Computing wildfire behaviour metrics from CFD simulation data

In this article, we demonstrate a new post-processing methodology which can be used to analyse CFD wildfire simulation outputs in a model-independent manner. CFD models produce a great deal of quantitative output but require additional post-processing to calculate commonly used wildfire behaviour metrics. Such post-processing has so far been model specific. Our method takes advantage of the 3D renderings that are a common output from such models and provides a means of calculating important fire metrics such as rate of spread and flame height using image processing techniques. This approach can be applied similarly to different models and to real world fire behaviour datasets, thus providing a new framework for model validation. Furthermore, obtained information is not limited to average values over the complete domain but spatially and temporally explicit metric distributions are provided. This feature supports posterior statistical analyses, ultimately contributing to more detailed and rigorous fire behaviour studies.Peer ReviewedPostprint (published version

Collected notes from the Benchmarks and Metrics Workshop

In recent years there has been a proliferation of proposals in the artificial intelligence (AI) literature for integrated agent architectures. Each architecture offers an approach to the general problem of constructing an integrated agent. Unfortunately, the ways in which one architecture might be considered better than another are not always clear. There has been a growing realization that many of the positive and negative aspects of an architecture become apparent only when experimental evaluation is performed and that to progress as a discipline, we must develop rigorous experimental methods. In addition to the intrinsic intellectual interest of experimentation, rigorous performance evaluation of systems is also a crucial practical concern to our research sponsors. DARPA, NASA, and AFOSR (among others) are actively searching for better ways of experimentally evaluating alternative approaches to building intelligent agents. One tool for experimental evaluation involves testing systems on benchmark tasks in order to assess their relative performance. As part of a joint DARPA and NASA funded project, NASA-Ames and Teleos Research are carrying out a research effort to establish a set of benchmark tasks and evaluation metrics by which the performance of agent architectures may be determined. As part of this project, we held a workshop on Benchmarks and Metrics at the NASA Ames Research Center on June 25, 1990. The objective of the workshop was to foster early discussion on this important topic. We did not achieve a consensus, nor did we expect to. Collected here is some of the information that was exchanged at the workshop. Given here is an outline of the workshop, a list of the participants, notes taken on the white-board during open discussions, position papers/notes from some participants, and copies of slides used in the presentations

Tools for Assessing Climate Impacts on Fish and Wildlife

Climate change is already affecting many fish and wildlife populations. Managing these populations requires an understanding of the nature, magnitude, and distribution of current and future climate impacts. Scientists and managers have at their disposal a wide array of models for projecting climate impacts that can be used to build such an understanding. Here, we provide a broad overview of the types of models available for forecasting the effects of climate change on key processes that affect fish and wildlife habitat (hydrology, fire, and vegetation), as well as on individual species distributions and populations. We present a framework for how climate-impacts modeling can be used to address management concerns, providing examples of model-based assessments of climate impacts on salmon populations in the Pacific Northwest, fire regimes in the boreal region of Canada, prairies and savannas in the Willamette Valley-Puget Sound Trough-Georgia Basin ecoregion, and marten Martes americana populations in the northeastern United States and southeastern Canada. We also highlight some key limitations of these models and discuss how such limitations should be managed. We conclude with a general discussion of how these models can be integrated into fish and wildlife management

UAV Navigation System for Prescribed Fires

Since the beginning of mankind, a lot of fires have happened and have taken millions of lives, whether they were human or animal lives. On average, there are about twenty thousand forest fires annually in the world and the burnt area is one per thousand of the total forest area on Earth. In the last years, there were a lot of big fires such as the fires in Pedrogão Grande, Portugal, the SoCal fires in the US coast, the big fire in the Amazon Forest in Brazil and the bush fires in Australia, later 2019. When fires take such dimensions, they can also cause several environmental and health problems. These problems can be damage to millions of hectares of forest resources, the evacuation of thousands of people, burning of homes and devastation of infrastructures. When a big fire starts, the priority is the rapid rescue of lives and then, the attempt to control the fire. In these scenarios, autonomous robots are a very good assistance because they can help in the rescue missions and monitoring the fire. These autonomous robots include the unmanned aerial vehicle, or commonly called the UAV. This dissertation begins with an intensive research on the work that has already been done relative to this subject. It will then continue with the testing of different simulators and see which better fits for this type of work. With this, it will be implemented a simulation that can represent fires and has physics for test purposes, in order to test without causing any material damage in the real world. After the simulation part is done, algorithm testing and bench marking are expected, in order to compare different algorithms and see which are the best for this type of applications. If everything goes according to plan, in the end, it is expected to have an autonomous navigation system for UAVs to navigate through burnt areas and wildfires to monitor the development of these.Desde o início da humanidade muitos incêndios têm acontecido e têm levado milhões de vidas, quer estas sejam humanas ou animais. Em média, no planeta, existem cerca de vinte mil incêndios florestais anualmente e a área queimada é um por mil da área total de florestas do mundo e na última década, houveram grandes incêndios. Alguns destes são os de Pedrogrão Grande, em Portugal, os incêndios no sul da Califórnia, na costa dos EUA, o incêndio que deflagrou na floresta Amazónia, no Brasil e os incêndios na Austrália, no final de 2019. Quando os incêndios assumem estas dimensões, podem vir a causar vários problemas ambientais e de saúde. Estes problemas podem ser danos a milhões de hectares de recursos florestais, a evacuação de milhares de pessoas e podem haver habitações e infraestruturas ardidas. Quando um grande incêndio começa, a primeira prioridade é o resgate rápido e de seguida a tentativa de controlar o incêndio. Nestes cenários, robôs autónomos são uma excelente assistência. Estes robôs incluem o veículo aéreo não tripulado, o UAV. Esta dissertação começa com uma intensa pesquisa sobre o trabalho já realizado em relação a este tema. De seguida, vários testes irão ser realizados para testar diferentes simuladores e ver qual melhor se adapta ao trabalho que se irá realizar. Com isto, será implementada uma simulação que consiga representar um incêndio e suporte várias fisícas do mundo real. Após a secção da simulação estar concluída, espera-se vários testes de algoritmos e comparação entre eles, para ver qual o que se adequa melhor a este tipo de situações. Se tudo correr conforme planeado, é esperado no final desta dissertação ter-se um sistema de navegação autónoma para UAVs percorrem áreas florestais e ser possível monitorizar incêndios

Fire behaviour in canyons due to symmetric and asymmetric ignitions

The eruptive propagation of a fire is a particular behaviour characterized by a sudden increase in the rate of spread and intensity without any change in the external driving forces such as wind velocity and ambient temperature, vegetation type and moisture content. It is, therefore, a local or internal dynamic connected with the terrain configuration and the ignition position that regulates fire spreading and causes its acceleration. This phenomenon is particularly evident and common in canyons. This work aims to study the effect ignition position on the fire propagation in a canyon by means of a physically-based computational code. The code WFDS was shown to be effective to describe the fire behaviour throughout such a terrain configuration