• Energy Research
• Closed Access close
• engineering and technology close
• QA close
• BH close

Here we investigate the irreversibility aspects in magnetohydrodynamics flow of viscous nanofluid by a variable thicked surface. Viscous dissipation, Joule heating and heat generation/absorption in energy expression is considered. Behavior of Brownian diffusion and thermophoresis are also discussed. The nanoliquid is considered electrical conducting under the behavior of magnetic field exerted transverse to the sheet. Using similarity variables the nonlinear PDEs are altered to ordinary one. The obtained system are computed through Newton built in shooting method. Significant behavior of various involving parameters on entropy generation rate, velocity, concentration, Bejan number and temperature are examined. Gradient of velocity and heat transfer rate are numerically computed through tabulated form. Velocity field is augmented versus power index (n). Temperature and velocity profiles have opposite characteristics for larger approximation of Hartmann number. Concentration profile has similar impact against Brownian diffusion variable and Lewis number. Entropy optimization is boost up via rising values of Brinkman and Hartmann numbers. Bejan number is declined for increasing value of Hartmann number.

Here we investigate the irreversibility aspects in magnetohydrodynamics flow of viscous nanofluid by a variable thicked surface. Viscous dissipation, Joule heating and heat generation/absorption in energy expression is considered. Behavior of Brownian diffusion and thermophoresis are also discussed. The nanoliquid is considered electrical conducting under the behavior of magnetic field exerted transverse to the sheet. Using similarity variables the nonlinear PDEs are altered to ordinary one. The obtained system are computed through Newton built in shooting method. Significant behavior of various involving parameters on entropy generation rate, velocity, concentration, Bejan number and temperature are examined. Gradient of velocity and heat transfer rate are numerically computed through tabulated form. Velocity field is augmented versus power index (n). Temperature and velocity profiles have opposite characteristics for larger approximation of Hartmann number. Concentration profile has similar impact against Brownian diffusion variable and Lewis number. Entropy optimization is boost up via rising values of Brinkman and Hartmann numbers. Bejan number is declined for increasing value of Hartmann number.

Abstract A deep plan layout and internal partitioning and resistances along the wind path in interior spaces make single-sided ventilation the only strategy possible for residential units. In this work, the potential of single-sided ventilation through the introduction of a balcony was investigated. The design of the wing wall at the balcony was investigated to determine its effect on the performance of single-sided indoor ventilation. The methodology used in was computer simulation (computational fluid dynamics or CFD) validated by data on an existing wind tunnel study and the measurements from a selected case study. CFD simulation was conducted on a simplified model of a medium-rise and low-cost residential building in Johor Bahru, Malaysia. Upon validation, the four wing wall depths and three wing wall angles were applied to the balcony, and each was evaluated numerically. Results showed that the provision of wing wall in the balcony could increase the indoor air velocity and airflow rate and improve the airflow distribution in the room if appropriately designed. The indoor ventilation performance was improved by increasing the wing wall size up to a certain depth and deviation of 22.5°. The provision of a balcony with wing walls can be a facade design alternative to improve indoor ventilation and possibly reduce energy consumption in buildings.

Abstract A deep plan layout and internal partitioning and resistances along the wind path in interior spaces make single-sided ventilation the only strategy possible for residential units. In this work, the potential of single-sided ventilation through the introduction of a balcony was investigated. The design of the wing wall at the balcony was investigated to determine its effect on the performance of single-sided indoor ventilation. The methodology used in was computer simulation (computational fluid dynamics or CFD) validated by data on an existing wind tunnel study and the measurements from a selected case study. CFD simulation was conducted on a simplified model of a medium-rise and low-cost residential building in Johor Bahru, Malaysia. Upon validation, the four wing wall depths and three wing wall angles were applied to the balcony, and each was evaluated numerically. Results showed that the provision of wing wall in the balcony could increase the indoor air velocity and airflow rate and improve the airflow distribution in the room if appropriately designed. The indoor ventilation performance was improved by increasing the wing wall size up to a certain depth and deviation of 22.5°. The provision of a balcony with wing walls can be a facade design alternative to improve indoor ventilation and possibly reduce energy consumption in buildings.

