1,720,968 research outputs found
Heat Release Rate Markers for the Adelaide Jet in Hot Coflow Flame
No full textIn the present work, the correlation between the Heat Releaser Rate (HRR) and species mole fractions and net reaction rates is studied. The PaSR closure model is employed in a RANS framework to implement a detailed kinetic scheme, including the excited species OH*, used as a HRR marker. The effect of oxygen dilution on the combustion regime is investigated, as it can lead to Moderate or Intense Low-Oxygen Dilution (MILD) conditions. Two cases with different levels of oxygen concentration are analyzed. The results suggest the possibility of combining chemical species to construct an appropriate scalar to achieve better correlation with the HRR. It is found that typical markers such as radicals O, OH, OH* correlate fairly well with the HRR but improved correlations can be achieved with appropriate species mole fractions combinations, particularly for the MILD region of the flame.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 714605. The author Marco Ferrarotti also wishes to thank Fonds de la Recherche Scientifique FNRS Belgium for financing his research.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Evaluation of Modeling Approaches for MILD Combustion Systems With Internal Recirculation
No full textNumerical simulations employing two different modeling approaches are performedand validated against experimental results from a moderate or intense low-oxygendilution (MILD) system with internal recirculation. The flamelet-generated manifold (FGM)and partially stirred reactor (PaSR) closures are employed in a Reynolds-averagedNavier–Stokes (RANS) framework to carry out the numerical simulations. The resultsshow that the FGM model strongly overpredicts temperature profiles in the reactiveregion, while yielding better results along the central thermocouple. The PaSR closuresbased on a prescribed mixing time constant, Cmix, of 0.01, 0.1, and 0.5 are compared,showing that a Cmix value of 0.5 is the most appropriate choice for the cases underinvestigation. A PaSR formulation allowing local estimation of the Cmix value is found toprovide improved results for both the lateral and central thermocouples. A flame indexanalysis, used to assess the ability of FGM and PaSR to capture intense mixing of thecyclonic burner, indicates how the FGM model predicts a typical non-premixed regionafter the injection zone, contrary to the experimental observation.info:eu-repo/semantics/publishe
Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows
No full textLarge Eddy Simulations (LES) of turbulent reacting flows carried out with detailed kinetic mechanisms have a key role for the discovery of the physical and chemical processes occurring in combustion systems, and are essential for the development of efficient, stable, and non-pollutant technologies. Nevertheless, these simulations require a large amount of computational resources, making their utilization for large-scale systems, such as industrial burners and gas turbines, impractical. In this work, we combine state-of-the-art machine learning algorithms and model reduction methods to deliver a fully automated strategy for performing LES with adaptive chemistry. This strategy is based on the Sample-Partitioning Adaptive Chemistry (SPARC) algorithmic procedure, which consists of four steps: the generation of a training dataset, its partitioning in clusters, the generation of a set of reduced chemical mechanisms specifically tailored to each cluster and, lastly, the numerical simulation of the case of interest with adaptive chemistry enabled by an on-the-fly classification of every grid point. The SPARC approach has already been demonstrated to substantially reduce the computational effort of reactive flows simulations. However a non-negligible level of user interventions is needed, upon which the method's success critically depend. Therefore, with the goal of boosting the performance of this workflow and minimise the user-specified degrees of freedom, we plug in and exploit the Local Principal Component Analysis augmented with an automated Bayesian-optimised search for optimal clustering solutions, and the Computational Singular Perturbation method with an additional layer of automation based on the Tangential Stretching Rate for minimally-sized reduced mechanisms. We employ a cheap and easy-to-generate 1-dimensional-flames training database and we demonstrate the efficiency, accuracy and robustness of this strategy with an application to LES of the Adelaide Jet in Hot Coflow (AJHC) burner, a turbulent reacting flow exhibiting intense turbulence-chemistry interactions.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Parameter Estimation Using a Gaussian Process Regression-Based Reduced-Order Model and Sparse Sensing: Application to a Methane/Air Lifted Jet Flame
