Modelling of combustion and sorbent reactions in three-dimensional flow environment of a circulating fluidized bed furnace

This thesis presents a three-dimensional, semi-empirical, steady state model for simulating the combustion, gasification, and formation of emissions in circulating fluidized bed (CFB) processes. In a large-scale CFB furnace, the local feeding of fuel, air, and other input materials, as well as the limited mixing rate of different reactants produce inhomogeneous process conditions. To simulate the real conditions, the furnace should be modelled three-dimensionally or the three-dimensional effects should be taken into account. The only available methods for simulating the large CFB furnaces three-dimensionally are semi-empirical models, which apply a relatively coarse calculation mesh and a combination of fundamental conservation equations, theoretical models and empirical correlations. The number of such models is extremely small. The main objective of this work was to achieve a model which can be applied to calculating industrial scale CFB boilers and which can simulate all the essential sub-phenomena: fluid dynamics, reactions, the attrition of particles, and heat transfer. The core of the work was to develop the model frame and the required sub-models for determining the combustion and sorbent reactions. The objective was reached, and the developed model was successfully used for studying various industrial scale CFB boilers combusting different types of fuel. The model for sorbent reactions, which includes the main reactions for calcitic limestones, was applied for studying the new possible phenomena occurring in the oxygen-fired combustion. The presented combustion and sorbent models and principles can be utilized in other model approaches as well, including other empirical and semi-empirical model approaches, and CFD based simulations. The main achievement is the overall model frame which can be utilized for the further development and testing of new sub-models and theories, and for concentrating the knowledge gathered from the experimental work carried out at bench scale, pilot scale and industrial scale apparatus, and from the computational work performed by other modelling methods.

CFD Modelling of Direct Contact Condensation in Suppression Pools by Applying Condensation Models of Separated Flow

The condensation rate has to be high in the safety pressure suppression pool systems of Boiling Water Reactors (BWR) in order to fulfill their safety function. The phenomena due to such a high direct contact condensation (DCC) rate turn out to be very challenging to be analysed either with experiments or numerical simulations. In this thesis, the suppression pool experiments carried out in the POOLEX facility of Lappeenranta University of Technology were simulated. Two different condensation modes were modelled by using the 2-phase CFD codes NEPTUNE CFD and TransAT. The DCC models applied were the typical ones to be used for separated flows in channels, and their applicability to the rapidly condensing flow in the condensation pool context had not been tested earlier. A low Reynolds number case was the first to be simulated. The POOLEX experiment STB-31 was operated near the conditions between the ’quasi-steady oscillatory interface condensation’ mode and the ’condensation within the blowdown pipe’ mode. The condensation models of Lakehal et al. and Coste & Lavi´eville predicted the condensation rate quite accurately, while the other tested ones overestimated it. It was possible to get the direct phase change solution to settle near to the measured values, but a very high resolution of calculation grid was needed. Secondly, a high Reynolds number case corresponding to the ’chugging’ mode was simulated. The POOLEX experiment STB-28 was chosen, because various standard and highspeed video samples of bubbles were recorded during it. In order to extract numerical information from the video material, a pattern recognition procedure was programmed. The bubble size distributions and the frequencies of chugging were calculated with this procedure. With the statistical data of the bubble sizes and temporal data of the bubble/jet appearance, it was possible to compare the condensation rates between the experiment and the CFD simulations. In the chugging simulations, a spherically curvilinear calculation grid at the blowdown pipe exit improved the convergence and decreased the required cell count. The compressible flow solver with complete steam-tables was beneficial for the numerical success of the simulations. The Hughes-Duffey model and, to some extent, the Coste & Lavi´eville model produced realistic chugging behavior. The initial level of the steam/water interface was an important factor to determine the initiation of the chugging. If the interface was initialized with a water level high enough inside the blowdown pipe, the vigorous penetration of a water plug into the pool created a turbulent wake which invoked the chugging that was self-sustaining. A 3D simulation with a suitable DCC model produced qualitatively very realistic shapes of the chugging bubbles and jets. The comparative FFT analysis of the bubble size data and the pool bottom pressure data gave useful information to distinguish the eigenmodes of chugging, bubbling, and pool structure oscillations.

