Please contact us if you are interested in our software solutions or if you want to contribute.
pafiX
We develop in collaboration with 
UCLouvain a multi-physics tool to simulate hazards to the operational safety of powder and fluid flow operations in process industries.
The tool is open-souce and distributed on gitlab: https://gitlab1.ptb.de/Holger/pafix
Key publications:
- H. Grosshans, W. Xu, and T. Matsuyama, Simulation of PMMA powder flow electrification using a new charging model based on single-particle experiments, Chemical Engineering Science, 254, 117623, 2022
- M. Zeybek and H. Grosshans, Eulerian formulation for the triboelectric charging of polydisperse powder flows, Physics of Fluids, 33, 63304, 2021
- H. Grosshans, C. Bissinger, M. Calero, and M.V. Papalexandris, The effect of electrostatic charges on particle-laden duct flows, Journal of Fluid Mechanics, 909, A21, 2021
OpenFOAM
We develop our own OpenFOAM solver to study ignitions by electrical discharges of low energy. Moreover, we apply OpenFOAM to a wide range of flows in the field of explosion protection. For example, to compute the structural integrity of flameproof enclosures, flow and reaction kinetics in jet-stirred reactors, ignition by hot surfaces, dust explosions, or large-scale plant explosions.
Our Reynolds-averaged Navier-Stokes (RANS) simulations, Large-Eddy Simulations (LES), and Direct Numerical Simulations (DNS) span a wide range of scales. The simulations run on PTB's high-performance computing cluster.
Key publications:
- S. Velagala, P. Raval, S.C.S. Chowhan, G. Esmaeelzade, M. Beyer, and H. Grosshans, Simulation of the flow of an explosive atmosphere exposed to a hot surface, Journal of Loss Prevention in the Process Industries, 73, 104610, 2021
- G. Esmaeelzade, K. Moshammer, R. Fernandes, D. Markus, and H. Grosshans, Numerical study of the mixing inside a jet stirred reactor using large eddy simulations, Flow, Turbulence and Combustion, 102, 331–343, 2019
- R. Shekhar, S. Gortschakow, H. Grosshans, U. Gerlach, and D. Uhrlandt, Numerical investigation of transient, low-power metal vapour discharges occurring in near limit ignitions of flammable gas, Journal of Physics D: Applied Physics, 52(4), 045202, 2018
Machine Learning
We pioneer in integrating artificial intelligence into the realm of explosion protection. In our research, we train machine learning algorithms using state-of-the-art three-dimensional simulation data. By leveraging the power of AI, these algorithms minimize errors in our measurement systems, ensuring higher accuracy and reliability. In the long run, these algorithms will enable autonomous process safety.
(in collaboration with WG 8.41)