Data driven forecast of droplet combustion

The characteristics of a diffusion flame resulting from the gasification of a condensed fuel are predicted from the synthesis of simple models and data. Combustion of a droplet in microgravity is used as a canonical configuration to illustrate the methodology. The simplicity of the spherical configuration and the detail of the measurements make the available experimental data ideal for this study. The approach followed combines the classical analytical solution first proposed by Spalding to describe the condensed phase gasification with a numerical method that describes the gas phase. Available data on flame geometry and regression rates are used to initialize the model and produce adequate predictions of the time evolution of all relevant variables. The method was shown to make proper predictions under numerous configurations and with very small computational cost

On Realization of Cinema Hall Fire Simulation Using Fire Dynamics Simulator

Currently known fire models are capable to describe fire dynamics in complex environments incorporating a wide variety of fire-related physical and chemical phenomena and utilizing large computational power of contemporary computers. In this paper, some issues related to realization of the simulation of fire in a cinema hall with sloping floor and curved ceiling furnished by upholstered seats modelled by FDS (Fire Dynamics Simulator) are discussed. The paper concentrates particularly on the impact of a computational meshes choice on resolving flow field and turbulence in the simulation and indicates problems related to parallelization of the calculation illustrated comparing sequential and parallel MPI calculation using 6 CPU cores. Results of the simulation described and their discussion demonstrate the ability of FDS simulation to capture main tendencies of smoke spread and to forecast the related safety risks realistically

Compatible finite element methods for geophysical fluid dynamics

This article surveys research on the application of compatible finite element methods to large scale atmosphere and ocean simulation. Compatible finite element methods extend Arakawa's C-grid finite difference scheme to the finite element world. They are constructed from a discrete de Rham complex, which is a sequence of finite element spaces which are linked by the operators of differential calculus. The use of discrete de Rham complexes to solve partial differential equations is well established, but in this article we focus on the specifics of dynamical cores for simulating weather, oceans and climate. The most important consequence of the discrete de Rham complex is the Hodge-Helmholtz decomposition, which has been used to exclude the possibility of several types of spurious oscillations from linear equations of geophysical flow. This means that compatible finite element spaces provide a useful framework for building dynamical cores. In this article we introduce the main concepts of compatible finite element spaces, and discuss their wave propagation properties. We survey some methods for discretising the transport terms that arise in dynamical core equation systems, and provide some example discretisations, briefly discussing their iterative solution. Then we focus on the recent use of compatible finite element spaces in designing structure preserving methods, surveying variational discretisations, Poisson bracket discretisations, and consistent vorticity transport.Comment: correction of some typo

Data Driven Forecast for Fire

Being able to forecast the evolution of a fire is essential for fire safety design and fire response strategies. Despite advances in understanding fire dynamics and improvements in computational capability, the ability to predict the evolution of a fire remains limited due to large uncertainties associated to multiple scales and the non-linearities. The data-driven approach provides a viable technique from models corrected by observations. However, the complicated coupling between gaseous and condensed phases has, in the past, limited proper prediction with a positive leading time. This work proposes and investigates a series of approaches to data-driven hybrid modelling that integrate analytical and numerical descriptions to address the coupling effects. The data-driven hybrid model is developed for different scenarios covering various complexity and scales. Different approaches are evaluated to reflect the dominant physics; nevertheless, they are structured by differentiating the condensed and gas phases. The initial scenario corresponds to one-dimensional convective-diffusive droplet combustion in micro and normal gravity. Then, concurrent flame spread in micro and normal gravity where a two-dimensional boundary layer combustion approach is implemented. Finally, the Malveira fire test represents a large-scale, three-dimensional, travelling fire. Coefficients assimilated with their experimental observations are used to alter analytical formulations describing the gas and condensed phases. By separating the phases, the data-driven hybrid model can forecast various types of variables while reducing processing resources. Convergence of the assimilated coefficients is used as an indicator for an appropriate representation of the model and therefore is suitable for predictions. The proposed methodology still requires ongoing research, however. This work provides evidence for specific approaches and of areas where additional attention is necessary. It has become apparent that to adequately predict real-scale fire, it is necessary for more sophisticated explanations of heat and mass transfer and descriptions of the interactions between fire and its environment

Data assimilation for micrometeorological applications with the fluid dynamics model Code_Saturne

