Rainald Loehner

Short CV

Rainald Löhner received his Diplom Ingenieur (Maschinenbau) degree from the Technical University of Braunschweig, and his PhD and a DSc in civil engineering from the University College of Swansea, Wales. After being appointed lecturer in Swansea and teaching for a year, he moved to the Naval Research Laboratory in Washington, DC. This was followed by a research professorship at the department of civil, mechanical and environmental engineering of the George Washington University in Washington, DC. He was appointed associate professor at the Institute for Computational Sciences of the George Mason University in Fairfax, VA, and then promoted to full professor in 1995 and distinguished professor in 2004. Since 2003 he leads the Center for Computational Fluid Dynamics at George Mason University.

Selected Awards

  • 2020, Nr. 15119 in the Stanford List of Most Influential Scientists of the World; Nr. 8 in Aerospace and  Aeronautics
  • 2010, Distinguished International Career Award, Argentine Association of Computational Mechanics
  • 2008, Fellow, International Association for Computational Mechanics
  • 2006, Associate Fellow, AIAA
  • 2005, Honorary Professor, University of Wales Swansea
  • 2005, Advisory Professor, Shanghai Jiao Tong University
  • 2004, Distinguished Professor of Fluid Dynamics, School of Computational Sciences, George Mason University, Fairfax, Virginia
  • 1999, Computational Mechanics Achievements Award, Computational Mechanics Division, Japan Society of Mechanical Engineering
  • 1993, Doctor of Science in Civil Engineering, University College of Swansea, Wales, United Kingdom
  • 1979–1983, Studienstiftung des Deutschen Volkes (Top 1% of German Students)

Research Interests

More than 35 years of broad multidisciplinary for along the complete pipeline of numerical solvers/ simulation tools. Contributions in the areas of pre-processing, grid generation, numerical methods, field solvers, parallel/high performance computing, adaptive mesh refinement, fluid-structure interaction, shape and process optimization, system identification, visualization, data reduction, visualization and computational crowd dynamics.

Currently developing advanced Field Solvers (forward and adjoint) for Compressible and Incompressible Flows, Acoustics and Electromagnetic Wave Propagation, Heat and Mass Transfer Simulation, Structural Mechanics, as well as Fluid-Structure Interaction; concentrating on strategic areas of applications: blast, ship hydrodynamics, bloodflow, contaminant and pathogen transport, optimal shape/process design and system identification. At the same time working on pedestrian movement simulation, computational crowd dynamics and pathogen transmission.

Prof. Löhner’s codes and methods have been applied in many fields, including aerodynamics or airplanes, drones, cars and trains, hydrodynamics of ships, submarines and UUVs, shock-structure interaction, dispersion and pathogen analysis in urban areas and the built environment, haemodynamics of vascular diseases, system identification and pedestrian safety assessments. These have been documented in more than 800 articles covering the fields enumerated above, as well as a textbook on Applied CFD Techniques.

Selected Publications

  • A. Stueck, F. Camelli and R. Löhner: Adjoint-Based Design of Shock Mitigation Devices; Int. J. Num. Meth. Fluids 64, 443-472 (2010).
  • A. Michalski, P.D. Kermel, E. Haug, R. Löhner, R. Wüchner, K.-U. Bletzinger: Validation of the Computational Fluid–Structure Interaction Simulation at Real-Scale Tests of a Flexible 29 m Umbrella in Natural Wind Flow; J. Wind Eng. Ind. Aerodyn. 99, 400–413 (2011).
  • K.-R. Wichmann, M. Kronbichler, R. Löhner and W. Wall: Practical Applicability of Optimizations and Performance Models to Complex Stencil-Based Loop Kernels in CFD; Int. J. of High Performance Computing Applications 33, 4, 602-618 (2019). doi: http://journals.sagepub.com/doi/10.1177/1094342018774126
  • R. Löhner and H. Antil: Determination of Volumetric Material Data from Boundary Measurements: Revisiting Calderon’s Problem; Int. J. Num. Meth. Heat and Fluid Flow  30, 11, 4837-4863 (2020). doi: 10.1108/HFF-12-2019-0931.
  • H. Antil, T.S. Brown, R. Löhner, F. Togashi and D. Veerma: Deep Neural Nets with Fixed Bias Configuration; Numerical Algebra, Control and Optimization (2022). doi: 10.3934/naco.2022016