Modeling Multicomponent Fuel Droplet Vaporization with Finite Liquid Diffusivity Using Coupled Algebraic-Dqmom with Delumping

Multicomponent fuel droplet vaporization models for use in combustion CFD codes often prioritize computational efficiency over model complexity. This leads to oversimplifying assumptions such as single component droplets or infinite liquid diffusivity. The previously developed Direct Quadrature Method of Moments (DQMoM) with delumping model demonstrated a computationally efficient and accurate approach to solve for every discrete species in a well-mixed vaporizing multicomponent droplet. To expand the method to less restrictive cases, a new solution technique is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM). In contrast to previous moment methods for droplet vaporization, CA-DQMoM solves for the evolution of two liquid distributions by coupling a monovariate, homogeneous DQMoM approach with additional algebraic moment equations, allowing for a more complex droplet vaporization model with finite rates of liquid diffusion to be solved with computational efficiency. To further decrease computational expense, an approximation that employs the same nodes for both distributions can be used in certain cases. Finally, a delumping technique is adapted to the finite diffusivity model to reconstruct discrete species information at minimal computational cost. The model is proven to be accurate relative to a full discrete component model for both a kerosene droplet comprised of 36 species and a multicomponent droplet of 200 species while maintaining the computational efficiency of continuous thermodynamics models. The combined accuracy and computational efficiency demonstrated by the CA-DQMoM with delumping model for a multicomponent fuel droplet with finite liquid diffusivity makes it ideal for incorporation into CFD models for complex combustion process

An Efficient Coal Pyrolysis Model for Detailed Tar Species Vaporization

An accurate and computationally efficient model for the vaporization of many tar species during coal particle pyrolysis has been developed. Like previous models, the molecular fragments generated by thermal decomposition are partitioned into liquid metaplast, which remains in the particle, and vapor, which escapes as tar, using a vapor-liquid equilibrium(VLE) sub-model. Multicomponent VLE is formulated as a rate-based process, which results in an ordinary differential equation (ODE) for every species. To reduce the computational expense of solving many ODEs, the model treats tar and metaplast species as a continuous distribution of molecular weight. To improve upon the accuracy of previous continuous thermodynamic approaches for pyrolysis, the direct quadrature method of moments (DQMoM) is proposed to solve for the evolving distributions without assuming any functional form. An inexpensive delumping procedure is also utilized to recover the time-dependent mole fractions and fluxes for every discrete species. The model is well-suited for coal-to-chemicals processes, and any application which requires information on a range of tar species. Using a modified CPD model as the basis for implementation of the VLE submodel, agreement between the full discrete model and DQMoM with delumping is excellent, with substantial computational savings

Direct Quadrature Method of Moments with Delumping for Modeling Multicomponent Droplet Vaporization

A multicomponent droplet vaporization model which combines the computational efficiency of continuous thermodynamic approaches with the detailed species information provided by discrete component models has been developed. The Direct Quadrature Method of Moments (DQMoM) is used to efficiently solve for the evolution of the nodes and weights of the equivalent liquid-phase mole fraction distribution without assuming any functional form. The novelty of the approach is an inexpensive delumping procedure that is used to reconstruct the time-dependent mole fractions and fluxes for all discrete species. When applied to a vaporizing kerosene droplet, agreement between the full discrete component model, which solves ODEs for every individual species, and DQMoM with delumping, which solves only a few ODEs, is excellent. This computationally inexpensive model is well-suited for implementation in CFD codes with detailed kinetic mechanisms, as it enables accurate calculation of species source terms from the droplets without incurring an unrealistic computational cost

