Simulation Software as a Service and Service-Oriented Simulation Experiment

Simulation software is being increasingly used in various domains for system analysis and/or behavior prediction. Traditionally, researchers and field experts need to have access to the computers that host the simulation software to do simulation experiments. With recent advances in cloud computing and Software as a Service (SaaS), a new paradigm is emerging where simulation software is used as services that are composed with others and dynamically influence each other for service-oriented simulation experiment on the Internet. The new service-oriented paradigm brings new research challenges in composing multiple simulation services in a meaningful and correct way for simulation experiments. To systematically support simulation software as a service (SimSaaS) and service-oriented simulation experiment, we propose a layered framework that includes five layers: an infrastructure layer, a simulation execution engine layer, a simulation service layer, a simulation experiment layer and finally a graphical user interface layer. Within this layered framework, we provide a specification for both simulation experiment and the involved individual simulation services. Such a formal specification is useful in order to support systematic compositions of simulation services as well as automatic deployment of composed services for carrying out simulation experiments. Built on this specification, we identify the issue of mismatch of time granularity and event granularity in composing simulation services at the pragmatic level, and develop four types of granularity handling agents to be associated with the couplings between services. The ultimate goal is to achieve standard and automated approaches for simulation service composition in the emerging service-oriented computing environment. Finally, to achieve more efficient service-oriented simulation, we develop a profile-based partitioning method that exploits a system’s dynamic behavior and uses it as a profile to guide the spatial partitioning for more efficient parallel simulation. We develop the work in this dissertation within the application context of wildfire spread simulation, and demonstrate the effectiveness of our work based on this application

Sensitivity Analysis of Aerial Wildfire Fighting Tactics with Heterogeneous Fleets Using an Agent Based Simulation Framework

The increase in the average temperature of the global surface temperature caused longer wildfire seasons, which have caused more severe and frequent incidents, resulting in higher expenses, unrecoverable losses and civilian casualties. Moreover, the increased number of wildfires has contributed to higher levels of carbon in the atmosphere, further exacerbating global warming. Fighting wildfires is a complex phenomenon that requires various resources, and the System of Systems (SoS) approach can be leveraged to analyze the problem. This study utilizes an SoS simulation framework to model wildfire suppression missions, focusing on a mixed fleet composition of suppression drones with different characteristics such as airframe configurations, payload capacity, flight velocity, and powertrain architectures. The study evaluates multiple suppression tactics, considering factors such as fleet composition, available agents, and resources. The results of the analysis show the impact of various environmental parameters on fire growth and provide a rigorous sensitivity analysis for wildfire containment use cases. The use of the SoS framework helps to reveal nuanced patterns at the SoS level, which can aid in the development of new solutions for wildfire fighting. This study highlights the importance of considering the complexities of the problem and the need for innovative approaches to combat wildfires effectively

The prevalence of complexity in flammable ecosystems and the application of complex systems theory to the simulation of fire spread

