Search for: All records

A computationally efficient one-shot approach with a low memory footprint is presented for unsteady optimization. The proposed technique is based on a novel and unique approach that combines local-in-time and fixed-point iteration methods to advance the unconverged primal and adjoint solutions forward and backward in time to evaluate the sensitivity of the globally time-integrated objective function. This is in some ways similar to the piggyback iterations in which primal and adjoint solutions are evaluated at a frozen design. During each cycle, the primal, adjoint, and design update problems are solved to advance the optimization problem. This new coupled approach is shown to provide significant savings in the memory footprint while reducing the computational cost of primal and adjoint evaluations per design cycle. The method is first applied to an inverse design problem for the unsteady lid-driven cavity. Following this, vortex suppression and mean drag reduction for a circular cylinder in crossflow is considered. Both of these objectives are achieved by optimizing the rotational speeds for steady or periodically oscillating excitations. For all cases presented in this work, the proposed technique is shown to provide significant reductions in memory as well as computational time. It is also shown that the unsteady optimization problem converges to the same optimal solution obtained using a conventional approach. 

A memory efficient framework is developed for the aerodynamic design optimization of helicopter rotor blades in hover. This framework is based on a fully-automated discrete-adjoint toolbox called FDOT. The in-house toolbox is capable of computing sensitivity or gradient information very accurately, and uses an operator-overloading technique that takes advantage of a unique expression-template-based approach for memory and computational efficiency while still being fully-automated with minimal user interventions. The main goal of the present work is to "design" helicopter rotor blades with increased figure-of-merit. Therefore, the flow around the Caradonna-Tung rotor in non-lifting and lifting hover conditions is studied in order to validate the primal and adjoint solvers based on a rotating frame of reference formulation. The efficacy of the optimization framework is first demonstrated for drag minimization of a rotating NACA 0012 airfoil, which resembles a Vertical-Axis Wind Turbine (VAWT) configuration. Finally, the single- and multi-point design optimization results for the Caradonna-Tung rotor are presented. It is important to note that the current approach (FDOT) can be directly coupled -- in a "black-box" manner -- to other existing codes in the Helios computational platform, which is part of CREATE-AV. 

A computationally efficient "one-shot" approach with a low memory footprint is presented for unsteady design optimization. The proposed technique is based on a novel and unique approach that combines "local-in-time" and fixed-point iteration methods to advance the unconverged primal and adjoint solutions forward and backward in time to evaluate the sensitivity of the globally time-integrated objective function. This is in some ways similar to the "piggyback" iterations where primal and adjoint solutions are evaluated at a frozen design. During each cycle, the primal, adjoint, and design update problems are solved to advance the optimization problem. This new coupled approach is shown to provide significant savings in the memory footprint while reducing the computational cost of primal and adjoint evaluations per design cycle. The method is first applied to an inverse design problem for the unsteady lid-driven cavity. Following this, vortex suppression and mean drag reduction for a circular cylinder in cross-flow is considered. Both of these objectives are achieved by optimizing the rotational speeds for steady or periodically oscillating excitations. For all cases presented in this work, the proposed technique is shown to provide significant reductions in memory as well as computational time. It is also shown that the unsteady design optimization problem converges to the same optimal solution obtained using a conventional approach. 

An improved version of the Fast automatic Differentiation using Operator-overloading Technique (FDOT) toolbox is developed in this work. The enhanced sensitivity analysis toolbox utilizes an expression-based tape approach -- a first-of-its-kind implementation in Fortran programming language -- that can significantly reduce the memory footprint while improving the computational efficiency of the adjoint-based automatic differentiation (AD). In the proposed approach, the partial derivatives are calculated for each expression using the reverse adjoint accumulation for the active variables involved on the right-hand-side of that expression. The recorded partial derivative information is then used in a very efficient adjoint evaluation process to calculate the entire Jacobian information. The enhanced toolbox is coupled with the in-house UNstructured PArallel Compressible (UNPAC) flow solver for a robust design optimization framework, called UNPAC-DOF. The efficiency and robustness of the proposed technique and the resulting framework are tested for aerodynamic shape optimization problems applied to airfoil and wing geometries.