2017
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Determining the optimal size of a ground source heat pump within an airconditioning system with economic and emission considerations
2
2
One of the most challenging issues in modernday building energy management involves equipping the buildings with more energy efficient facilities. In this paper, a hybrid system for cooling/heating for a residential building is developed and optimized. The system consists of a ground source heat pump (GSHP) as well as an electric chiller (EC) and boiler. The model is implemented in MATLAB and optimized using NSGAII. Two economic and environmental objective functions are considered: Net Present Cost (NPC) and Carbon Emission (CE); which are minimized simultaneously. The results indicated that when the building load is completely met by GSHP, much less carbon is emitted to the environment, while when the majority of the load is provided by EC and boiler, NPC is lower and CE is much higher.
1

219
226


Hossein
Yousefi
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Faculty of New Sciences and Technologies,
Iran
hosseinyousefi@ut.ac.ir


Mohammad Hasan
Ghodusinejad
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Faculty of New Sciences and Technologies,
Iran


Younes
Noorollahi
Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Faculty of New Sciences and Technologies,
Iran
noorollahi@ut.ac.ir
Ground Source Heat Pump
Net Present Cost
Carbon Emission
Genetic algorithm
[[1] Narei H., Ghasempour R., Noorollahi Y., The Effect of Employing Nanofluid on Reducing the Bore Length of a Vertical GroundSource Heat Pump, Energy Conversion and Management (2016)123: 581591. ##[2] Zeng R., Hongqiang L., Lifang L., Xiaofeng Z., Guoqiang Z., A Novel Method Based on MultiPopulation Genetic Algorithm for CCHP–GSHP Coupling System Optimization, Energy Conversion and Management (2015) 105: 11381148. ##[3]Zhou S., Wenzhi C., Zhisong L., Xianyan L., Feasibility Study on Two Schemes for Alleviating the Underground Heat Accumulation of the Ground Source Heat Pump, Sustainable Cities and Society (2016) 24: 19. ##[4]Yousefi H., Noorollahi Y., Abedi S., Panahian K., MirAbadi A. H., Abedi S., Economic and Environmental Feasibility Study of Greenhouse Heating and Cooling using Geothermal Heat Pump in Northwest Iran, Proceedings World Geothermal Congress, Melbourne, Australia (2015). ##[5]Barbieri E. S., Dai Y. J., Morini M., Pinelli M., Spina P. R., Sun P., Wang R. Z., Optimal Sizing of a MultiSource Energy Plant for Power Heat and Cooling Generation,Applied Thermal Engineering (2014)71(2): 736750. ##[6]Gabrielli L., Bottarelli M., Financial and Economic Analysis for GroundCoupled Heat Pumps Using Shallow Ground Heat Exchangers, Sustainable Cities and Society (2016)20: 7180. ##[7] Noorollahi Y., Bigdelou P., Pourfayaz F., Yousefi H., Numerical Modeling and Economic Analysis of a Ground Source Heat Pump for Supplying Energy for a Greenhouse in Alborz Province, Iran, Journal of Cleaner Production (2016)131: 145154. ##[8]Huang B., Mauerhofer V., Life Cycle Sustainability Assessment of Ground Source Heat Pump in Shanghai, China. Journal of Cleaner Production (2016)119: 207214. ##[9] Yousefi H., Roumi S., Tabasi S., Hamlehdar M., Economic and Environmental Analysis of Replacement of Natural Gas Heating System with Geothermal Heat Pump in District 11 of Tehran, PROCEEDINGS of 41st Workshop on Geothermal Reservoir Engineering, Stanford California (2016). ##[10]Desideri U., Sorbi N., Arcioni L., Leonardi D., Feasibility Study and Numerical Simulation of a Ground Source Heat Pump Plant, Applied to a Residential Building, Applied Thermal Engineering (2011) 31(16): 35003511. ##[11] Garber D., Choudhary R., Soga K., Risk Based Lifetime Costs Assessment of a Ground Source Heat Pump (GSHP) System Design, Methodology and Case Study, Building and Environment (2013) 60: 6680. ##[12] Zeng R., Hongqiang L., Runhua J., Lifang L., Guoqiang Z., A Novel MultiObjective Optimization Method for CCHP–GSHP Coupling Systems, Energy and Buildings (2016)112: 149158. ##[13] Yousefi H., Ghodusinejad M.H., Kasaeian A.,. Multiobjective optimal component sizing of a hybrid ICE+ PV/T driven CCHP microgrid. Applied Thermal Engineering (2017)122: 126138. ##[14]http://www.eia.gov/oiaf/1605/emission_ factors.html [Accessed February 16, 2016]. ##[15]2006 IPCC Guidelines for National Greenhouse Gas Inventories, Intergovernmental Panel On Climate Change (2006).##]
Electromagnetic field analysis of novel low cogging force, linear switched reluctance motor, based on 2D finite element method
2
2
This paper deals with electromagnetic design and 2D (twodimensional) magnetic field analysis of novel low force ripple linear switched reluctance (LSR) motor. The configuration that has been presented here has a higher number of rotor poles than stator poles, and the purpose of this configuration is to improve the force ripple, which is the weak point of LSRMs. In order to illustrate the conformity of the design parameter’s stage in this study, the calculated values of the magnetic field and cogging force characteristics are compared with that of their desired values. Also, the proposed configuration is compared to a 64 and 3phase conventional LSRM with similar number of stator teethes, number of phases, and constraints in volume. From the numerical analysis of a proposed novel configuration, it has been observed that this machine produces higher force per unit volume and almost similar cogging force when compared to a conventional LSRM with identical number of stator teethes, number of phases, and constraints in volume. The obtained primary electric and magnetic characteristics for the proposed configuration are verified with the help of 2D FE computations.
1

