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A high-speed multi-stage centrifugal pump building upon a transient investigation into the hydrodynamic characteristics, is conducted for 16 sets of clocking positions. The most significance of the pump head has a maximum increase of 3.6% at 0.8Qd. Phase differences emerge in the pressure pulsations at the impeller-guide vane gaps across stages. It results in waveform distortions that are primarily influenced by impeller clocking effects. Innovative average energy flux density analysis shows that impeller-guide vane clocking positions have the greatest influence on pressure pulsations near their gaps. The cumulative propagation of pressure pulsations has a relatively minor effect on fluid domains. Worst stability is observed when impellers rotated with a 3/4 maximum offset angle and guide vane positions remain unchanged. The best clocking position solution was adapted in a prototype pump for performance and operational stability measuring, achieving an efficiency of 53.67% and 1 mm/s vibration. This paper aims to provide valuable insights for enhancing the energy efficiency and operational safety of multi-stage pumps, offering beneficial guidance for engineering practices within the field of fluid machinery applications. [ABSTRACT FROM AUTHOR]

Among the most significant solutions suggested for estimating energy consumption and cooling load, one can refer to enhancing energy efficiency in non-residential and residential buildings. A structure's characteristics must be considered when estimating how much heating and cooling is required. To design and develop energy-efficient buildings, it can be helpful to research the characteristics of connected structures, such as the kinds of cooling and heating systems needed to ensure sui interior air quality. As an important part of energy consumption and demand of buildings, the assessment of cooling load conditions from the envelope of large buildings has not been comprehensively understood yet. In the present paper, a new conceptual system has been developed to anticipate cooling load in the sector of residential buildings. Also, the paper briefly describes the major models of the developed system to maintain continuity and concentrate on the prediction model of the cooling load. To predict cooling load, authors have modelled two methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in conjunction with teaching-learning-based optimization (TLBO). This article aims to illustrate how artificial intelligence (AI) approaches play an essential role in addressing the mentioned necessity and help estimate the optimal design parameters for various stations. The value of the multiple determination coefficient is also determined. The values of the training R² (coefficient of multiple determination) are 0.96446 and 0.97585 for TLBO-MLP and TLBO-ANFIS in the training stage and 0.95855 and 0.9721 in the testing stage, respectively, with an unknown dataset which is acceptable. The training RMSE values for TLBO-MLP and TLBO-ANFIS are 0.0685 and 0.11176 for training and 0.07074 and 0.12035 for testing, respectively, for the unknown dataset, which is acceptable. The lowest RMSE value and the higher R² value indicate the favourable accuracy of the TLBO-MLP technique. According to the high value of R² (97%) and the low value of RMSE, TLBO-MLP can predict residential buildings' cooling load. [ABSTRACT FROM AUTHOR]

To create a highly efficient photovoltaic-thermal (PV-T) system and maximise the energy and exergy efficiency, this study aims to propose an innovative configuration of a PV-T system comprising wavy tubes with twisted-tape inserts. Following the validation of a numerical model, a parametric study has been conducted to assess the geometrical effects of twisted tape and wavy tubes, as well as the coolant fluid type and velocity, on the overall performance of a PV-T system, located in Shiraz, Iran. It is found that employing twisted tape improves the energy and exergy efficiency by approx. 6.3%. The best configuration yields 12.4% and 16.8% increase in energy and exergy efficiency compared to conventional PV systems. This is achieved at 15% volumetric concentration of microencapsulated phase change material slurry. The monthly variation of global horizontal irradiance in Shiraz highly affected the energy and exergy efficiency of PV-T, with July and October exhibiting the most efficient months, corresponding to 90% and 11.3%, respectively. [ABSTRACT FROM AUTHOR]

Centrifugal pumps are widely used in various fields and have enormous energy-saving potential. In order to predict efficiency quickly and accurately, firstly, by establishing a hydraulic loss model for centrifugal pump impellers, the functional relationship between impeller hydraulic loss and hydraulic efficiency is constructed to establish the objective function. Secondly, calculate the main dimensions of the impeller using the velocity coefficient method and establish the objective function variables of the number of blades Z and outlet placement angle β2. And the velocity coefficient k0. Then, the method of mathematical statistics, namely response surface analysis, is used to solve the relationship between hydraulic efficiency and dependent variables within the range of variables, which plays the role of predicting hydraulic efficiency. Finally, the accuracy of the predictions is verified by numerical simulation. The results show that the hydraulic efficiency of a high specific speed centrifugal pump reaches its maximum at 1000m³/h operating conditions with a blade number of 3, a speed coefficient of 3.9 and an outlet angle of 30°. The study provides a new direction for the hydraulic design of high specific speed centrifugal pumps to achieve more accurate predictions of high specific speed centrifugal pump efficiency. [ABSTRACT FROM AUTHOR]

