The aim of this research was to investigate the cause of the severe localised corrosion that sometimes occurs at welds in carbon steel pipelines carrying hydrocarbons and inhibited brines saturated with carbon dioxide. A rotating cylinder electrode apparatus was designed so that electrodes machined from the weld metal, heat affected zone and parent material of welded X65 pipeline steel could be galvanically coupled and tested in high shear stress conditions. The galvanic currents flowing between the weld regions were recorded using zero resistance ammeters, and their self-corrosion rates were found by polarisation resistance measurements. The total corrosion rate of each weld region was obtained from the sum of the self-corrosion and galvanic contributions. In uninhibited conditions, the weld metal and heat affected zone were both cathodic to the parent material, and localised corrosion was prevented. However, when an oilfield corrosion inhibitor was present, a current reversal took place, which resulted in accelerated weld corrosion. Electrochemical impedance spectroscopy showed that the inhibitor film had lower electrical resistance and was less protective on the weld metal than on the parent material. At the highest shear stress, a second current reversal could occur when the inhibitor was removed from all regions of the weld, and there was a return to the original galvanic behaviour. It was concluded that preferential weld corrosion was caused by unstable conditions in which the inhibitor film was selectively disrupted on the weld metal but remained effective on the other weld regions.

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Since centrifugal pumps consume a mammoth amount of energy in various industrial applications, their design and optimization are highly relevant to saving maximum energy and increasing the system’s efficiency. In the current investigation, a centrifugal pump has been designed and optimized. The study has been carried out for the specific application of transportation of slurry at a flow rate of m3/hr to a head of 20 m. For the optimization process, a multi-objective genetic algorithm (MOGA) and response surface methodology (RSM) have been employed. The process is based on the mean line design of the pump. It utilizes six geometric parameters as design variables, i.e., number of vanes, inlet beta shroud, exit beta shroud, hub inlet blade draft, Rake angle, and the impeller’s rotational speed. The objective functions employed are pump power, hydraulic efficiency, volumetric efficiency, and pump efficiency. In this reference, five different software packages, i.e., ANSYS Vista, ANSYS DesignModeler, response surface optimization software, and ANSYS CFX, were coupled to achieve the optimized design of the pump geometry. Characteristic maps were generated using simulations conducted for 45 points. Additionally, erosion rate was predicted using 3-D numerical simulations under various conditions. Finally, the transient behavior of the pump, being the highlight of the study, was evaluated. Results suggest that the maximum fluctuation in the local pressure and stresses on the cases correspond to a phase angle of 0°–30° of the casing that in turn corresponds to the maximum erosion rates in the region.

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Design and optimization of a radial turbine for a Rankine cycle were accomplished ensuring higher thermal efficiency of the system despite the low turbine inlet temperature. A turbine design code (TDC) based on the meanline design methodology was developed to construct the base design of the turbine rotor. Best design practices for the base design were discussed and adopted to initiate a robust optimization procedure. The baseline design was optimized using the response surface methodology and by coupling it with the genetic algorithm. The design variables considered for the study are rotational speed, total to static speed ratio, hub radius ratio, shroud radius ration, and number of blades. Various designs of the turbine were constructed based on the Central Composite Design (CCD) while performance variables were computed using the in-house turbine design code (TDC) in the MATLAB environment. The TDC can access the properties of the working fluid through a subroutine that links NIST’s REFPROP to the design code through a subroutine. The finalization of the geometry was made through an iterative process between 3D-Reynolds-Averaged Navier-Stokes (RANS) simulations and the one-dimensional optimization procedure. 3D RANS simulations were also conducted to analyze the optimized geometry of the turbine rotor for off-design conditions. For computational fluid dynamics (CFD) simulation, a commercial code ANSYS-CFX was employed. 3D geometry was constructed using ASYS Bladegen while structured mesh was generated using ANSYS Turbogrid. Fluid properties were supplied to the CFD solver through a real gas property (RGP) file that was constructed in MATLAB by linking it to REFPROP. Computed results show that an initial good design can reduce the time and computational efforts necessary to reach an optimal design successfully. Furthermore, it can be inferred from the CFD calculation that Response Surface Methodology (RSM) employing CFD as a model evaluation tool can be highly effective for the design and optimization of turbomachinery.

The role of a pre-cooler is critical to the sCO2-BC as it not only acts as a sink but also controls the conditions at the main compressor’s inlet that are vital to the cycle’s overall performance. Despite their prime importance, studies on the pre-cooler’s design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number (Nu) and friction factor (f). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle’s performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler’s operating regime. Cycle simulation results suggest that the cycle’s performance calculation can be misleading without considering the pre-cooler’s pumping power. Moreover, the optimization study indicates that the compressor’s inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel’s Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle’s efficiency and the pre-cooler’s size.

In the present study, an erosion analysis of an industrial pump’s casing and impeller blades has been performed computationally. Effects of various critical parameters, i.e., the concentration and size of solid particles, exit pressure head, and cavitation on the erosion rate density of the casing and blade have been investigated. Commercial codes CFX, ICEM-CFD, and ANSYS Turbogrid are employed to solve the model, mesh generation for the casing, and mesh generation of the impeller, respectively. The Eulerian-Eulerian method is employed to model the pump domain’s flow to solve the two phases (water and solid particles) and the interaction between the phases. Published experimental data was utilized to validate the employed computational model. Later, a parametric study was conducted to evaluate the effects of the parameters mentioned above on the erosion characteristics of the pump’s casing and impeller’s blade. The results show that the concentration of the solid particles significantly affects the pump’s erosion characteristics, followed by the particle size and distribution of the particle size. On the other hand, the exit pressure head and cavitation do not affect the erosion rates considerably but significantly influence the regions of high erosion rate densities.

For adhesive bonding of stainless steel to itself, a surface treatment involving chromates is used. However, chromates are environmentally unfriendly so a replacement is being sought. In this paper, an alternative to chromate was investigated. The standard test method, ASTM D 1002, was used to measure the failure load of adhesively bonded stainless steel samples. A general-purpose epoxy adhesive was used. To simulate marine exposure, adhesively bonded samples were placed in a 5% salt spray for extended periods of time, up to five weeks. Results indicated that the initial shear strength of adhesive joints prepared with a traditional chromate preparation was 25% greater than the new, alternative coating. However, more importantly, the rate of decrease in strength with salt spray exposure was greater for the chromate than for the alternative. After 21 days, both bonding surface treatments had the same strength. However, after this period of time, the alternative was stronger than the chromate treatment, indicating that the alternative was a more durable coating. An adhesive/adherent coated system was investigated using a finite element method in order to investigate the influence of adhesive thickness between the adhesive and the adherent, and the residual stress in the adhesive layer.