Modelo y diseño de interacción basado en confianza para espacios inteligentes orientados a la eSalud

La computación ubicua se ha convertido en un paradigma de referencia para el diseño de espacios inteligentes y los desarrollos actuales en el campo de la inteligencia ambiental están cada vez más relacionados con el concepto de Internet de las Cosas y la Internet del Futuro. A pesar de que este paradigma ofrece soluciones verdaderamente rentables en los ámbitos de la Teleasistencia, la sanidad y Ambient Assisted Living, no proporciona al usuario final un rol claramente defi?nido en el desarrollo de las interacciones dinámicas con el entorno. Por lo tanto, el despliegue de servicios de eSalud inteligentes en un espacio privado, como la casa, está todavía sin resolver. En este artículo se define un modelo de interacción persona-ambiente para fomentar la confianza y aceptación de usuarios en entornos privados mediante la aplicación de los conceptos de seguridad centrada en el usuario, diseño guiado por la actividad y la teoría de la acción

Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction problems. Using a canonical simulation scenario, we assessed the performance of control variates Monte-Carlo (MC) and multilevel MC strategies, achieving speedups of up to 100x in comparison to a standard MC method. This improvement was leveraged to quantify aleatoric uncertainties and analyze the sensitivity of the fire rate of spread (RoS) to weather and fuel parameters using a full-physics fire model, namely the Wildland-Urban Interface Fire Dynamics Simulator (WFDS), at an affordable computation cost. The proposed methodology may also be used to analyze uncertainty in other relevant fire behavior metrics such as heat transfer, fuel consumption and smoke production indicators

Multifidelity approaches for uncertainty estimation in wildfire spread simulators

A variety of wildfire models are currently used for prescribed fire management, fire behaviour studies and decision support during wildfire emergencies, among other applications. All these applications are based on predictive analysis, and therefore require careful estimation of aleatoric and epistemic uncertainties such as weather conditions, vegetation properties and model parameters. However, the large computational cost of high-fidelity computaional fluid dynamics models prohibits the straightforward utilization of traditional Monte Carlo methods. Conversely, low-fidelity fire models are several orders of magnitude faster but they typically do not provide enough accuracy and they do not resolve all relevant phenomena. Multifidelity frameworks offer a viable solution to this limitation through the efficient combination of high-and low-fidelity simulations. While high-fidelity models provide the required level of accuracy, low-fidelity simulations are used to economically improve the confidence on estimated uncertainty. In this work, we assessed the suitability of multifidelity methodologies to quantify uncertainty in wildfire simulations. A collection of different multifidelity strategies, including Multilevel and Control Variates Monte Carlo, were tested and their computational efficiency compared. Fire spread was predicted in a canonical scenario using popular simulators such as the Wildland-Urban Interface Fire Dynamics Simulator (WFDS) and FARSITE. Results show that multifidelity estimators allow speedups in the order of 100× to 1000× with respect to traditional Monte Carlo

Short-term fire front spread prediction using inverse modelling and airborne infrared images

A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.Peer ReviewedPostprint (author's final draft

Remote characterization of fire behavior during the FireFlux II experiment

The FireFlux II field experiment was conducted on January 30th, 2013 in south-east Texas, USA, under high fire danger conditions. The experiment was designed to study the behavior of a head fire progressing through a flat, tall grass prairie, and it was informed by the use of a coupled fire-atmosphere model. Vegetation properties and fuel moisture were measured shortly before the experiment. Near-surface atmospheric conditions were monitored during the experiment using an elaborate meteorological instrumentation array. Fire behavior was observed through a combination of remote and in-situ sensors. Clements et al. (2019) presented the analysis of the experiment micrometeorology and in-situ fire behavior observations acquired using a thermocouple array. In this paper, we extend the study of fire behavior during the FireFlux II experiment with the analysis of remote sensing observations. Two thermal infrared and two visible cameras were deployed during the experiment. One thermal and one visible camera were mounted on a helicopter, whereas the other two cameras were installed on a 40-m-height tower next to the burn unit. The tower infrared camera covered a reduced area of interest coincident with the thermocouple array and it allowed monitoring the fire spread as well as measuring the spatially-resolved evolution of brightness temperature. Imagery collected from the helicopter allowed extending fire behavior measurements to the complete burn unit. While airborne IR footage was saturated and did not allow estimation of emitted radiant heat, its analysis allowed tracking fire progression through the plot and therefore estimating rate of spread and fire time of arrival. The existence of in-situ temperature observations provides an outstanding opportunity to validate remote sensing methodologies. In addition, the combination of remote observations with in-situ fire and fuel measurements allows a comprehensive characterization of fire behavior, including spatially-resolved fire rate of spread and fire time of arrival, fire radiative power, Byram’s fire line intensity, and air temperature during fire front passage. This paper presents preliminary results from this analysis. Such results demonstrate the usefulness of the selected datasets and the potential of the proposed methodology, encouraging further work. Possible applications of the resulting dataset include (i) the validation of existing fire behavior models that are able to predict any of the measured variables, (ii) the development of data-driven fire behavior models, and (iii) the investigation of the relative contribution of radiative and convective heat transfer mechanisms to fire spreadPostprint (published version

