Comparison of static and dynamic engine models on the transient performance of a passenger vehicle powertrain

This paper presents experimental and computational results obtained on the Ford Barra 190 4.0 litres I6 gasoline engine and on the Ford Falcon car equipped with this engine. Measurements of steady engine performance, fuel consumption and exhaust emissions were first collected using an automated test facility for a wide range of cam and spark timings vs. throttle position and engine speed. Simulations were performed for a significant number of measured operating points at full and part load by using a coupled Gamma Technologies GT-POWER/GT-COOL engine model for gas exchange, combustion and heat transfer. The fluid model was made up of intake and exhaust systems, oil circuit, coolant circuit and radiator cooling air circuit. The thermal model was made up of finite element components for cylinder head, cylinder, piston, valves and ports and wall thermal masses for pipes. The model was validated versus measured steady state air and fuel flow rates, cylinder pressure parameters, indicated and brake mean effective pressures, and temperature of metal, oil and coolant in selected locations. Computational results agree well with experiments, demonstrating the ability of the approach to produce fairly accurate steady state maps of BMEP and BSFC, as well as to optimize engine operation changing geometry, throttle position, cam and spark timing. Measurements of the transient performance and fuel consumption of the full vehicle were then collected over the NEDC cycle. Simulations were performed by using a coupled Gamma Technologies GT-POWER/GT-COOL/GT-DRIVE model for instantaneous engine gas exchange, combustion and heat transfer and vehicle motion. The full vehicle model is made up of transmission, driveshaft, axles, and car components and the previous engine model. The model was validated with measured fuel flow rates through the engine, engine throttle position, and engine speed and oil and coolant temperatures in selected locations. Instantaneous engine states following a time dependent demand for torque and speed differ from those obtained by interpolating steady state maps of BSFC vs. BMEP and speed. Computational results agree well with experiments, demonstrating the utility of the approach in providing a more accurate prediction of the fuel consumption over test cycles.

Timing and frequency data for passenger divesting at Melbourne domestic airport

The data covers the timing and frequency for divesting in the passenger screening process at Melbourne Airport, Victoria. Divesting is defined as the process of removing items from one's person before going through the screening process and placing them and cabin luggage onto the X-ray conveyor. This may also include removing items from bags to assist in the screening process.

Timing and frequency data for passenger divesting at Sydney domestic airport

The data covers the timing and frequency for divesting in the passenger screening process at Sydney Airport, New South Wales. Divesting is defined as the process of removing items from one's person before going through the screening process and placing them and cabin luggage onto the X-ray conveyor. This may also include removing items from bags to assist in the screening process.

Timing and frequency data for passenger divesting at Adelaide domestic airport

The data covers the timing and frequency for divesting in the passenger screening process at Adelaide Airport, South Australia. Divesting is defined as the process of removing items from one's person before going through the screening process and placing them and cabin luggage onto the X-ray conveyor. This may also include removing items from bags to assist in the screening process.

Timing and frequency data for passenger composing at Melbourne domestic airport

The data covers the timing and frequency for composing in the passenger screening process at Melbourne Airport, Victoria. Composing is defined as the process of collecting personal items after the screening process.

Timing and frequency data for passenger composing at Sydney domestic airport

The data covers the timing and frequency for composing in the passenger screening process at Sydney Airport, New South Wales. Composing is defined as the process of collecting personal items after the screening process.

Timing and frequency data for passenger composing at Adelaide domestic airport

The data covers the timing and frequency for composing in the passenger screening process at Adelaide Airport, South Australia. Composing is defined as the process of collecting personal items after the screening process.

Timing and frequency data for passenger walk through metal detection at Melbourne domestic airport

The data covers the timing and frequency for passengers walking through the metal detectors as part of the screening process at Melbourne Airport, Victoria.

Timing and frequency data for passenger walk through metal detection at Sydney domestic airport

The data covers the timing and frequency for passengers walking through the metal detectors as part of the screening process at Sydney Airport, New South Wales.

Timing and frequency data for passenger walk through metal detection at Adelaide domestic airport

The data covers the timing and frequency for passengers walking through the metal detectors as part of the screening process at Adelaide Airport, South Australia.

Passenger walking speed data for Australian domestic airports

The data covers the speed at which passengers walk through Australian domestic airport terminals, based on their group size.

Passenger monitoring in moving bus video

In this paper, we present a novel person detection system for public transport buses tackling the problem of changing illumination conditions. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modeling mechanism with a human shape model into a weighted Bayesian framework to detect passengers on-board buses. SIFT background modeling extracts local stable features on the pre-annotated background seat areas and tracks these features over time to build a global statistical background model for each seat. Since SIFT features are partially invariant to lighting, this background model can be used robustly to detect the seat occupancy status even under severe lighting changes. The human shape model further confirms the existence of a passenger when a seat is occupied. This constructs a robust passenger monitoring system which is resilient to illumination changes. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from bus cameras and the experimental results show that it is superior to state-of-art people detection systems.