The computational efficiency of Monte Carlo breakage of articles using serial and parallel processing : a comparison

This paper presents a GPU-based parallelized and a CPU-based serial Monte-Carlo method for breakage of a particle. We compare the efficiency of the graphic card’s graphics processing unit (GPU) and the general-purpose central processing unit (CPU), in a simulation using Monte Carlo (MC) methods for processing the particle breakage. Three applications are used to compare the computational performance times, clock cycles and speedup factors, to find which platform is faster under which conditions. The architecture of the GPU is becoming increasingly programmable; it represents a potential speedup for many applications compared to the modern CPU. The objective of the paper is to compare the performance of the GPU and Intel Core i7-4790 multicore CPU. The implementation for the CPU was written in the C programming language, and the GPU implemented the kernel using Nvidia’s CUDA (Compute Unified Device Architecture). This paper compares the computational times, clock cycles and the speedup factor for a GPU and a CPU, with various simulation settings such as the number of simulation entries (SEs), for a better understanding of the GPU and CPU computational efficiency. It has been found that the number of SEs directly affects the speedup factor.fi=vertaisarvioitu|en=peerReviewed

Detailed population balance modelling of industrial titania synthesis

This thesis presents an efficient and robust detailed population balance framework for simulating aerosol synthesis of structured particles using a stochastic method. This is developed in the context of the industrial titania (TiO2) process to enable extensive numerical characterisation of the pigmentary product. A reactor network model is used to provide a modular treatment of the reactor and account for key features, including multiple reactant injections, and tubular reaction and cooling zones. This approach simplifies the flow field in order to focus computational effort on resolving particle structure using a high-dimensional particle model and its modularity offers flexibility to investigate different configurations. Initial results are presented using a pre-defined temperature profile in the network, and the particulate product is characterised by its property distributions. Numerical performance is studied, highlighting the high computational cost of simulating strong phase-coupling, fast process rates, and broad particle size distributions. A novel hybrid particle model is developed to address these challenges. The hybrid particle model employs a univariate description of small particles and switches to a detailed particle model to resolve morphology of more complicated, aggregate particles. New simulation algorithms are presented to manage interactions between particles of each type. The hybrid model is shown to improve efficiency (resolution versus computational cost) and robustness (sensitivity to numerical parameters), while generating the same solutions and convergence behaviour as earlier models. The reactor model is extended, utilizing the superior numerical performance of the new hybrid particle model to enable inclusion of a system energy balance for more accurate study of a broad range of process conditions, and a more sophisticated particle model to resolve particle geometry. These contributions facilitate the study of particle structure and its sensitivity to reactor design and operational choices, providing insight into how operation affects characteristics of the particles and allowing direct comparison with experimental images of the pigmentary product.This research was supported by the National Research Foundation, Prime Minister's Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme, and by Venator

Simulations of an ASA flow crystallizer with a coupled stochastic-deterministic approach

A coupled solver for population balance systems is presented, where the flow, temperature, and concentration equations are solved with finite element methods, and the particle size distribution is simulated with a stochastic simulation algorithm, a so-called kinetic Monte-Carlo method. This novel approach is applied for the simulation of an axisymmetric model of a tubular flow crystallizer. The numerical results are compared with experimental data

Prediction of soot particles in Gas Turbine Combustors using Large Eddy Simulation

Expected stringent legislation on particulate matter (PM) emission by gas turbine combustors is currently motivating considerable efforts to be better understand, model and predict soot formation. This complex phenomenon is very difficult to study in detail with experiment, and numerical simulation is an essential complementary tool. Considering that the chemistry of soot particles strongly depends on their size, the numerical prediction of soot formation requires the description of their size distribution. To do so, either Eulerian methods (sectional or moments) or stochastic Lagrangian approaches are reported in the literature. In the present work, a far more simple semi-deterministic Lagrangian approach is proposed. An accurate description of the gaseous phase including first Polycyclic Aromatic Hydrocarbons is also developed as a necessary input to detail soot model. The combination of reduced chemistries with Lagrangian soot tracking is applied to canonical laminar sooting flames, later to two complex configurations representative of an aeronautical combustors. The first one is the FIRST configuration, a gaseous confined pressurized swirled flame studied experimentally at DLR. Impact of precursors species and radiative transfers through the resolution of Radiative Transfer Equation (RTE). Good predictions are obtained compared to experiments for predicted temperature and soot volume fraction. The second target configuration is the UTIAS Jet A-1 burner and corresponds to a confined turbulent spray flame burning aviation jet fuel A-1 studied experimentally at UTIAS Toronto. LES of this configuration provides a qualitative and quantitative understanding of soot evolution in turbulent spray flames. Numerical predicted soot volume fraction using Lagrangian soot tracking and an ARC mechanism including pyrolysis method is compared to experimental measurements. Results show the ability of the proposed methodology relying on ARC chemistry for Jet A-1 including pyrolysis method and Lagrangian soot tracking, to predict accurately soot compared to available measurements

