Analisi numerica della combustione di gas a basso potere calorifico per applicazioni a microturbine a gas

Il presente lavoro si occupa dell’analisi numerica di combustione di gas a basso potere calorifico (gas di sintesi derivanti da pirolisi di biomasse). L’analisi è stata condotta su due principali geometrie di camera di combustione. La prima è un bruciatore sperimentale da laboratorio adatto allo studio delle proprietà di combustione del singas. Esso è introdotto in camera separatamente rispetto ad una corrente d’aria comburente al fine di realizzare una combustione non-premiscelata diffusiva in presenza di swirl. La seconda geometria presa in considerazione è la camera di combustione anulare installata sulla microturbina a gas Elliott TA 80 per la quale si dispone di un modello installato al banco al fine dell’esecuzione di prove sperimentali. I principali obbiettivi conseguiti nello studio sono stati la determinazione numerica del campo di moto a freddo su entrambe le geometrie per poi realizzare simulazioni in combustione mediante l’utilizzo di diversi modelli di combustione. In particolare è stato approfondito lo studio dei modelli steady laminar flamelet ed unsteady flamelet con cui sono state esaminate le distribuzioni di temperatura e delle grandezze tipiche di combustione in camera, confrontando i risultati numerici ottenuti con altri modelli di combustione (Eddy Dissipation ed ED-FR) e con i dati sperimentali a disposizione. Di importanza fondamentale è stata l’analisi delle emissioni inquinanti, realizzata per entrambe le geometrie, che mostra l’entità di tali emissioni e la loro tipologia. Relativamente a questo punto, il maggior interesse si sposta sui risultati ottenuti numericamente nel caso della microturbina, per la quale sono a disposizione misure di emissione ottenute sperimentalmente. Sempre per questa geometria è stato inoltre eseguito il confronto fra microturbina alimentata con singas a confronto con le prestazioni emissive ottenute con il gas naturale. Nel corso dei tre anni, l’esecuzione delle simulazioni e l’analisi critica dei risultati ha suggerito alcuni limiti e semplificazioni eseguite sulle griglie di calcolo realizzate per lo studio numerico. Al fine di eliminare o limitare le semplificazioni o le inesattezze, le geometrie dei combustori e le griglie di calcolo sono state migliorate ed ottimizzate. In merito alle simulazioni realizzate sulla geometria del combustore della microturbina Elliott TA 80 è stata condotta dapprima l’analisi numerica di combustione a pieno carico per poi analizzare le prestazioni ai carichi parziali. Il tutto appoggiandosi a tecniche di simulazione RANS ed ipotizzando alimentazioni a gas naturale e singas derivato da biomasse. Nell’ultimo anno di dottorato è stato dedicato tempo all’approfondimento e allo studio della tecnica Large Eddy Simulation per testarne una applicazione alla geometria del bruciatore sperimentale di laboratorio. In tale simulazione è stato implementato l’SGS model di Smagorinsky-Lilly completo di combustione con modelli flamelet. Dai risultati sono stati estrapolati i profili di temperatura a confronto con i risultati sperimentali e con i risultati RANS. Il tutto in diverse simulazioni a diverso valore del time-step imposto. L’analisi LES, per quanto migliorabile, ha fornito risultati sufficientemente precisi lasciando per il futuro la possibilità di approfondire nuovi modelli adatti all’applicazione diretta sulla MTG.

