Integrating remote sensing datasets into ecological modelling: a Bayesian approach

Process-based models have been used to simulate 3-dimensional complexities of forest ecosystems and their temporal changes, but their extensive data requirement and complex parameterisation have often limited their use for practical management applications. Increasingly, information retrieved using remote sensing techniques can help in model parameterisation and data collection by providing spatially and temporally resolved forest information. In this paper, we illustrate the potential of Bayesian calibration for integrating such data sources to simulate forest production. As an example, we use the 3-PG model combined with hyperspectral, LiDAR, SAR and field-based data to simulate the growth of UK Corsican pine stands. Hyperspectral, LiDAR and SAR data are used to estimate LAI dynamics, tree height and above ground biomass, respectively, while the Bayesian calibration provides estimates of uncertainties to model parameters and outputs. The Bayesian calibration contrasts with goodness-of-fit approaches, which do not provide uncertainties to parameters and model outputs. Parameters and the data used in the calibration process are presented in the form of probability distributions, reflecting our degree of certainty about them. After the calibration, the distributions are updated. To approximate posterior distributions (of outputs and parameters), a Markov Chain Monte Carlo sampling approach is used (25 000 steps). A sensitivity analysis is also conducted between parameters and outputs. Overall, the results illustrate the potential of a Bayesian framework for truly integrative work, both in the consideration of field-based and remotely sensed datasets available and in estimating parameter and model output uncertainties

Chemical flux analysis of low-temperature plasma-enhanced oxidation of methane and hydrogen in argon

Plasma can be used to enhance the reactivity of combustible mixtures at low temperatures. In this article, the chemical pathways predicted by three different reaction mechanisms are investigated for the low-temperature oxidation of hydrogen and methane. To validate our model and the reaction mechanisms, the numerical results are compared against experimental results in a diluted flow reactor. Our model with all three reaction mechanisms predicts trends similar to those observed in the experiments. Moreover, all predicted quantities show reasonable quantitative agreement with the experiments. Flux analysis is used to identify the main pathways of oxidation at different temperatures. Three different modes, each active in a different temperature range, are identified in the oxidation of hydrogen. When the temperature is increased, these modes become increasingly self-sustained. Similarly, three different pathways are identified in the oxidation of methane. Below 1000K, methane quickly removes hydroxyl radicals from the radical pool, inhibiting self-sustained oxidation. From our analysis, we conclude that plasma provides activation of the low-temperature chemistry by the generation of radicals

Single-molecule Studies Of p53 Sliding Along DNA

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A computationally efficient approach for soot modeling with discrete sectional method and FGM chemistry

A novel approach for the prediction of soot formation in combustion simulations within the framework of discrete sectional method (DSM) based univariate soot model and Flamelet Generated Manifold (FGM) chemistry, referred to as FGM-CDSM, is proposed in this study. The FGM-CDSM considers the clustering of soot sections derived from the original soot particle size distribution function (PSDF) to minimize the computational cost. Unlike conventional DSM, in FGM-CDSM, governing equations for soot mass fractions are solved for the clusters, by using a pre-computed lookup table with tabulated soot source terms from the flamelet manifold, while the original soot PSDF is re-constructed in a post-processing stage. The flamelets employed for the manifold are computed with detailed chemistry and the complete sectional soot model. A comparative assessment of FGM-CDSM is conducted in laminar diffusion flames for its accuracy and computational performance against the detailed kinetics-based classical sectional model. Numerical results reveal that the FGM-CDSM can favorably reproduce the global soot quantities and capture their dynamic response predicted by detailed kinetics with a good qualitative agreement. Furthermore, compared to detailed kinetics, FGM-CDSM is shown to substantially reduce the computational cost of the complete reacting flow simulation with soot particle transport. Primarily, the use of FGM reduces the overall calculation by about two orders of magnitude compared to detailed kinetics, which is advanced further with the clustering of sections at a low memory footprint. Therefore, the present work demonstrates the promising capabilities of FGM-CDSM in the context of computationally efficient soot calculations and provides an excellent framework for extending its application to the simulations of turbulent sooting flames

A five dimensional implementation of the flamelet generated manifolds technique for gas turbine application

Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014 (ICNAAM-2014), 22–28 September 2014 Location: Rhodes, Greece ISBN 978-0-7354-1287-3 In the present paper the Flamelet-Generated Manifold (FGM) chemistry reduction method is implemented and extended for the inclusion of all the features that are typically observed in stationary gas-turbine combustion. These consist of stratification effects, heat loss and turbulence. The latter is included by coupling FGM with the Reynolds Averaged Navier Stokes(RANS)model. Three control variables are included for the chemistry representation: the reaction evolution is described by the reaction progress variable, the heat loss is described by the enthalpy and the stratification effect is expressed by the mixture fraction. The interaction between chemistry and turbulence is considered through a presumed probability density function (PDF) approach, which is considered for progress variable and mixture fraction. This results in two extra control variables: progress variable variance and mixture fraction variance. The resulting manifold is five-dimensional, in which the dimensions are progress variable, enthalpy, mixture fraction, progress variable variance and mixture fraction variance. In addition, a highly turbulent and swirling flame in a gas turbine model combustor is computed, in order to test the 5-D FGM implementation. The use of FGM as a combustionmodel shows that combustion features at gas turbine conditions can be satisfactorily reproduced with a reasonable computational effort. The implemented combustionmodel retains most of the physical accuracy of a detailed simulation while drastically reducing its computational time, paving the way for new developments of alternative fuel usage in a cleaner and more efficient combustion. In the present paper the Flamelet-Generated Manifold (FGM) chemistry reduction method is implemented and extended for the inclusion of all the features that are typically observed in stationary gas-turbine combustion. These consist of stratification effects, heat loss and turbulence. The latter is included by coupling FGM with the Reynolds Averaged Navier Stokes(RANS)model. Three control variables are included for the chemistry representation: the reaction evolution is described by the reaction progress variable, the heat loss is described by the enthalpy and the stratification effect is expressed by the mixture fraction. The interaction between chemistry and turbulence is considered through a presumed probability density function (PDF) approach, which is considered for progress variable and mixture fraction. This results in two extra control variables: progress variable variance and mixture fraction variance. The resulting manifold is five-dimensional, in which the dimensions are progress variable, enthalpy, mixture fraction, progress variable variance and mixture fraction variance. In addition, a highly turbulent and swirling flame in a gas turbine model combustor is computed, in order to test the 5-D FGM implementation. The use of FGM as a combustionmodel shows that combustion features at gas turbine conditions can be satisfactorily reproduced with a reasonable computational effort. The implemented combustionmodel retains most of the physical accuracy of a detailed simulation while drastically reducing its computational time, paving the way for new developments of alternative fuel usage in a cleaner and more efficient combustion