Welcome to the von Karman Institute for Fluid Dynamics Store

Adjoint-Based Optimization of Turbomachinery with Application to Axial and Radial Turbines - Lasse Müller - Ph.D. Thesis - Free download

Be the first to review this product

Availability: In stock

€0.00

Quick Overview

VKI PHDT 2019-03, Lasse Müller, Adjoint-Based Optimization of Turbomachinery with Application to Axial and Radial Turbines, ISBN 978-2-87516-144-4, 266 pgs


https://doi.org/10.35294/phdt201903

Adjoint-Based Optimization of Turbomachinery with Application to Axial and Radial Turbines - Lasse Müller - Ph.D. Thesis - Free download

Double click on above image to view full picture

Zoom Out
Zoom In

More Views

* Required Fields

€0.00

Details

Adjoint-Based Optimization of Turbomachinery with Application to Axial and Radial Turbines
By Lasse Müller

PhD Thesis from the von Karman Institute / Université libre de Bruxelles, January 2019, ISBN 978-2-87516-144-4, 266 pgs

https://doi.org/10.35294/phdt201903


Abstract

Numerical optimization methods have made significant progress over the last decades and play an important role in modern industrial design processes. In most cases, gradient-free algorithms are used, which only require the value of the objective function in each optimization step. These methods are robust and can be integrated into a standard design process at low implementation effort. However, in aerodynamic design problems using high-fidelity Computational Fluid Dynamics (CFD), the computational cost is high, especially when a large number of design parameters are used. Gradient-based methods, on the other hand, are particularly suited for problems involving large design spaces and generally converge to a local optimum in a few design cycles. However, the computational efficiency of these methods is mainly determined by the gradient calculation.

This thesis presents the development of an efficient gradient-based optimization framework for the aerodynamic design of turbomachinery applications. In particular, the adjoint approach is used to evaluate the gradients of the objective function with respect to all design parameters at low computational cost. The present work covers the various components of the optimization framework, including the solution of the flow governing equations, adjoint-based sensitivity analysis, geometry parameterization, and mesh generation. A substantial part of the thesis describes the implementation and validation of those components. The flow solver is a Reynolds-Averaged Navier-Stokes code applicable to multiblock structured grids. The spatial discretization is realized with a Roe-type upwind scheme with a MUSCL extrapolation for second order spatial accuracy. Viscous fluxes are centrally discretized, and for the turbulence closure problem the Spalart-Allmaras and the Shear-Stress Transport (SST) models are used. The code uses an implicit multistage Runge-Kutta time-stepping scheme, accelerated by local time-stepping and geometric multigrid. The corresponding discrete adjoint solver uses the same time marching scheme as the flow solver and features similar performance characteristics in terms of runtime and memory footprint. The adjoint solver has been implemented primarily by hand with selective use of algorithmic differentiation (AD) to simplify the development. The geometry parameterization is based on B-Spline representations which has two main advantages: (a) the simple integration of geometric constraints for structural requirements, and (b) the connection to Computer-Aided Design (CAD) software for manufacturing. The whole optimization framework is driven by a Sequential Quadratic Programming (SQP) algorithm.

The proposed framework has been successfully applied to optimize axial and radial turbines on multiple operating points subject to aerodynamic and geometric constraints. The different studies show the effectiveness of the developed method in terms of improved performances and computational cost. In particular, a comparative study shows that the proposed method is able to find optimized blade shapes at a computational time which is about one order of magnitude lower compared to a gradient-free optimization algorithm.

Additional Information

Manufacturer von Karman Institute for Fluid Dynamics

Product Tags

Use spaces to separate tags. Use single quotes (') for phrases.