Alessandro Parente

Dimension reduction of non-equilibrium plasma kinetic models using principal component analysis

The chemical complexity of non-equilibrium plasmas poses a challenge for plasma modeling because of the computational load. This paper presents a dimension reduction method for such chemically complex plasmas based on principal component analysis (PCA). PCA is used to identify a low-dimensional manifold in chemical state space that is described by a small number of parameters: the principal components. Reduction is obtained since continuity equations only need to be solved for these principal components and not for all the species. Application of the presented method to a CO2 plasma model including state-to-state vibrational kinetics of CO2 and CO demonstrates the potential of the PCA method for dimension reduction. A manifold described by only two principal components is able to predict the CO2 to CO conversion at varying ionization degrees very accurately.

Transient Simulations of a T100 Micro Gas Turbine Converted Into a Micro Humid Air Turbine

Micro Gas Turbines (mGTs) have arisen as a promising technology for Combined Heat and Power (CHP) thanks to their overall energy efficiencies of 80% (30% electrical + 50% thermal) and the advantages they offer with respect to internal combustion engines. The main limitation of mGTs lies in their rather low electrical efficiency: whenever there is no heat demand, the exhaust gases are directly blown off and the efficiency of the unit is reduced to 30%. Operation in such conditions is generally not economical and can eventually lead to shutdown of the machine. To address this issue, the mGT cycle can be modified so that in moments of low heat demand the heat in the exhaust gases is used to warm up water which is then re-injected in the cycle, thereby increasing the electrical efficiency. The introduction of a saturation tower allows for water injection in mGTs: the resulting cycle is known as a micro Humid Air Turbine (mHAT).The static performance of the mGT Turbec T100 working as an mHAT has been characterised through previous numerical and experimental work at Vrije Universiteit Brussel (VUB). However, the dynamic behaviour of such a complex system is key to protect the components during transient operation. Thus, we have modelled the Turbec T100 mHAT with the TRANSEO tool in order to simulate how the cycle performs when the demanded power output fluctuates. Steady-state results showed that when operating with water injection, the electrical efficiency of the unit is incremented by 3.4% absolute. The transient analysis revealed that power increase ramps higher than 4.2 kW/s or power decrease ramps lower than 3.5 kW/s (absolute value) lead to oscillations which enter the unstable operation region of the compressor. Since power ramps in the controller of the Turbec T100 mGT are limited to 2kW/s, it should be safe to vary the power output of the T100 mHAT when operating with water injection.Copyright

Assessment of On-the-Fly Chemistry Reduction and Tabulation Approaches for the Simulation of Moderate or Intense Low-Oxygen Dilution Combustion

The current paper focuses on the numerical simulation of the Delft jet in hot co-flow (DJHC) burner, fed with natural gas and biogas, using the eddy dissipation concept (EDC) model with dynamic chemistry reduction and tabulation, i.e., tabulated dynamic adaptive chemistry (TDAC). The central processing unit (CPU) time saving provided by TDAC is evaluated for various EDC model constants and chemical mechanisms of increasing complexity, using a number of chemistry reduction approaches. Results show that the TDAC method provides speed-up factors of 1.4–2.0 and more than 10 when using a skeletal mechanism (DRM19) and a comprehensive kinetic mechanism (POLIMIC1C3HT), respectively. The directed relation graph with error propagation (DRGEP), dynamic adaptive chemistry (DAC), and elementary flux analysis (EFA) reduction models show superior performances when compared to other approaches, such as directed relation graph (DRG) and path flux analysis (PFA). All of the reduction models have been adapted for run-time reduction. Furthermore, the contribution of tabulation is more important with small mechanisms, while reduction plays a major role with large ones.

Thermochemical oscillation of methane MILD combustion diluted with N2/CO2/H2O

ABSTRACT Strict environmental rules endorse moderate or intense low oxygen dilution (MILD) combustion as a promising technology to increase efficiency while reducing pollutants emission. The experimental and theoretical investigation of oscillatory behaviours in methane MILD combustion is of interest to prevent undesired unstable combustion regimes. In this study new speciation measurements were obtained in a jet-stirred flow reactor (JSR) for stoichiometric mixtures of CH4 and O2, diluted in N2, CO2 and N2-H2O, at p = 1.1 atm and T = 720–1200 K. Oscillations were experimentally detected under specific temperature ranges, where system reactivity is sufficient to promote ignition, but not high enough to sustain complete methane conversion. A thorough kinetic discussion highlights reasons for the observed phenomena, mostly focusing on the effects of different dilutions.

Edcsmoke: A new combustion solver for stiff chemistry based on OpenFOAM®

In the present work, two new OpenFOAM solvers for combustion problems requiring detailed kinetic mechanisms are presented. The Eddy Dissipation Concept (EDC) [1] is used for turbulence-chemistry interactions and for the integration of detailed chemistry. The solvers, called ′edcSimpleSMOKE′ for steady state problems and ′edcPimpleSMOKE′ for unsteady ones, were developed for a robust handling of large and detailed chemical mechanisms in the context of RANS simulations. The solver was validated using high-fidelity experimental data from several Sandia flames and Jet in Hot Co-flow burner. In general, good agreement is observed between the simulations and the experimental results, for both temperature and species mass fraction profiles. What’s more, different formulations of EDC model are tested and the results are compared.