227
240


Hassan
Moradi CheshmehBeigi
Electrical Engineering Department, Faculty of Engineering, Razi University, Kermanshah 67149, Iran
Electrical Engineering Department, Faculty
Iran
FE Computation
Magnetic Field Analysis
2D FEM
SR Motor
[[1] Krishnan K., Switched Reluctance Motor Drives, Modeling, Simulation, Analysis, Design, and Applications, CRC Press (2001). ##[2] Baoming G., Almeida A. T., Ferreira F., Design of Transverse Flux Linear Switched Reluctance Motor, IEEE Transactions on Magnetics (2009) 45(1): 113–119. ##[3] Ma C., Qu L., Multiobjective Optimization of Switched Reluctance Motors Based on Design of Experiments and Particle Swarm Optimization, IEEE Transactions on Energy Conversion, DOI: 10.1109/TEC.2015.2411677 (2015) (99):110. ##[4] Jin Y. , Bilgin B., Emadi A., An Offline Torque Sharing Function for Torque Ripple Reduction in Switched Reluctance Motor Drives, IEEE Transactions on Energy Conversion (2015) 30(2): 726735. ##[5] Boldea I., Linear Electric Machines, Drives and MAGLEVs Handbook, CRC Press (2013). ##[6] Amoros J. G., Andrada P., Sensitivity Analysis of Geometrical Parameters on a DoubleSided Linear Switched Reluctance Motor, IEEE Transactions on Industrial Electronics (2010) 57(1). ##[7] Lim H. S., Krishnan R., Lobo N. S., Design and Control of a Linear Propulsion System for an Elevator Using Linear Switched Reluctance Motor Drives, IEEE Transactions on Industrial Electronics (2008)55(2): 534–542. ##[8] Liu C.T., Su K.S., Chen J.W., Operational Stability Enhancement Analysis of a Transverse Flux Linear SwitchedReluctance Motor, in IEEE Transactions on Magnetics (2000) 36(5): 3699 – 3702. ##[9] Yanni L., Aliprantis D.C., Optimum Stator Tooth Shapes for Torque Ripple Reduction in Switched Reluctance Motors, IEEE International Electric Machines & Drives Conference (IEMDC), DOI: 10.1109/IEMDC.2013.6556224 (2013) 1037 – 1044. ##[10] Sheth N. K., Rajagopal K. R., Optimum Pole Arcs for a Switched Reluctance Motor for Higher Torque with Reduced Ripple, IEEE Transactions on Magnetics (2003) 39(5): 3214–3216. ##[11] Lee J.W., Kim H.S., Kwon B., Kim B.T., New Rotor Shape Design for Minimum Torque Ripple of SRM Using FEM, IEEE Transactions Magnetics (2004) 40 (2): 754  757,. ##[12] Mikail R., Husain I., Sozer Y., Islam M., Sebastian T.,TorqueRipple Minimization of Switched Reluctance Machines Through Current Profiling, IEEE Transactions on Industry Applications (2013) 49 (3):1258 1267. ##[13] Husain I., Minimization of Torque Ripple in SRM Drive, IEEE Transactions on Industrial Electronics (2002) 49(1):28 39. ##[14] Lee D. H., Lee Z. G., Ahn J. W., A Simple Nonlinear Logical Torque Sharing Function for LowTorque Ripple SR Drive, IEEE Transactions on Industrial Electronics (2009) 56(8): 3021 3028. ##[15] Kiwoo P., Liu X., Chen Z., A NonUnity Torque Sharing Function for Torque Ripple Minimization of Switched Reluctance Generators, The European Conference on Power Electronics and Applications (2013) 1 10. ##[16] Choi Y.K, Koh C. S, Pole Shape Optimization of Switched Reluctance Motor for Reduction of Torque Ripple, 12th Biennial IEEE Conference on Electromagnetic Field Computation, DOI: 10.1109/CEFC06.2006.1633125 (2006). ##[17] Choi Y.K., Yoon H. S., Koh C. S., PoleShape Optimization of a SwitchedReluctance Motor for Torque Ripple Reduction, IEEE Transactions on Magnetics (2007) 43(4). ##[18] Wang H., Lee D.H., Park T.H., Ahn J.W., Hybrid StatorPole Switched Reluctance Motor to Improve Radial Force for Bearing Less Application, Energy Convers Manage (2011)52:1371–6. ##[19] Faghihi F., Heydari H., Reduction of Leakage Magnetic Field in Electromagnetic Systems Based on Active Shielding Concept Verified by Eigenvalue Analysis, Progress In Electromagnetics Research (2009) 96: 217236. ##[20] Rezaeealam J.B., Yamada S., Coupled FiniteElement/BoundaryElement Analysis Of A Reciprocating SelfExcited Induction Generator In A Harmonic Domain, IEEE Transactions On Magnetics (2005) 41(11). ##[21] Tian J., Lv Z. Q., Shi X. W., Xu L., Wei F., An Efficient Approach for Multifrontal Algorithm to Solve NonPositiveDefinite Finite Element Equations In Electromagnetic Problems”, Progress In Electromagnetics Research (2009) 95121133. ##[22] Norhisam M., Ridzuan S., Firdaus R. N., Aravind C. V., Wakiwaka H., Nirei M., Comparative Evaluation On PowerSpeed Density Of Portable Permanent Magnet Generators For Agricultural Application, Progress In Electromagnetics Research (2012) 129:345363. ##[23] Jian L., Xu G., Gong Y., Song J., Liang J., Chang M., Electromagnetic Design And Analysis Of A Novel MagneticGearIntegrated Wind Power Generator Using TimeStepping Finite Element Method, Progress In Electromagnetics Research(2011.)113:351367. ##[24] Mahmoudi A., Kahourzade S., Rahim N. A., Hew W. P., Ershad N. F., SlotLess Torus SolidRotorRinged LineStart AxialFlux PermanentMagnet Motor, Progress In Electromagnetics Research (2012) 131:331355. ##[25] Liang J., Jian L., Xu G., Shao Z., Analysis Of Electromagnetic Behavior in Switched Reluctance Motor for The Application Of Integrated Air Conditioner OnBoard Charger System, Progress in Electromagnetics Research, (2012) 124:347364. ##[26] DanCristian Popa, VasileIoan Gliga, and Lorand Szabo, Theoretical and Experimental Study of a Modular Tubular Transverse Flux RelucTance Machine, Progress in Electromagnetics Research (2013) 139:4155. ##[27] Cheshmehbeigi H. M., Afjei S. E., Nasiri B., Electromagnetic Design Based on Hybrid Analytical and 3D Finite Element Method for Novel Two Layers Bldc Machine, Progress In Electromagnetics Research,DOI:10.2528/PIER12111301 (2013) 136: 141155. ##[28] Liu C., Chau K. T., Electromagnetic Design and Analysis of DoubleRotor FluxModulated PermanentMagnet Machines, Progress in Electromagnetics Research (2012) 131:8197. ##[29] Li X., Chau K.T., Cheng M., Hua W., Comparison of MagneticGeared PermanentMagnet Machines, Progress in Electromagnetics Research (2013) 133: 177198. ##[30] Jian L., Liang J., Shi Y., Xu G., A Novel DoubleWinding Permanent Magnet Flux Modulated Machine for StandAlone Wind Power Generation, Progress In Electromagnetics Research (2013) 142: 275289. ##[31] Yan L., Zhang L., Wang T., Jiao Z., Chen C. Y., Chen IM., Magnetic Field of Tubular Linear Machines with Dual Halbach Array, Progress in Electromagnetics Research (2013) 136: 283299,. ##[32] Kahourzade S., Gandomkar A., Mahmoudi A., Abd Rahim N., Hew W. P., Uddin M.N., Design Optimization and Analysis of AFPM Synchronous Machine in Corporating Density, Thermal analysis NDBACKEMFTHD Progress in Electromagnetics Research (2013) 136: 327367. ##[33] Andreux R., Fontchastagner J., Takorabet N., Labbe N., Metral J.S., A General Approach for Brushed DC Machines Simulation Using a Dedicated Field/Circuit Coupled Method, Progress in Electromagnetics Research (2014) 145, 213227. ##[34] Musolino A., Rizzo R., Tripodi E., Tubular Linear Induction Machine as a Fast Actuator, Analysis and Design Criteria, Progress in Electromagnetics Research (2012) 132:603619. ##[35] Khalil A., Modeling and Analysis of Four Quadrant Sensorless Control of a Switched Reluctance Machine over the Entire Speed Range, Dissertation, University of Akron (2005). ##[36] Rahman M., Chancharoensook P., Dynamic Modeling of a FourPhase 8/6 Reluctance Motor Using Current and Torque Lookup Tables, IECON 02 (Industrial Electronics Society) 28th Annual Conference, IEEE 2002 . ##[37] Ren Z., TΩ Formulation for EddyCurrent Problems in Multiply Connected Regions, IEEE Transactions on Magnetics (2002) 38(2). ##[38] Webb J. P., Singular Tetrahedral Finite Elements of High Order for Scalar Magnetic and Electric Field Problems, IEEE Transactions on Magnetics (2008) 44 (6). ##]
Theoretical analysis of a novel combined cooling, heating, and power (CCHP) cycle
2
2
This study presents a theoretical analysis of a new combined cooling, heating, and power cycle by the novel integration of an organic Rankine cycle (ORC), an ejector refrigeration cycle (ERC), and a heat pump cycle (HPC) for producing cooling output, heating output, and power output simultaneously. Three different working fluids—namely R113, isobutane, and R141b—have been used in power, refrigeration, and heating subcycles, respectively. Energetic and exergetic analyses of the proposed cycle have been conducted to demonstrate its efficiency. The thermal and exergy efficiencies are obtained as 71.08% and 38.3%, respectively. The exergy destruction rate of each component and the overall cycle have been calculated where it is shown that among all the components, the generator has a main contribution in the cycle inefficiency. Finally, the sensitivity analysis of the different key parameters on the performance of the proposed cycle has been investigated. It has been demonstrated that the proposed cycle performs well in high generator pressure and low evaporator outlet pressure, based on the first and second laws of thermodynamics.
1