Air quality is a major health and environmental issue worldwide. Similarly, the accuracy of wind resource assessment triggers significant economic and environmental repercussions. In order to study these two topics, it is necessary to accurately determine local wind fields using numerical models of micrometeorology. Such simulations are extremely sensitive to meteorological conditions at the domain borders. Up to present, the boundary conditions (BC) were estimated based on the results of larger scale simulations, which provide information that is not accurate enough, or even incomplete, for local scale purposes. As a matter of fact, the lack of knowledge about the BC represents a major source of error and uncertainty for micrometeorological studies.The potential sites for wind farm installation as well as built environments (urban areas or industrial sites) can be equipped with instruments measuring meteorological variables or pollutant concentration. The observations provided by these instruments represent a second source of information, insufficiently exploited for micrometeorological studies. Indeed, the in situ measurements are perturbed by the complex geometrical features on sites and might be difficult to exploit. In order to improve the exactitude and the accuracy of the BC, and consequently of the locale-scale atmospheric simulations, data assimilation (DA) methods, suited to this micrometeorological problem, could be applied to take benefit from the available observations.So far, DA methods have been mainly developed for large-scale meteorology and employed to correct the initial conditions (IC). In order to broaden the application scope of DA to micrometeorology, existing DA methods must be adapted to be able to correct the BC instead of IC.Two of the existing DA methods seem compatible with computational fluid dynamics (CFD) models used for micrometeorology over complex geometries: the back and forth nudging (BFN) algorithm and the iterative ensemble Kalman smoother (IEnKS). We have adapted these two methods, from a theoretical perspective, so as to include the BC in the control variables. The performances of the adapted versions of the BFN algorithm and the IEnKS have first been assessed with a simplified, 1D model of atmospheric flow with two layers, based on the shallow-water equations. The BFN algorithm and the IEnKS have then been tested in 2D and 3D with the atmospheric module of the open-source CFD model Code_Saturne.The first study case with Code_Saturne corresponds to a real application of wind resource assessment in a mountainous region with steep topography where three meteorological masts have been installed during a few months and provided in situ wind observations. The second case is a study of pollutant dispersion in an urban area, based on the measurements of wind and pollutant concentration coming from the ``Mock Urban Setting Test'' field campaign carried out in the USA. In this second case, the turbulence is also included in the BC and thus in the control variables. For both studies, some observations are assimilated and the remaining ones are used to validate the results.The experiences performed for the wind resource assessment study have revealed that the CFD models present too strong nonlinearities (flow recirculation after obstacles) for the BFN algorithm, which is based on a linearity assumption. However, both cases have shown the ability of the IEnKS to reduce the error and the uncertainty of the BC by assimilating a few observations, with operationally affordable computational costs. Consequently, the simulated wind fields with Code_Saturne are also closer to the validation observations and the confidence intervals are reduced. Eventually, the IEnKS allows, in one case to estimate the wind potential, and in the other case to build the pollution maps, with much more exactitude and accuracy.La qualité de l’air est un enjeu sanitaire et environnemental majeur. Par ailleurs, l'estimation précise des potentiels éoliens est la source d’importantes retombées économiques et environnementales. Pour étudier ces deux sujets, il est nécessaire de reconstituer précisément les champs de vent locaux grâce à des modèles numériques de micro-météorologie. Ces simulations sont extrêmement sensibles aux conditions météorologiques aux limites du domaine d’étude. Jusqu’à présent, les conditions aux limites (CL) étaient estimées à partir de simulations à plus grande échelle, qui fournissent des informations imprécises, voire incomplètes pour l’utilisation à micro-échelle. Par conséquent, la méconnaissance des CL représente une source majeure d’erreur et d’incertitude dans les études micro-météorologiques. Les sites susceptibles d’accueillir un parc éolien et les environnements bâtis (quartiers urbains ou sites industriels) peuvent être équipés d’instruments de mesures météorologiques et de concentration de polluants. Les observations fournies par ces instruments constituent une seconde source d’information, jusqu’à ce jour peu exploitée pour les études micro-météorologiques. En effet, étant à l’intérieur du domaine, les observations sont perturbées par la géométrie complexe des sites étudiés. Afin d'améliorer la précision des CL et donc des simulations atmosphériques à l'échelle locale, des méthodes d'assimilation de données (AD) adaptées à cette problématique pourraient permettre de mettre à profit les observations disponibles. Jusqu’à présent, les méthodes d’AD ont été principalement développées pour répondre aux besoins de la météorologie à grande échelle et donc utilisées pour corriger les conditions initiales (CI). Afin d'élargir le champ d'application de l’assimilation de données aux simulations à l’échelle locale, il faut adapter les méthodes d'AD pour qu'elles permettent de corriger les CL plutôt que les CI. Parmi les méthodes d'assimilation de données existantes, deux semblent compatibles avec les modèles de mécanique des fluides atmosphérique (CFD) utilisés pour la micro-météorologie en géométrie complexe : l’algorithme de nudging direct et rétrograde (BFN) et le lisseur de Kalman d’ensemble itératif (IEnKS). Nous avons adapté ces deux méthodes d’un point de vue théorique pour inclure les CL dans les variables de contrôle. Les performances des versions adaptées du BFN et de l'IEnKS ont tout d'abord été étudiées avec un modèle simplifié d’écoulement atmosphérique à deux couches en 1D, basé sur les équations de Saint-Venant. Le BFN et l’IEnKS ont ensuite été testés en deux puis trois dimensions avec le module atmosphérique du modèle open-source de CFD Code_Saturne. Le premier cas d’étude avec Code_Saturne correspond à une application réelle d’estimation de potentiel éolien dans une région montagneuse au relief très accidenté où trois mâts de mesure fournissent des observations de vent. Le second cas d’étude correspond à une étude de dispersion de polluants en milieu urbain, basé sur les observations de vent et de concentration, provenant de la campagne de mesures « Mock Urban Setting Test » aux USA. Dans ce second cas, la turbulence est également incluse dans les conditions aux limites. Dans les deux cas, une partie des observations est utilisée pour l’assimilation et le reste pour la validation des résultats. Les expériences menées sur le premier cas ont révélé que les modèles de CFD présentent des non-linéarités trop fortes (recirculations derrière les obstacles) pour l’algorithme de BFN, fondé sur une hypothèse de linéarité. Les études avec cette méthode n'ont donc pas été poursuivies. En revanche, les deux cas d'étude ont montré la capacité de l'IEnKS à réduire l'erreur et l'incertitude sur les CL grâce à l'assimilation d'une petite dizaine d'observations, en un nombre raisonnable de calculs. Par suite, l'écart entre les champs de vent simulés et les observations de validation est également réduit. De même, l'incertitudesur les simulations est plus faible. Finalement, l'IEnKS permet d'estimer le potentiel éolien dans un cas et les concentrations en polluant dans l'autre, avec beaucoup plus de précision

