Mohammed Rafiuddion Ahmed

This experimental work involves employing a solar water heating system to elevate the collector air temperatures of an 8 m tall solar chimney power plant (SCPP). Hot water at varying temperatures (40 °C to 70 °C) and mass flow rates (0.025 to 0.0512 kg/s) was supplied to the collector and the effects of supplying hot water to the collector on the resulting air temperature distribution within the collector and along the chimney height as well as the flow velocity at the turbine section and the corresponding turbine power output were studied. Results demonstrate a 14.6% increase in collector temperature rise during daytime and a notable 209% enhancement at night with 70 °C hot water, sustaining a temperature difference above 5 °C from midnight to 6:00 a.m., compared to 2 °C under ambient conditions. Temperature drops along the chimney peaked at 16.2 °C from 70 °C at 2:00 p.m., against 7.2 °C at 1:00 p.m. without solar boosting. Air velocity at the turbine section rose from 8.3 m/s under ambient conditions to 9 m/s at 70 °C, with a maximum enhancement of 378.5% at 4:00 a.m., attributed to heightened buoyancy at low ambient temperatures. Turbine power output was found to improve from 3.5 W normally to 4.5 W at 70 °C. Increasing the mass flow rate from 0.025 to 0.0512 kg/s increased the air velocity and power. These findings highlight the significant potential of solar boosting to enhance SCPP efficiency, offering a viable pathway for transition to renewable energy in small island nations and providing power around the clock.

Recently, energy harvesting through human locomotion has emerged as a promising source of clean energy for fueling low-power smart electronic devices. As energy plays a pivoted role in all sectors, the prospect of harnessing clean energy from human footsteps has become a focal point in the quest for sustainable energy solutions. This article presents a comprehensive exploration of various kinetic energy harvesting technologies—such as piezoelectric, rack and pinion, triboelectric nanogenerators, human mounted, Pavegen, airflow-based, and fluid-flow based energy harvesting systems that are being employed to produce biomechanical energy from simple human motion. The systems require small displacements such as walking and movement of human limbs. The discussion explores the complexity of design, operating principles, and performance evaluations of these cutting-edge human motion energy harvesters. Furthermore, the article delves into a critical assessment of the limitations, benefits, and challenges confronting piezoelectric and electromagnetic energy harvesting technologies in the realm of low-power practical applications. The review indicates that piezoelectric flooring systems, rack and pinion harvesters, triboelectric nanogenerators and wave spring generators hold great promise for footstep energy harvesting. With improvements in piezoelectric materials, it is expected that the efficiency of footstep energy harvesters will improve and with the ongoing reduction in the power requirements of portable devices, they will be able to serve as a clean and mobile source of energy for such devices.

Ocean thermal energy is an emerging energy source that holds great promise, especially for tropical countries. Ocean thermal energy conversion (OTEC) efficiency can be improved by raising the temperature difference between the hot water and the cold water. In the work reported here, a laboratory-scale closed-cycle OTEC system was constructed and tested for power output and efficiency. A solar heating system heated the water up to 70 °C. Experiments were conducted at cold water inlet temperatures of 5 °C, 8 °C, and 11 °C. The mass flow rate of the hot water was varied, while that of the cold water was kept constant. Increasing the hot water inlet temperature from 30 °C to 70 °C while keeping the cold water inlet temperature constant at 5 °C at the highest mass flow rate of hot water increased the power output from 32.07 W to 66.68 W (107.9% increase) and the thermal efficiency from 1.96% to 4.37% (123% increase). The pressure drop across the turbine was higher for a larger temperature difference between the hot water and cold water, indicating a higher transfer of energy to the working fluid. Increasing the mass flow rate of the hot water for the increasing temperature difference between the hot water and cold water increased the power output and efficiency due to the increase in the energy transfer from the hot water to the working fluid. Experimental works on solar-boosted OTEC systems are very rare, and this work should pave the way for practical implementation.

The energy present in the ocean surface waves can be extracted using an oscillating water column (OWC) device. In the present work, the performance characteristics of a normal OWC, an opposite OWC and a novel double OWC employing Savonius rotors are investigated at different sea states in a numerical wave tank (NWT) using the computational fluid dynamics (CFD) code ANSYS-CFX. The Savonius rotor is known to be an inexpensive energy harvesting device. The waves in the NWT were generated using a piston type wave-maker. The results from the computational work were compared with experimental work. For the normal OWC, a maximum power output of 18.85 kW, which corresponds to an efficiency of 22.44% was recorded at 60 rpm for the mean wave condition. On the other hand, for the opposite OWC, the peak power and efficiency were 10.4 kW and 12.38% respectively for the mean sea state. For the novel double OWC, the flow in the front OWC has more energy compared to the flow in the rear OWC for both advancing flow and retreating flow, resulting in a combined peak power of 19.5 kW at the mean sea state. This represented an increase of 3.45% in the power production.

Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco‐friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness‐of‐fit () value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved.

