Hurricane events combine ocean storm surge penetration with inland runoff flooding. This article presents a new methodology to determine coastal flood levels caused by the combination of storm surge and surface runoff. The proposed approach couples the Simulating Waves Nearshore model and the Advanced Circulation (ADCIRC) model with the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) two-dimensional hydrologic model. Radar precipitation data in a 2D hydrologic model with a circulation model allows simulation of time and spatially varied conditions. The method was applied to study flooding scenarios occurring during the passage of Hurricane Georges (1998) on the east coast of Puerto Rico. The combination of storm surge and surface runoff produced a critical scenario, in terms of flood depth, at this location. The paper describes the data collection process, circulation and hydrologic models, their assemblage and simulation scenarios. Results show that peak flow from inland runoff and peak flow due to storm surge did not coincide in the coastal zone; however, the interaction of both discharges causes an aggravated hazardous condition by increasing flood levels beyond those obtained with storm surge penetration only. Linking of storm surge and hydrologic models are necessary when storm surge conditions occur simultaneously with high precipitation over steep and small coastal watersheds.

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The determination of a reliable tortuosity index is lacking in the aerospace industry. Therefore, a methodology is formulated via direct and indirect characterization methods of a fluid-filled porous media. Chemical, thermal, and mechanical characterization was performed to the PolyuMACTM polyimide foam. Tortuosity was measured considering a pressure difference as the resistivity variable, rather than electrical resistivity or molecular diffusivity, as proposed on previous models. This is an empirical establishment of the tortuosity index considering the correlation among hydraulic and structural dimensionless parameters obtained through the Buckingham’s Pi theorem. The behavior of the polyimide was studied for samples of different lengths compressed at 30%, 60%, and 90% of its original length on the foaming direction. Results show that, porosity, sample length, and fluid viscosity are relevant for the insulation performance of the material. Regression analysis produced a significant statistical model fit to the data correlated from the dimensionless parameters for each dynamic compression series.

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