Large eddy simulation of coal combustion

In this work an in-house code for large-eddy simulations of coal combustion is developed and tested, with a special focus on the issue of modelling radiative heat transfer effects inside a furnace. An Eulerian-Lagrangian approach is used to describe the continuous gas phase and the discrete particle phase, with a two-way coupling between the two phases (implemented by another group member). The radiative transfer equation is solved using the discrete ordinates method, testing several different angular and spatial discretisation schemes. The spectral properties of the participating media are approximated with different grey gas models of varying complexity and accuracy. The accuracy of the radiative solver is initially assessed on simple idealised static cases in both two- and three-dimensions, and validated against benchmark data found in literature. The code is then integrated, parallelised and optimised with the LES flow and combustion solver, and used to simulate a large 2.4 MW coal combustion furnace. The results of the simulations are compared quantitatively against experimental data in terms of velocity, temperature, species distribution and solid particle analysis, showing a good agreement overall. A parametric study is then also performed on the variables and parameters of the radiation solver, showing great sensitivity on the outcome of the simulations in certain cases, further highlighting the importance of accurate radiation modelling for closed coal combustion furnaces.Open Acces

Parallel Multi-Physics Simulation of Biomass Furnace and Cloud-based Workflow for SMEs

Biomass combustion is a well-established process to produce energy that offers a credible alternative to reduce the consumption of fossil fuel. To optimize the process of biomass combustion, numerical simulation is a less expensive and time-effective approach than the experimental method. However, biomass combustion involves intricate physical phenomena that must be modeled (and validated) carefully, in the fuel bed and in the surrounding gas. With this level of complexity, these simulations require the use of High-Performance Computing (HPC) platforms and expertise, which are usually not affordable for manufacturing SMEs. In this work, we developed a parallel simulation tool for the simulation of biomass furnaces that relies on a parallel coupling between Computation Fluid Dynamics (CFD) and Discrete Element Method (DEM). This approach is computation-intensive but provides accurate and detailed results for biomass combustion with a moving fuel bed. Our implementation combines FOAM-extend (for the gas phase) parallelized with MPI, and XDEM (for the solid particles) parallelized with OpenMP, to take advantage of HPC hardware. We also carry out a thorough performance evaluation of our implementation using an industrial biomass furnace setup. Additionally, we present a fully automated workflow that handles all steps from the user input to the analysis of the results. Hundreds of parameters can be modified, including the furnace geometry and fuel settings. The workflow prepares the simulation input, delegates the computing-intensive simulation to an HPC platform, and collects the results. Our solution is integrated into the Digital Marketplace of the CloudiFacturing EU project and is directly available to SMEs via a Cloud portal. As a result, we provide a cutting-edge simulation of a biomass furnace running on HPC. With this tool, we demonstrate how HPC can benefit engineering and manufacturing SMEs, and empower them to compute and solve problems that cannot be tackled without

A theoretical and empirical investigation into the growth of ultralong carbon nanotubes

Carbon nanotubes (CNTs) were first discovered and named as such by Iijima in 1991. Various institutes and researchers have since widely conducted ongoing research on carbon nanotube growth. The exceptional properties of CNTs, including their electrical and mechanical properties, aim to revolutionise the applications of electronics and devices in the future such as transmission power lines and lightweight high-strength carbon nanotube fibres. Therefore, understanding the mechanisms of growing ultra-long carbon nanotubes (UL-CNTs) that can increase the length to more than a centimetre long can unlock the full potential of the CNTs. This PhD project will have three parts: (Ⅰ) the growth experiments using different types of monometallic & bimetallic iron based catalysts for growing carbon nanotubes; (Ⅱ) the computational simulation of flow fields around carbon nanotube geometry in a micro-scale; (Ⅲ) the applications of carbon nanotubes produced from waste plastics, such as Ethernet & audio cables, and public engagement events about the research. In the growth experiment topic, the primary objective of this research is to study the catalyst activities on the rate of carbon nanotube growth using monometallic (Fe) & bimetallic catalysts (Fe-Cu, Fe-Co, Fe-Ni, Fe-Sn, Fe-Ga, Fe-Mg & Fe-Al) dissolved in deionised water, and find which catalysts have the potential to grow the longest carbon nanotubes with improved characteristics, such as G/D (graphene/ disorders) ratio. As we know, the carbon source gas flow rate and reactor temperature profiles can affect the length of carbon nanotubes from the literature; an effective way to optimise experimental conditions to grow UL-CNTs is to use computational fluid dynamics (CFD) modelling methods. So far, there has been little research on the growth of ultra-long carbon nanotubes under a non-continuous flow environment on a nanoscale. Most computational modelling studies have only focused on the continuity of flow in a traditional approach. This research uses the BGK-Boltzmann equation and molecular collision models to investigate flow behaviours at the nanoscopic scale. Thus, this study provides an exciting opportunity to advance the knowledge of growing ultra-long carbon nanotubes (UL-CNTs) of centimetre length or higher and may be used in applications including the carbon nanotube Ethernet and audio cables as mentioned in this project