Les forêts sont une ressource naturelle importante sur le plan écologique, culturel et économique, et sont confrontées à des défis croissants en raison des changements climatiques. Ces défis sont difficiles à prédire en raison de la nature complexe des interactions entre le climat et la végétation, dont une le feu. Compte tenu de l’importance des écosystèmes forestiers, des dangers potentiels des feux de forêt et de la complexité de leurs interactions, il est primordial d'acquérir une compréhension de ces systèmes à travers le prisme de la science des systèmes complexes. La science des systèmes complexes et ses techniques de modélisation associées peuvent fournir des informations sur de tels systèmes que les techniques de modélisation traditionnelles ne peuvent pas. Là où les techniques statistiques et basées sur équations cherchent à contourner la dynamique non-linéaire, auto-organisée et émergente des systèmes complexes, les approches de modélisation telles que les automates cellulaires et les modèles à base d'agents (MBA) embrassent cette complexité en cherchant à reproduire les interactions clés de ces systèmes. Bien qu'il existe de nombreux modèles de comportement du feu qui tiennent compte de la complexité, les MBA offrent un terrain d'entente entre les modèles de simulation empiriques et physiques qui peut fournir de nouvelles informations sur le comportement et la simulation du feu. Cette étude vise à améliorer notre compréhension du feu dans le contexte de la science des systèmes complexes en développant un tel MBA de propagation du feu. Le modèle utilise des données de type de carburant, de terrain et de météo pour créer l'environnement des agents. Le modèle est évalué à l'aide d’une étude de cas d'un incendie naturel qui s'est produit en 2001 dans le sud-ouest de l'Alberta, au Canada. Les résultats de cette étude confirment la valeur de la prise en compte de la complexité lors de la simulation d'incendies de forêt et démontrent l'utilité de la modélisation à base d'agents pour une telle tâche.Forests are an ecologically, culturally, and economically important natural resource that face growing challenges due to climate change. These challenges are difficult to predict due to the complex nature of the interactions between climate and vegetation. Furthermore, fire is intrinsically linked to both climate and vegetation and is, itself, complex. Given the importance of forest ecosystems, the potential dangers of forest fires, and the complexity of their interactions, it is paramount to gain an understanding of these systems through the lens of complex systems science. Complex systems science and its attendant modeling techniques can provide insights on such systems that traditional modelling techniques cannot. Where statistical and equation-based techniques seek to work around the non-linear, self-organized, and emergent dynamics of complex systems, modelling approaches such as Cellular Automata and Agent-Based Models (ABM) embrace this complexity by seeking to reproduce the key interactions of these systems. While there exist numerous models of fire behaviour that account for complexity, ABM offers a middle ground between empirical and physical simulation models that may provide new insights into fire behaviour and simulation. This study seeks to add to our understanding of fire within the context of complex systems science by developing such an ABM of fire spread. The model uses fuel-type, terrain, and weather data to create the agent environment. The model is evaluated with a case study of a natural fire that occurred in 2001 in southwestern Alberta, Canada. Results of this study support the value of considering complexity when simulating forest fires and demonstrate the utility of ABM for such a task

Distributed Particle Filters for Data Assimilation in Simulation of Large Scale Spatial Temporal Systems

Assimilating real time sensor into a running simulation model can improve simulation results for simulating large-scale spatial temporal systems such as wildfire, road traffic and flood. Particle filters are important methods to support data assimilation. While particle filters can work effectively with sophisticated simulation models, they have high computation cost due to the large number of particles needed in order to converge to the true system state. This is especially true for large-scale spatial temporal simulation systems that have high dimensional state space and high computation cost by themselves. To address the performance issue of particle filter-based data assimilation, this dissertation developed distributed particle filters and applied them to large-scale spatial temporal systems. We first implemented a particle filter-based data assimilation framework and carried out data assimilation to estimate system state and model parameters based on an application of wildfire spread simulation. We then developed advanced particle routing methods in distributed particle filters to route particles among the Processing Units (PUs) after resampling in effective and efficient manners. In particular, for distributed particle filters with centralized resampling, we developed two routing policies named minimal transfer particle routing policy and maximal balance particle routing policy. For distributed PF with decentralized resampling, we developed a hybrid particle routing approach that combines the global routing with the local routing to take advantage of both. The developed routing policies are evaluated from the aspects of communication cost and data assimilation accuracy based on the application of data assimilation for large-scale wildfire spread simulations. Moreover, as cloud computing is gaining more and more popularity; we developed a parallel and distributed particle filter based on Hadoop & MapReduce to support large-scale data assimilation