241
249


Hadi
Rostamzadeh
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
Department of Aerospace Engineering, Sharif
Iran


Hadi
Ghaebi
Department of Mechanical Engineering, Faculty of Engineering, University of Mohaghegh Ardabili, P.O.Box 179, Ardabil, Iran
Department of Mechanical Engineering, Faculty
Iran
hghaebi@uma.ac.ir


Keivan
Mostoufi
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
Department of Aerospace Engineering, Sharif
Iran
Organic Rankine Cycle (ORC)
Ejector Refrigeration Fycle (ERC)
Heat Pump Cycle (HPC)
Combined Cooling, Heating and Power (CCHP) Cycle
Working Fluid
[[1] Goswami, D.Y., Xu, F. Analysis of a new Thermodynamic Cycle for Combined Power and Cooling Using Low and Mid Temperature Solar Collectors, The Journal of Solar Energy Engineering (1999) 121: 9197. ##[2] Xu, F., Goswami, D.Y., Bhagawat, S.S. A Combined Power/Cooling Cycle, Energy (2000) 25: 233246. ##[3] Hasan A.A., Goswami D.Y., Vijayaraghavan S., First and Second Law Analysis of a New Power and Refrigeration Thermodynamic Cycle Using a Solar Heat Source, The Journal of Solar Energy Engineering (2002) 73:385393. ##[4] Vijayaraghavan S., Goswami D.Y. On Evaluating Efficiency of a Combined Power and Cooling Cycle, Journal of Energy Resources Technology (2003) 125: 221227. ##[5] Zheng B., Weng Y.W., A Combined Power and Ejector Refrigeration Cycle for Low Temperature Heat Sources, The Journal of Solar Energy Engineering (2010) 84: 784791. ##[6] Habibzadeh A., Rashidi M.M., Galanis N. Analysis of a Combined Power and Ejector Refrigeration Cycle Using Low Temperature Heat, Energy Converse Manage(2013) 65: 381391. ##[7] Wang J.J., Yang Y., Energy, Exergy and Environmental Analysis of a Hybrid Combined Cooling, Heating, and Power System Utilizing Biomass and Solar Energy, Energy Convers Manage (2016) 124: 566577. ##[8] Sun F., Fu L., Sun J., Zhang S., A New Waste Heat District Heating System with Combined Heat and Power (CHP) Based on Ejector Heat Exchangers and Absorption Heat Pumps, Energy(2014) 69: 516524 ##[9] Li M., Mu H., Li N., Ma B., Optimal Design and Operation Strategy for Integrated Evaluation of CCHP (Combined Cooling, Heating, and Power) System, Energy (2016) 99: 202220. ##[10] Javan S., Mohamadi V., Ahmadi P., Hanafizadeh P., Fluid Selection Optimization of a Combined Cooling, Heating, and Power (CCHP) System for Residential Applications, Applied Thermal Engineering (2016) 96: 2638. ##[11] Bejan A., Tsataronis G., Moran M., Thermal Design and Optimization (1996) Jhon Wiley & Sons, NY.##]
An investigation on effect of backbone geometric anisotropy on the performance of infiltrated SOFC electrodes
2
2
Design of optimal microstructures for infiltrated solid oxide fuel cell (SOFC) electrodes is a complicated process because of the multitude of the electrochemical and physical phenomena taking place in the electrodes in different temperatures, current densities and reactant flow rates. In this study, a stochastic geometric modeling method is used to create a range of digitally realized infiltrated SOFC electrode microstructures to extract their geometryrelated electrochemical and physical properties. Triple Phase Boundary (TPB), active surface density of particles along with the gas transport factor is evaluated in those realized models to adapt for various infiltration strategies. Recently, additive manufacturing or freeze type casting methods enable researchers to investigate the performance of directional electrodes to get the maximum electrochemical reaction sites, gas diffusivity and ionic conductivity simultaneously. A series of directional backbones with different amount of virtually deposited electrocatalyst particles are characterized in the first step. The database of microstructural parameters (inputs) and effective geometric properties (outputs) is used to train a range neural network. A microstructure property hull is created using the best neural network model to discover the range of effective properties, their relative behaviour and optimum microstructure. The characteristics of models is shown that there is not any contradiction between the high level of TPB and contact surface density of particles, but the highest amount of gas diffusivity can be found in the microstructures with lower level of reaction sites. Also increasing the contact surface density has a negative effect on gas transport but the high level of TPB density is feasible in the full range of microstructures. In the other hand, TPB density and gas diffusion into the models are inversely related, although there are a limited number of microstructures with high level of reaction sites and acceptable gas diffusivity. Finally, using a simple optimization process, the microstructures with the highest level of reaction sites and gas transport factor are identified which have the backbone porosity of about 50%, and extremely higher gain growth rate normal to the electrolyte. Additive manufacturing and 3D printing methods will enhance researchers in the future to create the real directional electrodes on the base of these proposed models.
1

251
264


Mehdi
Tafazoli
Mechanical Engineering Department, Babol Noshirvani University of Technology, Babol, Iran
Mechanical Engineering Department, Babol
Iran


Mohsen
Shakeri
Mechanical Engineering Department, Babol Noshirvani University of Technology, Babol, Iran
Mechanical Engineering Department, Babol
Iran
shakeri@nit.ac.ir


