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This study evaluated the effects of the adding of flow momentum using artificial external source on the controlling of oblique shock wave of a supersonic flow in a three dimensional duct with low-aspect-ratio (based on experimental data) through a numerical investigation by employing an OpenFoam extended solver. The oblique shock occurred due to a 20° compression ramp which placed on the top of the low-aspect-ratio duct. The flow characteristics were consistent with the published data, suggesting that the numerical methodology successfully resolved the interaction between the shock-waves and flow boundary-layer. To investigate the effect of momentum adding, based on literature, three different cases with various forces exerted on the upper and lower walls of the duct were considered. The results showed that momentum adding led to a decrement in the separation region and shock strength. Moreover, adding the momentum sources on the lower wall of the duct suppressed the lambda shock that happened by the collision of separated and incident shocks and Mach stem. However, by applying the momentum source on the upper wall (upstream of the compression ramp) had more effects on the interaction between shock wave and boundary layer, as compared to the lower wall. Therefore, based on the obtained results, to achieve the best performance of momentum source, it must apply at the critical point with the greatest effect on discontinuities in the flow structure.

The present study aimed to conduct an experimental examination of heat transfer increase using tubes with extended surface of twisted fins for the condensation of vapor R-406A in horizontal tubes. ...

Power consumption of wellbore drilling in oil and gas exploitations count for 40% of total costs, hence power saving of WBM (water-based mud) by adding different concentrations of Al2O3, TiO2 and SiO2 nanoparticles is investigated here. A high-speed Taylor–Couette system (TCS) was devised to operate at speeds 0–1600 RPM to simulate power consumption of wellbore drilling using nanofluids in laminar to turbulent flow conditions. The TCS control unit uses several sensors to record current, voltage and rotational speed and Arduino microprocessors to process outputs including rheological properties and power consumption. Total power consumption of the TCS was correlated with a second-order polynomial function of rotational speed for different nanofluids, and the correlated parameters were found using an optimization technique. For the first time, energy saving of three nanofluids at four low volume concentrations 0.05, 0.1, 0.5 and 1% is investigated in the TCS simulating wellbore drilling operation. It is interesting to observe that the lower concentration nanofluids (0.05%) have better power savings. In average, for the lower concentration nanofluids (0.05%), power was saved by 39%, 30% and 26% for TiO2, Al2O3 and SiO2 WBM nanofluids, respectively. TiO2 nanofluids have better power saving at lower concentrations of 0.05 and 0.1%, while Al2O3 nanofluids have saved more power at higher concentrations of 0.5 and 1.0% compared with their counterpart nanofluids.

This research attempts to develop a systematic method to address climate changes by studying temperature-humidity patterns as the two major meteorological parameters and to moderate these effects using a commercially available window film on interior windows of an office building with double-glazing. A three-floor educational building at Australian College of Kuwait (ACK) was equipped with temperature, humidity and illuminance sensors for two similar size offices, one with 3 M Neutral 20 window films. Total readings of 50,000 entries from each sensor was recorded every 3 min by a home-designed microprocessor-based logging system for three months of June, July, and August 2019. In parallel, the ACK building was simulated in EnergyPlus and DesignBuilder software and calibrated with the experimental measurements. Histograms and a probability density function (PDF) of temperature and humidity are built by a representative rational function as a model of main climate parameters variations. By examining min-mean-max values of data, it is observed that the office with window films has increased indoor humidity and, in most instances, reduced temperature by 2–5 °C compared to double-glazed bare windows which can significantly reduce cooling loads in extreme hot-arid climates. Simulation results are presented on the energy saved and the reduced CO2 footprints by applying the solar window films in the ACK building.