Inverse modelling in wildfire spread forecasting: towards a data-driven system

Wildfires are an ecological phenomenon inherent to earth dynamics and widely spread over the globe. In addition to the environmental impact, when wildfires grow beyond controllable magnitudes, they pose a principal threat to human lives and properties. On many countries, the rural abandonment of last decades, the forest continuity growth and the Wildland Urban Interface increase are exposing entire communities and rendering them vulnerable to a major fire event. Coupled together with a global warming that seems to be enlarging and worsening wildfire-prone weather conditions, the wildfire problem is becoming a recurrent and repetitive natural hazard that is in urgent needs of research development, planning and organizational changes to minimize its impact. In this context, the thesis at hand focuses on the development, implementation and initial validation of a wildfire perimeter spread prediction model that might help emergency responders on taking sound decisions to efficiently employ resources and protect valuable assets. This forecasting model is a particular implementation of a data-driven system. That is, available data are used to improve and calibrate the spread model results with the aim of delivering a more accurate and timely forecast of the fire spread for the upcoming hours. This thesis builds up the mentioned system by increasing its complexity and tackling required improvements and adaptations on fuel characterization and wind projection on topography. Initially, a simplified proof of concept that uses front perimeter (isochrones) evolution extracted from infrared imagery of the fire is challenged with data from real-scale burning experiments conducted in Australia. Despite the positive outcome of this initial investigation, some advancements are identified to further upgrade the system. Thus, the following chapters focus on the fuel and wind sub-models together with the spread model topographic upgrade and the different mathematical algorithms and strategies necessary to conduct the data-driven process. Regarding fuels, the thesis presents an in-depth analysis of fuel characterization to be used by fire spread models. This is done by a thorough sensitivity analysis of the most commonly used fuel characterization systems. In the light of these results, a simplified model that integrates all different fuel properties is proposed to be used by the data-driven framework at hand. To properly resolve the wind interaction with the terrain and to couple it into the data-driven system, the WindNinja diagnose software is employed. However, long computational times do not allow for its integration into any data-assimilation strategy. Thus, a full interpolating framework is developed and validated to allow fast and computationally inexpensive wind field updates. This key element becomes then a cornerstone of the full data-driven approach. For the optimization process (embedded into any data-driven systems) six different mathematical algorithms were compared and evaluated. Three of them being line-search strategies and the other three being global search. It was found that the algorithm selection has an impact on the final results in terms of forecast accuracy and computing time. Finally, the overall system is verified and validated using two source of available data: (1) well characterized, homogeneous slope, medium-scale experimental fires conducted in Portugal and (2) with synthetically generated fronts reproducing a real large-scale fire. These validations were aimed at studying the overall performance, checking the system functionality and highlighting possible flaws and necessary improvements if the tool is to be deployed in a real emergency situation. Whereas the results show the potential of the approach by delivering an acceptable forecast usable for emergency responders, further validations are required to check the robustness and reliability of the tool before using it in operational situations.Els incendis forestals són, al cap i a la, un fenomen ecològic inherent a la dinàmica de la terra i àmpliament escampats per tot el món. A més de l'impacte ambiental, quan els incendis forestals excedeixen, en magnitud i intensitat, la capacitat d’extinció, representen una amenaça per a propietats i vides humanes. En molts països, l’abandó rural de les últimes dècades, el creixement de la continuïtat