We are working on state-of-the-art aerodynamic design, analysis, and optimization for aerospace and non-aerospace applications. Our research group develops advanced experimental and computational techniques to study complex fluid phenomena and to design and optimize aerodynamic bodies. We have applied our methods to analyze and design complex aerospace bodies on the applications side, including wind turbine blades, fluid machinery, and unmanned aerial vehicles. Current research that we are currently working on include:
1. Design and development of high-altitude long endurance (HALE) UAV.
2. Machine learning techniques for fluid flow analysis and aerodynamic optimization.
3. Development of meshless computational fluid dynamics solvers.
Design and Development of High-Altitude Long Endurance (HALE) UAV
Faculties involved: Dr. -Ing. Moch. Agoes Moelyadi
High Altitude Long Endurance is an Unmanned Aerial Vehicle designed for various missions such as surveillance, mapping, telecommunication. The evolution of last design configuration with tandem wings connected with triples booms presented in the right hand side. This research has involved lecturer, research assistants from many discipline such as aerodynamics, stability& controls, performances, propulsions, structure and systems.
Development of Meshless Computational Fluid Dynamics Solvers
Faculties involved: Lavi Rizki Zuhal
The aim of the development of mesh-free CFDs is to solve intricate fluid dynamics problems that are difficult to be handled by conventional mesh-based CFDs. The main advantage of mesh-free CFD techniques is that they eliminate the need for the time-consuming mesh generation process. The mesh-free Computational Fluid Dynamics (CFD) and Computational Solid Mechanics (CSM) methods that have been successfully developed are Vortex Element Method, Smooth Particle Hydrodynamics (SPH), Particle Strength Exchange (PSE), and Discretization Correction Particle Strength Exchange (DC-PSE).
Machine learning techniques for Fluid Flow Analysis and Aerodynamic Design
Faculties involved: Pramudita Satria Palar, Lavi Rizki Zuhal
Our group is working on the development of advanced machine learning techniques that are tailored for applications to aerodynamic design optimization. Combined with advanced computational and experimental techniques, our machine learning techniques are capable of discovering important fluid phenomenon and optimized aerodynamic bodies. Another research topic that we are currently focusing on is robust optimization, which aims to find high-performance optimal designs that can withstand the impact of uncertainty. We have applied our methods on various problems, including wind turbine design, axial compressor design, biomedical flow, and flapping wing mechanism. The figure below shows an application of our method to multi-objective optimization of an axial transonic compressor.
The following figures show another application of our method to airfoil optimization to reduce the wave drag of a wing in a transonic flow.