نشریه یافته های نوین کاربردی و محاسباتی در سیستم های مکانیکی
سال یکم شماره 1 (بهار 1400)

Drilling mud is one of the most important and basic requirements for drilling oil and gas wells. Optimization of drilling fluid properties leads to reduce drilling time and costs. Water-based drilling mud is one type of drilling fluid that is mainly used in drilling wells due to its environmental compatibility. In this research, in order to increase the efficiency of this type of drilling fluid, graphene and silicon oxide nanoparticles in equal weight ratio in water-based drilling mud have been used. This study investigates the effect of different concentrations (0.25, 0.5, 0.75 and 1% volume fraction) of these nanoparticles on different properties of drilling fluid such as plastic viscosity, yield point, gel strength of 10 minutes and 10 seconds, circulation loss of the samples and the coefficient of thermal conductivity are discussed. All rheological properties tests are performed according to the standard (API RP 13B). The transient hot wire method has been used to determine the thermal conductivity of the fluid. The results show that the increase of nanoparticles has a significant effect on the rheological and thermo-physical properties of water-based drilling mud and reduces plastic viscosity (15%), decreases circulation loss of the samples (25%) and gel strength and also increases the thermal conductivity by 16% in the volume fraction of 1%.

The performance improvement of a PV module with the application of phase change material (PCM) in a device known as a PV/PCM module was examined in an experiment. Two 90 watts PV module have been used in the test. One of which is used as reference module and the other as a cooled module. The aluminum enclosure is mounted on the back of the cooled module and is equipped by 50 aluminum pin fins to increase heat transfer. The study has been evaluated outdoors in two cases at two different angles using mixture of salt and water. Both the integrated cases maintained lower PV temperature than the reference PV module. The lower PV temperatures effected by the use of the PCMs prevented the associated PV power loss and increased PV conversion efficiencies.

Lubrication is one of the most effective ways to reduce friction and reduce the excess heat generated in a mechanical system. Identifying the right additive to improve lubrication properties by additives that have features such as accessibility and higher quality performance is important. The aim of this study was to investigate the effect of silicon dioxide (SiO2) nanoparticles with different concentrations as additives on the thermal and anti-wear properties of lubricants. For this purpose, nanoparticles with concentrations of 0.2, 0.4 and 0.5 wt% were combined with oil. Span 80 surfactant, ultrasonic bath and high speed agitator were used to disperse the nanoparticles into the base fluid and achieve a stable nanofluid. The static stability of nanofluids was also visually investigated. The obtained results showed that the addition of nanoparticles to the oil does not cause any change in its state and over time no sedimentation and phase change was observed, which indicates the very good stability of this nanofluid. In the last stage, wear tests, determination of friction coefficient and thermal conductivity coefficient were performed on the samples. According to the results, the lowest amount of disc wear was related to the lubricating mixture with a concentration of 0.5% by weight. The weight loss of the discs due to wear for this mixture was 60.76%, the reduction of the coefficient of friction was 15.07% and the increase of the thermal conductivity was 2.4% compared to the base oil.

In this study, two types of dual-purpose compression refrigeration systems including single-compressor and dual-compressor (cascade) with different eco-friendly refrigerants to achieve two temperature ranges above zero and below zero have been investigated. The purpose of this study is to investigate the annual energy consumption of dual-purpose refrigeration systems with eco-friendly refrigerants and also to investigate the parameters affecting the efficiency of these systems. Studies have shown that the cascade system has a higher efficiency than single-compressor system and the best refrigerant in terms of performance in this system is R290. It was also found that in the single-compressor system only R134a refrigerant is applicable and the other refrigerants in the evaporator create pressure under atmospheric pressure. Also, this system is only applicable in hot and humid climates for all seasons of the year, while the cascade system is applicable in all seasons of the year and in all climates. Comparison of these two systems shows that assuming the total load capacity of the refrigerator is constant, increasing the cooling load in the low temperature range leads to increased energy consumption of the cascade system. Also founded that the annual energy consumption of cascade system is 7% lower than single compressor in the case of equal distribution of cooling load between two temperature ranges.

Depending on the temperature and pressure of the heat source, single-effect absorption chillers are categorized in two types of hot water and steam single-effect chillers. Due to the ability to use the waste steam in oil, gas and petrochemical industries for air conditioning and process cooling purposes, the steam type chiller is more widely used. In this study, the artificial neural network is exploited in the prediction of the steam single-effect absorption chiller performance since it is faster and has lower computational cost compared to thermodynamic modeling methods. The perceptron multilayer neural network with the error backpropagation algorithm, the hyperbolic tangent excitation function and the Levenberg-Marquardt learning method with 15285 data points and also the mean squared error estimation index are used. Inputs of the artificial neural network are the inlet cooling tower water temperature, inlet chilled water temperature, inlet steam temperature, outlet chilled water temperature and the solution heat exchanger efficiency respectively. Also, outputs of the neural network are the coefficient of performance and thermal energy consumption of the chiller. Results of this study show that the artificial neural network is capable to predict the coefficient of performance and the thermal energy consumed by the single-effect absorption chiller while the values of mean squared error are 3.183×10^(-7) and 7.466×10^(-8) respectively which verify the accuracy of the method proposed here in absorption chiller performance prediction.

The purpose of this research is to investigate the stress distribution in the post and core set in the maxillary first premolar that has two roots. In such a tooth, the post and core will be united or separated. An orthodontic patient's extracted tooth was used to prepare the basic tooth model. Then, a basic tooth model was prepared for 3D computer modeling using a 3D scanner. Then the computer model was completed in the SolidWorks engineering modeling software. Then, for stress analysis, the model was submitted to ANYSIS software under two axial and 45 oblique loads at 400 N. The material chosen for the post and core in both united form and separated one was nickel-chromium alloy. Evaluation of the data revealed that the use of ferrule in all cases reduced stress in roots. It was also found that the use von ferrule did not necessarily decrease stress of post and core. The maximum equivalent von Mises stress in roots, independent of the load applied, was lower in the two pieces post and core than in the one piece post and core; this was not generally seen in post and core. Under oblique load, more stress is observed on the components of the post and core.