The SIAM International Meshing Roundtable Workshop 2027 is pleased to announce the following plenary talks to appear at this year’s conference.
-
Prof. Kenji Shimada, Carnegie Mellon University
- Dr. Shahrokh Shahpar, Rolls-Royce
Beyond the Mesh: Two Decades of Innovation in Automated Meshing for Jet-Engine Design Optimisation - Prof. Per-Olof Persson, University of California, Berkeley
Prof. Kenji Shimada
Abstract and biography will be announced closer to the conference.
Dr. Shahrokh Shahpar
Beyond the Mesh: Two Decades of Innovation in Automated Meshing for Jet-Engine Design Optimisation
Abstract: High-fidelity computational fluid dynamics (CFD) simulation plays a vital role in the design optimisation of turbomachinery components. However, a major bottleneck has long been the generation of high-quality meshes within practical computational and engineering timescales.
The automatic generation of high-quality hexahedral (HEX) meshes for complex turbomachinery geometries has challenged mesh-generation developers for decades. At Rolls-Royce, more than two decades of development have led to the creation of a comprehensive templated meshing library, implemented in the in-house PADRAM (Parametric Design and Rapid Meshing) code, to address this challenge.
A key enabler has been the integration of parametric representations of real geometric features directly into the meshing process, providing the foundation for robust, fully automated design optimisation systems.
This talk traces the major milestones in developing reliable meshing methods capable of handling the complex geometries of modern turbomachinery components, enabling high-fidelity CFD simulations to be performed routinely within Rolls-Royce. It will explore advances in templated meshing, evolutionary techniques and mesh refinement strategies for generating high-quality, all-hexahedral meshes. Examples from multidisciplinary design optimisation of jet-engine components will demonstrate the capabilities of PADRAM and the contribution of automated meshing to advanced engineering design.
Biography: Dr. Shahrokh is a Rolls-Royce Fellow in Aerothermal Design Systems. Recently moved from Innovation Hub-Future Methods to Design and Service Engineering (Fluid Mechanics), where he focuses on development and application of AI, ML, and Adjoint based MDO techniques for turbo-machinery components. Whilst at Innovation hub developed methodology applied to Clean aviation, which included eVTOL, Liquid Hydrogen, Fuel cells for aviation, DAC (Direct Air capture for CO2 capture) as well modelling and optimisation of the Contrail to reduce its radiation effect in the upper atmosphere. He has a national and international reputation in the field of CFD meshing and automatic aerothermal design optimisation of turbomachinery components and systems, publishing more than 200 internal and external technical papers (3300 citations) and holds fifteen patents. He has written & pioneered a number of successful aerothermal design systems currently used by Rolls-Royce, most-notably: FAITH and SOPHY (SOFT-PADRAM-HYDRA), which provide systematic design-optimisation tools that focus on high-fidelity aerodynamic, thermodynamic and acoustic analyses. His current research is on aerothermal topology optimisation, manufacturing and in service deterioration, uncertainty quantification and robust design.
Prof. Per-Olof Persson
Abstract will be announced closer to the conference.
Biography: Prof. Per-Olof Persson is a Professor of Mathematics at the University of California, Berkeley, and a Faculty Senior Scientist at the Berkeley Lab. He received his Ph.D. from the Massachusetts Institute of Technology in 2005, where he also developed the widely used DistMesh algorithm for unstructured mesh generation in implicit and deforming geometries. He has also worked for several years on the development of commercial numerical software, such as the finite element package COMSOL Multiphysics. His research focuses on high-order discontinuous Galerkin methods for computational fluid and solid mechanics, spanning efficient discretizations, scalable solvers, and adjoint-based optimization. A second key area of his research is mesh generation, where he has developed methods for space-time and curved meshes, as well as new approaches based on machine learning.