Probabilistic Modelling of Sensitivity in Fire Simulations

The objective of this thesis is to apply probabilistic sensitivity analyses to the emerging field of toxic hazard simulation in fire science. Fire simulation based on computational fluid dynamics (CFD) plays an important role in performance-based fire design. However, the thermal-physical process of material decomposition in fires and the chemical reactions of fire effluents are not well enough understood to make useful predictions about their burning behaviour. Input parameters are subject to uncertainties which can cause deviations in the results. Conventional engineering approaches are not suitable for reliable prediction of effects of input uncertainties on the results. In this work, probabilistic sensitivity analysis based on random sampling is used to determine the most important input variables and how uncertainties in their value influences the output. The theoretical basis for hazard analyses is summarised and uncertainties are described which typically influence the results of fire tests and numerical fire simulations. The simulations are run with the Fire Dynamics Simulator (FDS), version 6. The proposed method was used for both mixing-controlled and finite-rate combustion with Large-Eddy- and Direct Numerical Simulation. Results of three different sensitivity analyses, each based on up to 500 samples are presented. The input parameter sets were systematically generated using advanced Latin Hypercube Sampling. The results of the sensitivity analyses were evaluated using the Metamodel of Optimal Prognosis which provides a measure of the predictability of the simulation that can be applied as an indicator for model quality. The results allow conclusions to be made, which quality of prognosis can be achieved using current fire simulation technology. The most influential parameters have been identified. Based on these simulation results a recommendation is made as to how the technology of probabilistic fire simulation and sensitivity analysis can be developed further in order to allow material parameter identification as a basis for the prediction of toxic effluents from solid fuels

MIST: a portable and efficient toolkit for molecular dynamics integration algorithm development

The main contribution of this thesis is MIST, the Molecular Integration Simula- tion Toolkit, a lightweight and efficient software library written in C++ which provides an abstract interface to common Molecular Dynamics codes, enabling rapid and portable development of new integration schemes for Molecular Dynamics. The initial release provides plug-in interfaces to NAMD-Lite, GROMACS, Amber and LAMMPS and includes several standard integration schemes, a constraint solver, temperature control using Langevin Dynamics, temperature and pressure control using Nosé-Hoover chains, and five advanced sampling schemes. I describe the architecture, functionality and internal details of the library and the C and Fortran APIs which can be used to interface additional MD codes to MIST. As an example to future developers, each of the existing plug-ins and the integrators that are included with MIST are described. Brief instructions for compilation and use of the library are also given as a reference to users. The library is designed to be expressive, portable and performant, and I show via a range of test systems that MIST introduces negligible overheads for serial, parallel, and GPU-accelerated cases, except for Amber where the native integrators run directly on the GPU itself, but only run on the CPU in MIST. The capabilities of MIST for production-quality simulations are demonstrated through the use of a simulated tempering simulation to study the free energy landscape of Alanine-12 in both vacuum and detailed solvent conditions. I also present the evaluation and application of force-field and ab initio Molecular Dynamics to study the structural properties and behaviour of olivine melts. Three existing classical potentials for fayalite are tested and found to give lattice parameters and Radial Distribution Functions in good agreement with experimental data. For forsterite, lattice parameters at ambient pressure and temperature are slightly over-predicted by simulation (similar to other reported results in the literature). Likewise, higher-than expected thermal expansion coefficients and heat capacities are obtained from both ab initio and classical methods. The structure of both the crystal and melt are found to be in good agreement with experimental data. Several methodological improvements which could improve the accuracy of melting point determination and the thermal expansion coefficients are discussed