Modeling Pyrolysis of Large Coal Particles with Many Species

Coal currently supplies 40% of the world’s electricity needs, and is one of the most important energy sources. As the initial stage of coal combustion, pyrolysis is a thermal decomposition process which converts coal into light gases and tars, which are subsequently consumed in combustion reactions, as well as solid char. Recently there has been interest in using slow pyrolysis as a stand-alone process for the production of chemicals and fuels from large (mm-scale) coal particles. Simulations can be used to efficiently study the impact of pyrolysis conditions on gas, tar and char yields, as well as gas and tar species compositions, which are an important output for a coal-to-chemicals process. In order to simulate pyrolysis of large coal particles, the Chemical Percolation Devolatilization (CPD) model, which predicts the mass fractions of char, tar and light gas, has been modified and improved. A transient multicomponent vaporization sub-model has been developed to predict the partitioning of heavy species into gaseous tar and liquid metaplast. The Direct Quadrature Method of Moments (DQMoM) is introduced as a computationally efficient method to solve for the evolution of the distribution of tar species as a function of molar mass, and the full discrete tar species distribution can be reconstructed by a novel delumping procedure. Finally, a heat transfer model that can predict temperature gradients within the particles has been incorporated using the finite volume method to discretize the energy equation, with the improved CPD model implemented at every position within the particle. The results show the necessity of resolving large particles spatially, due to the impact of the local temperature evolution on tar and gas mass fractions and the production of certain species. Higher pyrolysis temperatures result in increased yields of gas and especially large tar species, while decreasing pressures also increase the production of heavier tar species. The agreement between the full discrete species model, which solves differential equations for every tar species, and DQMoM with delumping, which solves many fewer equations, is excellent, while yielding a large improvement in computational efficiency

Computational Methods for Modeling Multicomponent Droplet Vaporization

Computational fluid dynamics (CFD) models for combustion of multicomponent hydrocarbon fuels must often prioritize computational efficiency over model complexity, leading to oversimplifying assumptions in the sub-models for droplet vaporization and chemical kinetics. Therefore, a computationally efficient hybrid droplet vaporization-chemical surrogate approach has been developed which emulates both the physical and chemical properties of a multicomponent fuel. For the droplet vaporization/physical portion of the hybrid, a new solution method is presented called the Coupled Algebraic-Direct Quadrature Method of Moments (CA-DQMoM) with delumping which accurately solves for the evolution of every discrete species in a vaporizing multicomponent fuel droplet with the computational efficiency of a continuous thermodynamics model. To link the vaporization model to the chemical surrogate portion of the hybrid, a Functional Group Matching (FGM) method is developed which creates an instantaneous surrogate composition to match the distribution of chemical functional groups in the vaporization flux of the full fuel. The result is a hybrid method which can accurately and efficiently predict time-dependent, distillation-resolved combustion properties of the vaporizing fuel and can be used to investigate the effects of preferential vaporization on combustion behavior

Computational models for the simulation of turbulent poly-dispersed flows: Large Eddy Simulation and Quadrature-Based Moment Method

This work focuses on the development of efficient computational tools for the simulation of turbulent multiphase polydispersed flows. In terms of methodologies we focus here on the use of Large Eddy Simulation (LES) and Quadrature-Based Methods of Moments (QBMM). In terms of applications the work is finalised, in order to be applied in the future, to particle production processes (precipitation and crystallisation in particular). An important part of the work concerns the study of the flow field in a Confined Impinging Jets Reactor (CIJR), frequently used in particle production processes. The first part is limited to the comparison and analysis of micro Particle Image Velocimetry (μPIV) experiments, carried out in a previous work, and Direct Numerical Simulation (DNS), carried out in this thesis. In particular the effects of boundary and operating conditions are studied and the numerical simulations are used to understand the experimental predictions and demonstrate the importance of unavoidable fluctuations in the experimental inlets. This represents a preparatory work for the LES modelling of the CIJR. Before investigating the accuracy of LES predictions for this particular application, the model and the implementation are studied in a more general context, represented by a well-known test case such as the periodic turbulent channel flow: the LES model implementation in TransAT, the code used in this work, is compared with DNS data and with predictions of other codes. LES simulations for the CIJR, provided with the proper boundary conditions obtained by the previous DNS/μPIV study, are then performed and compared with experiments, validating the model in a more realistic test case. Since particle precipitation and crystallization often result in complex interactions between particles and the continuous phase, in the second part of the work particular attention has been paid in the modelling of the momentum transfer and the resulting velocity of the particles (relative to the fluid). In particular the possibility of describing poly-disperse fluid-solid systems with QBMM together with LES and Equilibrium Eulerian Model (EEM) is assessed. The study is performed by comparing our predictions with DNS Lagrangian data in the turbulent channel flow previously described, seeded with particles corresponding to a realistic Particle Size Distribution (PSD). The last part of the work deals with particle collisions, extending QBMM to the investigation of non-equilibrium flows governed by the Boltzmann Equation with a hard-sphere collision kernel. The evolution of the particle velocity distribution is predicted and compared with other methods for kinetic equations such as Lattice Boltzmann Method (LBM), Discrete Velocity Method (DVM) and Grad’s Moment Method (GM). The overall results of this thesis can be extended to a broad range of other applications of single-phase, dispersed multiphase and non-equilibrium flows