فهرست مطالب

  • Volume:8 Issue: 1, 2020
  • تاریخ انتشار: 1399/02/15
  • تعداد عناوین: 6
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  • Mostafa Mostafavi Sani, Alireza Noorpoor *, Majid Shafie, Pour Motlagh Pages 1-22
    This paper uses the energy hub concept to meet the water, heat and electricity demand of a power plant in the Qeshm island in south of Iran. Given the power plant’s high potential for waste heat recovery and some scenarios were considered using the hub energy concept based on energy, exergy, environmental and economic analyzes in terms of meeting the demands of the hub and purchasing/selling energy carriers including electricity, heating, freshwater as well as its production using gas turbine, steam turbine, boiler, Reverse Osmosis (RO) and Multi-Effect Desalination (MED) system. Energy hubs are optimized based on the Genetic Algorithm (GA) with the goal of supplying demand, as well as reducing costs and pollutants and increasing the exergy efficiency which ultimately will be selected using the concept of an energy hub at its optimal capacity. By comparing the two energy supplying systems of the current case study and optimal energy hub, results showed that the Total Annual Cost (TAC) level decreased by about 257904 $/year and exergy efficiency increased by 34.31%. CO2 emission will also decrease by about 471 tons/year.
    Keywords: Energy hub, Multi-Effect Desalination (MED), Reverse Osmosis (RO), Total Annual Cost (TAC), genetic algorithm
  • Sahar Nazer, Fateme Ahmadi Boyaghchi * Pages 23-44
    In this work, a novel dual-nozzle ejector enhanced triple-evaporator refrigeration cycle (DETRC) without separator is proposed to improve the performance of the conventional ejector one (CETRC). The performance of DETRC is analyzed and compared with CETRC in term of energy coefficient of performance (COPen). Under given operating conditions, the COPen improvement of the novel cycle could reach about 24.35% which shows the excellent energy-saving potential of DETRC in comparison with CETRC. Then, a comprehensive comparison between R717, R600a, R1234yf and R290 as low global warming potential (GWP) refrigerants of DETRC is conducted from the energy, exergy, economic and environmental impact (EI) aspects. It is observed that R717 gives better energetic and exergetic performances by 3.21 and 0.583 and R1234yf causes the lowest total product cost and EI rates of 8.186 $/h and 0.665 Pts/h, respectively for DETRC. Moreover, increasing the high evaporating temperature improves all desired performances of DETRC, simultaneously due to the reduction of compressor consumed power. Finally, a multi-objective optimization based on an evolutionary algorithm and LINMAP decision making are carried out to ascertain the optimum exergetic, economic and EI performances of DETRC for each refrigerant.
    Keywords: Dual-Nozzle Ejector, Triple-Evaporator, Exergoeconomic Analysis, Exergoenvironmental Analysis, Optimization
  • Masoud Jedari Ghourichaei, Mohammad Goharkhah *, Naiyer Razmara Pages 45-54

    In the current research, thermal conductivity of magnetite (Fe3O4) has been calculated using molecular dynamic simulation. The rNEMD Molecular Dynamics Method provided in the LMMPS package is used for the simulation of the thermal conductivity. The effects of magnetite layer size and temperature on the thermal conductivity have been investigated. The numerical results have been validated by experimental data. Results show that the thermal conductivity of magnetite can be predicted appropriately using Buckingham potential function. Moreover, Thermal conductivity of magnetite is shown to be a decreasing function of temperature. The obtained results provide a benchmark for magnetite ferrofluid heat transfer simulations, which has been extensively increased in recent years.

    Keywords: molecular dynamics, Thermal conductivity, Magnetite, rNEMD
  • Mojtaba Tahani, Mohsen Razavi, Mojtaba Mirhosseini *, Fatemeh Razi Astaraei Pages 55-80
    H-rotor Vertical Axis Wind Turbine (VAWT) is one of the most efficient energy suppliers which have been investigated in many recent types of research. The aim of this work is to study the aerodynamic performance of a doubled-row H-Darrieus VAWT. First, an ordinary three-bladed VAWT with NACA4415 profile is simulated by means of 3D computational fluid dynamics (CFD) and results are compared to a recently published research work based on Blade Element Momentum (BEM) theory. Afterward, a doubled-row H-Darrieus VAWT is simulated and analyzed in two different geometric configurations. In the first configuration, a second row with the same blade characteristics of the first row is added aligned with the first row and with 0.2 m distance toward it. In the second one, again with the same blade characteristics, the secondary blade is added with 0.2 m distance toward first row, but with 60 degrees angular offset. Renormalization-Group (RNG) k-ɛ turbulence model besides wall function is applied in all unsteady simulations. As comparative tools, based on other studies using the same coefficients, momentum coefficient ( ) and power coefficient   are calculated in all simulations to investigate which case operates more efficiently. It is observed that adding a second row to an ordinary H-Darrieus VAWT will improve these coefficients up to 314% which is a considerable leap in power production ability of the VAWT. Also, different turbulence models, geometries (with a central shaft and without central shaft) and solution methods were also analyzed and the effect of each one was computed and compared with other cases.
    Keywords: H-Darrieus, Doubled-Row VAWT, BEM, CFD, Performance
  • Arman Mohammadi, Pooyan Nayyeri, MohammadReza Zakerzadeh *, Farzad Ayatollahzadehshirazi Pages 81-90

    Energy harvesting is a conventional method to collect the dissipated energy of a system. In this paper, we investigate the optimal location of a piezoelectric element to harvest maximum power concerning different excitation frequencies of a vibrating cantilever beam. The cantilever beam oscillates by a concentrated sinusoidal tip force, and a piezoelectric patch is integrated on the beam to generate electrical energy. To this end, the system is modeled with analytical governing equations, then a Deep Neural Network (DNN)-based surrogate model is developed to appropriately model the system within the range of its first three natural frequencies. The surrogate model has significantly abated the computation cost. Thus, the optimization time is reduced drastically. Our investigations led to an optimal piezoelectric location for different excitation frequencies, which can result in maximum electrical output power. This location is highly dependent on the excitation frequency. When excitation frequency equals to natural frequencies, the maximum harvested power increases considerably.

    Keywords: Cantilever Beam, Piezoelectric, Surrogate Model, Deep Neural Network, Energy Optimization
  • MohammadMahdi Rastegardoost, Erfan Kosari *, Soroush Habibi Pages 91-102

    A successful cogeneration system design project needs an estimation of the economical parameters of the project, including capital investment, costs of fuel, expenses in maintenance and operating, and the proper cost for the products. This study describes the economic consideration of the benchmark cogeneration systems, called CGAM system located in the United States. To evaluate the profitability of alternative investments, cost estimation of the capital investment, calculation of the main product cost under the realistic assumption of fuel inflation, electricity inflation, and discount rate are required. Probabilistic analysis of lifetime discounted costs, including fuel and electricity cost changes, are defined by using the Monte-Carlo method for the next 20 years. Also, the total Revenue Requirement (TRR) method is selected as the main evaluation method for the economic model. As the result of calculations, the range of optimized value for inlet and outlet temperature of the combustion chamber, the efficiency of the gas turbine, efficiency and pressure ratio of air compressor in which the plant is economically and functionally in the best operation for the minimum cost of products of the cycle are achieved.

    Keywords: Cantilever Beam, Piezoelectric, Surrogate Model, Deep Neural Network, Energy Optimization