Contact Dr Tom-Robin Teschner
- Tel: +44 (0) 1234 754531
- Email: Tom.Teschner@cranfield.ac.uk
- ORCID
- ResearchGate
Areas of expertise
- Computational Fluid Dynamics
- Vehicle Aerodynamics
Background
Dr Teschner is an experienced aerospace and software engineer working in the field of Computational Fluid Dynamics (CFD). He received his BEng in Aerospace Engineering from the Hamburg University of Applied Sciences (Germany) and the University of Seville (Spain), along with his MSc in Computational Fluid Dynamics and PhD in Aerospace from Cranfield University.
Dr Teschner has been awarded the Erich Becker scholarship for his contribution to the aerospace industry, as well as the Lord Kings Norton medal for his PhD research (best PhD thesis ranked among 151 graduates).
His research interests include applied aircraft aerodynamics (laminar flow promoting designs, transition modelling), vehicle aerodynamics (high-performance vehicles), as well as algorithmic research to reduce computational costs of CFD solvers through hybrid CFD and deep-learning.
Before joining Cranfield, Dr Teschner was a software engineer developing a commercial CFD solver at the German Aerospace Center (DLR), working in particular on the high-resolution discontinuous Galerkin (DG) method for aeronautical applications in collaboration with Onera and Airbus.
Research opportunities
Dr Teschner main area of research are in the field of applied aerodynamics (vehicle and aerospace applications) as well as algorithmic design to reduce the cost of Computational Fluid Dynamics solvers (through hybrid CFD/deep-learning applications).
Past research projects included:
- Investigation of laminar flow-promiting aircraft designs through transition-based turbulence modelling using CFD
- Investigation of surface roughness on transition in deep-dynamic stall applications
- Investigation of high-performance vehicles with asymmetric wing loading
- Detection of vortices in CFD simulations through deep learning
Clients
- ITP Aero (Rolls-Royce Holdings PLC)
- Defence Science and Technology Laboratory
- Aston Martin Lagonda Global Holdings PLC
- McLaren Automotive Limited
Publications
Articles In Journals
- Amran Abolholl HA, Teschner T-R & Moulitsas I. (2024). A Hybrid Computer Vision and Machine Learning Approach for Robust Vortex Core Detection in Fluid Mechanics Applications. Journal of Computing and Information Science in Engineering, 24(6)
- Rijns S, Teschner T-R, Blackburn K & Brighton J. (2024). Integrated Numerical and Experimental Workflow for High-Performance Vehicle Aerodynamics. SAE Technical Paper Series, 1
- Rijns S, Teschner T-R, Blackburn K, Proenca AR & Brighton J. (2024). Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions. Physics of Fluids, 36(4)
- Rijns S, Teschner T-R, Blackburn K & Brighton J. (2024). Effects of cornering conditions on the aerodynamic characteristics of a high-performance vehicle and its rear wing. Physics of Fluids, 36(4)
- Abolholl HAA, Teschner T-R & Moulitsas I. (2023). Surface Line Integral Convolution-Based Vortex Detection Using Computer Vision. Journal of Computing and Information Science in Engineering, 23(5)
- Townsend JF, Wang J, Teschner T-R & Xu G. (2023). Towards an optimized design for an elevated cyclonic home using numerical simulations and active learning framework. Journal of Wind Engineering and Industrial Aerodynamics, 239(August)
- Rijns S, Teschner T-R, Blackburn K & Brighton J. (2023). Aerodynamic Analysis of the Ride Height Dependency of a High-Performance Vehicle Equipped with a Multichannel Diffuser in Ground Effect. SAE Technical Paper Series, 1
- Xu Z, Maria A, Chelli K, Premare TDD, Bilbao X, .... (2023). Vortex and Core Detection using Computer Vision and Machine Learning Methods. European Journal of Computational Mechanics, 32(5)
- Townsend JF, Teschner T-R, Xu G, Zou L, Han Y, .... (2022). Experimental and numerical aerodynamic analysis of an elevated beachfront house. Journal of Wind Engineering and Industrial Aerodynamics, 231(December)
- Gil JMS, Teschner T-R & Könözsy L. (2021). Implementation of the fractional-step, artificial compressibility with pressure projection (FSAC-PP) method into openfoam for unsteady flows. Multidiszciplináris tudományok, 11(5)
- Teschner T-R, Könözsy L & Jenkins KW. (2019). A generalised and low-dissipative multi-directional characteristics-based scheme with inclusion of the local Riemann problem investigating incompressible flows without free-surfaces. Computer Physics Communications, 239
- Teschner T-R, Könözsy L & Jenkins K. (2018). Predicting Non-Linear Flow Phenomena through Different Characteristics-Based Schemes. Aerospace, 5(1)
- Teschner T-R, Könözsy L & Jenkins KW. (2016). Progress in particle-based multiscale and hybrid methods for flow applications. Microfluidics and Nanofluidics, 20(4)
- Othman H Alburaidi F, Jenkins K & Teschner T-R. Virtual Reality's Effects on Air Crash Accident Investigation Learning Interaction. Computer Science and Information Technologies, 4(2)
Conference Papers
- Xiong X, Teschner T-R & Moulitsas I. (2023). Simulate cavitation bubble with single component multi-phase Lattice Boltzmann method
- Rijns S, Teschner T-R, Blackburn K & Brighton J. (2023). Comparative analysis of RANS and DDES methods for aerodynamic performance predictions for high performance vehicles at low ground clearances
- Althaf AM, Bangaru YS, Fawcett R, Guled VM, Maloo TG, .... (2023). Numerical modeling and tunnel specific considerations for CFD model development of low-speed wind tunnels
- Alburaidi FO, Jenkins K & Teschner TR. (2023). A Framework for Air Crash Accident Investigation with the Help of Virtual Reality
- Farah E & Teschner T-R. (2022). Aerodynamic performance investigation through different chemistry modelling approaches for space re-entry vehicles using the DSMC method
- Townsend JF, Wang J, Teschner T-R & Xu G. (2022). A Machine Learning-Based Geometry Optimization for an Elevated Beachfront House under Typhoon Wind Conditions using Numerical Simulations
- Teschner T-R. (2022). A high-resolution, unified incompressible solver framework for turbulent flows in OpenFOAM
- Abolholl HAA, Teschner T-R & Moulitsas I. (2022). A hybrid computer vision and machine learning approach for robust vortex core detection in fluid mechanics applications
- Szoke M, Nurani Hari N, Devenport WJ, Glegg SA & Teschner T-R. (2021). Flow Field Analysis Around Pressure Shielding Structures
- Figueroa-González A, Oliveira J, Teschner TR, Könözsy L, Moulitsas I, .... (2020). Validation of an in-house lattice Boltzmann solver for a multiphase flow application
- Teschner T-R, Könözsy L & Jenkins KW. (2017). A Three-Stage Algorithm for Solving Incompressible Flow Problems
- Józsa TI, Szőke M, Teschner T-R, Könözsy L & Moulitsas I. (2016). VALIDATION AND VERIFICATION OF A 2D LATTICE BOLTZMANN SOLVER FOR INCOMPRESSIBLE FLUID FLOW
- Teschner T-R, Könözsy L & Jenkins KW. (2016). Numerical Investigation of an Incompressible Flow Over a Backward Facing Step Using a Unified Fractional-Step, Artificial Compressibility and Pressure-Projection (FSAC-PP) Method
- Smith K, Teschner T-R & Könözsy L. On Approximate Riemann Solvers within the Concept of the Unified Fractional-Step, Artificial Compressibility and Pressure Projection Method