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Development and Study of an Implicit Model for Rapid and Accurate Simulation of Hurricane Storm Surge

Faculty #63
Discipline: Technology and Engineering
Subcategory: STEM Research

Muhammad Akbar - Tennessee State University


In this poster, a parallel storm surge model based on hybrid finite element and finite volume techniques to solve hurricane induced storm surge flow problem is presented. As a hurricane approaches the coastline, a combination of meteorological and hydrological forces causes sea water to rise and rush inland, causing a storm surge. A quick and accurate prediction of a storm surge, its extent, and the possibility of breaching levees are crucial for disaster planning. Storm surge models are used to predict these surges. Storm surge models solve shallow water equations to simulate the hurricane induced floods. The hurricane induced wind stress and pressure, bottom friction, Coriolis Effect, and tidal forcing conditions are used as inputs to this model. Almost all surge models use explicit solvers. An explicit solver finds solution at a new time step based on that at the previous step. The algorithm is easy to implement, but stability requirements heavily restrict the time step size – leading to a longer surge forecasting period. An implicit solver, on the other hand, finds the solution at a new time step using the information of the same step. This algorithm has its own challenges, but it can use larger time steps potentially minimizing simulation time. The implicit solver technique developed in the first year, has been implemented in ADCIRC framework this year. The implementation is currently being benchmarked. Application of the implicit solver in the ADCIRC framework was one of the ultimate objective of the present research. The developed storm surge model is used to hindcast Hurricane Katrina (2005). The simulated Maximum Envelope of Water (MEOW) and High Water Marks (HWM) are compared with published data. The comparison is reasonably good. The results are used to compare parallel performance of the model to the sequential version of the model.

Funder Acknowledgement(s): This study was supported by a HBCU-UP Research Initiative Award grant from NSF to Dr. Muhammad Akbar, Assistant Professor, Tennessee State University, 3500 John A. Merritt Blvd Nashville, TN 37209.

Faculty Advisor: None Listed,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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