forestal i l'augment de la interfície urbana-forestal (Wildand-Urban Interface) està comportant l'augment de comunitats exposades al foc forestal alhora que les fan més vulnerables a un gran incendi. A més a més, l'escalfament global sembla que està afavorint i facilitant la recurrència de les condicions climàtiques propícies pels incendis forestals. El problema de l'incendi forestal s’està convertint en un perill natural recurrent i repetitiu que clama avanços urgents en recerca, planificació i gestió per tal de minimitzar-ne el seu impacte. En aquest context, la present tesi se centra en el desenvolupament, la implementació i la validació inicial d'un model de predicció de la propagació del perímetre d'incendis forestals que podria ajudar als responsables de l’emergència a prendre decisions més oportunes per a emprar els recursos de forma eficient tot protegint els actius valuosos. Aquest model predictiu és una implementació particular d'un sistema basat en dades. És a dir, les dades disponibles s'utilitzen per a millorar i calibrar els resultats del model de propagació del front amb l'objectiu de proporcionar un pronòstic més precís i oportú de l’evolució del perímetre del foc en les pròximes hores. Aquesta tesi construeix el sistema esmentat pas a pas, tot incrementant-ne la seva complexitat i abordant les millores i adaptacions necessàries en cada etapa, per exemple, en la caracterització del combustible i la projecció i interacció del vent amb la topografia. Inicialment, s'utilitzen imatges infraroges de l’evolució del perímetre (isòcrones) de dos cremes experimentals dutes a terme a Austràlia per a realitzar una prova de concepte del sistema. Malgrat el resultat favorable d'aquesta primera investigació, s'identifiquen alguns avenços per millorar-ne l'efectivitat i permetre'n l’aplicació en incendis reals. Així, els capítols següents se centren en els submodels de combustible i vent juntament amb l’actualització topogràfica del model de propagació i els diferents algorismes i estratègies matemàtiques necessàries per dur a terme el procés d’assimilació basat en dades. Pel que fa als combustibles, la tesi presenta una anàlisi en profunditat de la caracterització dels combustibles que han d'utilitzar els models de propagació de foc. Això es fa mitjançant una anàlisi minuciosa de la sensibilitat dels paràmetres de caracterització del sistema més utilitzats. A la llum d'aquests resultats, es proposa un model simplificat que integri totes les propietats de combustible diferents per ser utilitzat pel model predictiu desenvolupat en la present tesi. Per resoldre adequadament la interacció del vent amb el terreny i acoblar-la al model de propagació bastat en dades, s'utilitza el programa de diagnòstic WindNinja. Els temps necessaris de computació, però, no permeten la seva directa integració en una estratègia d’assimilació de dades. Així doncs, en aquesta tesi, es desenvolupa i valida un marc interpolador que permeti actualitzacions ràpides i computacionalment assequibles del camp de vents a nivell topogràfic. Aquest element clau es converteix en una peça clau per aconseguir el model de propagació basat en dades que es cerca en aquesta tesi. Per al procés d’optimització (present en qualsevol model conduit per dades) es comparen i s'avaluen sis algorismes matemàtics diferents. Tres d'ells són estratègies de cerca basades en programació lineal i les altres tres són estratègies de recerca global. L’exploració conclou que la selecció d'algorismes té un gran impacte en els resultats finals en termes de la precisió de pronòstic i temps de computació. Finalment, tot el sistema es verifica i valida utilitzant les dades de dues fonts disponibles: (1) incendis experimentals de mitjana escala realitzats a Portugal en un pendent homogeni ben caracteritzat i (2) amb fronts generats sintèticament que reprodueixen un incendi real a gran escala. Aquestes validacions estan orientades a estudiar el rendiment general, comprovar la funcionalitat del sistema això com ressaltar possibles defectes i millores necessàries per tal de poder utilitzar l'eina en una situació d’emergència real. Malgrat els resultats mostren els potencials del sistema tot proporcionant un pronòstic acceptable, utilitzable com a eina de suport per a la gestió d'una emergència, queda també palès que es requereixen més validacions per comprovar la robustesa i fiabilitat de l'eina abans d'utilitzar-la en situacions operatives