Structural modelling of liquid ZrO2 and glassy B2O3 by using AB INITIO Molecular Dynamics method

Atomic structure of ZrO2 and B2O3 was investigated in this work. New data under extreme conditions (T = 3100 K) was obtained for the liquid ZrO2 structure. A fractional number of boron was investigated for glassy structure of B2O3. It was shown that it is possible to obtain an agreement for the fractional number between NMR and DFT techniques using a suitable initial configuration.

Simulation methods in the modelling of bioaffinity assays

Computational model-based simulation methods were developed for the modelling of bioaffinity assays. Bioaffinity-based methods are widely used to quantify a biological substance in biological research, development and in routine clinical in vitro diagnostics. Bioaffinity assays are based on the high affinity and structural specificity between the binding biomolecules. The simulation methods developed are based on the mechanistic assay model, which relies on the chemical reaction kinetics and describes the forming of a bound component as a function of time from the initial binding interaction. The simulation methods were focused on studying the behaviour and the reliability of bioaffinity assay and the possibilities the modelling methods of binding reaction kinetics provide, such as predicting assay results even before the binding reaction has reached equilibrium. For example, a rapid quantitative result from a clinical bioaffinity assay sample can be very significant, e.g. even the smallest elevation of a heart muscle marker reveals a cardiac injury. The simulation methods were used to identify critical error factors in rapid bioaffinity assays. A new kinetic calibration method was developed to calibrate a measurement system by kinetic measurement data utilizing only one standard concentration. A nodebased method was developed to model multi-component binding reactions, which have been a challenge to traditional numerical methods. The node-method was also used to model protein adsorption as an example of nonspecific binding of biomolecules. These methods have been compared with the experimental data from practice and can be utilized in in vitro diagnostics, drug discovery and in medical imaging.

Application and development of numerical methods for the modelling of innovative gas cooled fission reactors

Innovative gas cooled reactors, such as the pebble bed reactor (PBR) and the gas cooled fast reactor (GFR) offer higher efficiency and new application areas for nuclear energy. Numerical methods were applied and developed to analyse the specific features of these reactor types with fully three dimensional calculation models. In the first part of this thesis, discrete element method (DEM) was used for a physically realistic modelling of the packing of fuel pebbles in PBR geometries and methods were developed for utilising the DEM results in subsequent reactor physics and thermal-hydraulics calculations. In the second part, the flow and heat transfer for a single gas cooled fuel rod of a GFR were investigated with computational fluid dynamics (CFD) methods. An in-house DEM implementation was validated and used for packing simulations, in which the effect of several parameters on the resulting average packing density was investigated. The restitution coefficient was found out to have the most significant effect. The results can be utilised in further work to obtain a pebble bed with a specific packing density. The packing structures of selected pebble beds were also analysed in detail and local variations in the packing density were observed, which should be taken into account especially in the reactor core thermal-hydraulic analyses. Two open source DEM codes were used to produce stochastic pebble bed configurations to add realism and improve the accuracy of criticality calculations performed with the Monte Carlo reactor physics code Serpent. Russian ASTRA criticality experiments were calculated. Pebble beds corresponding to the experimental specifications within measurement uncertainties were produced in DEM simulations and successfully exported into the subsequent reactor physics analysis. With the developed approach, two typical issues in Monte Carlo reactor physics calculations of pebble bed geometries were avoided. A novel method was developed and implemented as a MATLAB code to calculate porosities in the cells of a CFD calculation mesh constructed over a pebble bed obtained from DEM simulations. The code was further developed to distribute power and temperature data accurately between discrete based reactor physics and continuum based thermal-hydraulics models to enable coupled reactor core calculations. The developed method was also found useful for analysing sphere packings in general. CFD calculations were performed to investigate the pressure losses and heat transfer in three dimensional air cooled smooth and rib roughened rod geometries, housed inside a hexagonal flow channel representing a sub-channel of a single fuel rod of a GFR. The CFD geometry represented the test section of the L-STAR experimental facility at Karlsruhe Institute of Technology and the calculation results were compared to the corresponding experimental results. Knowledge was gained of the adequacy of various turbulence models and of the modelling requirements and issues related to the specific application. The obtained pressure loss results were in a relatively good agreement with the experimental data. Heat transfer in the smooth rod geometry was somewhat under predicted, which can partly be explained by unaccounted heat losses and uncertainties. In the rib roughened geometry heat transfer was severely under predicted by the used realisable k − epsilon turbulence model. An additional calculation with a v2 − f turbulence model showed significant improvement in the heat transfer results, which is most likely due to the better performance of the model in separated flow problems. Further investigations are suggested before using CFD to make conclusions of the heat transfer performance of rib roughened GFR fuel rod geometries. It is suggested that the viewpoints of numerical modelling are included in the planning of experiments to ease the challenging model construction and simulations and to avoid introducing additional sources of uncertainties. To facilitate the use of advanced calculation approaches, multi-physical aspects in experiments should also be considered and documented in a reasonable detail.