This paper reports the results of a numerical study on the dynamics of isothermal multiphase fluids using an energy-law-preserving method. A phase-field model is taken into account. Different to previous studies, a continuous finite element technique is used to simulate the Navier-Stokes-Cahn-Hilliard coupled model. A modified discrete energy law of the numerical simulation is derived in detail. A penalty formulation is applied for continuous conditions to ensure the stability of the pressure. The coalescence of two kissing bubbles and the rising of a lighter drop are simulated as numerical examples, and the estimated orders of the velocity gradient are computed to examine the accuracy of the numerical solution. The paper shows that in the computing process the energy law is preserved for each time step and that the errors in the discrete energy law equal less than 10−8. [ABSTRACT FROM AUTHOR]

Optimal dilution and the lowest possible energy consumption are essential environmental and economic objectives for deep sea sewage discharge. In this paper, a new method with multiple parameters and objectives, nonlinear, genetically optimized and coupled inversion for determining jet ratios and angles was established by coupling Genetic Algorithms (GAs) with Anisotropic Turbulent Buoyant Jet Models with variable densities to achieve these objectives. The multiple objectives were taken into account for the jet model and the Operation Energy Consumption Equation was input into the GAs. Real coding methodology was utilized to avoid the precision losses of binary coding. A multiple parameters matching designed cases methodology was used to improve the convergence velocity. The fluctuated convergence curves that were utilized for searching for the optimal multiple parameters showed that the present method is suitable for the multiple objectives optimizing inversion problem. The numerical results of the re-calibrations and comparisons showed that the multiple parameters and objectives, nonlinear, genetically optimized and coupled inversion method has the advantages of improving the jet's water mixing efficiency with environmental waters, while also reducing the energy consumption. The optimal multiple parameters of a jet ratio of 4, and a jet angle of 90 degrees, satisfy the demands of the multiple objectives. [ABSTRACT FROM AUTHOR]

In this paper, modeling and analysis of a hybrid solar power plant are presented. Within a theoretical framework, thermodynamic modeling of several components of the cycles has been conducted through simultaneous consideration of energy, exergy, and emissions. The energy and exergy of the combined cycle of the solar-gas turbine and organic Rankine cycle (ORC) have been investigated. Heat loss from the solar receiver has been studied considering essential factors such as wind speed, air pressure, and ambient temperature. The emissions, mass flow rate of working fluids, pressure ratio, and concentration ratio are evaluated and compared with a standard fossil-fueled power plant. The results show that the energy efficiency and exergy of the overall system are substantially improved as the solar radiation intensity increases. Furthermore, the output power of the ORC is reported to be higher than that of the Rankin steam cycle. Consequently, when solar energy is used, the carbon dioxide emission is reduced by 150%. [ABSTRACT FROM AUTHOR]

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced through a process that dissociates it from its compounds. Several methods are used for hydrogen production, which first of all differ in the energy used in this process. Investigating the viability and exact applicability of a method in a specific context requires accurate knowledge of the parameters involved in the method and the interaction between these parameters. This can be done using top-down models relying on complex mathematically driven equations. However, with the raise of computational intelligence (CI) and machine learning techniques, researchers in hydrology have increasingly been using these methods for this complex task and report promising results. The contribution of this study is to investigate the state of the art CI methods employed in hydrogen production, and to identify the CI method(s) that perform better in the prediction, assessment and optimization tasks related to different types of Hydrogen production methods. The resulting analysis provides in-depth insight into the different hydrogen production methods, modeling technique and the obtained results from various scenarios, integrating them within the framework of a common discussion and evaluation paper. The identified methods were benchmarked by a qualitative analysis of the accuracy of CI in modeling hydrogen production, providing extensive overview of its usage to empower renewable energy utilization. [ABSTRACT FROM AUTHOR]

Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. In this study, an optimization method based on the particle swarm optimization (PSO) algorithm and least squares support vector regression (LSSVR) surrogate model is proposed. The head and efficiency are taken as the optimization objectives of the multistage pump, and the optimization design of the two impellers is carried out. Through the optimization of the surrogate model, the head and efficiency of the multistage pump are increased by 3.99% and 2.91%, respectively. It is found that the optimized impeller has more stable flow characteristics than original impeller. Further, the entropy production method is introduced to analyze the energy loss of pump. The result shows that the pump optimized by PSO-LSSVR surrogate model can reduce 9% energy consumption under nominal flow rate. It is attributed to the inhibitory effect of the optimization scheme on the horseshoe vortex at leading edge and the wake vortex at trailing edge of impeller. This study provides a new idea for the optimization design of high efficiency and low energy consumption of multistage centrifugal pumps. [ABSTRACT FROM AUTHOR]

The diagonal flow fan, known for its innovative airflow guiding technique, presents a dual advantage of heightened efficiency and reduced noise levels. Despite garnering attention in energy production systems, research on optimizing outlet guide vanes for diagonal flow fan performance remains limited. This study introduces a novel machine learning approach, utilizing Opt LHD sampling instead of traditional simulations and experiments to accumulate ample sample data. The fusion of LS-SVM and improved GA-PSO is employed for multi-objective optimization of outlet guide vanes, aiming to simultaneously enhance fan performance and reduce energy consumption. Experimental assessments reveal the optimized fan's significant improvements, including a 106 Pa increase in total pressure, a 3.6 dB reduction in noise levels, and a remarkable 16.3% enhancement in total pressure efficiency. These results highlight the robustness of the machine learning approach in optimizing diagonal flow fan exit guide vanes, effectively addressing the research gap in this area. Additionally, numerical analysis of internal flow dynamics and acoustic properties pre and post-optimization uncovers the intrinsic mechanisms influencing the overall performance of the diagonal flow fan. [ABSTRACT FROM AUTHOR]