Majid
Baniassadi
School of Mechanical Engineering, College of Engineering, University of Tehran,Tehran, Iran
School of Mechanical Engineering, College
Iran
m.baniassadi@ut.ac.ir


Alireza
Babaei
School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Iran
School of Metallurgy and Materials Engineering,
Iran
alireza.babaei@ut.ac.ir
Backbone Anisotropy
Infiltrated Electrode
Realization of Microstructure
Solid Oxide Fuel Cells
[[1] Minh N.Q., Takahashi T., Science and Technology of Ceramic Fuel Cells (1995) Elsevier. ##[2] Steele B.C., Heinzel A., Materials for FuelCell Technologies, Nature (2001) 414(6861): 34552. ##[3] Minh N.Q., Ceramic Fuel Cells, Journal of the American Ceramic Society (1993) 76(3): 563588. ##[4] Brown M., Primdahl S., Mogensen M., Structure/Performance Relations for Ni/Yttria‐Stabilized Zirconia Anodes for Solid Oxide Fuel Cells, Journal of the Electrochemical Society, (2000)147(2):47585. ##[5] Smith J., Chen A., Gostovic D., Hickey D., Kundinger D., Duncan K., Evaluation of the Relationship between Cathode Microstructure and Electrochemical Behavior for SOFCs, Solid State Ionics, (2009)180(1):908. ##[6] Kishimoto M., Lomberg M., RuizTrejo E., Brandon N.P., Enhanced TriplePhase Boundary Density in Infiltrated Electrodes for Solid Oxide Fuel Cells Demonstrated by HighResolution Tomography, Journal of Power Sources, (2014)266:2915. ##[7] Ni M., Zhao T.S., Solid Oxide Fuel Cells, Royal Society of Chemistry (2013). ##[8] Shearing P., Brett D., Brandon N., Towards Intelligent Engineering of SOFC Electrodes, A Review of Advanced Microstructural Characterisation Techniques, International Materials Reviews (2010)55(6): 347363. ##[9] Jiang S.P., Nanoscale and NanoStructured Electrodes of Solid Oxide Fuel Cells by Infiltration, Advances and Challenges, International Journal of Hydrogen Energy (2012) 37(1): 449470. ##[10] Vohs J.M., Gorte R.J., High‐Performance SOFC Cathodes Prepared by Infiltration. Advanced Materials (2009) 21(9): 943956. ##[11] Sarikaya A., Petrovsky V., Dogan F., Development of the Anode Pore Structure and its Effects on the Performance of Solid Oxide Fuel Cells, International Journal of Hydrogen Energy (2013) 38(24): 1008110091. ##[12] Othman M.H.D., Droushiotis N., Wu Z., Kelsall G., Li K., High‐Performance, Anode‐Supported, Microtubular SOFC Prepared from Single‐Step‐Fabricated, Dual‐Layer Hollow Fibers. Advanced Materials (2011) 23(21): 24802483. ##[13] Cable T.L., Setlock J.A., Farmer S.C., Eckel A.J., Regenerative Performance of the NASA Symmetrical Solid Oxide Fuel Cell Design, International Journal of Applied Ceramic Technology (2011) 8(1): 112. ##[14] Gannon P., Sofie S., Deibert M., Smith R., Gorokhovsky V., Thin Film YSZ Coatings on Functionally Graded Freeze Cast NiO/YSZ SOFC Anode Supports, Journal of Applied Electrochemistry (2009)39(4): 497502. ##[15] Chen Y., Bunch J., Li T., Mao Z., Chen F., Novel Functionally Graded Acicular Electrode for Solid Oxide Cells Fabricated by the FreezeTapeCasting Process, Journal of Power Sources (2012)213: 9399. ##[16] Chen Y., Zhang Y., Baker J., Majumdar P., Yang Z., Han M., Hierarchically Oriented Macroporous AnodeSupported Solid Oxide Fuel Cell with thin Ceria Electrolyte Film, ACS Applied Materials & Interfaces (2014) 6(7): 51305136. ##[17] Sofie S.W., Fabrication of Functionally Graded and Aligned Porosity in Thin Ceramic Substrates With the Novel Freeze–Tape‐Casting Process. Journal of the American Ceramic Society (2007) 90(7): 20242031. ##[18] Hen Y., Liu Q., Yang Z., Chen F., Han M., High Performance Low Temperature Solid Oxide Fuel Cells with Novel Electrode Architecture, RSC Advances (2012) 2(32): 1211812121. ##[19] Chen Y., Lin Y., Zhang Y., Wang S., Su D., Yang Z., Low Temperature Solid Oxide Fuel Cells with Hierarchically Porous Cathode NanoNetwork, Nano Energy (2014)8: 2533. ##[20] Chen Y., Zhang Y., Lin Y., Yang Z., Su D., Han M., DirectMethane Solid Oxide Fuel Cells with Hierarchically Porous NiBased Anode Deposited with Nanocatalyst layer, Nano Energy (2014) 10: 19. ##[21] Torabi A., Hanifi A.R., Etsell T.H., Sarkar P., Effects of Porous Support Microstructure on Performance of Infiltrated Electrodes in Solid Oxide Fuel Cells, Journal of the Electrochemical Society (2011)159(2): B201B210. ##[22] Cassidy M., Doherty D.J., Yue X., Irvine J.T., Development of Tailored Porous Microstructures for Infiltrated Catalyst Electrodes by Aqueous Tape Casting Methods. ECS Transactions (2015) 68(1): 20472056. ##[23] Zhang Y., Sun Q., Xia C., Ni M., Geometric Properties of Nanostructured Solid oxide Fuel Cell Electrodes, Journal of The Electrochemical Society (2013)160(3): F278F289. ##[24]Zhang Y., Ni M., Xia C., Microstructural insights into dualphase infiltrated solid oxide fuel cell electrodes. Journal of The Electrochemical Society (2013)160(8): F834F839. ##[25] Bertei A., Pharoah J.G., Gawel D.A., Nicolella C., Microstructural Modeling and Effective Properties of Infiltrated SOFC Electrodes, ECS Transactions (2013)57(1): 25272536. ##[26] Synodis M.J., Porter C.L., Vo N.M., Reszka A.J., Gross M.D., Snyder R.C., A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells. Journal of The Electrochemical Society (2013)160(11): F1216F1224. ##[27]Hardjo E.F., Monder D.S., Karan K., An Effective Property Model for Infiltrated