Ventilating of multi pane-glazed windows using wasted air of buildings is an effective technique to minimize heat loss through windows and save heating energy in cold regions. In low-scaled occupancy buildings with high WWR ratio, buildings supply a low flow rate of wasted air to windows ventilation systems, resulting in a declination in its thermal performance. Therefore, this study introduces methods of managing the utilisation of wasted air in windows ventilation to optimise the energy saving. Two methods have been implemented experimentally on a small-scaled room. The first method is a time-based division of air pump operation, an air pump ventilates multiple windows, one window at a time repetitively. The second method shares the available wasted air to multiple windows. The experimental results and mathematical heat transfer model have been employed to evaluate thermal performance of the system in different methods. The first method showed a best energy saving with a duty cycle of 50% for the air pump, and on/off operation every 10 s. An energy saving of 42.6% has been realized compared to the traditional double-glazed windows, and the heat transfer coefficient was declined from 3.82 to 2.8 W/m2 K. The second method showed an optimum thermal performance when the available flow rate of wasted air was shared with three double-glazed windows. An energy saving of 83.1% was achieved compared to the traditional double-glazed windows, and the heat transfer coefficient dropped from 3.82 to 2.36 W/m2 K.
The visual linking of a building’s occupants with the outside views is a basic property of windows. However, vision through windows is not yet a metricized factor. The previous research employs a human survey methods to assess the vision through conventional windows. The recently fabricated smart films add a changeable visual transparency feature to the windows. The varied operating transparency challenges the evaluation of vision. Therefore, surveying human preferences is no longer a feasible approach for smart windows. This paper proposes an image-processing-based approach to quantify the vision quality through smart windows. The proposed method was experimentally applied to a polymer dispersed liquid crystal (PDLC) double-glazed window. The system instantaneously determines the available contrast band of the scenes seen through the window. The system adjusts the excitation of the PDLC film to maintain a desired vision level within the determined vision band. A preferred vision ratio (PVR) is proposed to meet the requirements of occupant comfort. The impact of the PVR on vision quality, solar heat gain, and daylight performance was investigated experimentally. The results show that the system can determine the available vision comfort band during daytime considering different occupant requirements.
Polymer-dispersed liquid crystal automated quantification system for vision through polymer-dispersed liquid crystal double-glazed windows: Circuit implementation (PDLC)-windows played an essential role in providing a visual comfort for occupants in commercial buildings recently. PDLC windows adjust the visible transparency of the glazing to control the daylight accessed to internal environments. A former study proposed an algorithm to quantify the vision through the PDLC glazing in terms of image contrast. The quantification algorithm determines the minimum level of transparency that maintains a comfortable vision through the window. This study introduced the implementation of a real-time automated system that achieves the vision quantification process. Firstly, system on-chip was utilised to realise the quantification algorithm, including contrast estimation. Secondly, the contrast determination action was re-implemented using MATLAB, Cortex-A9 microcontroller, and Cyclone V field programmable gate array field programmable gate array-chip. The implemented systems were evaluated based on the latency, throughput, power consumption, and cost. K E Y W O R D SCortex-A9 microcontroller, FPGA, PDLC glazing, smart windows, system on-chip, vision quantificationThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
In the oil extraction industry, igniting the flare stacks is an essential operation. Oil sites have two kinds of flares, ground flares and flares that installed on towers. The ignition systems generate electrical sparks to burn the gases blowing out of the flares. Due to the permanent high operating temperature and the need for special thermal isolation, classical igniters have low reliability and high cost. In this work, two novel ignition systems have been implemented, the first is the robotic ignition system for ground flares, it utilises a mobile robot which moves toward the flare, avoiding the obstacles in its way and stops after detecting the gas, then it starts igniting the flare before heading to a safe point with no gas and low temperature. The second solution is the automated ignition system to light up the flares on the towers, which is a car that moves on a rail vertically, and begins igniting once it arrives at the tip of the tower, then it comes back to its starting point. As the igniters in both suggested systems are movable, so the system will be exposed to the heat generated by the flame within a very short time, this new feature increases the reliability of the igniter and reduces the complexity and the cost of the system.
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