Statistical analysis and simulation techniques in wall-bounded turbulence

The present work is devoted to the assessment of the energy fluxes physics in the space of scales and physical space of wall-turbulent flows. The generalized Kolmogorov equation will be applied to DNS data of a turbulent channel flow in order to describe the energy fluxes paths from production to dissipation in the augmented space of wall-turbulent flows. This multidimensional description will be shown to be crucial to understand the formation and sustainment of the turbulent fluctuations fed by the energy fluxes coming from the near-wall production region. An unexpected behavior of the energy fluxes comes out from this analysis consisting of spiral-like paths in the combined physical/scale space where the controversial reverse energy cascade plays a central role. The observed behavior conflicts with the classical notion of the Richardson/Kolmogorov energy cascade and may have strong repercussions on both theoretical and modeling approaches to wall-turbulence. To this aim a new relation stating the leading physical processes governing the energy transfer in wall-turbulence is suggested and shown able to capture most of the rich dynamics of the shear dominated region of the flow. Two dynamical processes are identified as driving mechanisms for the fluxes, one in the near wall region and a second one further away from the wall. The former, stronger one is related to the dynamics involved in the near-wall turbulence regeneration cycle. The second suggests an outer self-sustaining mechanism which is asymptotically expected to take place in the log-layer and could explain the debated mixed inner/outer scaling of the near-wall statistics. The same approach is applied for the first time to a filtered velocity field. A generalized Kolmogorov equation specialized for filtered velocity field is derived and discussed. The results will show what effects the subgrid scales have on the resolved motion in both physical and scale space, singling out the prominent role of the filter length compared to the cross-over scale between production dominated scales and inertial range, lc, and the reverse energy cascade region lb. The systematic characterization of the resolved and subgrid physics as function of the filter scale and of the wall-distance will be shown instrumental for a correct use of LES models in the simulation of wall turbulent flows. Taking inspiration from the new relation for the energy transfer in wall turbulence, a new class of LES models will be also proposed. Finally, the generalized Kolmogorov equation specialized for filtered velocity fields will be shown to be an helpful statistical tool for the assessment of LES models and for the development of new ones. As example, some classical purely dissipative eddy viscosity models are analyzed via an a priori procedure.

Carbon fluxes and allocation pattern in an apple orchard

Carbon fluxes and allocation pattern, and their relationship with the main environmental and physiological parameters, were studied in an apple orchard for one year (2010). I combined three widely used methods: eddy covariance, soil respiration and biometric measurements, and I applied a measurement protocol allowing a cross-check between C fluxes estimated using different methods. I attributed NPP components to standing biomass increment, detritus cycle and lateral export. The influence of environmental and physiological parameters on NEE, GPP and Reco was analyzed with a multiple regression model approach. I found that both NEP and GPP of the apple orchard were of similar magnitude to those of forests growing in similar climate conditions, while large differences occurred in the allocation pattern and in the fate of produced biomass. Apple production accounted for 49% of annual NPP, organic material (leaves, fine root litter, pruned wood and early fruit drop) contributing to detritus cycle was 46%, and only 5% went to standing biomass increment. The carbon use efficiency (CUE), with an annual average of 0.68 ± 0.10, was higher than the previously suggested constant values of 0.47-0.50. Light and leaf area index had the strongest influence on both NEE and GPP. On a diurnal basis, NEE and GPP reached their peak approximately at noon, while they appeared to be limited by high values of VPD and air temperature in the afternoon. The proposed models can be used to explain and simulate current relations between carbon fluxes and environmental parameters at daily and yearly time scale. On average, the annual NEP balanced the carbon annually exported with the harvested apples. These data support the hypothesis of a minimal or null impact of the apple orchard ecosystem on net C emission to the atmosphere.

Techniques for Lagrangian modelling of dispersion in geophysical flows

Basic concepts and definitions relative to Lagrangian Particle Dispersion Models (LPDMs)for the description of turbulent dispersion are introduced. The study focusses on LPDMs that use as input, for the large scale motion, fields produced by Eulerian models, with the small scale motions described by Lagrangian Stochastic Models (LSMs). The data of two different dynamical model have been used: a Large Eddy Simulation (LES) and a General Circulation Model (GCM). After reviewing the small scale closure adopted by the Eulerian model, the development and implementation of appropriate LSMs is outlined. The basic requirement of every LPDM used in this work is its fullfillment of the Well Mixed Condition (WMC). For the dispersion description in the GCM domain, a stochastic model of Markov order 0, consistent with the eddy-viscosity closure of the dynamical model, is implemented. A LSM of Markov order 1, more suitable for shorter timescales, has been implemented for the description of the unresolved motion of the LES fields. Different assumptions on the small scale correlation time are made. Tests of the LSM on GCM fields suggest that the use of an interpolation algorithm able to maintain an analytical consistency between the diffusion coefficient and its derivative is mandatory if the model has to satisfy the WMC. Also a dynamical time step selection scheme based on the diffusion coefficient shape is introduced, and the criteria for the integration step selection are discussed. Absolute and relative dispersion experiments are made with various unresolved motion settings for the LSM on LES data, and the results are compared with laboratory data. The study shows that the unresolved turbulence parameterization has a negligible influence on the absolute dispersion, while it affects the contribution of the relative dispersion and meandering to absolute dispersion, as well as the Lagrangian correlation.

Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.