Electrodes in Solid Oxide Fuel Cells. Journal of The Electrochemical Society (2014)161(1): F83F93. ##[28]Reszka A.J., Snyder R.C., Gross M.D., Insights into the Design of SOFC nfiltrated Electrodes with Optimized Active TPB Density via Mechanistic Modeling. Journal of The Electrochemical Society (2014)161(12): F1176F1183. ##[29] Kishimoto M., Lomberg M., RuizTrejo E., Brandon N.P., Towards the Microstructural Optimization of SOFC Electrodes Using Nano Particle Infiltration. ECS Transactions (2014) 64(2): 93102. ##[30] Jemeı S., Hissel D., Péra M.C., Kauffmann J.M., OnBoard Fuel Cell Power Supply Modeling on the Basis of Neural Network Methodology, Journal of Power Sources (2003)124(2): 479486. ##[31]Ou S., Achenie L.E., A hybrid neural network model for PEM fuel cells. Journal of Power Sources (2005)140(2): 319330. ##[32] Hagan M.T., Demuth H.B., Beale M.H., De Jesús O., Neural Network Design, PWS Publishing Company Boston(1996) 20. ##[33] Marra D., Sorrentino M., Pianese C., Iwanschitz B., A Neural Network Estimator of Solid Oxide Fuel Cell Performance for onField Diagnostics and Prognostics Applications, Journal of Power Sources (2013)241: 320329. ##[34]Bozorgmehri S., Hamedi M., Modeling and Optimization of Anode‐Supported Solid Oxide Fuel Cells on Cell Parameters via Artificial Neural Network and Genetic Algorithm. Fuel Cells (2012) 12(1): 1123. ##[35] Tafazoli M., Shakeri M., Baniassadi M., Babaei A., Geometric Modeling of Infiltrated Solid Oxide Fuel Cell Electrodes with Directional Backbones, Fuel Cells (2017)17(1):6774. ##[36] Baniassadi M., Ahzi S., Garmestani H., Ruch D., Remond Y., New Approximate Solution for NPoint Correlation Functions for Heterogeneous Materials, Journal of the Mechanics and Physics of Solids (2012)60(1): 104119. ##[37] Baniassadi M., Garmestani H., Li D., Ahzi S., Khaleel M., Sun X., ThreePhase Solid Oxide Fuel Cell Anode Microstructure Realization using TwoPoint Correlation Functions, Acta Materialia (2011)59(1): 3043. ##[38] Rüger B., Joos J., Weber A., Carraro T., IversTiffée E., 3D Electrode Microstructure Reconstruction and Modelling, ECS Transactions (2009)25(2): 12111220. ##[39] Joos J., Rüger B., Carraro T., Weber A., IversTiffée E., Electrode Reconstruction by FIB/SEM and Microstructure Modeling, ECS Transactions (2010)28(11): 8191. ##[40]Cai Q., Adjiman C.S., Brandon N.P., Modelling the 3D microstructure and performance of solid oxide fuel cell electrodes: computational parameters. Electrochimica Acta (2011)56(16): 58045814. ##[41]Jiang Z., C. Xia, Chen F., NanoStructured Composite Cathodes for intermediatetemperature solid oxide fuel cells via an infiltration/impregnation technique. Electrochimica Acta (2010) 55(11): 35953605. ##[42]Adler S.B., Lane J., Steele B., Electrode kinetics of porous mixed‐conducting oxygen electrodes. Journal of the Electrochemical Society (1996)143(11): 35543564. ##[43]Babaei A., Jiang S.P., Li J., Electrocatalytic promotion of palladium nanoparticles on hydrogen oxidation on Ni/GDC anodes of SOFCs via spillover. Journal of the Electrochemical Society (2009)156(9): B1022B1029. ##[44]Janardhanan V.M., Heuveline V., Deutschmann O., Threephase boundary length in solidoxide fuel cells: A mathematical model. Journal of Power Sources (2008)178(1): 368372. ##[45] Kishimoto M., Lomberg M., RuizTrejo E., Brandon N.P.,Towards the DesignLed Optimization of Solid Oxide Fuel Cell Electrodes. in ECS Conference on Electrochemical Energy Conversion & Storage with SOFCXIV (2015). ##[46]Cronin J.S., ThreeDimensional Structure Combined with Electrochemical Performance Analysis for Solid Oxide Fuel Cell Electrodes (2012). ##[47] Shikazono N., Kanno D., Matsuzaki K., Teshima H., Sumino S., Kasagi N., Numerical Assessment of SOFC Anode Polarization Based on ThreeDimensional Model Microstructure Reconstructed from FIBSEM Images, Journal of The Electrochemical Society (2010) 157(5): B665B672. ##[48]He W., Lv W., Dickerson J., Gas transport in solid oxide fuel cells (2014) Springer. ##[49] Fullwood D.T., Niezgoda S.R., Adams B.L., Kalidindi S.R., Microstructure Sensitive Design for Performance Optimization. Progress in Materials Science (2010)55(6): 477562. ##[50]Adams B.L., Kalidindi S., Fullwood D.T., MicrostructureSensitive Design for Performance Optimization,ButterworthHeinemann (2013).##]
Performance evaluation of trapezoidal teeth labyrinth seal
2
2
The present paper investigates the effects of the trapezoidal teeth labyrinth seal on the leakage amount in gas turbines. The influences of increasing the number of teeth from 1 to 6 with step 1 and the tip clearance s=0.5 to 7.5 mm on the leakage flow at different pressure ratios of PR=1.5, 2 and 2.5 are examined, comprehensively. The analysis is performed numerically using a FiniteVolume software with the kε turbulent model. The obtained results show a good agreement in comparison with the other research results. The results show that an increase in the number of teeth causes a decrease in the leakage flow. An increase in the tip clearance (s) from 0.5 mm to around 7.5 mm leads to a decrease in the leakage flow, like the step labyrinth behavior, while an increase beyond 7.5 mm results into a leakage increase, the behavior of the straight labyrinth.
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273


