Additive manufacturing (AM) offers new geometric freedom for the design of flow components. Static mixers are a prominent example with growing industrial relevance across a wide range of applications, such as the chemical or pharmaceutical industry. Despite new opportunities the transfer of innovative designs into industrial applications remains limited. A reason is the lack of expertise concerning new design methodologies and robust evaluation strategies for such complex geometries. This contribution aims to bridge this gap between industry and academic research, by presenting a framework for the automated optimization of flow components, using static mixers as an example. Due to their strong sensitivity to geometric details, static mixers provide an ideal benchmark to demonstrate the potential of the proposed approach. The framework combines two main components, coupled within an optimization loop: algorithmic geometry generation and numerical performance evaluation. The geometry generation enables the creation of fully parameterized mixer designs derived from a preselected rough structure. These include, among others, classical mixing elements as well as TPMS-based geometries such as gyroids. Additive manufacturing constraints, such as overhang limits and minimum wall thicknesses, are explicitly incorporated as design boundary conditions. The flow performance of the geometries is assessed using the Lattice Boltzmann Method (LBM) [1], within the open-source software OpenLB [2]. LBM is particularly advantageous in this application, as it resolves complex geometries on structured grids, without the need for elaborate mesh generation, and is highly parallelizable. The mixing efficiency is evaluated using a particle-based approach, where particles trace the previously computed velocity field. Performance metrics, including mixing quality and pressure drop, are fed back into the optimization loop to guide subsequent design iterations. The presented framework demonstrates how algorithmic design combined with simulation-based evaluation can enable the systematic exploration of complex geometries, thereby narrowing the gap between academia and industrial application.