H2020 project: MOTOR

Multi-ObjecTive design Optimization of fluid eneRgy machines

Logo MOTOR project

As part of the EU's Horizon 2020 research and innovation programme, the MOTOR project aims to develop new types of numerical simulation and automatic shape optimization technologies for aircraft engines, ship propellers, water turbines and rotary screw compressors. Coordinated by TU Delft, research is conducted by an international consortium that will new mathematical concepts and advanced computational tools over the next three years. The idea is to harmonize the geometry and mathematical language used in computer-aided design and engineering systems, making it possible to virtually analyse machine designs in greater detail.

The goal of this project is to create new software tools for optimizing the complex shapes of fluid energy machines to finally increase their overall efficiency and to make the design workflow more efficient. Mathematics is omnipresent in this project. It starts with the accurate algorithmic modelling of the rotor geometries, which are functional free-form surfaces typically described by splines or NURBS. Next, numerical simulation and optimization algorithms are entirely based on rigorous mathematical concepts like calculus of variation or iterative solution algorithms for systems of equations.

Coordination

Matthias Möller

Duration

01/09/2015 - 01/09/2018

Personnel

Jochen Hinz, Andrzej Jaeschke

Official website

https://cordis.europa.eu/project/id/678727

News reports

External collaborations

  • TU Delft collaborations with PAL-V

Selected contributions

  • Andrzej Jaeschke and Matthias Möller. High-order isogeometric methods for compressible flows. I. Scalar conservation laws. In Harald van Brummelen, Alessandro Corsini, Simona Perotto, and Gianluigi Rozza, editors, Numerical Methods for Flows: FEF 2017 Selected Contributions, pages 21--29, Cham, 2020. Springer International Publishing. [ DOI | arXiv | http ]
  • Matthias Möller and Andrzej Jaeschke. High-order isogeometric methods for compressible flows. II. Compressible Euler equations. In Harald van Brummelen, Alessandro Corsini, Simona Perotto, and Gianluigi Rozza, editors, Numerical Methods for Flows: FEF 2017 Selected Contributions, pages 31--39, Cham, 2020. Springer International Publishing. [ DOI | arXiv | http ]
  • Jochen Hinz, Michael Abdelmalik, and Matthias Möller. Goal-oriented adaptive THB-spline schemes for PDE-based planar parameterization, 2020. [ DOI | arXiv | http ]
  • Jochen Hinz, Jan Helmig, Matthias Möller, and Stefanie Elgeti. Boundary-conforming finite element methods for twin-screw extruders using spline-based parameterization techniques. Computer Methods in Applied Mechanics and Engineering, 361:112740, 2020. [ DOI | http ]
  • Jochen Hinz, Joost van Zwieten, Matthias Möller, and Fred Vermolen. Isogeometric analysis of the Gray-Scott reaction-diffusion equations for pattern formation on evolving surfaces and applications to human gyrification, 2019. [ DOI | arXiv | http ]
  • Jochen Hinz, Matthias Möller, and Cornelis Vuik. Elliptic grid generation techniques in the framework of isogeometric analysis applications. Computer Aided Geometric Design, 65:48--75, oct 2018. [ DOI | http ]
  • Jochen Hinz, Matthias Möller, and Cornelis Vuik. Spline-based parameterization techniques for twin-screw machine geometries. IOP Conference Series: Materials Science and Engineering, 425:012030, nov 2018. (pdf). [ DOI | http ]
  • Matthias Möller and Jochen Hinz. Isogeometric analysis framework for the numerical simulation of rotary screw machines. I. General concept and early applications. IOP Conference Series: Materials Science and Engineering, 425:012032, nov 2018. [ DOI | http ]
  • Matthias Möller and Andrzej Jaeschke. FDBB: Fluid dynamics building blocks. In Proceedings of the 6th European Conference on Computational Mechanics (ECCM 6) and the 7th European Conference on Computational Fluid Dynamics (ECFD 7), Glasgow, UK, jun 2018. ECCOMAS. (pdf). [ DOI | arXiv | http ]
  • Jochen Hinz. Isogeometric analysis of a reaction-diffusion model for human brain development, 2016. [ http ]