Razie
Abdous
Aerospace Department, Semnan University, Semnan, Iran
Aerospace Department, Semnan University,
Iran
r.abdous92@yahoo.com


Saadat
Zirak
Mechanical Engineering Department, Semnan University, Semnan, Iran
Mechanical Engineering Department, Semnan
Iran
s_zirak@semnan.ac.ir
Labyrinth
Seal
Trapezoidal
Number of Teeth
tip clearance
Leakage Flow
CFD
[[1] Chupp R. E., Hendricks R. C., Lattime S. B., Steinetz B. M., Sealing in Turbomachinery, Journal of Propulsion and Power (2006) 22(2): 313349. ##[2] Stoff H., Incompressible Flow in a Labyrinth Seal, The Journal of Fluid Mechanics (1980)100: 817829. ##[3] Rhoded L., Demko J. A., Morrison G. L., On the Prediction of Incompressible Flow in Labyrinth Seals. ASEM Journal of Fluids Engineering (1984)108(1):1925. ##[4] Malvano R., Vatta F., Vigliani A., Rotordynamic Coefficients for Labyrinth Gas Seal, Single Control Volume Model. Meccanica (2001) 36(66): 731744. ##[5] Rhode D. L., Johnson J. W., D. H. Broussard, Flow Visualization and Leakage Measurements of Stepped Labyrinth Seals, Part1 – Annular Groove, ASME Journal of Turbomachinery (1997)119(4): 839843. ##[6] Vakili A. D., Meganathan A. J., Michaud M., Radhakrishnan S., An Experimental and Numerical Study of Labyrinth Seal Flow, ASME Paper Gas Turbine 200568224 (2005). ##[7] Kim T. S., Cha K. S., Comparative Analysis of the Influence of Labyrinth Seal Configuration on Leakage Behavior, Journal of Mechanical Science and Technology (2009) 23: 28302838. ##[8] Wang W., Liu Y., Jiang P., Chen H., Numerical Analysis of Leakage Flow Through Two Labyrinth Seals, Journal of Hydrodynamics (2007)19(1):107112. ##[9] Hinze J. O., Turbulence, McGrawHill Publishing Company, New York (1975). ##[10] Choudhury D., Introduction to the Renormalization Group Method and Turbulence Modeling, Fluent Incorporated Technical Memorandum, TM107 (1993). ##[11] Sarkar S., Balakrishnan L., Application of a ReynoldsStress Turbulence Model to the Compressible Shear Layer, ICASE Report 9018, NASA CR 182002 (1990). ##[12] FLUENT 6.3 User's Guide, Fluent Incorporated (2006). ##[13] Bovand M., Valipour M. S., Eiamsaard S., Tamayol A., Numerical Analysis for Curved Vortex Tube Optimization, International Communications in Heat and Mass Transfer (2013) 50:98107. ##[14] Patankar S.V., Numerical Heat Transfer and Fluid Flow, Hemisphere, New York (1980). ##]
Gasification of potato shoots: An experimental and theoretical investigation
2
2
A thermodynamic equilibrium model was developed to predict the gasification process in a benchscale fluidized bed gasifier. Potato shoot (leaves and stems) was used as the feedstock of the gasifier. The experiments were done in five different gasification zone temperatures (650, 700, 750, 800 and 850°C), with a feeding rate of 0.166 kg/hour, and two equivalence ratios (ER: 0.2 and 0.25). The produced gas was analyzed and the portion of each component was calculated from a thermodynamic equilibrium model. The data from the experiments were compared with those of the modeling in order to validate the model. For 650°C, the closest results of the model to experiment data were observed for CO2 at ER = 0.2, followed by CO at ER = 0.25 with errors of 7% and 21%, respectively. The least difference between the model data and the experimental data at 700°C was observed for N2 with the error of 26% and 22% for ER= 0.2 and 0.25, respectively. At 750°C, the predicted values conformed reasonably well to the experimental data for CO with error less than 7%. Regarding the least error, the most admissible results were seen at 800°C for N2 with ER= 0.25 with an error of 7%. In this case, the most acceptable results of the model were obtained for 850°C, in which the error in predicting the amount of CH4 at ER= 0.25 was 0. Owing to the applicability of potato shoot in the gasification process, it can play a great role in energy production.
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275
284


Mojtaba
Javidi Gharacheh
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Biosystems Engineering, Ferdowsi
Iran
mo.javidi@mail.um.ac.ir


Mehdi
Khojastehpour
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Biosystems Engineering, Ferdowsi
Iran
mkhpour@um.ac.ir


Mohammadali
Ebrahiminik
Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Biosystems Engineering, Ferdowsi
Iran
ebrahiminik@um.ac.ir


