Date and Place: Thursdays and hybrid (live in 32-349/online via Zoom). For detailed dates see below!
Content
In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.
List of Talks
Event Information:
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Tue21Jun2022
SC Seminar: Nijso Beishuizen
12:00Hybrid (Room 32-349 and via Zoom)
Prof. Nijso Beishuizen, Bosch Deventer and Eindhofen University of Technology
Title:
Adjoint-based design optimization for combustion applicationsAbstract:
Combustion is used in many devices in the chemical industry, in transport, heating and power. Often, design requirements are competing with each other and Computational Fluid Dynamics is a necessary (but not always sufficient) tool to come to a final design. We present a framework for automatic adjoint-based design optimization of combustion devices and we focus on emission reduction and heat transfer optimization in a gas boiler. The flow solution is obtained from the preconditioned compressible Navier-Stokes equations and combustion is modeled using a laminar premixed flamelet approach. Fluid properties and reaction source terms are tabulated as a function of two controlling parameters: a progress variable and the total enthalpy. To increase the accuracy of pollutant emissions, additional transport equations for CO and NOx are solved.
The combustion model is implemented in SU2, the discrete adjoint method is handled by CoDiPack and the optimization cycle is driven by FADO. Automatic remeshing can optionally be performed using the Pointwise mesh generation software. We will demonstrate the method by simultaneously minimizing CO and NOx emissions as well as the outlet temperature of a steady, laminar, premixed methane-air flame in a simplified 2D model of a gas boiler with strong flue gas cooling.How to join online
The talk is held online via Zoom. You can join with the following link:
https://uni-kl-de.zoom.us/j/62521592603?pwd=VktnbVlrWHhiVmxQTzNWQlkxSy9WZz09