Towards predictive emission models for turbulent combustion using large scale simulations
In the past decades, the world energy consumption increased rapidly and is expected to further grow significantly in the future. As fossil fuel remains the dominant energy source, combustion devices remain the major technology for the conversion of chemically bound energy into electrical, mechanical, or thermal energy. In combustion processes of hydrocarbon-containing fuels, pollutants, such as unburned hydrocarbons (UHC), carbon monoxide (CO), oxides of nitrogen, collectively referred to as NOx, and particulates in the form of soot, are formed and emitted into the atmosphere. The severe environmental effects of increased ambient NOx concentrations have led to stringent emission laws and as a result, strong efforts are made to develop modern low emission combustion systems. A promising tool to accomplish this goal is the design and control of these systems using computational fluid dynamics (CFD), which in turn requires predictive models for NO formation.
Combustion modeling is complex, because of its multi-scale nature and the strong non-linearities of chemical source terms. Modeling depends on data, but experimentally measuring spatially and temporally resolved species concentrations in turbulent flames is extremely challenging. The so-called direct numerical simulation technique (DNS) has been established as a powerful tool in combustion science to complement theory and experiment, and is as a research area becoming ever more important. In DNS, the governing equations describing fluid flow and chemistry are solved without any modeling assumptions, thereby resolving turbulent motions and combustion in time and space ranging from the smallest scales associated with the fastest reactions up to largest scales of the turbulent motions. Because of the wide ranges of length and time scales in turbulent flows, these simulations can be extremely costly, but the rapid advancement of supercomputing has recently enabled a series of interesting DNS studies with practical relevance. Although DNS are presently not feasible for realistic combustion systems, the richness of DNS even for simpler cases, the level of detail, the availability of all desired quantities at all locations in the flow, and the well-defined and well-known details of boundary conditions encourage the use of DNS data for model development. Moreover, spatially and temporally resolved high quality data for key quantities like reaction rates including species and temperature distributions, high order moments, or correlation functions are challenging or impossible to obtain experimentally. Yet, these are available in DNS and can enable very systematic analyses as well as model development and validation.
Within a Gauss project awarded by the Leibniz Rechenzentrum, a peta-scale DNS of a temporally evolving lean premixed methane/air jet flame was carried out at the Institute for Combustion Technology at RWTH Aachen University, managed by JARA-ENERGY member Prof. Heinz Pitsch. This DNS is intended to closely mimic gas turbine combustion and can be regarded as an idealized representation of a premixed flame element inside a jet burner. In contrast to many other DNS, this configuration features a very detailed description of oxidation and pollutant formation chemistry and a mean shear driving the turbulence so that it does not rely on artificial turbulence forcing.
In order to realize high resolution of flame and turbulence and to obtain converged statistics the simulation domain is discretized with almost three billion grid points, which together with the chemistry model results in nearly 100 billion degrees of freedom. Following prior successful scaling tests, the DNS was performed on the SuperMUC supercomputer at the LRZ Munich on 65536 cores and required around 40 million CPUh in total. An instantaneous snapshot of the simulation shortly after the flames arrive in the shear layer region is shown in Figure 1.
In a first step, the NO formation process is split into different formation pathways and visualized in a flux diagram shown in Figure 2. This analysis highlights the advantage of DNS data over experimental data sets, as such a detailed view into the individual formation steps and the trajectory that N atoms undergo from N2 to NO, would not be possible with sparse experimental data. A main finding here is that in the present DNS configuration, the thermal and the NNH formation pathways for NO are most important ones.
While the thermal pathway is well understood and known to be initiated by high temperatures, the role of the NNH mechanism in lean premixed flames is less clear. Therefore, this finding encourages a more detailed inspection of the NNH pathway and an analysis of its individual reactions, which demonstrates that the largest errors exist for the reactions involving the hydrogen radical. In turn, this means that the H radical plays an important role for the NNH pathway and suggests that differential diffusion effects are responsible for the decorrelation of heat release and NO production. Visual evidence for the above outlined physical picture is provided in Figure 3. It can be clearly observed that the heat release is strongly correlated with a reaction progress variable. However, the NO source term field shows structures that are not in the C and heat release fields, but do appear in the YH field. Since C and YH are independent, the hydrogen mass fraction plays a crucial role in the formation of NO on top of the progress variable, which is attributed to differential diffusion of the hydrogen radical.
Figure 3: Contours plots of a reaction progress variable, heat release, NO source term and hydrogen mass fraction.