The present paper reports the impact of the Covid-19 pandemic on the electricity peak load and power generation in the State of Kuwait during the partial and full curfews imposed in March, April and May 2020 using historic data measured data and the predictions provided by a statistical genetic algorithm model. A quantitative assessment is made of the economic and environmental impacts caused by partial and full lockdowns. Comparison of measured peak demand for 2019 and 2020 with predicted peak demand for 2020 has: (i) enabled an accurate evaluation of residential energy consumption in the state of Kuwait at nearly 18 MWh yearly the highest energy consumption per capita in the world, (ii) shown that the imposition of the curfews to reduce the spread of COVID-19 caused a fall in the demand for electrical power of 17.6% compared with the expected demand and (iii) quantified the reduction in CO2, NOx and CO pollutant emissions produced by power plants due to less fuel being consumed. A mathematical model has been developed to predict the peak electric load in the national grid according to climatic data supplied by the Meteorological Department of Civil Aviation of Kuwait and National Control Center (NCC).

Drones/unmanned aerial vehicles (UAVs) have recently grown in popularity due to their inexpensive cost and widespread commercial use. The increased use of drones raises the possibility that they may be employed in illicit activities such as drug smuggling and terrorism. Thus, drone monitoring and automated detection are critical for protecting restricted areas or special zones from illicit drone operations. One of the most challenging difficulties in drone detection in surveillance videos is the apparent likeness of drones against varied backdrops. This paper introduces an automated image-based drone-detection system that uses an enhanced deep-learning-based object-detection algorithm known as you only look once (YOLOv5) to defend restricted territories or special zones from unauthorized drone incursions. The transfer learning to pretrain the model is employed for improving performance due to an insufficient number of samples in our dataset. Furthermore, the model can recognize the detected object in the images and mark the object’s bounding box by joining the results across the region. The experiments show outstanding results for the loss value, drone location detection, precision and recall.

Desalination is the sole proven technique that can provide the necessary fresh water in arid and semi-arid countries in sufficient quantities and meet the modern needs of a growing world population. Multi effect desalination with thermal vapour compression (MED-TVC) is one of most common applications of thermal desalination technologies. The present paper presents a comprehensive thermodynamic model of a 24 million litres per day thermal desalination plant, using specialised software packages. The proposed model was validated against a real data set for a large-scale desalination plant, and showed good agreement. The performance of the MED-TVC unit was investigated using different loads, entrained vapour, seawater temperature, salinity and number of effects in two configurations. The first configuration was the MED-TVC unit without preheating system, and the second integrated the MED-TVC unit with a preheating system. The study confirmed that the thermo-compressor and its effects are the main sources of exergy destruction in these desalination plants, at about 40% and 35% respectively. The desalination plant performance with preheating mode performs well due to high feed water temperature leading to the production of more distillate water. The seawater salinity was proportional to the fuel exergy and minimum separation work. High seawater salinity results in high exergy efficiency, which is not the case with membrane technology. The plant performance of the proposed system was enhanced by using a large number of effects due to greater utilisation of energy input and higher generation level. From an economic perspective, both indicators show that using a preheating system is more economically attractive.

The jet impingement technique is an effective method to achieve a high heat transfer rate and is widely used in industry. Enhancing the heat transfer rate even minimally will improve the performance of many engineering systems and applications. In this numerical study, the convective heat transfer process between orthogonal air jet impingement on a smooth, horizontal surface and a roughened uniformly heated flat plate is studied. The roughness element takes the form of a circular rib of square cross-section positioned at different radii around the stagnation point. At each location, the effect of the roughness element on heat transfer rate was simulated for six different heights and the optimum rib location and rib dimension determined. The average Nusselt number has been evaluated within and beyond the stagnation region to better quantify the heat transfer advantages of ribbed surfaces over smooth surfaces. The results showed both flow and heat transfer features vary significantly with rib dimension and location on the heated surface. This variation in the streamwise direction included both augmentation and decrease in heat transfer rate when compared to the baseline no-rib case. The enhancement in normalized averaged Nusselt number obtained by placing the rib at the most optimum radial location R/D = 2 was 15.6% compared to the baseline case. It was also found that the maximum average Nusselt number for each location was achieved when the rib height was close to the corresponding boundary layer thickness of the smooth surface at the same rib position.

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