Wood-plastic composites (WPCs) are becoming one of the most attractive materials in building envelopes. In addition to WPCs' architectural and design attraction, they can enhance the thermal performance of buildings by acting as insulation materials. The thermal performance of building materials requires new experimental methods that can simulate true indoor/outdoor temperatures. In this study, a simple quasi-steady heating film (QSHF) method is devised to measure the thermal conductivity of WPC samples utilizing blocks of standard materials with known thermal conductivity. QSHF device uses a 10cm×10cm×0.5mm silicon heating film controlled by a temperature regulator and several transparent acrylic square blocks of the same size with 10mm thickness as the standard materials along with various specially designed WPC samples for Kuwait. The WPC samples' top surface is considered the cold side of the system, which is open to indoor temperatures of 22 to 23 oC. The bottom layer is maintained at fixed temperatures ranging from 25 to 55 oC to simulate the outdoor temperatures of a hot subtropical desert environment like Kuwait. The thermal conductivity of several WPCs type namely FB16, FB18W, CD, and TD were obtained as 0.0912, 0.1174, 0.3453, and 0.3078 W/m.K, respectively. Experimental results for DP45-1 were not consistent at different temperatures. hence Multiphysics CFD simulation was conducted for DP45 which shows strong 2D effects. A typical building sample was also modelled in TRNSYS to compare cooling loads with and without WPC. Also, the limitations and advantages of using QSHF method are discussed.

The interaction between the shock wave and boundary layer and its induced separation is an undesirable phenomenon in the design of supersonic systems. The control of this phenomenon by active and passive methods is highly important in the design of aerodynamic systems. In the present work, different series of micro vortex generators were investigated as a passive control method (with the ability to increase the flow momentum), affecting the structure of a compressible flow at the Mach number of 2.05 in a low-aspect-ratio duct. In this regard, the finite volume method was used in the OpenFoam package. The obtained results were first validated against experimental data, and then four different vortex generators with a height of 10 μ m were investigated. The results revealed that the use of vortex generators caused a reduction in the shock wave strength and energy loss across the flow. Furthermore, applying a vortex micro-generator with the outer body of national advisory committee for aeronautics 4412 airfoil led to a significant decrease in the strength of shock waves. Also, a vortex generator could shift the shock wave occurrence location to the downstream.

The present paper aims at the optimization and exergy and thermoeconomic analyses of a combined cooling, heat, and power generation system equipped with a thermal energy storage for the use in a residential complex with a gas-fueled internal combustion engine as the prime mover. The system is optimized using the direct search method by minimizing annual cost in two cases of using/not using a thermal energy storage. In case of the use of a thermal energy storage, an engine with a capacity of 2 MW and an operating time of 4000 h are found to be optimal, but when a thermal energy storage is included, an engine with a capacity of 2 MW and an operating time of 5268 hours and a thermal energy storage with a capacity of 18.93 m3 are found to be the optimal options. Both systems are evaluated assuming selling/not selling surplus power to the public power grid. The best case for the performance of the system is to use a thermal energy storage and to sell surplus electricity to the grid. In this case versus the case of excluding the thermal energy storage, primary energy consumption, CO2 emission, operating cost of the system, and power purchase from the public grid are decreased by 20.8, 19.5, 14.3 and 17%, respectively while return on capital is increased by 3.1% resulting in 10.7% higher annual cost of the system.

The present paper simulates and dynamically analyzes a combined heat and power (CHP) system driven by a gas-fueled internal combustion engine. The CHP system uses a heat exchanger to convert the heat loss of the engine to meet heat demand and uses a generator for power generation. Then, the impact of the use or non-use thermal energy storage (TES) is examined on power and heat generation. Given the different demands of different seasons, two smart control strategies are developed concerning time to attain higher efficiency in different seasons. A TES is included in the control strategy for cold and hot seasons, and analyses are performed for a typical week. The gas engine of the CHP system uses time and temperature variables simultaneously to operate smartly by two strategies for a year instead of one single strategy. The results show that when a control strategy with variable partial loads based on temperature and time is employed, the efficiency of the CHP system’s gas engine is improved. Using the results of the new and smart control strategy, the CHP system exhibits an efficiency of 84.2% in the hot season and an efficiency of 87.0% in the cold seasons for a typical week.

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study’s main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

Extension of SIR type models has been reported in a number of publications in the mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce the SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm. Four sets of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible are used from the start of the outbreak until 23rd June 2020 to fit with SEIR-PAD model results. Results for trends of COVID-19 in GCC countries indicate that the disease may be terminated after 200 to 300 days from the start of the outbreak depends on current measures and policies. SEIR-PAD model provides a robust and strong tool to predict trends of COVID-19 for better management and/or foreseeing effects of certain enforcing laws by governments, health organizations or policy makers.

