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Direct Numerical Simulations of a strongly heated jet of water vapor discharging into a low-speed coflow of cold water vapor is fully coupled with a reciprocal Monte-Carlo method to account for radiative heat transfer. The spectral dependency of the radiative properties is modeled using the Correlated-k method. Such a numerical and modeling setup allows for studying accurately the turbulent jet development submitted to radiative heat transfer. Contrary to the previous study by Armengol et al. [International Journal of Heat and Mass Transfer 139 (2019), pp. 456–474], the present conditions show a direct effect of thermal radiation in the jet developed zone located downstream the jet potential core. Results are analyzed to identify the effects of thermal radiation on turbulence properties of the temperature field, and reciprocally. The analysis reveals a complex coupling between the mean temperature field and the mean radiative power as well as between their fluctuations. This is studied in various profiles of average and root-mean-square properties, and completed by considering budgets of enthalpy variance and turbulence spectra., SCOPUS: ar.j, info:eu-repo/semantics/published

The effects of thermal radiation in a heated jet of water vapor are studied with a direct numerical simulation coupled to a Monte-Carlo solver. The adequacy of the numerical setup is first demonstrated in the uncoupled isothermal and heated turbulent plane jets with comparisons to experimental and numerical data. Radiative energy transfer is then accounted for with spectral dependency of the radiative properties described by the Correlated-k (ck) method. Between the direct impact through modification of the temperature field by the additional radiative transfer and the indirect one where the varied flow density changes the turbulent mixing, the present study is able to clearly identify the second one in the jet developed region by considering conditions where effects of thermal radiation are moderate. When using standard jet scaling laws, the different studied cases without radiation and with small-to-moderate radiative heat transfer yield different profiles even when thermal radiation becomes locally negligible. By deriving another scaling law for the decay of the temperature profile, self-similarity is obtained for the different turbulent jets. The results of the study allow for distinguishing whether thermal radiation modifies the nature of heat transfer mechanisms in the jet developed region or not while removing the indirect effects of modified density., SCOPUS: ar.j, info:eu-repo/semantics/published

Modern society will have to meet its energy demands while ensuring low or virtually zero emissions in order to meet future challenges associated to air pollution, climate change and energy storage. Very often, renewable sources cannot be directly employed because of their intermittent nature and because many applications such as transport and other in- dustrial processes require high energy densities. Therefore, novel storage solutions for the energy that renewable sources contribute to produce is necessary and the transformation of this energy into chemical compounds represents the best choice in order to meet the aforementioned demands, which requires novel combustion technologies, such as Moder- ate and Intense Low-oxygen Dilution (MILD) combustion, to be efficient and fuel-flexible. In order to develop such technologies, several studies are being proposed and terabytes of data collected as more and more experiments and high-fidelity simulations are carried out. However, there are two main challenges to this: the huge amount of data available makes it hard for the researcher to distinguish useful from redundant data, with the risk that useful information might stay hidden; the production process of these data-sets re- quires substantial resources as combustion process are multi-physics, multi-scale and thus require high-fidelity computationally-intensive simulations and experiments over a wide range for their operating conditions or input parameters. Digital twins and Artificial Intel- ligence (AI) are shaping the fourth industrial revolution by building data-driven models that make use of machine learning. It makes sense then to extend this approach to combus- tion applications in order to alleviate the two aforementioned issues: the use of machine learning techniques can help automate the process of data interpretation as well as pro- vide a low-dimensional representation of the high-dimensional data produced by either experiments or simulations; they can speed, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

