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ISSN Online: 2377-424X

ISBN Print: 978-1-56700-474-8

ISBN Online: 978-1-56700-473-1

International Heat Transfer Conference 16
August, 10-15, 2018, Beijing, China

ADJOINT-BASED SHAPE OPTIMIZATION FOR TURBULENT CONVECTIVE HEAT TRANSFERWITH A HYBRID RANS-DNS APPROACH

Get access (open in a dialog) DOI: 10.1615/IHTC16.hte.022700
pages 5011-5018

要約

A new adjoint-based shape optimization algorithm for a turbulent heat transfer problem is proposed. In this algorithm, direct numerical simulation (DNS) of relevant velocity and thermal fields is first conducted. Based on the statistics obtained from DNS, the spatial distribution of the eddy viscosity and diffusivity are determined so as to reproduce the local productions of turbulent kinetic energy and temperature fluctuation, respectively. Then, the Reynolds averaged Navier-Stokes equations are constructed with the DNS-based eddy viscosity and diffusivity, and their adjoint equations are derived to achieve shape optimization. For validation, the present algorithm is applied to a wavy fin between two parallel isothermal walls under constant mean pressure gradient and uniform heating inside the fluid. Aiming at the heat transfer enhancement with less pressure drag, an analogy factor, i.e., the ratio of the Nusselt number to the friction factor multiplied by the bulk Reynolds number, is set as a cost functional. The results show that the present DNS-based eddy viscosity can predict the mean flow field well, including flow separation, recirculation and reattachment. The resultant optimal shape enhances heat transfer through the increase of heat transfer area near the reattachment region and edge of the fin. Meanwhile, pressure drag reduction is achieved by making holes in the wavy fin near the parallel walls. These holes contribute to reducing the cross sectional area in the streamwise direction.