Modeling and Sensitivity Analysis of Coronavirus Disease (COVID-19) Outbreak Prediction: The susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

SIR model is one of the simplest methods used in prediction of endemic/pandemic outbreaks. We examined SIRD model for development of COVID-19 in Kuwait which was started on 24 February 2020 by 5 patients in Kuwait. This paper investigates sensitivity of SIRD model for development of COVID-19 in Kuwait based on duration of progressed days of data. For Kuwait, we have fitted SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed sensitivity of the model with number of days of data. The parameters of SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of SIRD model indicates that after 40 days the peak infectious day can be adequately predicted; althogh, error percentage from sensetivity analysis indicates that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19 and more precise methods are needed to tackle nonlinear dynamics of a pandemic.

SIR model is one of the simplest methods used in prediction of endemic/pandemic outbreaks. We examined SIRD model for development of COVID-19 in Kuwait which was started on 24 February 2020 by 5 patients in Kuwait. This paper investigates sensitivity of SIRD model for development of COVID-19 in Kuwait based on duration of progressed days of data. For Kuwait, we have fitted SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed sensitivity of the model with number of days of data. The parameters of SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of SIRD model indicates that after 40 days the peak infectious day can be adequately predicted; althogh, error percentage from sensetivity analysis indicates that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19 and more precise methods are needed to tackle nonlinear dynamics of a pandemic.

Susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

On 30 July 2020, a total number of 301,530 diagnosed COVID-19 cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 infections, which makes its data difficult to analyze. The present study's main contribution is to forecast the near-future of COVID-19 trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21,2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 in Iran for NPI decisions. Between 180,000 to 250,000 infected cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 in Iran. There is currently no analytical method to project more waves of COVID-19 beyond Wave II.

This study examines the performance of solar window films installed on double-glazed windows of typical offices in actual working conditions in a hot and dry climate. Passive thin films stuck interiorly on double-glazed windows were used to affect various solaroptical impacts including energy saving, UV reduction, and increased thermal comfort. Real-time measurements of environmental parameters, including temperature, humidity, and luminous, were performed continuously in 5-minute intervals over three summer months of June, July, and August 2019 in Kuwait and data were stored automatically through a WiFi system. Arduino-type microprocessors and various temperature, humidity, Lux, and UV sensors were assembled and programmed to transfer and store data in real-time. Temporal and spectral analyses were conducted to compare the effects of the window films on these environmental parameters. It is evident that windows film has good potential to decrease interior temperature and its variability, to save energy and reduce CO 2 footprints, to save water by preserving interior humidity to level of human comfort, to restrict the solar UV rays to minimum level, and to reduce visual impacts of high solar luminous in hot-dry climates. The benefits and drawbacks of the selected windows film are discussed.

COVID-19 infectious started on 24 February 2020 with 5 patients returning to Kuwait. The ministry of health (MOH) has reported a total of 26,192 patients with 10,156 recovered, 15,831 under treatment, 205 deceased, 206 critical, and 23 quarantined in Kuwait on 30 May 2020. Accurate prediction of the number of expected infected patients, patients under treatment, patients in critical condition, and death will assist health authorities for better planning and the government policymakers a better approach to reduce the number of susceptible people to COVID-19. In this study, a modified SIR model is used to determine COVID-19 dynamics in Kuwait. COVID-19 data for 97 days consist of infectious, recovered, and deceased cases are used to study the SIRD model and to obtain the re-production number and the total susceptible (Sus) population. The accuracy of the fitted model is assessed using the coefficient of determination (R2). The re-production of the total susceptible (predicted) population of 123,102 is obtained to assess the dynamics of COVID-19 in Kuwait. It is predicted that the peak of COVID-19 infectious will be around 23 June 2020 with total infected cases of 56,533. However, a maximum of 26,039 people in the need of hospitals may be accelerated on 10 June 2020 and will quickly drop on 2 July 2020 to only 377 people. The total deceased cases will be 1,169 on this date; although, the death tolls may continue to the total value of 2,667 by the end of the pandemic.

