Prediction of Heat Transfer for Additively Manufactured Roughness using RANS Models

Background

Additive manufacturing (AM) is increasingly becoming a critical technology for the production of components with complex geometries, especially in industries such as aerospace and energy. One prominent example is Siemens' use of additive manufacturing to produce gas turbine cooling blades. These blades operate under extreme conditions where efficient heat transfer is essential for performance and durability. However, the rough surfaces inherent to additively manufactured components can significantly impact both flow behavior and heat transfer. Existing predictive models, particularly Reynolds-Averaged Navier-Stokes (RANS) models, are designed for smooth or mildly rough surfaces and often fail to accurately predict the complex interactions between flow, roughness, and heat transfer in highly roughened surfaces. These inaccuracies are critical, as roughness characteristics—such as height, spacing, and distribution—can vary widely, influencing heat transfer beyond the traditional dependence on Reynolds and Prandtl numbers. To ensure better performance and optimized designs, it is crucial to develop accurate predictive models that account for these parameters.

Aim of the Thesis

The primary objective of this master thesis is to evaluate the performance of existing RANS models in predicting heat transfer for surfaces with additively manufactured roughness and compare them with newer models, specifically the RANS models developed by Garg and Kadivar (manuscript in preparation). The student will perform simulations using OpenFOAM, an open-source computational fluid dynamics (CFD) software, to test the predictive capabilities of the traditional models against these newer approaches, which account for the complex characteristics of rough surfaces. By analyzing the accuracy of these models in a range of conditions, the thesis aims to identify whether the newer models provide significant improvements and to propose possible enhancements or adjustments for better prediction in industrial applications. 

Figure 1: (a) presenta on of fric on factor on the rough surface and temperature contours overlapped with surface streamlines. (b) mean flow profiles computed using advanced RANS models and their comparison with DNS data.  

Contact and more information

For more information, please contact Himani Garg, Assistant Professor at the Department of Energy Sciences: himani.garg@energy.lth.se.

References

1. Himani Garg et al.; Large eddy simulations of flow over additively manufactured surfaces: Impact of roughness and skewness on turbulent heat transfer. Physics of Fluids 1 August 2024; 36 (8): 085143. https://doi.org/10.1063/5.0221006 Links to an external site.
2. Himani Garg et al.; Heat transfer enhancement with additively manufactured rough surfaces: Insights from large-eddy simulations. Physics of Fluids 1 February 2024; 36 (2): 025109. https://doi.org/10.1063/5.01891153 Links to an external site..
3. Himani Garg et al.; Large eddy simulations of fully developed turbulent flows over additively manufactured rough surfaces. Physics of Fluids 1 April 2023; 35 (4): 045145. https://doi.org/10.1063/5.0143863 Links to an external site.