Population balance modelling of soot formation in laminar and turbulent flames

The reduction of soot emissions in combustion processes is a primary concern of combustion engineers due to the severe health impact of soot, and the prediction of the soot particle size distribution (PSD) has become important. The evolution of the PSD can be predicted by solving the population balance equation (PBE), and several approaches have been proposed for introducing soot morphology in the PBE. Furthermore, the PBE must be coupled with fluid dynamics, species transport and chemical kinetics in order to predict soot properties in laminar and turbulent flames. Finally, accurate and computationally efficient methods must be employed for solving the CFD-PBE approach. In the first part of this thesis, the recently developed conservative finite volume sectional method for the solution of the population balance equation (PBE) is extended to a two-PBE approach for modelling soot formation that distinguishes between coalescence and aggregation and accounts for finite-rate fusing of primary particles within aggregates, while providing a numerically accurate description of primary particle surface growth and oxidation within aggregates. The validation of the method is conducted by reproducing the self-preserving distributions of aggregates with varying fractal dimension. Subsequently, the one-PBE and two-PBE approaches are coupled with CFD and applied to the application of the Santoro laminar non-premixed co-flow sooting flame. By using a comprehensive soot kinetic model, the deficiencies of the one-PBE approach are analysed, and the two-PBE approach is shown to provide a significant improvement in the description of soot morphology using a properly adjusted particle fusing rate. At present, the model parameters for the fusing of soot primary particles are based on sintering models from silica and titania nanoparticles due to the lack of experimental data for soot. Therefore, a comprehensive sensitivity analysis of the model parameters is conducted. The results show the predictive potential of both the one-PBE and two-PBE approaches. With the presently available experimental measurements, the results suggest that one-PBE method is a reasonable choice for the applications associated with turbulent flame. Subsequently in the second part, the one-PBE method is incorporated into the LES-PBE-PDF approach developed within the group for modelling soot formation in turbulent flames. For the first time, the LES-PBE-PDF approach provides a comprehensive physicochemical model accounting for nucleation, surface growth, oxidation, condensation, coalescence and aggregation. The interaction between chemistry, turbulence and soot particles are accounted for by resolving an evolution equation for the LES-filtered one-point, one-time, joint scalar-number density probability density function (PDF). The Eulerian stochastic field method is used for the solution of the joint-scalar-number density PDF. By using the same kinetics and model parameters as tested in the laminar flame case, the LES-PBE-PDF approach is applied to model soot formation in the Sandia turbulent non-premixed sooting flame. The predicted thermochemical conditions and soot volume fraction are in reasonably good agreement with experimental measurements. The analysis and findings demonstrate good predictive capability and computational feasibility of the complete LES-PBE-PDF approach. In summary, this thesis presents a systematic study for soot formation in the laminar and turbulent flames. In particular, the key adjustable model parameters, surface reactivity $\alpha$ and cut-off point $d_c$, are calibrated in the laminar flame and employed in the turbulent flame. Yet, some limitations should be pointed out. For soot study, the current methodology does not capture the composition of soot during its formation and growth, thus the surface reactivity model applied is rather primitive and needs some adjustments, and the work assumes a constant fractal dimension, whose impact should be further investigated. For turbulent sooting flame, future investigation regarding the micromixing model is warranted.Open Acces

Study of industrial titania synthesis using a hybrid particle-number and detailed particle model

We apply a hybrid particle model to study synthesis of particulate titania under representative industrial conditions. The hybrid particle model employs a particle-number description for small particles, and resolves complicated particle morphology where required using a detailed particle model. This enables resolution of particle property distributions under fast process dynamics. Robustness is demonstrated in a network of reactors used to simulate the industrial process. The detailed particle model resolves properties of the particles that determine end product quality and post-processing efficiency, including primary particle size and degree of aggregate cohesion. Sensitivity of these properties to process design choices is quantified, showing that higher temperature injections produce more sintered particles; more frequent injections narrow the geometric standard deviation of primary particle diameter; and chlorine dilution reduces particle size and size variance. Structures of a typical industrial particle are compared visually with simulated particles, illustrating similar aggregate features with slightly larger primary particles

Simulations of an ASA flow crystallizer with a coupled stochastic-deterministic approach

A coupled solver for population balance systems is presented, where the flow, temperature, and concentration equations are solved with finite element methods, and the particle size distribution is simulated with a stochastic simulation algorithm, a so-called kinetic Monte-Carlo method. This novel approach is applied for the simulation of an axisymmetric model of a tubular flow crystallizer. The numerical results are compared with experimental data

The MESSy aerosol submodel MADE3 (v2.0b): description and a box model test

We introduce MADE3 (Modal Aerosol Dynamics model for Europe, adapted for global applications, 3rd generation; version: MADE3v2.0b), an aerosol dynamics submodel for application within the MESSy framework (Modular Earth Submodel System). MADE3 builds on the predecessor aerosol submodels MADE and MADE-in. Its main new features are the explicit representation of coarse mode particle interactions both with other particles and with condensable gases, and the inclusion of hydrochloric acid (HCl) / chloride (Cl) partitioning between the gas and condensed phases. The aerosol size distribution is represented in the new submodel as a superposition of nine lognormal modes: one for fully soluble particles, one for insoluble particles, and one for mixed particles in each of three size ranges (Aitken, accumulation, and coarse mode size ranges). <br><br> In order to assess the performance of MADE3 we compare it to its predecessor MADE and to the much more detailed particle-resolved aerosol model PartMC-MOSAIC in a box model simulation of an idealised marine boundary layer test case. MADE3 and MADE results are very similar, except in the coarse mode, where the aerosol is dominated by sea spray particles. Cl is reduced in MADE3 with respect to MADE due to the HCl / Cl partitioning that leads to Cl removal from the sea spray aerosol in our test case. Additionally, the aerosol nitrate concentration is higher in MADE3 due to the condensation of nitric acid on coarse mode particles. MADE3 and PartMC-MOSAIC show substantial differences in the fine particle size distributions (sizes &lesssim; 2 μm) that could be relevant when simulating climate effects on a global scale. Nevertheless, the agreement between MADE3 and PartMC-MOSAIC is very good when it comes to coarse particle size distributions (sizes &gtrsim; 2 μm), and also in terms of aerosol composition. Considering these results and the well-established ability of MADE in reproducing observed aerosol loadings and composition, MADE3 seems suitable for application within a global model