# Conjugate Heat Transfer Optimization for Turbine Blade Thermal Performance Using Field-Driven Design

Page: https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587/conjugate-heat-transfer-optimization-for-turbine-blade-thermal-performance-using-field-driven-design
Text version: https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587/conjugate-heat-transfer-optimization-for-turbine-blade-thermal-performance-using-field-driven-design.md
Podcast: [CDFAM Computational Design Symposium](https://stenobird.com/podcast/cdfam-computational-design-symposium-7077587)
Published: 2026-09-12T21:55:56+00:00
Episode link: https://www.designforam.com/p/conjugate-heat-transfer-optimization
Audio file: https://api.substack.com/feed/podcast/215419135/c87f32ff30638e68212b90c34ee59bc2.mp3
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JSON: https://stenobird.com/v1/public/podcasts/cdfam-computational-design-symposium-7077587/episodes/conjugate-heat-transfer-optimization-for-turbine-blade-thermal-performance-using-field-driven-design
Duration seconds: 1494

## Resource

CDFAM Computational Design Symposium — Barcelona 2026 Max Gaedtke, Markus Lempke · nTop, Siemens Energy Turbine blade thermal management demands tight coupling between design exploration and high-fidelity simulation—yet traditional workflows separate these domains, limiting the design space that can be practically explored. We present an integrated parametric optimization framework enabled by nTop’s robust implicit geometry engine. Field-driven design representation eliminates mesh regeneration between design variants, while a Lattice Boltzmann Method formulation for conjugate heat transfer removes the need for explicit fluid-solid interface handling. This architectural unity—implicit geometry paired with interface-agnostic thermal transport—permits fully automated design-simulate-optimize loops on complex internal cooling geometries. The GPU-native solver has been validated against finite-volume baselines on canonical heat sink geometries, demonstrating peak temperature agreement within 0.5% while achieving approximately 200x reduction in time-to-solution on consumer-grade hardware. These evaluation times make high-fidelity CHT practical as an inner-loop optimization objective rather than a final verification step. We demonstrate the workflow on turbine blade internal cooling channels, where parametric control over fin positioning drives systematic exploration of the thermal-structural design space. Results show automated identification of Pareto-optimal configurations balancing thermal performance, pressure drop, and additive manufacturing constraints. This collaboration between nTop and Siemens Energy bridges computational design research with industrial turbomachinery requirements. Links Talk page with full transcript Watch the talk on YouTube Cite this talk Search…

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