3D Modelling and Simulation of Reactive Fluidized Beds for Conversion of Biomass with Discrete Element Method

The use of biomass as a CO2–neutral renewable energy source gains more importance due to the decreasing resources of fossil fuels and their impact on the global warming. The thermochemical conversion of biomass in fluidized beds offers an economic and sustainable contribution to the global energy supply. Although the fluidized bed has reached a commercial status since many decades ago, its hydrodynamic behaviour is not completely understood. The availability of detail experimental information from real facilities is extremely difficult because the lack of accessibility, the measurement costs and the associated inevitable reduction in production. The numerical simulation provides an effective complement to the costly measurements. This requires besides the calculation of a gas-solid flow, an accurate description of particle–particle/wall collisions. Furthermore, kinetic models for pyrolysis, homogenous reactions, heterogeneous reactions and the related heat and mass transfer processes should be considered. Basically, there are two different methods for the representation of the gas–solid flow, viz. Euler–Euler and Euler–Lagrange models. The solid phase is treated as a continuum in the Euler–Euler model, while each particle trajectory is determined in the Euler–Lagrange model. In the Euler–Euler approach, the single particle-particle or particle-wall collision can be considered using additional assumptions. In the Euler–Lagrange approach, the particle-particle/wall collisions can be stochastically modeled or deterministically detected. The aim of this study is to develop a 3D program for the numerical simulation of biomass conversion in fluidized beds. The particle–particle/wall and gas–solid interactions are modeled by tracking all individual particles. For this purpose, the deterministic Euler–Lagrange/discrete element method (DEM) is applied and further developed. The fluid–particle interaction is studied using a new procedure, known as the offset method. The proposed method is highly precise in determining the interaction values, thus improving the simulation accuracy up to an order of magnitude. In this work, an additional grid, so-called particle grid, in which the physical values of solid phase is computed, is introduced. The suggested procedure allows the variation of the fluid grid resolution independent of the particle size and consequently improves the calculation accuracy. The collision detection between particle–particle/wall is performed with the aid of the particle search grid method. The use of the particle search grid method enhances the efficiency of collision detection between collision partners. The improved Euler–Lagrange/DEM model is validated towards the measurements obtained from a cold quasi–2D fluidized bed. The results suggest that the extended Euler–Lagrange/DEM model can predict accurately the motion of particles and the gas bubble expansion in the bed. The received results from the DEM model are also compared with other numerical approaches, namely the Euler-Euler and stochastic Euler–Lagrange models. Compared to measurements, the results show that the Euler–Euler model underestimates the bubble sizes and the bed expansions, while the stochastic Euler–Lagrange model reaches faster the maximum bed expansions. The efficiency and accuracy of the Euler–Lagrange/DEM model is investigated in detail. Parameter studies are carried out, in which stiffness coefficient, fluid time step and processor number are varied for different particle numbers and diameters. The obtained results are compared with the measurements in order to derive the optimum parameters for Euler–Lagrange/DEM simulations. The results suggest that the application of higher stiffness coefficients (more than 10^5 N/m) improves the simulation accuracy slightly, however, the average computing time increases exponentially. For time intervals larger than five milliseconds, the results show that the average computation time is independent of applied fluid time step, while the simulation accuracy decreases extremely by increasing the size of fluid time step. The use of fluid time steps smaller than five milliseconds leads to negligible improvements in the simulation accuracy, but to exponential rise in the average computing time. The parallel calculation accelerates the Euler–Lagrange/DEM simulation if the critical number of domain decomposition is not reached. Exceeding this number, the performance is not anymore proportional to the number of processors and the computational time increases again. The critical number of domain decomposition depends on particle numbers. An increase in solid contents results in a shift of critical decomposition number to higher numbers of CPUs. The local concentrations of solid and gaseous species, the local gas and particle temperatures, the local heat release and heat transfer rates can also be calculated with the developed program. In combination with the simulation of the gas–solid flow, it is possible to model the biomass conversion in the fluidized bed. Three series of warm simulations in a quasi–2D fluidized bed model are performed, viz. combustion with fuel gas without and with inert sand particles as well as combustion with solid fuel (a mixture of inert sand and pine wood particles). The received results realise the coupling of the Euler–Lagrange/DEM model with chemical reaction mechanism. The extended Euler–Lagrange/DEM model under the consideration of thermochemical reaction model is able to simulate, by the same token, the conversion of other solid fuels such as coal in fluidized beds

