of military applications, including liquid rockets, solid rockets, gas turbines, and augmentors, and non-military applications, including large gas turbines for land based power. REFERENCES: 1. Bui-Thanh, T., Willcox, K., Ghattas, O.,"Model reduction for large-scale systems with high-dimensional parametric input space,"SIAM J. Sci. Compute., V. 30, No. 6, pp. 3270-3288, 2008. 2. Quarteroni, A., Rozza, G., Manzoni, A.,"Certified reduced basis approximation for parameterized partial differential equations and applications,"J. Mathematics in Industry, 1:3, doi:10.1186/2190-5983-1-3, June 2011. 3. Schmid P.J.,"Dynamic mode decomposition of numerical and experimental data,"J. Fluid Mech., V. 656, pp. 5-28, 2010.
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