A numerical study of the performance of point absorber wave energy converters

Free-floating and submerged wave energy converters (SWECs) are regarded as promising technologies for renewable energy production. These converters rely on a heave-motion buoy to capture the kinetic energy of ocean waves and convert it into electrical energy through power conversion systems. To better understand the impact of various factors on power generation and efficiency, the effects of different buoy shapes (rectangular, circular cylinder, and trapezoidal fin), submergence depths (0, 0.1, and 0.2 m), wave heights (0.04, 0.06, and 0.1 m), and spring stiffness (50 and 100 N/m) were investigated. A 2D numerical wave tank with a buoy was simulated, and the results were validated against experimental data. Information on vorticity, vertical displacement, power absorption, and efficiency are provided. The findings indicate that the buoy shape and wave height significantly affect power absorption and efficiency. Additionally, this study reveals that increasing submergence leads to higher power absorption and lower conversion efficiency.UK Commonwealth Split-Site Commissio

Methanogenesis of organic wastes and their blend in batch anaerobic digester: Experimental and kinetic study

The scarcity of the fossil fuels and increasing energy demand urges the production of sustainable source of energy. The uncontrolled generation of wastes and their easy accessibilities gained a significant attention towards its use for the synthesis of renewable energy like biomethane. In order to cope up with the energy demand and urgency of alternative non-conventional energy source, the present study is focused on improvisation of biogas production qualitatively and quantitatively from different substrates viz. paper waste, Parthenium hysterophorus, canteen waste, and their mixture. The enhancement of the methane potential is accomplished by treating these substrates with catalyst (poultry litter, silica gel and cow urine) and active inoculum (gobar gas slurry) under the standard anaerobic digestion condition. The methanogesis process was carried out in a 1 l batch digester at 1:1 ratio of water:feed under mesophilic temperature (37 °C) for hydraulic retention time of 30 days. Moreover, cumulative gas yield for considered substrates were 167.32 ml/g VS, 149.05 ml/g VS, 197.72 ml/g VS, 290.69 ml/g VS respectively with methane content in biogas for each substrate of 25.5%, 56.8%, 60%, 62% respectively. Among various kinetic models studied, first order kinetic model was found to be best to describe the kinetics of biomethane synthesis for all employed wastes with maximum fitting accuracy (R2 = 0.966). Results of the study confirm the enrichment of quality and quantity of the product gas. The experimental study also revealed that the process is prominent for the efficient production of biomethane to meet the excessive energy thrust

Uncovering the effect of physical conditions and surface roughness on the maximum spreading factor of impinging droplets using a supervised artificial neural network model

The phenomenon of liquid droplet impingement on solid surfaces is particularly important in industrial applications related to spray coating, thermal spraying, inkjet printing, spray cooling, and powder generation industries. Atomised liquid metal droplet impact over surfaces where impingement on both on stationary and rotating surfaces such as rotating disks can be used to carefully control droplet sizes. Furthermore, several other aspects such as liquid properties (especially its surface tension), falling height, surface roughness, and wettability play a vital role in controlling characteristics that not only affect droplet size, but also influences droplet trajectories and spread. These parameters were studied in fine detail in a previous article where a series of experiments were conducted to investigate the phenomena of transient liquid spreading under varying conditions. In this paper, we further extend the previous study by demonstrating the effect of surface roughness, ra, the droplet Reynolds, and Weber numbers and the contact angle by fitting 342 data points to obtain a high-fidelity model using an artificial neural network (ANN) for predicting Bmax the dimensionless spreading diameter. By comparing the obtained model with ten models in the literature, the authors demonstrated the development of a more precise neural network-based predictive model and demonstrated its accuracy using a large set of experimental data. It is shown that the spreading is strongly affected in an inverse manner by the impinged surface roughness, which the ANN modelling well captures along with the complex interaction of the other independent factors.</p