Heterogeneous mass transfer in fluidized beds by computational fluid dynamics

The main objective of this research is to estimate and characterize heterogeneous mass transfer coefficients in bench- and pilot-scale fluidized bed processes by the means of computational fluid dynamics (CFD). A further objective is to benchmark the heterogeneous mass transfer coefficients predicted by fine-grid Eulerian CFD simulations against empirical data presented in the scientific literature. First, a fine-grid two-dimensional Eulerian CFD model with a solid and gas phase has been designed. The model is applied for transient two-dimensional simulations of char combustion in small-scale bubbling and turbulent fluidized beds. The same approach is used to simulate a novel fluidized bed energy conversion process developed for the carbon capture, chemical looping combustion operated with a gaseous fuel. In order to analyze the results of the CFD simulations, two one-dimensional fluidized bed models have been formulated. The single-phase and bubble-emulsion models were applied to derive the average gas-bed and interphase mass transfer coefficients, respectively. In the analysis, the effects of various fluidized bed operation parameters, such as fluidization, velocity, particle and bubble diameter, reactor size, and chemical kinetics, on the heterogeneous mass transfer coefficients in the lower fluidized bed are evaluated extensively. The analysis shows that the fine-grid Eulerian CFD model can predict the heterogeneous mass transfer coefficients quantitatively with acceptable accuracy. Qualitatively, the CFD-based research of fluidized bed process revealed several new scientific results, such as parametrical relationships. The huge variance of seven orders of magnitude within the bed Sherwood numbers presented in the literature could be explained by the change of controlling mechanisms in the overall heterogeneous mass transfer process with the varied process conditions. The research opens new process-specific insights into the reactive fluidized bed processes, such as a strong mass transfer control over heterogeneous reaction rate, a dominance of interphase mass transfer in the fine-particle fluidized beds and a strong chemical kinetic dependence of the average gas-bed mass transfer. The obtained mass transfer coefficients can be applied in fluidized bed models used for various engineering design, reactor scale-up and process research tasks, and they consequently provide an enhanced prediction accuracy of the performance of fluidized bed processes.

Computational models for and from biology : simple gene assembly and reaction systems

The advancement of science and technology makes it clear that no single perspective is any longer sufficient to describe the true nature of any phenomenon. That is why the interdisciplinary research is gaining more attention overtime. An excellent example of this type of research is natural computing which stands on the borderline between biology and computer science. The contribution of research done in natural computing is twofold: on one hand, it sheds light into how nature works and how it processes information and, on the other hand, it provides some guidelines on how to design bio-inspired technologies. The first direction in this thesis focuses on a nature-inspired process called gene assembly in ciliates. The second one studies reaction systems, as a modeling framework with its rationale built upon the biochemical interactions happening within a cell. The process of gene assembly in ciliates has attracted a lot of attention as a research topic in the past 15 years. Two main modelling frameworks have been initially proposed in the end of 1990s to capture ciliates’ gene assembly process, namely the intermolecular model and the intramolecular model. They were followed by other model proposals such as templatebased assembly and DNA rearrangement pathways recombination models. In this thesis we are interested in a variation of the intramolecular model called simple gene assembly model, which focuses on the simplest possible folds in the assembly process. We propose a new framework called directed overlap-inclusion (DOI) graphs to overcome the limitations that previously introduced models faced in capturing all the combinatorial details of the simple gene assembly process. We investigate a number of combinatorial properties of these graphs, including a necessary property in terms of forbidden induced subgraphs. We also introduce DOI graph-based rewriting rules that capture all the operations of the simple gene assembly model and prove that they are equivalent to the string-based formalization of the model. Reaction systems (RS) is another nature-inspired modeling framework that is studied in this thesis. Reaction systems’ rationale is based upon two main regulation mechanisms, facilitation and inhibition, which control the interactions between biochemical reactions. Reaction systems is a complementary modeling framework to traditional quantitative frameworks, focusing on explicit cause-effect relationships between reactions. The explicit formulation of facilitation and inhibition mechanisms behind reactions, as well as the focus on interactions between reactions (rather than dynamics of concentrations) makes their applicability potentially wide and useful beyond biological case studies. In this thesis, we construct a reaction system model corresponding to the heat shock response mechanism based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We also introduce for RS various concepts inspired by biology, e.g., mass conservation, steady state, periodicity, etc., to do model checking of the reaction systems based models. We prove that the complexity of the decision problems related to these properties varies from P to NP- and coNP-complete to PSPACE-complete. We further focus on the mass conservation relation in an RS and introduce the conservation dependency graph to capture the relation between the species and also propose an algorithm to list the conserved sets of a given reaction system.