Discrete event front tracking simulator of a physical fire spread model

International audienceSimulation of moving interfaces, like a fire front usually requires the resolution of a large scale and detailed domain. Such computing involves the use of supercomputers to process the large amount of data and calculations. This limitation is mainly due to the fact that large scale of space and time is usually split into nodes, cells or matrices, and the solving methods often require small time steps. This paper presents a novel method that enables the simulation of large scale/high resolution systems by focusing on the interface. Unlike the conventional explicit and implicit integration schemes, it is based on the discrete-event approach, which describes time advance in terms of increments of physical quantities rather than discrete time stepping. Space as well is not split into discrete nodes or cells, but we use polygons with real coordinates. The system is described by the behaviour of its interface, and evolves by computing collision events of this interface in the simulation. As this simulation technique is suited for a class of models that can explicitly provide rate of spread for a given configuration, we developed a specific radiation based propagation model of physical wildland fire. Simulations of a real large scale fire performed with an implementation of our method provide very interesting results in less than 30 seconds with a 3 metres resolution with current personal computers

Exploration of Aerial Firefighting Fleet Effectiveness and Cost by System of Systems Simulations

Wildfires are becoming a more frequent and devastating phenomena across the globe. The suppression of these wildfires is a dangerous and complex activity considering the vast systems that need to operate together to monitor, mitigate, and suppress the fire. In addition, the required cooperation spans multiple institutes in different capacities. Thus, the recognition of the wildfire suppression scenario as a System of Systems (SoS) is valid. Due to the dangers associated with firefighting and the increased occurrence, there is scope for the design of unmanned aerial vehicles for wildfire suppression. In this work, a SoS driven aircraft design, cost, and fleet assessment methodology is utilized together with a wildfire simulation to investigate several sensitivities relating to design and operational parameters. Further, this paper investigates their impacts on the measures of effectiveness, i.e. burnt area and operating cost. These two parameters enable the identification of optimal fleet size for wildfire suppression for a given scenario and aircraft definition

Exploration of Aerial Firefighting Fleet Effectiveness and Cost by System of Systems Simulations

Wildfires are becoming a more frequent and devastating phenomena across the globe. The suppression of these wildfires is a dangerous and complex activity considering the vast systems that need to operate together to monitor, mitigate, and suppress the fire. In addition, the required cooperation spans multiple institutes in different capacities. Thus, the recognition of the wildfire suppression scenario as a System of Systems (SoS) is valid. Due to the dangers associated with firefighting and the increased occurrence, there is scope for the design of unmanned aerial vehicles for wildfire suppression. In this work, a SoS driven aircraft design, cost, and fleet assessment methodology is utilized together with a wildfire simulation to investigate several sensitivities relating to design and operational parameters. Further, this paper investigates their impacts on the measures of effectiveness, i.e. burnt area and operating cost. These two parameters enable the identification of optimal fleet size for wildfire suppression for a given scenario and aircraft definition

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

Fire performance of residential shipping containers designed with a shaft wall system

seven story building made of shipping containers is planned to be built in Barcelona, Spain. This study mainly aimed to evaluate the fire performance of one of these residential shipping containers whose walls and ceiling will have a shaft wall system installed. The default assembly consisted of three fire resistant gypsum boards for vertical panels and a mineral wool layer within the framing system. This work aimed to assess if system variants (e.g. less gypsum boards, no mineral wool layer) could still be adequate considering fire resistance purposes. To determine if steel temperatures would attain a predetermined temperature of 300-350ºC (a temperature value above which mechanical properties of steel start to change significantly) the temperature evolution within the shaft wall system and the corrugated steel profile of the container was analysed under different fire conditions. Diamonds simulator (v. 2020; Buildsoft) was used to perform the heat transfer analysis from the inside surface of the container (where the fire source was present) and within the shaft wall and the corrugated profile. To do so gas temperatures near the walls and the ceiling were required, so these temperatures were obtained from two sources: (1) The standard fire curve ISO834; (2) CFD simulations performed using the Fire Dynamics Simulator (FDS). Post-flashover fire scenarios were modelled in FDS taking into account the type of fuel present in residential buildings according to international standards. The results obtained indicate that temperatures lower than 350ºC were attained on the ribbed steel sheet under all the tested heat exposure conditions. When changing the assembly by removing the mineral wool layer, fire resistance was found to still be adequate. Therefore, under the tested conditions, the structural response of the containers would comply with fire protection standards, even in the case where insulation was reduced.Postprint (published version

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table