ORCID Profile
0000-0002-7954-0506
Current Organisation
Karlsruhe Institute of Technology
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Publisher: Elsevier BV
Date: 08-2017
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 07-2018
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer Science and Business Media LLC
Date: 29-06-2020
DOI: 10.1007/S10494-020-00188-0
Abstract: A novel multiple mapping conditioning (MMC) approach has been developed for the modelling of turbulent premixed flames including mixture inhomogeneities due to mixture stratification or mixing with the cold surroundings. MMC requires conditioning of a mixing operator on characteristic quantities (reference variables) to ensure localness of mixing in composition space. Previous MMC used the LES-filtered reaction progress variable as reference field. Here, the reference variable space is extended by adding the LES-filtered mixture fraction effectively leading to a double conditioning of the mixing operator. The model is used to predict a turbulent stratified flame and is validated by comparison with experimental data. The introduction of the second reference variable also requires modification of the mixing time scale. Two different mixing time scale models are compared in this work. A novel anisotropic model for stratified combustion leads to somewhat higher levels of fluctuations for the passive scalar when compared with the original model but differences remain small within the flame front. The results show that both models predict flame position and flame structure with good accuracy.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Springer Science and Business Media LLC
Date: 12-05-2020
DOI: 10.1007/S10494-020-00140-2
Abstract: This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the “paradigm shift” approach where elastic light scattering signals (that depend on particulate number and size), OH- and SiO-LIF signals are computed from the simulation results and compared with “raw signals” from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for.
Publisher: Springer International Publishing
Date: 2016
Publisher: Elsevier BV
Date: 03-2018
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 2017
Publisher: Elsevier BV
Date: 08-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Oliver Thomas Stein.