Abstract By applying the principles of the second law of thermodynamics and utilizing the HSC Chemistry software, the thermodynamic equilibrium and efficiency analysis of the CaSO4 CaO water splitting cycle was performed in this investigation. The temperatures desirable and the equilibrium compositions allied with the thermal reduction of CaSO4 and the re-oxidation of CaO via water splitting reaction were estimated. The obtained results indicate that the thermal reduction temperature (TH) required to completely decompose the CaSO4 was decerased from 2220 to 1890 K due to the rise in the molar flow rate of ( n ˙ A r ) from 1 to 50 mol/s. In addition, the consequence of the TH, n ˙ A r , and the water splitting temperature ( T L ) on the process parameters such as total amount of solar energy needed, re-radiation losses, energy dissipated by the water splitting reactor and others associated with the CaSO4 CaO water splitting cycle was scrutinized. By utilizing higher n ˙ A r from 1 to 50 mol/s, the TH was decreased from 2200 to 1890 K. However, as the n ˙ A r was increased from 1 to 50 mol/s, the amount of heat energy needed to heat the Ar was also upsurged from 12.5 to 625.6 kW. This rise in the Q ˙ A r − h e a t i n g , directly reflected into an increase in the Q ˙ s o l a r − c y c l e from 1063.4 up to 2653.9 kW. The findings of this study further confirms that the maximum solar-to-fuel energy conversion efficiency ( η s o l a r − t o − f u e l ) equal to 27.4% was realized by conducting the CaSO4 CaO water splitting cycle at TH = 2220 K, n ˙ A r = 1 mol/s, and TL = 1100 K. By using 50% of the recuperable heat, the η s o l a r − t o − f u e l of the CaSO4 CaO water splitting cycle can be enhanced up to 36.2%.

Abstract By applying the principles of the second law of thermodynamics and utilizing the HSC Chemistry software, the thermodynamic equilibrium and efficiency analysis of the CaSO4 CaO water splitting cycle was performed in this investigation. The temperatures desirable and the equilibrium compositions allied with the thermal reduction of CaSO4 and the re-oxidation of CaO via water splitting reaction were estimated. The obtained results indicate that the thermal reduction temperature (TH) required to completely decompose the CaSO4 was decerased from 2220 to 1890 K due to the rise in the molar flow rate of ( n ˙ A r ) from 1 to 50 mol/s. In addition, the consequence of the TH, n ˙ A r , and the water splitting temperature ( T L ) on the process parameters such as total amount of solar energy needed, re-radiation losses, energy dissipated by the water splitting reactor and others associated with the CaSO4 CaO water splitting cycle was scrutinized. By utilizing higher n ˙ A r from 1 to 50 mol/s, the TH was decreased from 2200 to 1890 K. However, as the n ˙ A r was increased from 1 to 50 mol/s, the amount of heat energy needed to heat the Ar was also upsurged from 12.5 to 625.6 kW. This rise in the Q ˙ A r − h e a t i n g , directly reflected into an increase in the Q ˙ s o l a r − c y c l e from 1063.4 up to 2653.9 kW. The findings of this study further confirms that the maximum solar-to-fuel energy conversion efficiency ( η s o l a r − t o − f u e l ) equal to 27.4% was realized by conducting the CaSO4 CaO water splitting cycle at TH = 2220 K, n ˙ A r = 1 mol/s, and TL = 1100 K. By using 50% of the recuperable heat, the η s o l a r − t o − f u e l of the CaSO4 CaO water splitting cycle can be enhanced up to 36.2%.

Abstract The thermal performance of the thermosyphon water heater unit was analyzed to show its applicability in Bahrain, using data of several sunny, cloudy and hazy days in winter. The performance of this unit was studied under various maximum daily solar intensities, ranging from 1, 2 and 3 on a cloudy day, upto 695 W/m 2 on a sunny day, with the daily outside temperature ranges between 25–19°C. The results show that the system has an average efficiency of 38% with storage tank temperature above 50°C. These results show that this system is quite suitable for application in Bahrain weather conditions.

Abstract The thermal performance of the thermosyphon water heater unit was analyzed to show its applicability in Bahrain, using data of several sunny, cloudy and hazy days in winter. The performance of this unit was studied under various maximum daily solar intensities, ranging from 1, 2 and 3 on a cloudy day, upto 695 W/m 2 on a sunny day, with the daily outside temperature ranges between 25–19°C. The results show that the system has an average efficiency of 38% with storage tank temperature above 50°C. These results show that this system is quite suitable for application in Bahrain weather conditions.

The random forest (RF) classifier, which is a combination of tree predictors, is one of the most powerful classification algorithms that has been recently applied for fault detection and diagnosis (FDD) of industrial processes. However, RF is still suffering from some limitations such as the noncorrelation between variables. These limitations are due to the direct use of variables measured at nodes and therefore the only use of static information from the process data. Thus, this article proposes two enhanced RF classifiers, namely the Euclidean distance based reduced kernel RF (RK-RF $_{\text{ED}}$ ) and K-means clustering based reduced kernel RF (RK-RF $_{\text{Kmeans}}$ ), for FDD. Based on the kernel principal component analysis, the proposed classifiers consist of two main stages: feature extraction and selection, and fault classification. In the first stage, the number of observations in the training data set is reduced using two methods: the first method consists of using the Euclidean distance as dissimilarity metric so that only one measurement is kept in case of redundancy between samples. The second method aims at reducing the amount of the training data based on the K-means clustering technique. Once the characteristics of the process are extracted, the most sensitive features are selected. During the second phase, the selected features are fed to an RF classifier. An emulated grid-connected PV system is used to validate the performance of the proposed RK-RF $_{\text{ED}}$ and RK-RF $_{\text{Kmeans}}$ classifiers. The presented results confirm the high classification accuracy of the developed techniques with low computation time.