Computational Market Dynamics of Virtual Economies

The last two decades have provided a vast opportunity to live and explore the compulsive imaginary world or virtual world through massively multiplayer online role-playing games (MMORPGs). MMORPG gives a wide range of opportunities to its users to participate with multi-players on the same platform, to communicate and to do real time actions. There is a virtual economy in these games which is largely player-driven. In-game currency provides its users to build up their Avatars, to buy or sell the necessary goods to play, survive in the games and so on. As a part of virtual economies generated through EVE Online, this thesis mainly focuses on how the prices of the minerals in EVE Online behave by applying the Jabłonska- Capasso-Morale (JCM) mathematical simulation model. It is to verify up to what degree the model can reproduce the virtual economy behavior. The model is applied to buy and sell prices of two minerals namely, isogen and morphite. The simulation results demonstrate that JCM model ts reasonably well to the mineral prices, which lets us conclude that virtual economies behave similarly to the real ones.

Modelling the colour difference by artificial neural network

This thesis work studies the modelling of the colour difference using artificial neural network. Multilayer percepton (MLP) network is proposed to model CIEDE2000 colour difference formula. MLP is applied to classify colour points in CIE xy chromaticity diagram. In this context, the evaluation was performed using Munsell colour data and MacAdam colour discrimination ellipses. Moreover, in CIE xy chromaticity diagram just noticeable differences (JND) of MacAdam ellipses centres are computed by CIEDE2000, to compare JND of CIEDE2000 and MacAdam ellipses. CIEDE2000 changes the orientation of blue areas in CIE xy chromaticity diagram toward neutral areas, but on the whole it does not totally agree with the MacAdam ellipses. The proposed MLP for both modelling CIEDE2000 and classifying colour points showed good accuracy and achieved acceptable results.

Qualitative Air Flow Modelling and Analysis of Data Centre Air Conditioning as Multiple Jet Array

Data centre is a centralized repository,either physical or virtual,for the storage,management and dissemination of data and information organized around a particular body and nerve centre of the present IT revolution.Data centre are expected to serve uniinterruptedly round the year enabling them to perform their functions,it consumes enormous energy in the present scenario.Tremendous growth in the demand from IT Industry made it customary to develop newer technologies for the better operation of data centre.Energy conservation activities in data centre mainly concentrate on the air conditioning system since it is the major mechanical sub-system which consumes considerable share of the total power consumption of the data centre.The data centre energy matrix is best represented by power utilization efficiency(PUE),which is defined as the ratio of the total facility power to the IT equipment power.Its value will be greater than one and a large value of PUE indicates that the sub-systems draw more power from the facility and the performance of the data will be poor from the stand point of energy conservation. PUE values of 1.4 to 1.6 are acievable by proper design and management techniques.Optimizing the air conditioning systems brings enormous opportunity in bringing down the PUE value.The air conditioning system can be optimized by two approaches namely,thermal management and air flow management.thermal management systems are now introduced by some companies but they are highly sophisticated and costly and do not catch much attention in the thumb rules.

Molecular cloning and homology modelling of a subtilisin-like serine protease from the marine fungus, Engyodontium album BTMFS10

An alkaline protease gene (Eap) was isolated for the first time from a marine fungus, Engyodontium album. Eap consists of an open reading frame of 1,161 bp encoding a prepropeptide consisting of 387 amino acids with a calculated molecular mass of 40.923 kDa. Homology comparison of the deduced amino acid sequence of Eap with other known proteins indicated that Eap encode an extracellular protease that belongs to the subtilase family of serine protease (Family S8). A comparative homology model of the Engyodontium album protease (EAP) was developed using the crystal structure of proteinase K. The model revealed that EAP has broad substrate specificity similar to Proteinase K with preference for bulky hydrophobic residues at P1 and P4. Also, EAP is suggested to have two disulfide bonds and more than two Ca2? binding sites in its 3D structure; both of which are assumed to contribute to the thermostable nature of the protein.