Real-Time Simulation and Prognosis of Smoke Propagation in Compartments Using a GPU

The evaluation of life safety in buildings in case of fire is often based on smoke spread calculations. However, recent simulation models – in general, based on computational fluid dynamics – often require long execution times or high-performance computers to achieve simulation results in or faster than real-time. Therefore, the objective of this study is the development of a concept for the real-time and prognosis simulation of smoke propagation in compartments using a graphics processing unit (GPU). The developed concept is summarized in an expandable open source software basis, called JuROr (Jülich's Real-time simulation within ORPHEUS). JuROr simulates buoyancy-driven, turbulent smoke spread based on a reduced modeling approach using finite differences and a Large Eddy Simulation turbulence model to solve the incompressible Navier-Stokes and energy equations. This reduced model is fully adapted to match the target hardware of highly parallel computer architectures. Thereby, the code is written in the object-oriented programming language C++ and the pragma-based programming model OpenACC. This model ensures to maintain a single source code, which can be executed in serial and parallel on various architectures. Further, the study provides a proof of JuROr's concept to balance sufficient accuracy and practicality. First, the code was successfully verified using unit and (semi-) analytical tests. Then, the underlying model was validated by comparing the numerical results to the experimental results of scenarios relevant for fire protection. Thereby, verification and validation showed acceptable accuracy for JuROr's application. Lastly, the performance criteria of JuROr – being real-time and prognosis capable with comparable performance across various architectures – was successfully evaluated. Here, JuROr also showed high speedup results on a GPU and faster time-to-solution compared to the established Fire Dynamics Simulator. These results show JuROr's practicality.Die Bewertung der Personensicherheit bei Feuer in Gebäuden basiert häufig auf Berechnungen zur Rauchausbreitung. Bisherige Simulationsmodelle – im Allgemeinen basierend auf numerischer Strömungsdynamik – erfordern jedoch lange Ausführungszeiten oder Hochleistungsrechner, um Simulationsergebnisse in und schneller als Echtzeit liefern zu können. Daher ist das Ziel dieser Arbeit die Entwicklung eines Konzeptes für die Echtzeit- und Prognosesimulation der Rauchausbreitung in Gebäuden mit Hilfe eines Grafikprozessors (GPU). Zusammengefasst ist das entwickelte Konzept in einer erweiterbaren Open-Source-Software, genannt JuROr (Jülich's Real-time Simulation in ORPHEUS). JuROr simuliert die Ausbreitung von auftriebsgetriebenem, turbulentem Rauch basierend auf einem reduzierten Modellierungsansatz mit finiten Differenzen und einem Large Eddy Simulation Turbulenzmodell, um inkompressible Navier- Stokes und Energiegleichungen zu lösen. Das reduzierte Modell ist voll- ständig angepasst an hochparallele Computerarchitekturen. Dabei ist der Code implementiert mit C++ und OpenACC. Dies hat den Vorteil mit nur einem Quellcode verschiedenste serielle und parallele Ausführungen des Programms für unterschiedliche Architekturen erstellen zu können. Die Studie liefert weiterhin einen Konzeptnachweis dafür, ausreichende Genauigkeit und Praktikabilität im Gleichgewicht zu halten. Zunächst wurde der Code erfolgreich mit Modul- und (semi-) analytischen Tests verifiziert. Dann wurde das zugrundeliegende Modell durch einen Vergleich der numerischen mit den experimentellen Ergebnissen für den Brandschutz relevanter Szenarien validiert. Die Verifizierung und Validierung zeigten dabei ausreichende Genauigkeit für JuROr. Zuletzt, wurden die Kriterien von JuROr – echtzeit- und prognosefähig zu sein mit vergleichbarer Leistung auf unterschiedlichsten Architekturen – erfolgreich geprüft. Zudem zeigte JuROr hohe Beschleunigungseffekte auf einer GPU und schnellere Lösungszeiten im Vergleich zum etablierten Fire Dynamics Simulator. Diese Ergebnisse zeigen JuROr's Praktikabilität