Wan Azlina Wan
Ab Karim Ghani
Department of Chemical and Environmental Engineering, University Putra Malaysia, Malaysia
Department of Chemical and Environmental
Malaysia
FluidizedBed
Gasifier
Modeling
Potato Shoot
Thermodynamic Equilibrium Model
[[1] http://www.ren21.net/statusofrenewables/globalstatusreport/; [Accessed 29.10.16]. ##[2] Nieminen J., Kivela M., Biomass CFB Connected to a 350 MW/TH Steam Boiler Fired with Coal and Natural Gasthermie Demonstration Project in Lahti in Finland, Biomass Bioenergy (1998) 15: 251257. ##[3] Mory A., Zotter T., EUDemonstration Project Biococomb for Biomass Gasification and CoCombustion of the ProductGas in a CoalFired Power Plant in Austria, Biomass Bioenergy (1998) 15: 239244. ##[4] De Lange H. J., Barbucci P., The Thermie Energy Farm Project, Biomass Bioenergy (1998) 15: 219224. ##[5] Faaij A., Van Doorn J., Curvers T., Waldheim L., Olsson E., Van Wijk A., DaeyOuwens C., Characteristics and Availability of Biomass Waste and Residue in the Netherlands for Gasification, Biomass Bioenergy (1997) 12: 225240. ##[6] Olivares A., Aznar M.P., Caballero M.A., Gil J., Frances E., Corella J., Biomass Gasification: Produced Gas Upgrading by inbed Use of Dolomite, Industrial Engineering Chemistry Research (1997) 36: 52205226. ##[7] Monforti F., Bodis K., Scarlat N.,Dallemond J.F., The Possible Contribution of Agricultural Crop Residues to Renewable Energy Targets in Europe: A Spatially Explicit Study, Renewable and Sustainable Energy Reviews (2013) 19: 666–677. ##[8] Plis P., Wilk R.K., Theoretical and Experimental Investigation of Biomass Gasification Process in a Fixed Bed Gasifier, Energy (2011) 36: 3838–3845. ##[9] Arnavat, P. M., Performance Modeling and Validation of Biomass Gasifiers for Trigeneration Plant. PhD Thesis, Department of Mechanical Engineering, University of Rovira i Virgili (2011). ##[10] Food and Agriculture Organization of the United Nations, FAOSTAT, http://faostat.fao.org/beta/en/#data/QC; [Accessed 22.11.16]. ##[11] http://www.fao.org/potato2008/en/world/.2008; [Accessed 21.12.15]. ##[12] Khajehpour M., The Production of Industrial Crops 4th Edition (1999) 203 225. ##[13] https://research.cip.cgiar.org/confluence/display/wpa/Iran (2014) [Accessed 25.01.15]. ##[14] Mweetwaa A.M., Huntera D., Poea R., Kim C., Harich Ginzberg I., Tokuhisa J.G., Veilleux R. E., Steroidal Glycoalkaloids in Solanumchacoense, Phytochemistry (2012) 75: 32–40. ##[15] Dokhani Sh., Keramat J., Rofigari Haghighat Sh., Changes in Glycoalkaloids and Alfasolanine in Potatoes During Storage and Thermal Process, Journal of Soil and Water Sciences, Science and Technology of Agriculture and Natural Resources (2003) 7: 171183. ##[16] Xie J., Zhong W., Jin B., Shao Y., Liu H., Simulation on Gasification of Forestry Residues in Fluidized Beds by Eulerian–Lagrangian Approach, Bioresource Technology (2012) 121: 3646. ##[17] Vaezi M., PassandidehFard M., Moghiman M., Charmchi M., Gasification of Heavy Fuel Oils, A Thermochemical Equilibrium Approach, Fuel (2011) 90: 878885. ##[18] Jarungthammachotes S., Dutta A., Thermodynamic Equilibrium Model and Second Law Analysis of a Downdraft Waste Gasifier, Energy (2007) 32: 16601669. ##[19] Pengmei L., Zhenhong Y., Longlong M., HydrogenRich Gas Production from Biomass Air and Oxygen/Steam Gasification in a Downdraft Gasifier, Renewable Energy (2007) 32: 21732185. ##[20] Midilli A., Dogru M., Howarth C.R., Ayhan T., Hydrogen Production from Hazelnut Shell by Applying AirBlown Downdraft Gasification Technique, International Journal of Hydrogen Energy (2001) 26: 2937. ##[21] Zhao B., Zhang X., Sun L., Meng G., Chen L., Xiaolu Y., Hydrogen Production from Biomass Combining Pyrolysis and the Secondary Decomposition, International Journal of Hydrogen Energy (2010) 35: 26062611. ##[22] Colpan C.O., Fung A.S., Hamdullahpur F., Modeling of an Integrated TwoStage Biomass Gasiﬁer and Solid Oxide Fuel Cell System, Biomass and Bioenergy (2012) 42: 132 – 142. ##[23] Zainal Z.A., Ali R., Lean C.H., Seetharamu K.N., Prediction of the Performance of a Downdraft Gasifier Using Equilibrium Modeling for Different Biomass Materials, Energy Conversion and Management (2001) 42: 1499–1515. ##[24] Orikasa H., Tomita A., NO and N2 90: 878885.Formation Behavior during the HighTemperature O2 Gasification of Coal Char, Energy Fuels (2003) 17: 405–411. ##[25] Ghani W.A.W.K., Moghadam R.A., Salleh M.A.M., Tavasoli A., Gasification Performance of Rice Husk in Fluidized Bed Reactor, A HydrogenRich Production, Journal of Energy and Environment (2012) 4: 711. ##[26]Ghani W.A.W.K., Moghadam R.A., Salleh M.A.M., Alias A., Air Gasification of Agricultural Wastes in a Fluidized Bed Gasifier, Energies (2009) 2: 258268. ##[27] Leung D.Y.C., Wang C.L., Fluidized Bed Gasification of Waste tire Powders, Fuel Processing Technology (2003) 84: 175196. ##[28] Cao Y., Wang Y., Riley J.T., Pan W.P., A Novel Biomass Air Gasification Process for Producing TarFree Higher Heating Value Fuel Gas, Fuel Processing Technology (2006) 87: 343353. ##[29] Simone M., Nicolella C., Tognotti L., Numerical and Experimental Investigation of Downdraft Gasification of Woody Residue, Bioresource Technology (2013) 133: 92101. ##[30] Doranehgard M.H., Samadyar H., Mesbah M., Haratipour M., Samiezade S., HighPurity Hydrogen Production with in Situ CO2 Capture Based on Biomass Gasification, Fuel (2017) 202: 29–35. ##[31] Monteiro E., Ismail T.M., Ramos A., ElSalam M.A., Brito P.S.D., Rouboa A., Assessment of the Miscanthus Gasification in a SemiIndustrial Gasifier Using a CFD Model, Applied Thermal Engineering (2017) 123: 448–457. ##[32] Sales C.V.B., Maya D.M.Y., Lora E.E.S., Jaén R.L., Reyes A.M.M., Andrade A.M.G.R.V., Martínez J.D., Experimental Study on Biomass (eucalyptus spp.) Gasification in a TwoStage Downdraft Reactor by Using Mixtures of Air, Saturated Steam and Oxygen as Gasifying Agents, Energy Conversion and Management (2017) 145: 314–323. ##[33] Lv P.M., Xiong Z.H., Chan G.J., Wu C.Z., Chen Y., Zhu J.X., An Experimental Study on Biomass AirSteam Gasification in a Fluidized Bed, Bioresource Technology (2004) 95: 95101. ##[34] Xiao R., Jin B., Zhou H., Zhong Z., Zhong M., Air Gasification of Polypropylene Plastic Waste in Fluidized Bed Gasifier, Energy Conversion Management (2007) 48: 778786.##]
4E analysis and multiobjective optimization of gas turbine CCHP plant with variable ambient temperature
2
2
In this paper a gas turbine power plant including air preheater (recuperator), heat recovery steam generator and air cooler system was modeled. Eight parameters were selected as the design variables. Fast and elitist nondominated sorting genetic algorithm (NSGAII) was applied (to maximize the exergy efficiency and to minimize the total cost rate) for the mentioned cogeneration system. The total cost rate is included the investment cost, operational cost and environmental impact penalty cost. The presented work included Energy, Exergy, Economy and Environmental (4E) analysis in which all system design parameters were optimally estimated. The optimization problem was developed for variable ambient temperature (VAT) during a year and their results were compared with constant ambient temperature (CAT) during a year. The results for a simple gas turbine showed that at the optimum point, the exergy efficiency reduced about 5.6 percent and total cost rate increased about 4.4 percent when the results for VAT was compared with CAT situation. When the system included a gas turbine with preheater, the total cost decreased and exergy efficiency increased for 39% and 30% respectively (in comparison with a simple gas turbine system). The above percentages were 39.5% and 29.8% respectively for variable ambient temperature. Furthermore when the system included a gas turbine with both preheater and inlet cooling, the total cost decreased and exergy efficiency increased 41% and 34% respectively (in comparison with a simple gas turbine system).
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285
298


Zahra
Hajabdollahi
Department of Energy and Power Engineering, HUST University, Wuhan, China
Department of Energy and Power Engineering,
China
i201322168@hust.edu.cn


