Periodic Reporting for period 4 - MILESTONE (Multi-Scale Description of Non-Universal Behavior in Turbulent Combustion)
Periodo di rendicontazione: 2020-12-01 al 2021-05-31
Combustion is an extremely important field for our society. The development of new, step-change technologies is essential and greatly benefits from design based on numerical simulations. However, turbulent combustion physics are complex, highly non-linear, of multi-scale and multi-physics nature, and involve interactions at many time-scales. This makes modeling quite challenging such that accurate predictive models, especially for the formation of pollutants, are not available. Today, the two major challenges for developing predictive simulations of turbulent combustion are first to account for its multi-scale nature by considering the non-universal behavior of small-scale turbulence, which is known to be critically important for turbulence-chemistry interactions, and second, to provide data in sufficient detail for rigorous analysis of model deficiencies and unambiguous model development. These two issues are addressed in the MILESTONE work.
The main overall objectives are: 1) Establish a new multi-scale framework to analyze and model turbulent combustion phenomena based on a new way to describe turbulence using so-called dissipation elements, which are space-filling regions in a scalar field allowing to capture its small-scale morphology and non-universality. 2) Create new unprecedented datasets using direct numerical simulations (DNS) and provide new analysis methods to develop and validate combustion models; this will include automatically reducing and optimizing chemical kinetic mechanisms for use in DNS and developing an on-the-fly chemistry reduction technique. 3) Apply new modeling approaches to complex and highly non-linear modeling questions, such as pollutant formation in turbulent spray combustion. The successful outcome of the project will provide new and unprecedented datasets, a quantitative description of the impact of non-universality in small-scale turbulence on different aspects of turbulent combustion, and the basis for an entirely new multi-scale closure.
The main overall objectives are: 1) Establish a new multi-scale framework to analyze and model turbulent combustion phenomena based on a new way to describe turbulence using so-called dissipation elements, which are space-filling regions in a scalar field allowing to capture its small-scale morphology and non-universality. 2) Create new unprecedented datasets using direct numerical simulations (DNS) and provide new analysis methods to develop and validate combustion models; this will include automatically reducing and optimizing chemical kinetic mechanisms for use in DNS and developing an on-the-fly chemistry reduction technique. 3) Apply new modeling approaches to complex and highly non-linear modeling questions, such as pollutant formation in turbulent spray combustion. The successful outcome of the project will provide new and unprecedented datasets, a quantitative description of the impact of non-universality in small-scale turbulence on different aspects of turbulent combustion, and the basis for an entirely new multi-scale closure.
We performed very large Direct Numerical Simulation of complex flow including turbulence, combustion, and multi-phase phenomena such as sprays and atomization. The simulations run on massively parallel computers with more that 100.000 processors, employing hundreds of billions numerical degrees of freedom, and generating hundreds of terabytes of data. We focus both on fundamental scientific questions such as the scaling of the flame burning rate with respect to basic fluid dynamics parameters such as the Reynolds number and to practical questions related to the characteristics of specific fuels. We also investigate the pollutant formation in these system in order to develop strategies for emission modeling to help in the development of green device for energy production.
These simulations generated extremely large amount of data that should be processed in a systematic way. It would be desirable to switch from an approach which heavily relies on experience and intuition to a formal assessment based on methodical approaches. A rigorous data-driven approach allows to infer the deficiency of existing models with the final goal of quantifying and reducing model uncertainty. We combined a number of different approached such as the Dissipation Element analysis and the Optimal Estimator concept, which have been employed in different configurations.
These simulations generated extremely large amount of data that should be processed in a systematic way. It would be desirable to switch from an approach which heavily relies on experience and intuition to a formal assessment based on methodical approaches. A rigorous data-driven approach allows to infer the deficiency of existing models with the final goal of quantifying and reducing model uncertainty. We combined a number of different approached such as the Dissipation Element analysis and the Optimal Estimator concept, which have been employed in different configurations.
The Dissipation Element analysis has been applied to a number of premixed and non-premixed flames, computing statistics of the main parameters describing the complex flows in these reactive systems. We observed that selected statistics are universal across the possible regimes of the flames or unaffected by the presence of combustion itself, while other quantities show important peculiarities related to the energy addition due to combustion. A clear understanding of the degree of universality of turbulence in flames is key to formulate appropriate models.
In addition, modern methodologies developed in the context of Big Data analysis, Artificial Intelligence and Machine Learning are starting to be popular in the field of turbulence and combustion. Our team is exploiting and adapting these approaches for the analysis of complex reactive systems and for the development of models. One example is given by the use of Neural Networks, which provided a convenient framework for the statistical analysis of combustion-generated particulate in a turbulent flame.
The understanding gained applying the Dissipation Element analysis will be employed to refine and develop new strategies for modeling of turbulent combustion. The same type of analysis is being applied to pollutants and multiphase phenomena and is expected to clarify some of the mechanisms governing their behavior.
In addition, modern methodologies developed in the context of Big Data analysis, Artificial Intelligence and Machine Learning are starting to be popular in the field of turbulence and combustion. Our team is exploiting and adapting these approaches for the analysis of complex reactive systems and for the development of models. One example is given by the use of Neural Networks, which provided a convenient framework for the statistical analysis of combustion-generated particulate in a turbulent flame.
The understanding gained applying the Dissipation Element analysis will be employed to refine and develop new strategies for modeling of turbulent combustion. The same type of analysis is being applied to pollutants and multiphase phenomena and is expected to clarify some of the mechanisms governing their behavior.