Modeling of the minimized two-phase flow frictional pressure drop in a small tube with different correlations

The major parameters of interest in heat transfer research are the refrigerant charge, pressure drop, and heat transfer capacity. Smaller channels reduce the refrigerant charge with higher heat transfer capability due to the increased in surface area to volume ratio but at the expense of a higher pressure drop. Differences between the predicted and experimental frictional pressure drop of two-phase flow in small tubes have frequently been discussed. Factors that could have contributed to that effect have been attributed to the correlations used to model the flow, some being modified from the originals developed for a macro system. Experimental test-rigs have varied in channel geometry, refrigerant type, and flow conditions. Thousands of data have been collected to find a common point among the differences. This paper reports an investigation of four different two-phase friction factor correlations used in the modeling of the frictional two-phase flow pressure drop of refrigerant R-22. One had been specifically developed for laminar flow in a smooth channel, another was modified from a laminar flow in a smooth pipe to be used for a rough channel, and two correlations are specific for turbulent flow that consider internal pipe surface roughness. Genetic algorithm, an optimization scheme, is used to search for the minimum friction factor and minimum frictional pressure drop under optimized conditions of the mass flux and vapor quality. The results show that a larger pressure drop does come with a smaller channel. A large discrepancy exists between the correlations investigated; between the ones that does not consider surface roughness and that which does, as well as between flow under laminar and turbulent flow conditions

A review: Use of evolutionary algorithm for optimisation of machining parameters

Optimisation of machining parameters is crucial to ensure higher productivity and optimum outcomes in machining processes. By optimising machining parameters, a particular machining process can produce better machining outcomes within equivalent resources. This paper reviews past studies to achieve the desired outputs; minimum surface roughness (SR), highest material removal rate (MRR), lowest production cost, and the shortest production time of machining processes and various optimisation attempts in terms of varying parameters that affect the outcomes. The review deliberates the optimisation methods employed and analyses the performance discussing the relevant parameters that must have been considered by past researchers. To date, most studies have been focusing on optimising conventional machining processes such as turning, milling, and drilling. Optimisation works have been performed parametrically, experimentally, and numerically, where discrete variations of the parameters are investigated, while others are remained constant. Lately, evolutionary algorithm, statistical approaches such as genetic algorithm (GA), particle swarm optimisation (PSO), and cuckoo search algorithm (CSA) have been utilised in simultaneous optimisation of the parameters of the desired outputs and its great potential in optimising machining processes is recognisable

Comparison of a thermoacoustic refrigerator stack performance: Mylar spiral, celcor substrates and 3D printed stacks

Although the thermoacoustic refrigeration (TAR) system has been recognized as a potential alternative environmentally cooling system, the low coefficient of performance (COP) has yet to make it marketable. One major factor contributing towards the low COP is the fabrication method applied to the stack component which is the most important component in the TAR. In this paper, comparison of the performance of a (i) 3D printed stack, (ii) a hand fabricated Mylar stack and (iii) an off-the-shelf Celcor substrates stack has been done; these being based on optimized design parameters using Multi-Objective Genetic Algorithm (MOGA). The performance is determined from the temperature attained at the cold end of the stack and the temperature difference across the stack. Experimental results showed that the 3D printed stack has the best performance by achieving a temperature, Tc=18.9°C at the cold end and a temperature difference of δT=18.1°C across the stack, about 60% of the designed temperature difference even though the fabricated 3D printed stack deviated from the optimal design due to fabrication constraint as compared to that of the Mylar stack which was closer to the optimal design. This 3D printing of the stack promises a big potential in the improvement of the TAR performance because of the consistency achievable with the precise dimensions of the stack

Analysis of 3D printed stack in thermoacoustic cooling

Inconsistencies in fabricated thermoacoustic stacks are due to the methods available to obtain the desired geometry and dimensions even under optimized design parameters. This paper presents performance results from stacks fabricated using 3D printing technology which minimizes the error, disposes of irregularities and can reduce production time. In this study, the performance of a thermoacoustic refrigerator was determined from measurements of the temperature difference across various 3D printed stack lengths fabricated. Experiments were done at 400 Hz frequency with different stack plate spacing and thickness, in a 21-mm diameter resonator. Results show that a 0.7 mm stack plate spacing with a 0.5 mm plate thickness performed better compared to those with smaller spacing at the same thickness or with the same spacing but larger thickness. The outcomes of this study have shown the need for the fabrication technology to keep pace with optimized design to realize global efforts towards a sustainable environment