Molecular cloning and homology modelling of a subtilisin-like serine protease from the marine fungus, Engyodontium album BTMFS10

An alkaline protease gene (Eap) was isolated for the first time from a marine fungus, Engyodontium album. Eap consists of an open reading frame of 1,161 bp encoding a prepropeptide consisting of 387 amino acids with a calculated molecular mass of 40.923 kDa. Homology comparison of the deduced amino acid sequence of Eap with other known proteins indicated that Eap encode an extracellular protease that belongs to the subtilase family of serine protease (Family S8). A comparative homology model of the Engyodontium album protease (EAP) was developed using the crystal structure of proteinase K. The model revealed that EAP has broad substrate specificity similar to Proteinase K with preference for bulky hydrophobic residues at P1 and P4. Also, EAP is suggested to have two disulfide bonds and more than two Ca2? binding sites in its 3D structure; both of which are assumed to contribute to the thermostable nature of the protein.

Modelling of global crop production and resulting N2O emissions

Landwirtschaft spielt eine zentrale Rolle im Erdsystem. Sie trägt durch die Emission von CO2, CH4 und N2O zum Treibhauseffekt bei, kann Bodendegradation und Eutrophierung verursachen, regionale Wasserkreisläufe verändern und wird außerdem stark vom Klimawandel betroffen sein. Da all diese Prozesse durch die zugrunde liegenden Nährstoff- und Wasserflüsse eng miteinander verknüpft sind, sollten sie in einem konsistenten Modellansatz betrachtet werden. Dennoch haben Datenmangel und ungenügendes Prozessverständnis dies bis vor kurzem auf der globalen Skala verhindert. In dieser Arbeit wird die erste Version eines solchen konsistenten globalen Modellansatzes präsentiert, wobei der Schwerpunkt auf der Simulation landwirtschaftlicher Erträge und den resultierenden N2O-Emissionen liegt. Der Grund für diese Schwerpunktsetzung liegt darin, dass die korrekte Abbildung des Pflanzenwachstums eine essentielle Voraussetzung für die Simulation aller anderen Prozesse ist. Des weiteren sind aktuelle und potentielle landwirtschaftliche Erträge wichtige treibende Kräfte für Landnutzungsänderungen und werden stark vom Klimawandel betroffen sein. Den zweiten Schwerpunkt bildet die Abschätzung landwirtschaftlicher N2O-Emissionen, da bislang kein prozessbasiertes N2O-Modell auf der globalen Skala eingesetzt wurde. Als Grundlage für die globale Modellierung wurde das bestehende Agrarökosystemmodell Daycent gewählt. Neben der Schaffung der Simulationsumgebung wurden zunächst die benötigten globalen Datensätze für Bodenparameter, Klima und landwirtschaftliche Bewirtschaftung zusammengestellt. Da für Pflanzzeitpunkte bislang keine globale Datenbasis zur Verfügung steht, und diese sich mit dem Klimawandel ändern werden, wurde eine Routine zur Berechnung von Pflanzzeitpunkten entwickelt. Die Ergebnisse zeigen eine gute Übereinstimmung mit Anbaukalendern der FAO, die für einige Feldfrüchte und Länder verfügbar sind. Danach wurde das Daycent-Modell für die Ertragsberechnung von Weizen, Reis, Mais, Soja, Hirse, Hülsenfrüchten, Kartoffel, Cassava und Baumwolle parametrisiert und kalibriert. Die Simulationsergebnisse zeigen, dass Daycent die wichtigsten Klima-, Boden- und Bewirtschaftungseffekte auf die Ertragsbildung korrekt abbildet. Berechnete Länderdurchschnitte stimmen gut mit Daten der FAO überein (R2 = 0.66 für Weizen, Reis und Mais; R2 = 0.32 für Soja), und räumliche Ertragsmuster entsprechen weitgehend der beobachteten Verteilung von Feldfrüchten und subnationalen Statistiken. Vor der Modellierung landwirtschaftlicher N2O-Emissionen mit dem Daycent-Modell stand eine statistische Analyse von N2O-und NO-Emissionsmessungen aus natürlichen und landwirtschaftlichen Ökosystemen. Die als signifikant identifizierten Parameter für N2O (Düngemenge, Bodenkohlenstoffgehalt, Boden-pH, Textur, Feldfrucht, Düngersorte) und NO (Düngemenge, Bodenstickstoffgehalt, Klima) entsprechen weitgehend den Ergebnissen einer früheren Analyse. Für Emissionen aus Böden unter natürlicher Vegetation, für die es bislang keine solche statistische Untersuchung gab, haben Bodenkohlenstoffgehalt, Boden-pH, Lagerungsdichte, Drainierung und Vegetationstyp einen signifikanten Einfluss auf die N2O-Emissionen, während NO-Emissionen signifikant von Bodenkohlenstoffgehalt und Vegetationstyp abhängen. Basierend auf den daraus entwickelten statistischen Modellen betragen die globalen Emissionen aus Ackerböden 3.3 Tg N/y für N2O, und 1.4 Tg N/y für NO. Solche statistischen Modelle sind nützlich, um Abschätzungen und Unsicherheitsbereiche von N2O- und NO-Emissionen basierend auf einer Vielzahl von Messungen zu berechnen. Die Dynamik des Bodenstickstoffs, insbesondere beeinflusst durch Pflanzenwachstum, Klimawandel und Landnutzungsänderung, kann allerdings nur durch die Anwendung von prozessorientierten Modellen berücksichtigt werden. Zur Modellierung von N2O-Emissionen mit dem Daycent-Modell wurde zunächst dessen Spurengasmodul durch eine detailliertere Berechnung von Nitrifikation und Denitrifikation und die Berücksichtigung von Frost-Auftau-Emissionen weiterentwickelt. Diese überarbeitete Modellversion wurde dann an N2O-Emissionsmessungen unter verschiedenen Klimaten und Feldfrüchten getestet. Sowohl die Dynamik als auch die Gesamtsummen der N2O-Emissionen werden befriedigend abgebildet, wobei die Modelleffizienz für monatliche Mittelwerte zwischen 0.1 und 0.66 für die meisten Standorte liegt. Basierend auf der überarbeiteten Modellversion wurden die N2O-Emissionen für die zuvor parametrisierten Feldfrüchte berechnet. Emissionsraten und feldfruchtspezifische Unterschiede stimmen weitgehend mit Literaturangaben überein. Düngemittelinduzierte Emissionen, die momentan vom IPCC mit 1.25 +/- 1% der eingesetzten Düngemenge abgeschätzt werden, reichen von 0.77% (Reis) bis 2.76% (Mais). Die Summe der berechneten Emissionen aus landwirtschaftlichen Böden beträgt für die Mitte der 1990er Jahre 2.1 Tg N2O-N/y, was mit den Abschätzungen aus anderen Studien übereinstimmt.