Solar energy is an attractive renewable energy option for countries located in the tropical region. Harvesting this energy using simple yet innovative technologies such as solar chimney power plants (SCPP) will help the developing countries in meeting their sustainable development goals. In an SCPP, air is heated under a greenhouse collector and the hot air is passed to a chimney, where it drives a turbine while rising up. Research efforts have been directed in the past at improving the performance and power output of an SCPP by varying its geometric parameters. The chimney height of a previously optimized solar chimney power plant, having a divergent chimney, was increased from 4 to 6 m and then to 8 m in this first experimental work of this kind. The temperature variations inside the collector, along the chimney height, the velocity at the turbine section, the power available and the output power of an air turbine, estimated by applying mechanical load, are compared for the three chimney heights. The temperature rise of the air inside the collector was the highest for the 4 m tall SCPP and reduced as the chimney height was increased to 6 and 8 m due to the lower time of stay of air in the collector for greater chimney heights. Along the height of the divergent chimney, the temperature dropped with the maximum drop occurring for the 8 m tall SCPP indicating a lower enthalpy loss at the chimney exit. The air velocity at the turbine section was found to increase with chimney height for given solar insolation/time of the day due to the higher driving force which is the buoyancy effect produced by the hot air. The maximum turbine output power for the 8 m tall SCPP increased by 252% compared to the 4 m tall SCPP indicating that significant improvement in the power output can be achieved by increasing the height of a divergent chimney SCPP. An average power of about 40 kW will be available for a chimney height of 100 m which will be extremely beneficial for the sustainable development of small islands.

The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to assess energy at every location, especially at sites where low wind speeds are frequent, such as in tropical regions. In this work, a robust technique for wind resource assessment using a Bayesian approach for estimating Weibull parameters is first proposed. Secondly, the wind resource assessment techniques using a two-parameter Weibull distribution and a three-parameter Weibull distribution, which is a generalized form of two-parameter Weibull distribution, are compared. Simulation studies confirm that the Bayesian approach seems a more robust technique for accurate estimation of Weibull parameters. The research is conducted using data from seven sites in the tropical region from 1^∘ N of the Equator to 21^∘ S of the Equator. Results reveal that a three-parameter Weibull distribution with a non-zero shift parameter is a better fit for the wind data with a higher percentage of low wind speeds (0–1 m s⁻¹) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results with a two-parameter distribution that is a special case of the three-parameter Weibull distribution with a zero shift parameter. The proposed distribution can be incorporated into commercial software like WAsP to improve the accuracy of wind resource assessments. The results also demonstrate that the proposed Bayesian approach and application of a three-parameter Weibull distribution are extremely useful for accurate estimation of wind power density.

The two-parameter Weibull distribution has garnered much attention in the assessment of wind energy potential. The estimation of the shape and scale parameters of the distribution has brought forth a successful tool for the wind energy industry. However, it may be inappropriate to use the two-parameter Weibull distribution to assess energy at every location, especially at sites where low wind speeds are frequent, such as the Equatorial region. In this work, a robust technique for wind resource assessment using a Bayesian approach for estimating Weibull parameters is first proposed. Secondly, the wind resource assessment techniques using a two-parameter Weibull distribution and a three-parameter Weibull distribution, which is a generalized form of two-parameter Weibull distribution, are compared. Simulation studies confirm that the Bayesian approach seems a more robust technique for accurate estimation of Weibull parameters. The research is conducted using data from seven sites in Equatorial region from 1° N of Equator to 21° South of Equator. Results reveal that a three-parameter Weibull distribution with non-zero shift parameter is a better fit for wind data having a higher percentage of low wind speeds (0–1 m/s) and low skewness. However, wind data with a smaller percentage of low wind speeds and high skewness showed better results with a two-parameter distribution that is a special case of three-parameter Weibull distribution with zero shift parameter. The results also demonstrate that the proposed Bayesian approach and application of a three-parameter Weibull distribution are extremely useful for accurate estimate of wind power and annual energy production.

Wind energy resource assessments at two islands in the Cook Islands are carried out in the present work. The wind data were collected for one year from sites on Mauke and Rarotonga Islands in the Cook Islands and the daily, monthly and seasonal average wind speeds, the diurnal variations of the wind shear coefficient, average temperature and turbulence intensity were estimated. Eleven frequentist methods and a Bayesian technique were used to determine the Weibull parameters and the wind power density (WPD) for each site. The best method was determined using the goodness of fit test and error measures. The average wind speeds were 4.65 m/s and 3.86 m/s at 34 m above ground level for the sites on Mauke and Rarotonga respectively. Based on the goodness of fit tests and error measures, the Least Squares Method performed best for estimating the Weibull parameters at the Mauke site, while for the Rarotonga site, the median and quartiles method performed the best. For both the sites, the Bayesian method, which is being used for the first time for wind resource assessments, ranked second of the twelve methods, indicating good potential for this method. The annual energy production (AEP) was also determined which was calculated to be 2192.34 MWh from a total of ten Vergnet 275 kW turbines at the two sites. Finally, an economic analysis, carried out for the two sites, indicated a payback period of 7.72 years.