Coupling CFD and visualisation to model the behaviour and effect on visibility of small particles in air

The use of computational fluid dynamics (CFD) and lighting simulation software is becoming commonplace in building design. This study looks at a novel linkage between these two tools in the visualization of droplets or particles suspended in air. CFD is used to predict the distribution of the particles, which is then processed and passed to the lighting simulation tool. The mechanism for transforming CFD contaminant concentration predictions to a form suitable for visual simulation is explained in detail and an example presented which demonstrates this linkage. The CFD-visualisation simulations described in this paper have applications in both automotive and fire safety through the modelling of fog and smoke respectively. Historically, smoke and fog effects have been rendered in images with no attempt at modelling physical reality. The novelty of the work presented in this paper is that, for the first time, an attempt is made to model both the fluid mechanics and optical physics of small particles suspended in a primary fluid

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Prediction of smoke filling in large volumes by means of data assimilation-based numerical simulations

The concept of numerical simulations for real-time Numerical Fire Forecasting is illustrated for the case of natural smoke filling of a large-scale atrium in case of fire. The numerical simulations are performed within the Inverse Zone Modelling framework. The technique consists of assimilating collected data for a certain parameter, in casu the smoke layer height, into the zone model in order to estimate an unknown of the problem ('model invariant'), mainly the fire heat release rate. A forecast in terms of evolution of smoke level and temperature can then be produced. Because zone model calculations are very fast, positive lead times of several minutes are obtained. The developed model produces reliable forecasts for the cases considered. Equally important, the robustness of the technique is illustrated: the sensitivity of the results to the 'initial guess' of the model invariants is small (i.e. the method converges easily); one model invariant is sufficient to obtain reliable predictions for smoke layer height evolution; the data assimilation window length does not affect the results significantly. The method automatically provides a different value for the plume entrainment constant, depending on the position of the fire (in the middle of the atrium or in a corner)

Smoke and Shadows: Rendering and Light Interaction of Smoke in Real-Time Rendered Virtual Environments

Realism in computer graphics depends upon digitally representing what we see in the world with careful attention to detail, which usually requires a high degree of complexity in modelling the scene. The inevitable trade-off between realism and performance means that new techniques that aim to improve the visual fidelity of a scene must do so without compromising the real-time rendering performance. We describe and discuss a simple method for realistically casting shadows from an opaque solid object through a GPU (graphics processing unit) based particle system representing natural phenomena, such as smoke