Associação para o Desenvolvimento da Aerodinâmica Industrial

Every year worldwide some extraordinary wildfires occur, overwhelming suppression capabilities, causing substantial damages, and often resulting in fatalities. Given their increasing frequency, there is a debate about how to address these wildfires with significant social impacts, but there is no agreement upon terminology to describe them. The concept of extreme wildfire event (EWE) has emerged to bring some coherence on this kind of events. It is increasingly used, often as a synonym of other terms related to wildfires of high intensity and size, but its definition remains elusive. The goal of this paper is to go beyond drawing on distinct disciplinary perspectives to develop a holistic view of EWE as a social-ecological phenomenon. Based on literature review and using a transdisciplinary approach, this paper proposes a definition of EWE as a process and an outcome. Considering the lack of a consistent “scale of gravity” to leverage extreme wildfire events such as in natural hazards (e.g., tornados, hurricanes and earthquakes) we present a proposal of wildfire classification with seven categories based on measurable fire spread and behavior parameters and suppression difficulty. The categories 5 to 7 are labeled as EWE.

Wildfire fatalities remain a significant problem in Mediterranean Europe. Although there is a strong inter-annual variability with regard to their number, repeated tragic accidents remind us of this grim occurrence, despite the increasing firefighting capacity aimed to improve human safety. In this paper, we present an analysis of the 865 fatalities caused by wildfires in the 1945–2016 period. Data originating from national databases were merged, contextual and weather factors related to the accidents that caused these deaths were documented and analysed to explore probable relationships with the number and type of fatalities. Results show a major rise of fatalities in late 1970s in the four regions of Greece, Sardinia (Italy), Spain and Portugal. Fatalities present a strong seasonality in summer months, as expected. Overall, Spain has the highest absolute numbers of fatalities; however, normalisations by population, and burned and forest area show that annual number of fatalities is comparatively smaller. Certain other factors showed correlation with mortality. Civilians were the most affected group in Greece (65%) and Sardinia (58%), but not in Spain and Portugal. Findings indicate that an in-depth revision of fire-management policies and practices is required, with emphasis on prevention planning in urban areas, and better training of the firefighting resources.

The vectoring of wind and slope effects on a flame front is considered. Mathematical methods for vectoring are presented and compared to results of laboratory experiments. The concept of multiple standard fire spread directions is presented. The experimental laboratory study, included effects of variable wind velocity and direction on point source flame fronts on a 30° inclined fuel bed.

The very quick fire spread that occurs in some forest fires has been the cause of many fatalities in the past among fire fighters throughout the world. A theoretical model describing the convective interaction between the fire front and the surrounding air is proposed to explain the blowup phenomenon that is observed in nature. This model is based on a set of laboratory experiments of fire blowup in canyons that was used to validate it. The model predicts quite well the general fire behavior observed during two fatal accidents that occurred in the United States and one in Portugal.

A model was developed to predict the ignition of forest crown fuels above a surface fire based on heat transfer theory. The crown fuel ignition model (hereafter referred to as CFIM) is based on first principles, integrating: (i) the characteristics of the energy source as defined by surface fire flame front properties; (ii) buoyant plume dynamics; (iii) heat sink as described by the crown fuel particle characteristics; and (iv) energy transfer (gain and losses) to the crown fuels. Fuel particle temperature increase is determined through an energy balance relating heat absorption to fuel particle temperature. The final model output is the temperature of the crown fuel particles, which upon reaching ignition temperature are assumed to ignite. CFIM predicts the ignition of crown fuels but does not determine the onset of crown fire spread per se. The coupling of the CFIM with models determining the rate of propagation of crown fires allows for the prediction of the potential for sustained crowning. CFIM has the potential to be implemented in fire management decision support systems.

Canyons or ridges are associated with a large number of fatal accidents produced during forest fires all over the world. A contribution to the understanding of fire behaviour in these terrain conditions is given in this paper. The basic geometrical parameters of the canyon configuration are described. An analytical model assuming elliptical growth of point ignition fires and constant values of rate of spread is proposed. A non-dimensional formulation to transfer results from analytical, numerical, laboratory or field simulations to other situations is proposed. An experimental study at laboratory scale on a special test rig is described. A wide set of canyon configurations were covered in the experimental program. In spite of the relatively small scale of the experiments they were able to put in evidence some of the main features found in fires spreading in this type of terrain. They show that in practically all cases the rate of spread of the fire front is non-constant. On the contrary, the fire has a dynamic behaviour and its properties depend not only on the canyon geometry but on the history of fire development as well. The convection induced by the fire is enhanced by terrain curvature and the fire accelerates causing the well-known blow-up that is associated with canyon fires. The rate of spread of the head fire increases continuously even in the absence of wind or any other special feature or change of boundary conditions that are sometimes invoked to justify such fire behaviour. The results of the present study confirm the predictions of a previous numerical study of the flow and fire spread in canyons that showed the important feedback effect of the fire on the atmospheric flow and how this affects fire behaviour in canyons. Results from a field experiment carried out in a canyon-shaped plot covered by tall shrubs were used to validate the laboratory scale experiments. Case studies related to fatal accidents that occurred in canyon-shaped configurations are analysed and recommendations to deal with this problem are made. It is shown that these accidents may occur even in the absence of special fuel or atmospheric conditions as they are intrinsically related to terrain configuration.

Forest fire behaviour analysis and prediction is based on the assumption that for a given set of boundary conditions a steady-state of fire propagation exists with a well-defined rate of spread. The evolution of a fire front for linear and point ignited fires is analysed and it is shown that, even in nominally uniform and permanent conditions, the rate of spread of the head fire does not remain constant in the general case of slope- and wind-driven fires due to joint convection and radiation effects. The basic case of a linear fire front without slope and without wind is one of the few cases for which the rate of spread is well defined and remains constant. if there is slope or wind in point ignition fires, the rate of spread of the head fire tends to increase while for linear ignition fires the contrary happens. It is shown that convective effects induced by the fire for steep slope terrain can produce the so-called ‘blow-up’ effect even in the absence of any other special atmospheric conditions. Therefore the definition of rate of spread of a fire and its evaluation from laboratory and field experiments is strongly questioned.

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

Smoke from forest fires contains significant amounts of gaseous and particulate pollutants. Firefighters exposed to wildland fire smoke can suffer from several acute and chronic adverse health effects. Consequently, exposure data are of vital importance for the establishment of cause/effect relationships between exposure to smoke and firefighter health effects. The aims of this study were to (1) characterize the relationship between wildland smoke exposure and medical parameters and (2) identify health effects pertinent to wildland forest fire smoke exposure. In this study, firefighter exposure levels of carbon monoxide (CO), nitrogen dioxide (NO₂), and volatile organic compounds (VOC) were measured in wildfires during three fire seasons in Portugal. Personal monitoring devices were used to measure exposure. Firefighters were also tested for exhaled nitric oxide (eNO) and CO before and after their firefighting activities. Data indicated that exposure levels during firefighting activities were beyond limits recommended by the Occupational Exposure Standard (OES) values. Medical tests conducted on the firefighters also indicated a considerable effect on measured medical parameters, with a significant increase in CO and decrease in NO in exhaled air of majority of the firefighters.

Accurate and spatially explicit information on forest fuels becomes essential to designing an integrated fire risk management strategy, as fuel characteristics are critical for fire danger estimation, fire propagation, and emissions modelling, among other aspects. This paper proposes a new European fuel classification system that can be used for different spatial scales and purposes (propagation, behaviour, and emissions). The proposed classification system is hierarchical and encompasses a total of 85 fuel types, grouped into six main fuel categories (forest, shrubland, grassland, cropland, wet and peat/semi-peat land, and urban), plus a nonfuel category. For the forest cover, fuel types include two vertical strata, overstorey and understorey, to account for both surface and canopy fuels. In addition, this paper presents the methods to map fuel types at the European scale, including the first-level of the classification system. Land cover, biogeographic datasets, and bioclimatic modelling were used. The final map, which is publicly available (https://doi.org/10.21950/YABYCN; Aragoneses et al., 2022a), included 20 fuel categories at 1 km spatial resolution. A first assessment of this map was performed using field information obtained from LUCAS (Land Use and Coverage Area frame Survey), complemented with high-resolution data. This validation exercise provided an overall accuracy of 88 % for the main fuel types and 81 % for all mapped fuel types. To facilitate the use of this fuel dataset in fire behaviour modelling, an assignment of fuel parameters to each fuel type was performed by developing a crosswalk to the standard fuel models defined by Scott and Burgan (2005; FBFMs – Fire Behavior Fuel Models), considering European climate diversity. This work has been developed within the framework of the FirEUrisk project, which aims to create a European-integrated strategy for fire danger assessment, reduction, and adaptation.