This work examines the solar performance of double-glazed windows in four offices in ACK buildings with and without solar window film in actual working conditions. Window films are passive thin films stick to the interior or exterior of windows to effect various solar-optical impacts including energy saving, UV reduction, and increased thermal comfort. For this study, the 3M Neutral 20 and 70 are selected which are made of multilayer nanofilms of 220 SAN25/THV 95-nm thick reflective layers. Real-time measurements of temperature, humidity, and luminous were conducted in 5 min intervals over the months of June, July, and August 2019 and data stored through WiFi system in iClouds. Arduino-type microprocessors and various temperature-humidity, and LUX sensors were assembled and programmed to transfer and store data in real time. Exploring and analyzing the data collected for four offices with and without window films in summer of Kuwait, it is evident that window film has good potential to save water by preserving interior humidity to the level of human comfort, decrease interior temperature to save energy and reduce CO2 footprints, to restrict the solar UV rays to minimum level, and to reduce visual impacts of high solar luminous. The benefits and drawbacks of the selected window films are discussed here for the weather condition of Kuwait.

The susceptible-infectious-recovered-deceased (SIRD) model is an essential model for outbreak prediction. This paper evaluates the performance of the SIRD model for the outbreak of COVID-19 in Kuwait, which initiated on 24 February 2020 by five patients in Kuwait. This paper investigates the sensitivity of the SIRD model for the development of COVID-19 in Kuwait based on the duration of the progressed days of data. For Kuwait, we have fitted the SIRD model to COVID-19 data for 20, 40, 60, 80, 100, and 116 days of data and assessed the sensitivity of the model with the number of days of data. The parameters of the SIRD model are obtained using an optimization algorithm (lsqcurvefit) in MATLAB. The total population of 50,000 is equally applied for all Kuwait time intervals. Results of the SIRD model indicate that after 40 days, the peak infectious day can be adequately predicted. Although error percentage from sensitivity analysis suggests that different exposed population sizes are not correctly predicted. SIRD type models are too simple to robustly capture all features of COVID-19, and more precise methods are needed to tackle the correct trends of a pandemic.

In the oil and gas industry, application of nanoparticles is becoming a more common practice. There are a number of researches on lubricating effects of nanoparticles in different applications; therefore, some fundamental studies are eminent on mechanisms and functions of skin friction reduction of nanoparticles in pipe flows. In addition, studies on coagulation and agglomeration of nanoparticle precipitation on the pipe surfaces are of practical interest. In this research, development of a miniature Reynolds apparatus is sought to study different flow regimes from laminar to turbulent. The first goal is to measure rheological properties of Newtonian and non-Newtonian nanofluids at laminar flows. The miniature Reynolds apparatus should continuously circulate 1 L or lower amounts of nanofluid and measure pressure drop and flow rates for at least three Reynolds numbers. This paper summarizes the design procedure and provides CFD simulations for the designed system. Analysis of the measured data will provide useful information on skin friction reduction and precipitation of nanoparticles on pipe surfaces.

In this study, to simulate the pollution treatment as a separated phase a 3D incompressible turbulence model under isothermal conditions was considered. The hybrid RANS-LES method with a sub-grid scale(SGS) technique within the framework of two-phase flow was used to study pollution distribution in urban street canyons. An area source of pollutant with a constant concentration, located in the ground of the street canyon, was used to simulate vehicular emissions. The near-wall regions were simulated using the RANS(k-e) turbulence model and the rest of the domain was simulated with the LES model. To measure the effect of building height on pollutant dispersion, the parameter AR(Aspect Ratio) was calculated. Also to determine the effect of street width, a new parameter SWV(Street Width Value) was introduced. Governing equations were discretized using the well-known finite volume method on a staggered grid. To increase the accuracy, the second-order Van Leer Method(VLM) was applied and the Volume of Fluid(VOF) method was employed for the description of the interfaces. The Iterative line-by-line TDMA solution procedure and the Strongly Implicit Procedure(SIP) was used for the Pressure Correction Equation. A fine comparison of the results revealed that by increasing SWV, the number of vertical vortices in the canyon streets generally decreased and the vortices grew larger. In addition, increasing AR caused a rise in the pollution volume fraction in the first canyon street. The current numerical results indicate that the AR and SWV influence the flow field and volume fraction of phases inside an isolated street canyon, significantly.