Particle and gaseous emissions from commercial aircraft at each stage of the landing and takeoff cycle

A novel technique was used to measure emission factors for commonly used commercial aircraft including a range of Boeing and Airbus airframes under real world conditions. Engine exhaust emission factors for particles in terms of particle number and mass (PM2.5), along with those for CO2, and NOx were measured for over 280 individual aircraft during the various modes of landing/takeoff (LTO) cycle. Results from this study show that particle number, and NOx emission factors are dependant on aircraft engine thrust level. Minimum and maximum emissions factors for particle number, PM2.5, and NOx emissions were found to be in the range of 4.16×1015-5.42×1016 kg-1, 0.03-0.72 g.kg-1, and 3.25-37.94 g.kg-1 respectively for all measured airframes and LTO cycle modes. Number size distributions of emitted particles for the naturally diluted aircraft plumes in each mode of LTO cycle showed that particles were predominantly in the range of 4 to 100 nm in diameter in all cases. In general, size distributions exhibit similar modality during all phases of the LTO cycle. A very distinct nucleation mode was observed in all particle size distributions, except for taxiing and landing of A320 aircraft. Accumulation modes were also observed in all particle size distributions. Analysis of aircraft engine emissions during LTO cycle showed that aircraft thrust level is considerably higher during taxiing than idling suggesting that International Civil Aviation Organization (ICAO) standards need to be modified as the thrust levels for taxi and idle are considered to be the same (7% of total thrust) [1].

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Analysis and Improvements to the Green Falcon Solar Powered UAV

There has recently been a rapidly increasing interest in solar powered UAVs. With the emergence of high power density batteries, long range and low-power micro radio devices, airframes, and powerful micro-processors and motors, small/micro UAVs have become applicable in civilian applications such as remote sensing, mapping, traffic monitoring, search and rescue. The Green Falcon UAV is an innovative project from Queensland University of Technology and has been developed and tested during these past years. It comprises a wide range of subsystems to be analyses and studied such as Solar Panel Cells, Gas sensor, Aerodynamics of the wing and others. Previous test however, resulted in damage to the solar cells and some of the subsystems including motor and ESC. This report describes the repair and verification process followed to improve the efficiency of the Green Falcon UAV. The report shows some of the results obtained in previous static and flight tests as well as some of recommendations.