When it comes to forest management and protection, knowledge is key. Therefore, forest mapping is crucial to obtain the required knowledge towards profitable resource exploitation and increased resilience against wildfires. Within this context, this paper presents a literature review on tree classification and segmentation using data acquired by unmanned aerial vehicles, with special focus on the last decade (2013–2023). The latest research trends in this field are presented and analyzed in two main vectors, namely: (1) data, where used sensors and data structures are resumed; and (2) methods, where remote sensing and data analysis methods are described, with particular focus on machine learning approaches. The study and review methodology filtered 979 papers, which were then screened, resulting in the 144 works included in this paper. These are systematically analyzed and organized by year, keywords, purpose, sensors, and methods used, easily allowing the readers to have a wide, but at the same time detailed, view of the latest trends in automatic tree classification and segmentation using unmanned aerial vehicles. This review shows that image processing and machine learning techniques applied to forestry and segmentation and classification tasks are focused on improving the accuracy and interpretability of the results by using multi-modal data, 3D information, and AI methods. Most works use RGB or multispectral cameras, or LiDAR scanners, individually. Classification is mostly carried out using supervised methods, while segmentation mostly uses unsupervised machine learning techniques.

Wildfires are complex natural events that cause significant environmental and property damage, as well as human losses, every year throughout the world. In order to aid in their management and mitigate their impact, efforts have been directed towards developing decision support systems that can predict wildfire propagation. Most of the available tools for wildfire spread prediction are based on the Rothermel model that, apart from being relatively complex and computing demanding, depends on several input parameters concerning the local fuels, wind or topography, which are difficult to obtain with a minimum resolution and degree of accuracy. These factors are leading causes for the deviations between the predicted fire propagation and the real fire propagation. In this sense, this paper conducts a literature review on optimization methodologies for wildfire spread prediction based on the use of evolutionary algorithms for input parameter set calibration. In the present literature review, it was observed that the current literature on wildfire spread prediction calibration is mostly focused on methodologies based on genetic algorithms (GAs). Inline with this trend, this paper presents an application of genetic algorithms for the calibration of a set of the Rothermel model’s input parameters, namely: surface-area-to-volume ratio, fuel bed depth, fuel moisture, and midflame wind speed. The GA was validated on 37 real datasets obtained through experimental prescribed fires in controlled conditions.

When two fires approach each other, convective and radiative heat transfer processes are greatly enhanced. The interaction between two linear fire fronts making an angle θoi between them is of particular interest as it produces a very rapid advance of their intersection point with intense radiation and convection activity in the space between the fire lines. This fire is designated here as a ‘jump fire’ for when the value of θoi is small, the intersection point of the fire lines can reach unusually high rate of spread values that decrease afterwards in the course of time. A very simple analytical model based on the concept of energy concentration between the fire lines is proposed to explain this behaviour, which in large-scale fires can be of great concern to personnel and property safety. Experimental tests performed at laboratory scale on a horizontal fuel bed confirmed the basic assumptions of the model and provide a framework to extend the present analysis to more general conditions, namely to explain the behaviour of real fires. Given the rapid changes in fire behaviour, ‘jump fires’ can be considered as a form of extreme fire behaviour.

Junction fires, which involve the merging of two linear fire fronts intersecting at a small angle, are associated with very intense fire behaviour. The dynamic displacement of the intersection point of the two lines and the flow along the symmetry plane of the fire are analysed for symmetric boundary conditions. It is observed that the velocity of displacement of this point increases very rapidly owing to strong convective effects created by the fire that are similar to those of an eruptive fire. The change of fire geometry and of its associated flow gradually blocks the rate of spread increase and creates a strong deceleration of the fire, which ends up behaving like a linear fire front. Results from laboratory and field-scale experiments, using various fuel beds and slope angles and from a large-scale fire show that the processes are similar at a wide range of scales with little dependence on the initial boundary conditions. Numerical simulation of the heat flux from two flame surfaces to an element of the fuel bed show that radiation can be considered as the main mechanism of fire spread only during the deceleration phase of the fire.

The production of aluminum-carbon steel and aluminum-stainless steel clads is challenging, and explosive welding is one of the most suitable processes to achieve them. The present work aims to investigate the coupled effect of two strategies for optimizing the production of these clads by explosive welding: the use of a low-density interlayer and the use of a low-density and low-detonation velocity explosive mixture. A broad range of techniques was used to characterize the microstructural and the mechanical properties of the welds, specifically, optical microscopy, scanning electron microscopy, energy dispersive spectroscopy, electron backscatter diffraction, microhardness and tensile-shear testing with digital image correlation analysis. Although aluminum-carbon steel and aluminum-stainless steel have different weldabilities, clads with sound microstructure and good mechanical behavior were achieved for both combinations. These results were associated with the low values of collision point and impact velocities provided by the tested explosive mixture, which made the weldability difference between these combinations less significant. The successful testing of this explosive mixture indicates that it is suitable to be used for welding very thin flyers and/or dissimilar materials that easily form intermetallic phases.

