Uncertainty Quantification for Scale-Bridging Modeling of Multiphase Reactive Flows

• Main Author: Iavarone, Salvatore
• Other Authors: Parente, Alessandro
• Format: Doctoral Thesis
• Language: en
• Published: Universite Libre de Bruxelles 2019
• Subjects: Sciences de l'ingénieur; Uncertainty Quantification; Validation; Scale-bridging modeling; Reduced order modeling; Coal combustion; NOx formation; Turbulent combustion; Computational Fluid Dynamics
• Online Access: https://dipot.ulb.ac.be/dspace/bitstream/2013/286344/5/contratSI.pdf; https://dipot.ulb.ac.be/dspace/bitstream/2013/286344/4/Table_of_Contents.pdf; https://dipot.ulb.ac.be/dspace/bitstream/2013/286344/3/Thesis_IAVARONE.pdf; http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/286344

The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of novel and cost-effective combustion technologies and the minimization of environmental concerns at industrial scale. CFD simulations facilitate scaling-up procedures that otherwise would be complicated by strong interactions between reaction kinetics, turbulence and heat transfer. CFD calculations can be applied directly at the industrial scale of interest, thus avoiding scaling-up from lab-scale experiments. However, this advantage can only be obtained if CFD tools are quantitatively predictive and trusted as so. Despite the improvements in the computational capability, the implementation of detailed physical and chemical models in CFD simulations can still be prohibitive for real combustors, which require large computational grids and therefore significant computational efforts. Advanced simulation approaches like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) guarantee higher fidelity in computational modeling of combustion at, unfortunately, increased computational cost. However, with adequate, reduced, and cost-effective modeling of physical phenomena, such as chemical kinetics and turbulence-chemistry interactions, and state of the art computing, LES will be the tool of choice to describe combustion processes at industrial scale accurately. Therefore, the development of reduced physics and chemistry models with quantified model-form uncertainty is needed to overcome the challenges of performing LES of industrial systems. Reduced-order models must reproduce the main features of the corresponding detailed models. They feature predictivity and capability of bridging scales when validated against a broad range of experiments and targeted by Validation and Uncertainty Quantification (V/UQ) procedures. In this work, V/UQ approaches are applied for reduced-order modeling of pulverized coal devolatilization and subsequent char oxidation, and furthermore for modeling NOx emissions in combustion systems.For coal devolatilization, a benchmark of the Single First-Order Reaction (SFOR) model was performed concerning the accuracy of the prediction of volatile yield. Different SFOR models were implemented and validated against experimental data coming from tests performed in an entrained flow reactor at oxy-conditions, to shed light on their drawbacks and benefits. SFOR models were chosen because of their simplicity: they can be easily included in CFD codes and are very appealing in the perspective of LES of pulverized coal combustion burners. The calibration of kinetic parameters was required to allow the investigated SFOR model to be predictive and reliable for different heating rates, hold temperatures and coal types. A comparison of several calibration approaches was performed to determine if one-step models can be adaptive and able to bridge scales, without losing accuracy, and to select the calibration method to employ for wider ranges of coal rank and operating conditions. The analysis pointed out that the main drawback of the SFOR models is the assumption of a constant ultimate volatile yield, equal to the value from the coal proximate analysis. To overcome this drawback, a yield model, i.e. a simple functional form that relates the ultimate volatile yield to the particle temperature, was proposed. The model depends on two parameters that have a certain degree of uncertainty. The performances of the yield model were assessed using a collaboration of experiments and simulations of a pilot-scale entrained flow reactor. A consistency analysis, based on the Bound-to-Bound Data Collaboration (B2B-DC) approach, and a Bayesian method, based on Gaussian Process Regression (GPR), were employed for the investigation of experiments and simulations. In Bound-to- Bound Data Collaboration the model output, evaluated at specified values of the model parameters, is compared with the experimental data: if the prediction of the model falls within the experimental uncertainty, the corresponding parameter values would be included in the so-called feasible set. The existence of a non-empty feasible set signifies consistency between the experiments and the simulations, i.e. model-data agreement. Consistency was indeed found when a relative error of 19% for all the experimental data was applied. Hence, a feasible set of the two SFOR model parameters was provided. A