The iDesign Lab is part of the Aerospace Engineering Department at Iowa State University.
Our vision is develop revolutionary analysis, design, and optimization tools to impact educational and research activities, as well as industrial design.
Specifically, our research focuses on developing efficient algorithms and software, such as computational fluid dynamics and adjoint solvers, to understand the fundamental behavior of multidisciplinary engineering systems and to facilitate their design. The applications include aerodynamic, heat transfer, structural, and hydrodynamic analysis, design, and optimization for next-generation land, air, and sea vehicles, as well as improving long-range laser communication through deep turbulence. See details of the past and ongoing projects.
We have developed an efficient adjoint-based optimization framework (DAFoam) to handle general engineering design problems, as well as a massively-parallel direct numerical simulation solver (Hercules) to predict and understand small-scale turbulence characteristics under stably stratified conditions. Both of these codes are open source.
Our research is supported by the Air Force Office of Scientific Research, National Science Foundation, Department of Defense Supercomputing Resource Center, and Extreme Science and Engineering Discovery Environment.
DAFoam v3 released. A major update that integrated DAFoam with OpenMDAO for multidisciplinary design optimization. [Release notes]Jan. 3, 2022
Heyecan and Zilong presented their papers, Coupled Wing-Propeller Aerodynamic Optimization Using the Adjoint Method. [doi][preprint] and Unsteady Aerodynamic Analysis Using a Galerkin Reduced-order Modeling Approach [doi][preprint], in the AIAA Scitech conference 2022!August. 4, 2021
Our paper, Rapid airfoil design optimization via neural network-based parameterization and surrogate modeling [preprint], was accepted for publication in Aerospace Science and Technology!Dec. 24, 2020
DAFoam-v2.2 released! A major update that adds the Jacobian-free adjoint capability using automatic-differentiation. See the details [here]