Particle modelling in biomass combustion using orthogonal collocation

Development of an accurate and computational efficient biomass particle model to predict particle pyrolysis and combustion is the focus of this paper. Partial differential equations (PDEs) for heat and mass balance are transformed into a system of coupled ordinary differential equations (ODEs) with the use of orthogonal collocation as the particle discretization method. The orthogonal collocation method is incorporated with comprehensive physicochemical mechanisms to predict the behavior of biomass components during particle pyrolysis and combustion. Heat adsorption by evaporated gas and water movement by diffusion inside the biomass matrix are included in the present work, in parallel with the effect of Stefan flow on the heat and mass transfer rates at the particle surface. Abandoning the classical interface-based modelling approach, the present approach allows decoupling between biomass components and spatial resolution, and the prediction of continuous intra-particle profiles. The new particle model is proven to be accurate and stable through its high degree of agreement with simulation results for particle pyrolysis and combustion experiments using different particle moisture contents and geometrical shapes. The intra-particle temperature gradient, as well as particle mass and size evolution, can be predicted accurately, as validated against experimental data. It is shown that six collocation points provide satisfying resolution. The computational efficiency is confirmed by the short simulation time that was found to be approximately three orders of magnitude faster than mesh-based simulations. This implies that the current model can be used for computational fluid dynamic (CFD) analysis through implementation as sub-grid-scale models to design, for example, biomass furnaces

Parallelization of multidimensional hyperbolic partial differential equation on détente instantanée contrôlée dehydration process

The purpose of this research is to propose some new modified mathematical models to enhance the previous model in simulating, visualizing and predicting the heat and mass transfer in dehydration process using instant controlled pressure drop (DIC) technique. The main contribution of this research is the mathematical models which are formulated from the regression model (Haddad et al., 2007) to multidimensional hyperbolic partial differential equation (HPDE) involving dependent parameters; moisture content, temperature, and pressure, and independent parameters; time and dimension of region. The HPDE model is performed in multidimensional; one, two and three dimensions using finite difference method with central difference formula is used to discretize the mathematical models. The implementation of numerical methods such as Alternating Group Explicit with Brian (AGEB) and Douglas-Rachford (AGED) variances, Red Black Gauss Seidel (RBGS) and Jacobi (JB) method to solve the system of linear equation is another contribution of this research. The sequential algorithm is developed by using Matlab R2011a software. The numerical results are analyzed based on execution time, number of iterations, maximum error, root mean square error, and computational complexity. The grid generation process involved a fine grained large sparse data by minimizing the size of interval, increasing the dimension of the model and level of time steps. Another contribution is the implementation of the parallel algorithm to increase the speedup of computation and to reduce computational complexity problem. The parallelization of the mathematical model is run on Matlab Distributed Computing Server with Linux operating system. The parallel performance evaluation of multidimensional simulation in terms of execution time, speedup, efficiency, effectiveness, temporal performance, granularity, computational complexity and communication cost are analyzed for the performance of parallel algorithm. As a conclusion, the thesis proved that the multidimensional HPDE is able to be parallelized and PAGEB method is the alternative solution for the large sparse simulation. Based on the numerical results and parallel performance evaluations, the parallel algorithm is able to reduce the execution time and computational complexity compared to the sequential algorithm

Elucidation of molecular kinetic schemes from macroscopic traces using system identification

Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and recording events at the molecular level, this is still possible only in especially favorable cases. Therefore the development of methods to extract the dynamics and relationships between the different lower-level (microscopic) processes from the overall (macroscopic) response remains a crucial challenge in the understanding of many aspects of physiology. Here we have devised a hybrid computational-analytical method to accomplish this task, the SYStems-based MOLecular kinetic scheme Extractor (SYSMOLE). SYSMOLE utilizes system-identification input-output analysis to obtain a transfer function between the stimulus and the overall cellular response in the Laplace-transformed domain. It then derives a Markov-chain state molecular kinetic scheme uniquely associated with the transfer function by means of a classification procedure and an analytical step that imposes general biological constraints. We first tested SYSMOLE with synthetic data and evaluated its performance in terms of its rate of convergence to the correct molecular kinetic scheme and its robustness to noise. We then examined its performance on real experimental traces by analyzing macroscopic calcium-current traces elicited by membrane depolarization. SYSMOLE derived the correct, previously known molecular kinetic scheme describing the activation and inactivation of the underlying calcium channels and correctly identified the accepted mechanism of action of nifedipine, a calcium-channel blocker clinically used in patients with cardiovascular disease. Finally, we applied SYSMOLE to study the pharmacology of a new class of glutamate antipsychotic drugs and their crosstalk mechanism through a heteromeric complex of G protein-coupled receptors. Our results indicate that our methodology can be successfully applied to accurately derive molecular kinetic schemes from experimental macroscopic traces, and we anticipate that it may be useful in the study of a wide variety of biological systems

A fast GPU Monte Carlo Radiative Heat Transfer Implementation for Coupling with Direct Numerical Simulation

We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve radiative heat transfer in turbulent flows of non-grey participating media that can be coupled to fully resolved turbulent flows, namely to Direct Numerical Simulation (DNS). The spectrally varying absorption coefficient is treated in a narrow-band fashion with a correlated-k distribution. The implementation is verified with analytical solutions and validated with results from literature and line-by-line Monte Carlo computations. The method is implemented on GPU with a thorough attention to memory transfer and computational efficiency. The bottlenecks that dominate the computational expenses are addressed and several techniques are proposed to optimize the GPU execution. By implementing the proposed algorithmic accelerations, a speed-up of up to 3 orders of magnitude can be achieved, while maintaining the same accuracy

Performance analysis of direct N-body algorithms for astrophysical simulations on distributed systems

We discuss the performance of direct summation codes used in the simulation of astrophysical stellar systems on highly distributed architectures. These codes compute the gravitational interaction among stars in an exact way and have an O(N^2) scaling with the number of particles. They can be applied to a variety of astrophysical problems, like the evolution of star clusters, the dynamics of black holes, the formation of planetary systems, and cosmological simulations. The simulation of realistic star clusters with sufficiently high accuracy cannot be performed on a single workstation but may be possible on parallel computers or grids. We have implemented two parallel schemes for a direct N-body code and we study their performance on general purpose parallel computers and large computational grids. We present the results of timing analyzes conducted on the different architectures and compare them with the predictions from theoretical models. We conclude that the simulation of star clusters with up to a million particles will be possible on large distributed computers in the next decade. Simulating entire galaxies however will in addition require new hybrid methods to speedup the calculation.Comment: 22 pages, 8 figures, accepted for publication in Parallel Computin