No full textThe goal of this work is to perform parameter estimation by comparing a Reduced Order Model (ROM), built using Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR), with a Sparse Sensing (SpS) model. This framework is demonstrated by selecting the optimal set of the Partially Stirred Reactor (PaSR) coefficients used in the modelling of the Cabra flame. The Cabra flame is a methane flame in a vitiated coflow, consisting of the combustion products of hydrogen and air. The PaSR model necessitates the knowledge of 4 scalar coefficients, which are unknown a priori. To select the optimal set of coefficients, 57 simulations were performed with a different combination of PaSR coefficients. These simulations were used to build the ROM via POD and GPR. To compare the numerical solution with the experimental data, the SpS technique has been employed. SpS is a framework that leverages dimensionality reduction to predict the state of the system given few measurements. The optimal coefficients have been estimated by applying an optimization algorithm to the ROM, using the solution provided by SpS as target. Finally, the data assimilation framework has been used to provide a solution with lower uncertainty bounds. The results show that this framework is able to estimate the optimal set of coefficients, and it can be used to identify residual sources of uncertainty in the numerical model by highlighting the difference between the optimized model and the experimental values.SCOPUS: ar.jinfo:eu-repo/semantics/publishe
Simulation of a single-element GCH4/GOx rocket combustor using a non-adiabatic flamelet method
No full textIn this contribution we present numerical simulations of a single element gaseous-methane/gaseous-oxygen (GCH4/GOX) rocket combustor. The aim is to describe and validate a numerical framework for the simulation of mixing, combustion and wall heat transfer in liquid rocket engines (LRE). Such framework is based on a low-Mach number, unsteady Reynolds averaged Navier Stokes (URANS) approach where turbulent combustion modeling is tackled by means of a non-adiabatic flamelet-based method. The latter allows a detailed chemical description of the non-premixed flame structure by employing reasonable computational resources. Two-dimensional as well as three-dimensional results show similar trends in terms of temperature, velocity and composition fields. In addition, an overall good agreement with available experimental results is observed for both the simulations, in terms of pressure and wall heat transfer along the chamber wall. Moreover, the effects of the injector recess and oxidizer to fuel ratio are preliminarily investigated using two-dimensional axis-symmetric simulations
An enhanced Sample-Partitioning Adaptive Reduced Chemistry method with a-priori error estimation
Full text linkReactor-based approaches for handling the Turbulence–Chemistry-Interactions closure have the advantage of embedding finite-rate chemistry in the combustion model of RANS and LES simulations, which might be crucial for the solution accuracy when complex combustion regimes are investigated. However, the numerical solution of the chemical ODEs is burdened with stiffness and increased dimensionality, especially when large detailed mechanisms are required. To this end, the Sample-Partitioning Adaptive Reduced Chemistry (SPARC) methodology couples adaptive chemistry and machine learning to speed-up the chemistry integration in reactive flows simulations. It consists in building a library of skeletal mechanisms, associated to clusters of similar thermo-chemical states identified in a training dataset, and then, at run-time, each computational cell is assigned to a specific cluster, whose skeletal mechanism is retrieved and employed for the time integration. Such workflow builds on four interacting blocks, i.e. training dataset generation, clustering, mechanism simplification, and classification, and its success tightly depends on the quality of each block, which generally results from a combination of theoretical, methodological, and computational choices. In this paper, we explore the effects of the mechanism simplification strategy on the SPARC performance and we develop an ad-hoc procedure that automatically identifies the cluster-wise optimal reduction parameters, delivering a higher global reduction and therefore a larger computational speed-up compared to a standard approach, along with an explicit a-priori control on accuracy. We implement and test this novel procedure on a RANS simulation of the Adelaide Jet-in-hot-coflow (AJHC) burner, and we attain a ∼2x CPU time improvement with respect to the simulation obtained with a 36-species detailed mechanism. Novelty and significance This work makes a contribution towards the acceleration of chemistry integration in reactive flows simulations. More specifically, the Sample-Partitioning Adaptive Reduced Chemistry method, which couples adaptive chemistry and machine learning, is enhanced with automatic target species definition and a-priori error estimation. The novelties lie in the utilization of the computational singular perturbation (CSP) reduction algorithm, which provides means for automatically identifying an adaptive set of target species, and in the definition of a novel strategy for assessing the performance of the reduced mechanisms in the pre-processing phase, with the goal of estimating a measure of accuracy of the upcoming CFD simulation.SCOPUS: ar.jPollution Reduction Design for Innovative Combustion TechnologiesPReDICTMarie Skłodowska-Curie Actions; Individual Fellowship at the Université libre de Bruxelles; IFatULB; Grant Agreement number: 801505info:eu-repo/semantics/publishe
Uncertainty Assessment and Chemistry Acceleration for Numerical Simulations of Sustainable Combustion Technologies
