SBIR PHASE I: A New Approach to Particle Characterization - The Probability Density Function Propagation (PDFP) Model

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This Small Business Innovation Research (SBIR) Phase I project will develop an innovative particle transport and dispersion model called the Probability Density Function Propagation (PDFP) models, and implements the model into a commercial code. The PDFP model tracks the Probability Density Function (PDF) of particle position as a function of space and time. Only one trajectory for each class of particle size or type needs to be calculated, as compared to the 2000 to 6000 trajectories needed in current state-of-the-art particulate models. This makes the PDFP model very computationally efficient, which is important for simulations with large numbers of particles where the particle calculations take a significant percentage of the CPU time. From the particle PDF's calculated in the model, the properties of the dispersed phase (solid or liquid) can be calculated anywhere in the model, providing a complete statistical representation of the particle behavior. The combination of computational speed and capability to completely describe the particles (solid or liquid) will make the PDFP model a valuable tool for engineers analyzing two-phase flows. In Phase I, the feasibility of the model will be demonstrated by comparing predictions with published data.The Probability Density Function Propagation (PDFP) model will be developed and implemented in a commercial code. The capability will be implemented in an existing commercial code and will provide the capability to easily simulate two-phase flows that are difficult or very time consuming using current state-of-the-art particle models. Examples of applications where the PDFP model can be used include characterizing nonreacting sprays for ignition calculations, atmospheric dispersion, pneumatic transport, spray cooling, and any other application where large number of particles must be modeled.