Solidification behavior of high nitrogen stainless steels and establishment of a one-dimensional heat transfer framework

Duplex stainless steel (DSS) has excellent corrosion resistance and mechanical properties due to its dual-phase structure. The solidification process is the key to determining the structure of materials, and an in-depth investigation of solidification can help us better understand the properties of materials. The melting and solidification processes of S32101 DSS were investigated using high temperature confocal microscopy (HTCM)

Simulating radiation damage in austenitic stainless steel and Ni-based alloys

The evolution of materials at an atomistic level may have vital consequences for the properties of materials. Therefore, modelling long time scale behaviour of defects in a material is very important, particularly for those used in nuclear power plants. The materials used in nuclear power plants should have good mechanical properties to overcome the corrosive environment and high temperature. Examples of these materials are the austenitic stainless steel and the Ni-based alloys due to their high temperature properties. Molecular Dynamics (MD) and on the fly Kinetic Monte Carlo (otf-KMC) techniques have been used to model the radiation damage in austenitic stainless steel and the Ni-based alloys. This thesis represents the main findings obtained. Three potentials were implemented and used to study radiation damage in austenitic stainless steel. Structural properties such as the elastic constants for the point defects in the pure metals were first calculated. This was followed by calculating the formation energies and migration energies of vacancy and self interstitial defects in the pure metals. Different calculations were performed using each potential on the ternary alloy (Fe with 10 at.% Ni and 20 at.% Cr) and the binary alloy (Ni with 20 at.% Cr) . For example, the segregation in these alloys was investigated using Monte Carlo simulations and results obtained for both alloys at high temperature MD. Furthermore, the vacancy formation energies were calculated for both alloys using all the potentials. Radiation damage at Grain Boundaries (GBs) in fcc Ni and a Ni-Cr binary alloy has been studied using MD and otf-KMC techniques. From the results obtained, the mobility of interstitials were found to be higher than that of vacancies and tend to move quickly to the GB. Vacancies are found to migrate to the GB if they are near otherwise they tend to form clusters in the bulk. During the simulations, interesting mechanisms were observed for the point defects migration and recombinations. Large roughening at the GB was observed, especially in the alloy system and overall the total number of defects accumulated on the GB after multiple collision cascades were relatively small. The radiation in fcc Ni resulting from low energy collision cascades was also modelled using MD and otf-KMC techniques. This part of work aimed replicating the observations seen in experiment and trying to understand them. Recombinations between vacancies and interstitials were found to happen from large distances with low barriers. Most defects produced from low energy collision cascades were found to recombine or interstitials were found to form clusters. Modelling the evolution of the vacancies shows the possibility of producing Stacking Fault Tetrahedra (SFT) which were found to dissociate at 200°C

Numerical modelling of pulverised coal combustion

First Online: 30 September 2017Many thermal power generation plants rely on combustion of pulverised coal carried out in large furnaces. Design and improvement of these furnaces can be effectively assisted by using numerical modelling with Computational Fluid Dynamics (CFD) techniques to develop a detailed picture of the conditions within the furnace, and the effect of operating conditions, coal type, and furnace design on those conditions. The equations governing CFD models of pulverised coal combustion are described, with a focus on sub-models needed for devolatilisation, combustion and heat transfer. The use of the models is discussed with reference to examples of CFD modelling of brown coal fired furnaces in the Latrobe Valley in Australia and black coal fired furnaces described in the literature. Extensions to the CFD models that are required to tackle specific industrial and environmental issues are also described. These issues include control of NOx and SOx emissions and the effect of slagging and fouling on furnace and boiler operation.Zhao F. Tian, Peter J. Witt, Mark P. Schwarz, and William Yan