The aim of this work is to review the phenomenon of icing in marine operations. The focus is on two main sources of icing, namely atmospheric and sea spray. The literature reveals that sea spray icing is the main contributor to marine icing. This work discusses the available ice accretion prediction models on ships and offshore structures. It also reviews the anti-/de-icing technologies that can be implemented on ships operating in cold climate regions. The significance of ice detection is acknowledged, and a brief review of various ice detection technologies is provided.

An effort is made to analyse the stresses experienced by the human femur. In order to achieve these results a CAD model was developed by using the 3-D scanning of generic human femur for an individual of 70 kg weight (approx. averaged adult weight). The marrow cavity has been approximated as a hollow cylinder. The FEM model was built using solid tetrahedral element (20-noded 186 structural solid, ANSYS®). The model was analysed for its sensitivity. The results were computed for the range of loads. In this analysis, the maximum stress and its location were noted. In addition, the critical value of load was estimated for ultimate failure (i.e. fracture). The evaluated results give an understanding of the natural safety factor. The presented results are of significant importance in replication of the natural design parameters in creating the synthetic bone substitutes.

CFD-DEM (Computational Fluid Dynamics – Discrete Element Modelling) is a two-phase flow numerical modelling technique, where the Eulerian method is used for the fluid and the Lagrangian method for the particles. The two phases are coupled by a fluid-particle interaction force (i.e. drag force) which is computed using a correlation. In a two-phase flow, one critical parameter is the voidage (or void fraction), which is defined as the ratio of the volume occupied by the fluid to the total volume. In a CFD-DEM simulation the local voidage is computed by calculating the volume of particles in a given fluid cell. For spherical particles, this computation is difficult when a particle is on the boundary of fluid cells. In this case, it is usual to compute the volume of a particle in a fluid cell approximately. One such approximation divides the volume of a particle into each cell in the same ratio as an equivalent cube of width equal to the particle diameter. Whilst this approach is computationally straight forward, the approximation introduces an error in the voidage computation. Here we estimate the error by comparing the approximate volume calculation with an exact (numerical) computation of the volume of a particle in a fluid cell. The results show that the error varies with the position of the particle relative to the cell boundary. A new approach is suggested which limits the error to less than 2.5 %, without significantly increasing the computational complexity.

In this work, CFD-DEM (computational fluid dynamics - discrete element method) has been used to model the 2 phase flow composed of solid particle and gas in the fluidised bed. This technique uses the Eulerian and the Langrangian methods to solve fluid and particles respectively. Each particle is treated as a discrete entity whose motion is governed by Newton's laws of motion. The particle-particle and particle-wall interaction is modelled using the classical contact mechanics. The particles motion is coupled with the volume averaged equations of the fluid dynamics using drag law.In fluidised bed, particles start experiencing drag once the fluid is passing through. The solid particles response to it once drag experienced is just equal to the weight of the particles. At this moment pressure drop across the bed is just equal to the weight of particles divide by the cross-section area. This is the first regime of fluidization, also referred as ‘the regime of minimum fluidization’.In this study, phenomenon of ...

The massive increase in disposable plastic globally can be addressed through effective recovery methods, and one of these methods is pyrolysis. R software may be used to statistically model the composition and yield of pyrolysis products, such as oil, gas, and waxes to deduce an effective pyrolysis mechanism. To date, no research reports have been documented employing the Arrhenius equation in R software to statistically forecast the kinetic rate constants for the pyrolysis of high-density plastics. We used the Arrhenius equation in R software to assume two series of activation energies (Ea) and pre-exponential factors (A_o) to statistically predict the rate constants at different temperatures to explore their impact on the final pyrolysis products. In line with this, MATLAB (R2020a) was used to predict the pyrolysis products of plastic in the temperature range of 370–410 °C. The value of the rate constant increased with the temperature by expediting the pyrolysis reaction due to the reduced frequency factor. In both assumed series of Ea and A_o, a significantly larger quantity of oil (99%) was predicted; however, the number of byproducts increased in the first series analysis compared to the second series analysis. It was revealed that an appropriate combination of Ea, A_o, and the predicted rate constants could significantly enhance the efficiency of the pyrolysis process. The major oil recovery in the first assumed series occurred at 390 °C to 400 °C, whereas the second assumed series of Ea and Ao occurred at 380 °C to 390 °C. In the second series at 390 °C to 400 °C, the predicted kinetic rate constants behaved aggressively after 120 min of the pyrolysis process. The second assumed series and anticipated rate constants at 380 °C to 390 °C can be applied commercially to improve oil production while saving energy and heat.