No full textIn order to meet the required CO2 emissions targets by 2050 the global energy production, distribution and consumption framework needs to drastically change. While most decarbonisation will come from the electrification of most end-uses, for the so-called hard-to-abate sectors decarbonisation will be met through a combination of increased efficiency of existing processes and usage of alternative fuels. Moreover, chemical storage of surplus electricity will be key for a sustainable energy system based on renewable sources. The development of efficient, flexible and clean combustion technologies is necessary. In recent years, flameless, or Moderate or Intense Low-oxygen Dilution (MILD) combustion has been studied to this end. However, literature on its use in combination with alternative fuels such as hydrogen and ammonia in industrial-relevant conditions is still scarce. The objective of this work is threefold. First, to investigate flameless combustion of non-conventional fuel blends, such as methane/hydrogen and ammonia/hydrogen, to assess the robustness and accuracy of state-of-the-art numerical models for a variety of operating conditions. Second, to quantify the uncertainty associated with such combustion model predictions. Third, to provide tools to reduce the computational cost associated to turbulencechemistry interaction models for non conventional regimes. Experimental and numerical campaigns on the ULB furnace were carried out to assess MILD conditions in the furnace for different methane/hydrogen mixtures in different working conditions. Operating in MILD conditions above 50% content in H2 proved to be quite challenging, even while varying the equivalence ratio. Different solutions were investigated, aiming at delaying the injection of the fuel to allow mixing with exhausts before chemical reactions could take place. This was accomplished by modifying the injection system by increasing the fuel lance length, allowing MILD conditions up to a hydrogen content of 75%. Varying the air injector geometry had no significant effect on the combustion regime: co-axial and different multi-hole injectors were tested to modify the inlet momentum flux ratio without a noticeable effect on the combustion regime. Fuel dilution with CO2 or H2O was also found to lead to smoother temperature fields. An experimental campaign was then performed to assess the fuel flexibility of the ULB furnace fired with ammonia/hydrogen blends. Optimal operating configurations in terms of trade-off between NOx emissions and ammonia slip were identified. Ammonia slip was found to be negligible in lean conditions, while it became relevant in stoichiometric conditions. The optimal working point was identified for the equivalence ratio f = 0.95, which enabled reduced NO emissions with respect to leaner conditions while keeping NH3 slip below 10 ppm. In stoichiometric conditions, peak NO production was observed for both tested air injectors (internal diameter ID 16 mm and 25 mm) at 10% ammonia in volume and 90% hydrogen in volume (N10H90) fuel composition. Emissions then decreased up to extinction, which occurred above N80H20 fuel composition. The larger air injector (ID25) helped control pollutants emissions, as the resulting increased residence time enhanced NO conversion to N2. Experimental data were employed to validate the combustion model for ammonia/hydrogen oxidation. Temperature predictions were found to be satisfactory and nearly insensitive to different kinetic mechanisms. On the other hand, NOx emissions showed substantial differences between predictions made with different mechanisms. Because of this, an uncertainty quantification study on the kinetic scheme employing a well-stirred reactor network representing the ULB furnace was performed to assess the propagation of the kinetic sub-model uncertainty on the NOx emissions predictions. Sensitivity and rate of production analyses identified influential reactions for NO production, and two kinetic mechanisms were determined, representing the minimum and maximum NO distribution. Over-prediction of NO was still noticeable, therefore the characterisation of said reactions requires improvement, especially for diluted and stoichiometric conditions. To quantify the uncertainty associated with the combustion model predictions in nonconventional combustion regimes, an uncertainty quantification study for RANS simulations of the Cabra flame was then carried out. Polynomial Chaos Expansion was employed to build surrogate models to assess the effect of parametric uncertainty of the Partially-Stirred Reactor combustion model. In particular, the parameters controlling the local mixing time scale were investigated. Results indicate that that the two dissipation coefficients have the strongest influence on the variability of the model response across all regions of the flame. An optimisation procedure which employs experimental results as targets was proposed to determine an optimal set of parameters which yield improved predictions of the quantities of interest. Last, a framework to reduce the computational cost of finite-rate models in LES calculations was presented. The Sample-Partitioning Adaptive Chemistry (SPARC) methodology was advanced with automated algorithms for clustering and mechanism reduction and it was applied to LES of the Adelaide Jet-in-Hot-Coflow burner. The procedure begins with the generation of a sample composition space from cheap 1D simulations. The composition space is clustered and then for each cluster a reduced kinetic mechanism is built. Once these pre-processing steps are concluded, during the CFD simulation, each cell is classified and assigned to a cluster and the respective reduced mechanism is employed. High accuracy with respect to the reference LES computed with the full detailed mechanism was observed, and the chemistry integration time step was more than halved.Doctorat en Sciences de l'ingénieur et technologieinfo:eu-repo/semantics/nonPublishe
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