The ventilation system significantly affects the energy saving, thermal comfort, and pollutant dispersion characteristics of high-speed trains. Using Computational Fluid Dynamics (CFD), the effects of combining side-wall air supply with side roof or ceiling air supply, on the performance of a highspeed train ventilation system is studied. The results show that the addition of side-wall air supply aids energy saving and emission reduction, resulting in an increase in energy utilisation coefficient of between 5% and 30%. The number of measurement points with wind speed less than 0.3 m/s increases by 3%~9%, which can improve passenger comfort. Also, the concentration of pollutants can be reduced by 6%~29%, and the diffusion distance along the length of the train can be reduced by 18%~45%, which means more efficient ventilation and a shorter diffusion range of pollutants. However, it reduces temperature uniformity. The results of this study can be used for ventilation optimisation of high-speed trains, to reduce ventilation energy consumption, improve thermal comfort, and increase ventilation efficiency. [ABSTRACT FROM AUTHOR]

The fuel injection system in diesel engines has a consequential effect on the fuel consumption, combustion process and formation of emissions. Cavitation and turbulence inside a diesel injector play a critical role in primary spray breakup and development processes. Thus understanding the phenomenon of cavitation is significant in capturing the injection process with accuracy. In this study, the cavitating flow inside an injector nozzle hole was numerically investigated. The two-phase mixture model by Schnerr and Sauer (2001) was adopted along with k-ε turbulence model and Fluent CFD package was used to solve the governing equations numerically. The validation of the model was done by comparing the numerical results with the experimental results of Winklhofer et al. (2001) for U-throttle geometry and a good agreement was found. In this paper, a detailed parametric study on the effects of injection pressure, transient analysis for diesel and SME (Soy Methyl Esther) biodiesel and different geometries on cavitation phenomenon inside the injector nozzle hole was done. The results show that the bio-diesel inhibits the cavitation phenomenon compared to diesel fuel, and positive Kfactor nozzles have higher mass flow rate and higher exit velocity. [ABSTRACT FROM AUTHOR]

The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively. [ABSTRACT FROM AUTHOR]

Biodiesel can easily be used as an alternative fuel in diesel engines. It is environmentally friendly and can be produced from low-cost feedstocks such as waste cooking oil (WCO). WCO contains a significant amount of free fatty acid, which is extracted by a two-step process of converting the free fatty acid by acid catalysis (H2S04) and converting the triglycerides using an NaOH catalyst. Currently, the major challenge for the industrial production of biodiesel is optimizing the yield while meeting American Society for Testing and Materials (ASTM) standards. In this study, experiments were performed to optimize the reaction conditions. The studied experimental parameters were the alcohol types (methanol, ethanol), the alcohol-to-oil molar ratio (AOMR; 3:1, 6:1, 9:1), the amount of catalyst (0.5, 1.0, 1.5 wt% of the oil), the temperature of the reaction (50, 60, 70, 80°C), the mixing intensity (300, 600, 900 rpm), and the reaction time (30, 60, 90 min). The biodiesel production yield (BPY) was optimized based on the experimental data. The optimum value of the BPY based on methanol is 95.92%, which is obtained at 73.80°C, with a reaction time of 74.02 min, an AOMR of 6.58:1, a catalyst concentration of 1.13 and a mixing intensity of 824.45 rpm. In the case of ethanol, the optimum BPY is 95.53%. which is obtained at 64.96°C, with a reaction time of 88.02 min, an AOMR of 7.005:1, a catalyst concentration of 1.25 and a mixing intensity of 592.18 rpm. These results of biodiesel production were confirmed by the experimental data. [ABSTRACT FROM AUTHOR]

Comparing to pumps with moving parts water-jet pumps have a lower efficiency and surface roughness is an important factor for these types of pumps. The aim of this simulation study is to numerically determine how the scaling-up, downscaling and change in the absolute and relative roughness would impact on the energy efficiency of the pumps, using a commercial computational fluid dynamics (CFD) solver ANSYS FLUENT. In order to select the turbulence model that produces the predictions closest to the actual data from four turbulence models, a preliminary study was conducted on a full-scale jet pump. Using the transition SST model, which gives the best results among all the models, the effects of scale and roughness on the performance of the pumps were investigated in the scale range from 1/4 to 20/1. The optimum efficiencies for different area ratios over a wide range were determined according to the scale and size of roughness. It was seen that the efficiency increases significantly up to a given scale size at a constant absolute roughness, while it is generally independent of the scale size at constant relative roughness. The relative efficiency for the area ratio 5.92 reduces to 60% at the relative roughness value of 0.05. Moreover, CFD appears to be the most appropriate tool for model studies of jet pumps. [ABSTRACT FROM AUTHOR]