Use of Distributed Temperature Sensing Technology to Characterize Fire Behavior

We evaluated the potential of a fiber optic cable connected to distributed temperature sensing (DTS) technology to withstand wildland fire conditions and quantify fire behavior parameters. We used a custom-made ‘fire cable’ consisting of three optical fibers coated with three different materials—acrylate, copper and polyimide. The 150-m cable was deployed in grasslands and burned in three prescribed fires. The DTS system recorded fire cable output every three seconds and integrated temperatures every 50.6 cm. Results indicated the fire cable was physically capable of withstanding repeated rugged use. Fiber coating materials withstood temperatures up to 422 °C. Changes in fiber attenuation following fire were near zero (−0.81 to 0.12 dB/km) indicating essentially no change in light gain or loss as a function of distance or fire intensity over the length of the fire cable. Results indicated fire cable and DTS technology have potential to quantify fire environment parameters such as heat duration and rate of spread but additional experimentation and analysis are required to determine efficacy and response times. This study adds understanding of DTS and fire cable technology as a potential new method for characterizing fire behavior parameters at greater temporal and spatial scales

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

The Fire and Smoke Model Evaluation Experiment—A Plan for Integrated, Large Fire–Atmosphere Field Campaigns

The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models

A normative approach to multi-agent systems for intelligent buildings

Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to provide high-quality building services, and reduce the running cost of the building. However, most BMS are functionality-oriented and do not consider user personalization. The aim of this research is to capture and represent building management rules using organizational semiotics methods. We implement Semantic Analysis, which determines semantic units in building management and their relationship patterns of behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these management actions occur. Finally, we propose a multi-agent framework for norm based building management. This framework contributes to the design domain of intelligent building management system by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building

Effects of variable resistance on smart structures of cubic reconnaissance satellites in various thermal and frequency shocking conditions

Piezoelectric materials are widely used as smart structures in cubic reconnaissance satellites because of their sensing, actuating, and energy-harvesting abilities. In this study, an analytical model is developed in specific mechanical thermal shocking conditions. A special circuit and apparatus is designed for experimentation on the basis of the inverse piezoelectric effect. An equivalent circuit method is used to establish the relationship between the resistance and peak-to-peak voltage of lead zirconate titanate used as smart materials for cubic reconnaissance satellites. Various frequencies and resistance were applied in different mechanical thermal shocking conditions. Moreover, numerical simulations are conducted in various mechanical loading conditions to determine the accumulative effect. The model provides a novel mechanism to characterize the smart structures in cubic reconnaissance satellites. A rise in temperature increases peak-to-peak voltage; a rise in frequency decreases peak-to-peak voltage; and intensified resistance decreases peak-to-peak voltage. Based on experimentation and simulation, the optimum resistance is predicted for the various frequencies and temperatures. The various conditions may correspond to the different applications of smart structures for cubic reconnaissance satellites. The analytical calculations are in good agreement with experimental and numerical calculations. © 2017, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Fluid Phase Separation (FPS) experiment for flight on a space shuttle Get Away Special (GAS) canister

The separation of fluid phases in microgravity environments is of importance to environmental control and life support systems (ECLSS) and materials processing in space. A successful fluid phase separation experiment will demonstrate a proof of concept for the separation technique and add to the knowledge base of material behavior. The phase separation experiment will contain a premixed fluid which will be exposed to a microgravity environment. After the phase separation of the compound has occurred, small samples of each of the species will be taken for analysis on the Earth. By correlating the time of separation and the temperature history of the fluid, it will be possible to characterize the process. The experiment has been integrated into space available on a manifested Get Away Special (GAS) experiment, CONCAP 2, part of the Consortium for Materials Complex Autonomous Payload (CAP) Program, scheduled for STS-42. The design and the production of a fluid phase separation experiment for rapid implementation at low cost is presented

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial