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+62 811 1933 3635 prk@brin.go.id Bogor, Indonesia
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+62 811 1933 3635

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ACMath > Profile
about
Since 2022

Group Overview

Applied and Computational Mathematics Research Group, formerly known as Modelling and Simulation, is a research group established in 2022 as a part of Research Center for Computing, National Research and Innovation Agency (BRIN).

Our research scopes are on a wide range of applied and computational mathematics including numerical methods for ODEs and PDEs, nonlinear waves, numerical hydrodynamics, climate-related modelling and analysis, computational fluid dynamics for coastal-ocean environment and engineering.

Currently, our research focuses on:

  • 1. Surface water waves modelling and simulation
  • 2. Multiphase CFD model for fluid-structure-seabed interactions and landslide-induced impulsive wave/tsunami
  • 3. Climate impact on natural disaster
Focus
Research Focus

What We Do

Surface Water Wave Modelling and Simulation

The research deals with surface water wave modeling and simulation. Since collaborating with LabMath-Indonesia (LMI) in 2019, we have investigated wave evolution over various bathymetry using HAWASSI developed by LMI, which is based on a Bousinessq-type model. Our research explores mathematical and numerical modeling to understand the behavior of surface water waves due to bathymetry or other physical structures. The numerical wave simulation covers a laboratory or real scale from deep to shallow water. The main application areas include offshore and coastal engineering issues such as harbor design, coastal safety, tsunami, etc. We are also developing a coupling method between multiphase fluid and a Bousinessq-based model for numerical simulation of landslide-generated tsunami waves.

Climate Change Impact on Natural Disaster

Climate change has drawn attention as it plays a role in intensifying the natural disaster. The abundance of climate data from model simulation and observations motivate us to study the impact of climate change on natural disaster, for example, tropical cyclone, forest fire. In our study, the need for high-resolution climate data has been met by advancing dynamical and statistical downscaling methods. Also, using artificial intelligence techniques, we investigate environmental factors affecting the forest fire. Furthermore, we also develop a model-based fire spread prediction using Cellular automata.

Multiphase Granular Flow CFD Model

Develop a robust CFD-based model in the open-source platform for simulating granular flows and the fluid-structure-sediment interactions, applied for coastal/ocean engineering and physical processes.