The ability to accurately predict electro-optical signatures for high-value targets in an outdoor scene is a tremendous asset for defense agencies. In the thermal infrared wavebands, physical temperature is the primary contributor to imager-detected radiance. Consequently, enhancements to the fidelity of thermal predictions are desirable, and convection estimates are often the most significant source of uncertainty. One traditional method employed by MuSES applies a single global convection coefficient across the entire scene, and assumes that all exposed surfaces are in contact with the ambient air temperature. This results in a rapid prediction of convection coefficients for a large scene that changes dynamically with wind speed but lacks localized detail concerning how each target surface can experience a different convection coefficient and local air temperature. In practice, wind speed and directions typically change frequently, which coupled with the thermal mass of most targets reduces the negative impacts this approach can have on thermal predictions; numerous validations of MuSES predictions bear this out. Computational fluid dynamics (CFD) simulations provide additional spatial fidelity in the calculation of localized convection coefficients and air temperatures but only at great computational cost. A fully transient outdoor scene simulation would be nearly impossible. Boundary layers near surfaces must be resolved with a fine mesh, creating a numerical problem difficult to solve for large spatial scales when spanning large periods of simulated time. In this paper, a novel thermal fluid flow solver is presented. This proprietary flow solver models fluid flow at the spatial resolution and accuracy needed for convective heat transfer at the scale viewed by EO/IR (Electro-optical/Infrared) sensors, avoiding the burdens associated with conventional CFD codes. Many of the same Navier-Stokes equations are solved, albeit in simpler form. ThermoAnalytics-developed correlations directly calculate convection coefficients based on local bulk flow conditions of temperature, velocity, and pressure. The resulting accuracy is a large step beyond using a constant convection correlation universally across the scene. Intelligent simplification of the flow equations provides robust efficiency, requiring minimal effort and expertise compared to CFD codes.