posterior state of knowledge, indicating potential model forms that could be explored in yield modeling, was obtained by Gaussian Process Regression. The model form evaluated through the consistency analysis is included within the posterior derived from GPR, indicating that it can satisfactorily match the experimental data and provide reliable estimation in almost every range of temperatures. CFD simulations were carried out using the proposed yield model with first-order kinetics, as in the SFOR model. Results showed promising agreement between predicted and experimental conversion for all the investigated cases.Regarding char combustion modeling, the consistency analysis has been applied to validate a reduced-order model and quantify the uncertainty in the prediction of char conversion. The model capability to address heterogeneous reaction between char carbon and O2, CO2 and H2O reagents, mass transport of species in the particle boundary layer, pore diffusion, and internal surface area changes was assessed by comparison with a large number of experiments performed in air and oxy-coal conditions. Different model forms had been considered, with an increasing degree of complexity, until consistency between model outputs and experimental results was reached. Rather than performing forward propagation of the model-form uncertainty on the predictions, the reduction of the parameter uncertainty of a selected model form was pursued and eventually achieved. The resulting 11-dimensional feasible set of model parameters allows the model to predict the experimental data within almost ±10% uncertainty. Due to the high dimensionality of the problem, the employed surrogate models resulted in considerable fitting errors, which led to a spoiled UQ inverse problem. Different strategies were taken to reduce the discrepancy between the surrogate outputs and the corresponding predictions of the simulation model, in the frameworks of constrained optimization and Bayesian inference. Both strategies succeeded in reducing the fitting errors and also resulted in a least-squares estimate for the simulation model. The variety of experimental gas environments ensured the validity of the consistent reduced model for both conventional and oxy-conditions, overcoming the differences in mass transport and kinetics observed in several experimental campaigns.The V/UQ-aided modeling of coal devolatilization and char combustion was done in the framework of the Predictive Science Academic Alliance Program II (PSAAP-II) funded by the US Department of Energy. One of the final goals of PSAAP-II is to develop high-fidelity simulation tools that ensure 5% uncertainty in the incident heat flux predictions inside a 1.2GW Ultra-Super-Critical (USC) coal-fired boiler. The 5% target refers to the expected predictivity of the full-scale simulation without considering the uncertainty in the scenario parameters. The data-driven approaches used in this Thesis helped to improve the predictivity of the investigated models and made them suitable for LES of the 1.2GW USC coal-fired boiler. Moreover, they are suitable for scale-bridging modeling of similar multi-phase processes involved in the conversion of solid renewable sources, such as biomass.In the final part of the Thesis, the sensitivity to finite-rate chemistry combustion models and kinetic mechanisms on the prediction of NO emissions was assessed. Moreover, the forward propagation of the uncertainty in the kinetics of the NNH route (included in the NOx chemistry) on the predictions of NO was investigated to reveal the current state of the art of kinetic modeling of NOx formation. The analysis was carried out on a case where NOx formation comes from various formation routes, both conventional (thermal and prompt) and unconventional ones. To this end, a lab-scale combustion system working in Moderate and Intense Low-oxygen Dilution (MILD) conditions was selected. The results showed considerable sensitivity of the NO emissions to the uncertain kinetic parameters of the rate-limiting reactions of the NNH pathway when a detailed kinetic mechanism is used. The analysis also pointed out that the use of one-step global rate schemes for the NO formation pathways, necessary when a skeletal kinetic mechanism is employed, lacks the required chemical accuracy and dims the importance of the NNH pathway in this combustion regime. An engineering modification of the finite-rate combustion model was proposed to account for the different chemical time scales of the fuel-oxidizer reactions and NOx formation pathways. It showed an equivalent impact on the emissions of NO than the uncertainty in the kinetics of the NNH route. At the cost of introducing a small mass imbalance (of the order of ppm), the adjustment led to improved predictions of NO. The investigation established a possibility for the engineering modeling of NO formation in MILD combustion with a finite-rate chemistry combustion model that can incorporate a detailed mechanism at affordable computational costs. === Doctorat en Sciences de l'ingénieur et technologie === info:eu-repo/semantics/nonPublished