The global interest in nearly zero-energy buildings (NZEBs) has led to their establishment as mandatory building objectives in Europe for all new constructions starting in 2021. The principles outlined in the Energy Performance of Building Directive (EPBD) emphasize the significance of reducing energy demand through various energy efficiency measures to achieve NZEB status. Among these measures, the utilization of high-performance glazing systems plays a crucial role in ensuring natural light, ventilation, favorable solar gain, aesthetics, and positive psychological effects in buildings, while maintaining high energy performance and thermal comfort without burdening the budget or harming the environment. The use of increasingly larger glazing areas makes this topic of great relevance. Nevertheless, numerous studies frequently overlook certain crucial aspects of glazing systems in their assessments. This review study aims to assess different glazing solutions based on four critical perspectives called “EThCE”: Energy performance, thermal comfort, cost-effectiveness, and environmental impact, considering their interrelationships. Furthermore, the importance of adopting a comprehensive approach for selecting the optimal glazing solution for NZEBs is discussed. Additionally, the relationship between glazing systems and climate change is taken into account. Ultimately, the authors propose a comprehensive approach, including all the influential factors, to assist designers and homeowners in making informed decisions regarding glazing system selection for new NZEBs or NZEB retrofits in different situations.

Abstract This paper describes a user‐friendly software for the calculation of general piping system networks composed of virtually any parallel and series pipe arrangement. Solution of the network is made with recourse to the iterative method of Hardy Cross. Solution is provided for pressure and flow‐rate in each branch. Dimensioning problems, where pump characteristics or a pipe diameter are sought for achieving a pre‐specified flow‐rate condition, may also be tackled. © 2004 Wiley‐Liss, Inc. © 2004 Wiley Periodicals, Inc. Comput Appl Eng Educ 12: 117–125, 2004; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20006

The present study seeks to select the most important articles and reviews from the Web of Science database that approached alternative fuels towards the decarbonization of the maritime sector. Through a systematic review methodology, a combination of keywords and manual refining found a contribution of 103 works worldwide, the European continent accounting for 57% of all publications. Twenty-two types of fuels were cited by the authors, liquefied natural gas (LNG), hydrogen, and biodiesel contributing to 49% of the mentions. Greenhouse gases, sulfur oxide, nitrogen oxide, and particulate matter reductions are some of the main advantages of cleaner sources if used by the vessels. Nevertheless, there is a lack of practical research on new standards, engine performance, cost, and regulations from the academy to direct more stakeholders towards low carbon intensity in the shipping sector.

The initiation of crown fires in conifer stands was modelled through logistic regression analysis by considering as independent variables a basic physical descriptor of the fuel complex structure and selected components of the Canadian Forest Fire Weather Index (FWI) System. The study was based on a fire behaviour research database consisting of 63 experimental fires covering a relatively wide range of burning conditions and fuel type characteristics. Four models were built with decreasing input needs. Significant predictors of crown fire initiation were: canopy base height, wind speed measured at a height of 10 m in the open, and four components of the FWI System (i.e., Fine Fuel Moisture Code, Drought Code, Initial Spread Index and Buildup Index). The models predicted correctly the type of fire (i.e., surface or crown) between 90% and 66% of the time. The C index, a statistical measure, varied from 0.94 to 0.71, revealing good concordance between predicted probabilities and observed events. A comparison between the logistic models and Canadian Forest Fire Behaviour Prediction System models did not show any conclusive differences. The results of a limited evaluation involving two independent experimental fire data sets for distinctly different fuel complexes were encouraging. The logistic models built may have applicability in fire management decision support systems, allowing for the estimation of the probability of crown fire initiation at small and large spatial scales from commonly available fire environment and fire danger rating information. The relationships presented are considered valid for free-burning fires on level terrain in coniferous forests that have reached a pseudo steady-state and are not deemed applicable to dead conifer forests (i.e., insect-killed stands). Key words: Canadian Forest Fire Danger Rating System, crown fire initiation, fire behaviour, fire danger indices, logistic regression

A crown fuel ignition model (CFIM) describing the temperature rise and subsequent ignition of the lower portion of tree crowns above a spreading surface fire was evaluated through a sensitivity analysis, comparison against other models, and testing against experimental fire data. Results indicate that the primary factors influencing crown fuel ignition are those determining the depth of the surface fire burning zone and the vertical distance between the ground/surface fuel strata and the lower boundary of the crown fuel layer. Intrinsic crown fuel properties such as fuel particle surface area-to-volume ratio and foliar moisture content were found to have a minor influence on the process of crown fuel ignition. Comparison of model predictions against data collected in high-intensity experimental fires and predictions from other models gave encouraging results relative to the validity of the model system.