Statistical evidences of cyclic changes in volcanic gas chemistry composition by inverse modelling

The identification of compositional changes in fumarolic gases of active and quiescent volcanoes is one of the most important targets in monitoring programs. From a general point of view, many systematic (often cyclic) and random processes control the chemistry of gas discharges, making difficult to produce a convincing mathematical-statistical modelling. Changes in the chemical composition of volcanic gases sampled at Vulcano Island (Aeolian Arc, Sicily, Italy) from eight different fumaroles located in the northern sector of the summit crater (La Fossa) have been analysed by considering their dependence from time in the period 2000-2007. Each intermediate chemical composition has been considered as potentially derived from the contribution of the two temporal extremes represented by the 2000 and 2007 samples, respectively, by using inverse modelling methodologies for compositional data. Data pertaining to fumaroles F5 and F27, located on the rim and in the inner part of La Fossa crater, respectively, have been used to achieve the proposed aim. The statistical approach has allowed us to highlight the presence of random and not random fluctuations, features useful to understand how the volcanic system works, opening new perspectives in sampling strategies and in the evaluation of the natural risk related to a quiescent volcano

Modelling for Problem Solving in the Large

This a short presentation which introduces how models and modelling help us to solve large scale problems in the real world. It introduces the idea that dynamic behaviour is caused by interacting components in the system. Feedback in the system makes behaviour prediction difficult unless we use modelling to support understanding