La simulation numérique représente un outil important pour la conception des systèmes de combustion. La prédiction des flux aux parois joue un rôle déterminant dans le cycle de vie des chambres de combustion, car elle permet de prédire la fatigue thermique des parois. Le transfert de chaleur de la flamme aux parois est entraîné, outre la convection, également par le rayonnement des gaz chauds au sein de la chambre. Pour évaluer les transferts thermiques aux parois il faut donc tenir compte des flux radiatifs. Les pertes thermiques aux parois dépendent de la répartition de la température des gaz qui est, à son tour, fortement modifiée par le rayonnement des gaz brûlés. Afin d’intégrer les contributions convectives et radiatives au flux pariétal dans des simulations numériques, il est nécessaire de résoudre simultanément l'équation de transfert radiatif et les équations régissant l’écoulement réactif. De nos jours, les simulations couplées impliquant combustion et transfert de chaleur radiatif sont de plus en plus utilisées et ciblées. Grâce à l'augmentation de la puissance de calcul, l'utilisation des méthodes de Monte Carlo (MC) dans des simulations 3D instationnaires, telles que les simulations numériques directes (DNS) et les simulations aux grandes échelles (LES), est devenue. Cependant, de telles simulations restent très coûteuses. L’objectif de cette thèse est donc d’investiguer une technique pour améliorer l’efficacité de la méthode MC, basée sur un mécanisme alternatif d’échantillonnage et appelé intégration Quasi-Monte Carlo (QMC). Cette méthode a rarement été utilisée pour la résolution numérique du rayonnement thermique. Dans cette étude, la méthode QMC est appliquée sur plusieurs configurations 3D et comparée à celle de MC. L’amélioration de l’efficacité obtenue par QMC fait de cette méthode un candidat idéal pour des simulations haute-fidélité couplées avec des simulations LES ou DNS. Au cours de cette thèse, la méthode QMC a pu être appliquée à une conf, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

For stationary power sources such as utility boilers, it is useful to dispose of parametric models able to describe their behavior in a wide range of operating conditions, to predict some Quantities of Interest (QOIs) that need to be consistent with experimental observations. The development of predictive simulation tools for large scale systems cannot rely on full-order models, as the latter would lead to prohibitive costs when coupled to sampling techniques in the model parameter space. An alternative approach consists of using a Surrogate Model (SM). As the number of QOIs is often high and many SMs need to be trained, Principal Component Analysis (PCA) can be used to encode the set of QOIs in a much smaller set of scalars, called PCA scores. A SM is then built for each PCA score rather than for each QOI. The advantage of reducing the number of variables is twofold: computational costs are reduced (less SMs need to be trained) and information is preserved (correlation among the original variables).The strategy is applied to a CFD model simulating the Alstom 15 MWth oxy-pilot Boiler Simulation Facility (BSF). In practice, experiments cannot provide full coverage of the pulverized-coal utility boiler due to both practicality and costs. Values of the model’s parameters which guarantee consistency with the experimental data of this test facility for 121 QOIs are found, by training a SM based on the combination of Kriging and PCA, using only 5 latent variables., This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no 643134 and was also sponsored by the European Research Council, Starting Grant no 714605. This material is based upon work supported by the Department of Energy, National Nuclear Security Administration, under Award no DE-NA0002375., SCOPUS: ar.j, info:eu-repo/semantics/published

Detailed numerical simulations of detailed combustion systems require substantial computational resources, which limit their use for optimization and uncertainty quantification studies. Starting from a limited number of CFD simulations, a reduced-order model is derived using a few detailed function evaluations.In this work, the combination of Principal Component Analysis (PCA) with Kriging is considered to identify accurate low-order models. PCA is used to identify and separate invariants of the system, the PCA modes, from the coefficients that are instead related to the characteristic operating conditions. Kriging is then used to find a response surface for these coefficients. This leads to a surrogate model that allows to perform parameter exploration with reduced computational cost. Varia- tions of the classical PCA approach, namely Local and Constrained PCA, are also presented.This methodology is demonstrated on 1D and 2D flames produced by OpenSmoke++ and OpenFoam, respectively, for which accurate surrogate models have been developed., This project has received funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 643134., SCOPUS: ar.j, info:eu-repo/semantics/published