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

Main specifications of CFD codes for WUIVIEW activities

CFD simulations will be the core activity of the WUVIEW performance based fire safety analysis. The purpose of this document is to provide WUIVIEW partners with a general overview of the CFD codes to be used in the Action. The general simulation framework is described, particularly highlighting data inputs and scenario description requirements, to be developed in subsequent WUIVIEW WPs. This TN provides the technical foundations and main specifications of the databases to be designed within the WUIVIEW working program (ongoing action by UPC).Postprint (updated version

Performance analysis of a self-protection system for vehicles in case of WUI fire entrapment

Sheltering inside a civilian vehicle has proved to be a high risk strategy in case of wildfire entrapment. Survival is by no means guaranteed, especially in moderate to high-intensity wildfires. However, vehicles do offer a certain degree of fire protection, which can be reinforced by ad-hoc fire resistant technology. In this paper, we present the experimental performance analysis of a self-protection system that has been designed to protect people’s life in case of fire entrapment. Similar to a firefighter fire shelter, the designed system can be quickly deployed covering the whole vehicle. In case of fire exposure, this fabric provides additional heat protection to the occupants and the vehicle itself. An experimental burning was designed in order to simulate real fire exposure conditions in case of vehicle entrapment in a rural road. An ex-situ 2-m high fuel bed composed of Pinus halepensis fine logging slash was arranged in a 13 m long x 6 m wide area. Fire was ignited at one end of the fuel bed and spread driven by an induced constant air flow (3 m/s midflame wind speed). 2.8 m away from the other fuel bed end, a car covered with the fire protection fabric was placed, parallel to the fire. Data analysis provided mean values of fire rate of spread (2 m/s), fireline intensity (1800 kW/m), flame height (6.5 m), flame tilt angle (30º), flame depth (2 m), flame temperature (800 ºC) and flame emissive power (47.5 kW/m2 ). Maximum air temperatures inside the vehicle ranged around 41-42.5 ºC during a period between 20 min and 35 min after ignition. A thermocouple in contact with the internal side of the driver’s window registered a maximum value of 47.3 ºC. These results evidenced the good performance of the fabric when protecting eventual vehicle occupants against thermal exposure from wildfires of moderate intensity.Peer ReviewedPostprint (author's final draft

Automated location of active fire perimeters in aerial infrared imaging using unsupervised edge detectors

A variety of remote sensing techniques have been applied to forest fires. However, there is at present no system capable of monitoring an active fire precisely in a totally automated manner. Spaceborne sensors show too coarse spatio-temporal resolutions and all previous studies that extracted fire properties from infrared aerial imagery incorporated manual tasks within the image processing workflow. As a contribution to this topic, this paper presents an algorithm to automatically locate the fuel burning interface of an active wildfire in georeferenced aerial thermal infrared (TIR) imagery. An unsupervised edge detector, built upon the Canny method, was accompanied by the necessary modules for the extraction of line coordinates and the location of the total burned perimeter. The system was validated in different scenarios ranging from laboratory tests to large-scale experimental burns performed under extreme weather conditions. Output accuracy was computed through three common similarity indices and proved acceptable. Computing times were below 1¿s per image on average. The produced information was used to measure the temporal evolution of the fire perimeter and automatically generate rate of spread (ROS) fields. Information products were easily exported to standard Geographic Information Systems (GIS), such as GoogleEarth and QGIS. Therefore, this work contributes towards the development of an affordable and totally automated system for operational wildfire surveillance.Peer ReviewedPostprint (author's final draft

A data-driven fire spread simulator: validation in Vall-llobrega's Fire

A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega’s Fire Oriol Rios, Mario Miguel Valero, Elsa Pastor* and Eulàlia Planas Department of Chemical Engineering, Centre for Technological Risk Studies, Universitat Politècnica de Catalunya, Barcelona, Spain While full-physics fire models continue to be unsuitable for wildfire emergency situations, the so-called operational fire spread simulators are incapable of providing accurate estimations of the macroscopic fire behavior while quickly reacting to a change of governing spread mechanisms. A promising approach to overcome these limitations are data-driven simulators, which assimilate observed data with the aim of improving their forecast with affordable computation times. Although preliminary results obtained by several data-driven simulators are promising, this scheme needs intensive validation. Detailed studies of the particular aspects related to data assimilation are essential to gain insight about the applicability of this approach to operational wildfire simulation. This paper presents the validation of the simulator presented in Rios et al. (2014b, 2016, 2018) with a large scenario of real complexity with intricate terrain. The study case corresponds to a wildfire of significant repercussions occurred in Catalonia in March 2014. We employed as reference data the event reconstruction performed by the Catalan Fire Service and validated with operational observations. Detailed information about fuel and meteorology was collected by the fire brigades and allowed reconstructing the fire development with Farsite, a widely employed simulator. Subsequently, our simulator was tested without a detailed description of the fuel and wind parameters, i.e., imitating its intended deployment conditions. It proved capable of automatically estimating them and correctly simulating the fire spread. Additionally, the effect of the assimilation window on the forecast accuracy was analyzed. These results showed that the simulator is able to correctly handle complex terrain and wind situations to successfully deliver a short-term fire-front forecast in those real and complex scenarios.Peer ReviewedPostprint (author's final draft