Reduction of a collisional-radiative argon model comparing a modified binning method with principal component analysis

Summary form only given. Considerable effort has been carried out to reduce the complexity of detailed chemistry models for plasma flows. Plasma involves many species and different complex reactions, each of them evolving at a different time-scale. Because of this complexity, numerical simulations often remain restricted to simple zero-or one-dimensional calculations. Different reduction techniques have been developed over the years. Time-scale based reductions are possible through rate-controlled constrained equilibrium methods or singular perturbation methods as investigated by the combustion community. Other techniques aim to limit the number of species. Coarse-grain models resulting from binning methods, for example, have been developed for simulating rovibrational nitrogen chemistry. The energy levels of ab initio databases are being lumped into several bins. The number of governing equations is significantly reduced as the considered species equal the amount of bins.This paper compares two techniques for obtaining a reduced model for a 34-species collisional-radiative argon mechanism. The first technique relies on a modified binning method. The excited states are separated from the ground state and averaged through a Maxwell-Boltzmann distribution in a separate bin. Separating the ground state from the bin allows to keep the entire reactive scheme as the excitation reactions are not canceled out. An extra energy equation is solved for the regrouped states. The state model uses an extra temperature to characterize the temperature evolution of the bin. This state to state reduction technique is compared with previously investigated work on Principal Component Analysis. PCA reduces the number of variables of the simulation by projecting the system on a base formed by the principal components. These principal components are retrieved by solving an eigenvalue problem on the covariance matrix of the full dataset and correspond to the eigenvectors with the highest eigenvalues. The advantage of PCA lies in the conservation of detailed chemistry information for every species. Such information is usually lost when applying binning techniques. The present work gives a detailed comparison between both reduction techniques and their resulting model.

An Optimization-based Approach for the Development of a Combustion Chamber for Residential Micro-gas Turbine Applications

In the field of micro gas turbine, attention must be paid on the design of the combustion chamber to reduce NOx formation without compromising combustion efficiency. To this goal flameless combustion represents an appealing solution. The present work aims at the design and optimization of a combustion chamber for a micro gas-turbine, operating in flameless combustion regime. The feasibility of such system is analysed with numerical simulations, using CFD-tools. Among the several configurations under investigations, it is possible to identify one that guarantees the maximum combustion efficiency, the minimum pressure losses as well as the minimum overall dimension.

Chemistry reduction for modelling flameless combustion of ethylene

Flameless combustion is a novel combustion technology able to ensure high combustion efficacies with low pollutant emissions thanks to the dilution of reactants, usually achieved through recirculation of combustion products. The technology has been successfully applied in several processes and has been found to be able to handle a large variety of fuels, including low grade fuels, industrial by-products and hydrogen. Further development of this innovative combustion technology would benefit of Computational Fluid Dynamics (CFD) tools; however, modelling flameless combustion is much more challenging than conventional flames, because of the strong coupling between turbulent mixing and chemical kinetics. In particular the chemical kinetics plays a fundamental role, even though there no common opinion on the degree a mechanism can be reduced. Some useful works may be found on flameless burners fed with methane, but there is lack of information on different fuels. The present work describes the numerical modelling of an ethylene jet flame issuing in a hot coflow burner, emulating flameless combustion and fully characterised in literature, with the scope of investigating the potential for chemistry reduction in the context of flameless combustion. A Principle Component/Variable Analysis is used to investigate the most important species in the chemical mechanism and subsequently a dimension reduction technique based on the Rate-controlled constrained-equilibrium (RCEE) principle is applied to the detailed mechanism to be coupled to the CFD code, in order to make simulations more affordable. Results indicated that the use of Principle Component/Variable Analysis leads to a good choice of the variables to be retained as results with the reduced scheme were found to be consistent with those obtained with the full mechanism.

MG-local-PCA Method for the Reduction of a Collisional-Radiative Argon Plasma Mechanism

The present paper introduces the use of a locally applied Manifold Generated Principal Component Analysis (MG-local-PCA) on a collisional-radiative mechanism for a 34-species argon mixture. MG-PCA is applied on 1D shock relaxation simulations. The method is validated against shock tube experiments from the University of Toronto (UTIAS). The reduced model is obtained by projecting the original variable set on a reduced basis containing its principal components. An important reduction of simulation cost is obtained as the number of equations has been reduced to the number of principal components.

Principal Component Analysis on a LES of a Squared Ribbed Channel

The present paper reports on the application of Principal Component Analysis (PCA) on the flow and thermal fields generated by the large-eddy simulation (LES) of a square ribbed duct heated by a constant heat flux applied over the bottom surface of the duct. PCA allows to understand the complexity of the resulting turbulent heat transfer process, identifying the flow and thermal quantities which are most relevant to the process. Different algorithms have been employed to perform this analysis, showing high correlation between turbulent coherent structures, identified by Q − criterion, and the heat transfer quantified by the non-dimensional magnitude Enhancement Factor (EF), both identified as Principal Variables (PV) of the process.