Hassan
Hajabdollahi
Department of Mechanical Engineering, ValieAsr University of Rafsanjan, Rafsanjan, Iran
Department of Mechanical Engineering, ValieAsr
Iran
h.hajabdollahi@vru.ac.ir
Gas Turbine
CFD
optimization
Genetic algorithm
Thermodynamic Analysis
[[1] Beyene A., Combined Heat and Power Sizing Methodology, in: Proceeding of ASME Turbo Expo 2002, June 36, Amsterdam, The Netherlands. ##[2] Sahoo P.K., Exergoeconomic Analysis and Optimization of a Cogeneration System Using Evolutionary Programming. Applied Thermal Engineering (2008)28(13):15808. ##[3] Kwak H.Y., Byun G.T., Kwon Y.H., Yang H., Cost Structure of CGAM Cogeneration System. International Journal of Energy Research (2004) 28:11451158. ##[4] Khaliq A., Choudhary K., Thermodynamic Evaluation of Gas Turbines for Cogeneration Applications, International Journal of Exergy (2009) 6(1). ##[5] Rosen M., Le M., Dincer I., Exergetic Analysis of CogenerationBased Energy Systems, Proceedings of the Institution of Mechanical Engineers, Part A, Journal of Power and Energy (2004)218(6):36975. ##[6] Balli O., Aras H., Hepbasli A., Exergetic Performance Evaluation of a Combined Heat and Power (CHP) System in Turkey. International Journal of Energy Research (2007)3: 84966. ##[7] Balli O., Aras H., Hepbasli A., Exergoeconomic Analysis of a Combined Heat and Power (CHP) System, International Journal of Energy Research (2008)32: 27389. ##[8] Ameri M., Behbahaninia A., Tanha A.A., Thermodynamic Analysis of a Trigeneration System Based on MicroGas Turbine with a Steam ejector refrigeration system. Energy, The International Journal (2010) 35:22039. ##[9] Valero A., Lozano M.A., Serra L., Torres C., Application of the Exergetic Cost Theory to the CGAM Problem, Energy  The International Journal (1994) 19:365. ##[10] Spakovsky M.R., Application of Engineering Functional Approach to the Analysis and Optimization of the CGAM Problem, Energy the International Journal 1994; 19:343. ##[11] Fumo N., Mago P.J., Chamra L.M., Emission Operational Strategy for Combined Cooling, Heating, and Power Systems, Applied Energy (2009) 86: 2344–2350 ##[12] Wang J.J., Zhang C.F., Jing Y.Y., MultiCriteria Analysis of Combined Cooling, Heating and Power Systems in Different Climate Zones in China, Applied Energy (2010) 87: 1247–1259. ##[13] Roque Diaz P., Benito Y.R., Parise J.A.R., Thermoeconomic Assessment of a MultiEngine, MultiHeatPump CCHP (combined Cooling, Heating and Power generation)System, A Case Study, doi:10.1016/j.energy.2010.04.002 ##[14] MartinezLera S., Ballester J., A Novel Method for the Design of CHCP (combined heat, cooling and power) systems for buildings, Energy 35(2010)29722984. ##[15] Mago P.J., Hueffed A.K., Evaluation of a Turbine Driven CCHP System for Large Office Buildings under Different Operating Strategies, doi:10.1016/j.enbuild.2010.04.005 ##[16] Sanaye S., Meybodi M. A., Shokrollahi S., Selecting the Prime Movers and Nominal Powers in Combined Heat and Power Systems, Applied Thermal Engineering (2008) 28: 1177–1188. ##[17] Sanaye S., Ardali M. R., Estimating the Power and Number of Microturbines in SmallScale Combined Heat and Power Systems, Applied Energy (2009) 86: 895–903. ##[18] Sanaye S., Ziabasharhagh M., Ghazinejad M., Optimal Design of Gas Turbine CHP Plant, International Journal of Energy Research(2009)33(8): 766777. ##[19] Dorer V., Weber A., Energy and CO2 Emissions Performance Assessment of Residential MicroCogeneration Systems with Dynamic WholeBuilding Simulation Programs Energy Conversion and Management (2009) 50: 648–657. ##[20] Frangopoulos C.A., Application of the Thermoeconomical Functional Approach to the CGAM Problem, Energy (1994) 19:323–42. ##[21] Valero A., Lozano M.A., Serra L., Tsatsaronis G., Pisa J., Frangopoulos C., CGAM Problem, Definition and Conventional Solution, Energy (1994) 19:279–86. ##[22] Tsatsaronis G., Pisa J., Exergoeconomic Evaluation and Optimization of Energy Systems, Application to the CGAM Problem, Energy (1994)19:287–321. ##[23] Hanafizadeh P., Parhizgar T., Nouri Gheimasi A., Analysis of MicroTecuperators in SmallSized Gas Turbines–Manufacturing Potential of Iran, Energy Equipment and Systems (2015) 3(1):11. ##[24] Kotas T.j., The Exergy Method of Thermal Plant Analysis, Butterworths, London (1985). ##[25] Bejan A., Tsatsaronis G.., Moran M., Thermal Design and Optimization. Wiley, New York (1996). ##[26] Ozgur B., Haydar A.,Energetic and Exergetic Performance Evaluation of a Combined Heat and Power System with the Micro Gas Turbine (MGTCHP) ,InternationalJournal of Energy Research, (2007)31:1425–1440. ##[27] Kurt H., Recebli Z., Gredik E.. Performance Analysis of Open Cycle Gas Turbines, International Journal of Energy Research (2009) 33(2):28594. ##[28] Aljundi I., Energy and Exergy Analysis of a Steam Power Plant in Jordan, Applied Thermal Engineering (2009) 29:3248. ##[29] Goldberg D.E., Genetic Algorithms in Search, Optimization and Machine Learning, AddisonWesley, Reading (1989). ##[30] Schaffer JD., Multiple Objective Optimization with Vector Evaluated Genetic Algorithms, In Proceedings of the International Conference on Genetic Algorithm and Their Applications (1985). ##[31] Srinivas N., Deb K., MultiObjective Optimization Using NonDominated Sorting in Genetic Algorithms, Journal of Evolutionary Computation (1994) 2(3):221–48. ##[32] Deb K., Pratap A., Agarwal S., Meyarivan T., A Fast and Elitist MultiObjective Genetic Algorithm, NSGAII, IEEE Transactions on Evolutionary Computation (2002) 6(2):182–97. ##[33] Deb K., Goel T., Controlled Elitist NonDominated Sorting Genetic Algorithms for Better Convergence, In Proceedings of the First International Conference on Evolutionary MultiCriterion Optimization, Zurich (2001) 385–99. ##[34] Deb K., MultiObjective Optimization Using Evolutionary Algorithms, Chichester, John Wiley and Sons Ltd (2001). ##[35] Tichi S.G., Ardehali M.M., Nazari M.E.,Examination of Energy Price Policies in Iran for Optimal Configuration of CHP and CCHP Systems Based on Particle Swarm Optimization Algorithm, Energy Policy (2010) 38: 6240–6250. ##[36] Sanaye S., Hajabdollahi H., ThermalEconomic Multiobjective Optimization of Plate Fin Heat Exchanger Using Genetic Algorithm, Applied Energy (2010) 87:1893–1902. ##[37] Gülder, Flame Temperature Estimation of Conventional and Future Jet Fuels, Journal of Engineering for Gas Turbine and Power (1986) 108(2):37680. ##[38] Toffolo A., Lazzaretto A., Energy, Economy and Environment as Objectives in MultiCriteria Optimization of Thermal System Design, Energy (2004) 29: 11391157. ##[39] Sayyaadi H., Mehrabipour R., Efficiency Enhancement of a Gas Turbine Cycle Using an Optimized Tubular Recuperative Heat Exchanger, Energy (2012)38: 362375. ##]
The energy and exergy analysis of a novel cogeneration organic Rankine power and twostage compression refrigeration cycle
2
2
The energy crisis in recent years has led to the use of thermodynamic cycles that work based on renewable energies. Lowtemperature cycles—such as organic cycles—are suitable strategies for the application of renewable energies. The present study proposes a novel cycle through the integration of a twostage compression refrigeration cycle with a combined Rankine power and ejector refrigeration cycle by using the cascade condenser method. The fundamental idea of this cycle is to obtain refrigeration production at lower temperatures, and to achieve higher thermal and exergy efficiencies. The results showed that the new cycle recorded an 11.67 percent improvement in thermal efficiency and a 16.89 percent improvement in exergy efficiency compared to the basic cycle. Even though the network output of the cycle is reduced, a significant increase in the refrigeration capacity of the cycle is observed.
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299
312


Hamed
Mortazavi Beni
Department of Mechanical Engineering, Shahrekord University, Shahrekord,Iran
Department of Mechanical Engineering, Shahrekord
Iran
mortazavi.hamed.s@gmail.com


Afshin
Ahmadi Nadooshan
Department of Mechanical Engineering, Shahrekord University, Shahrekord,Iran
Department of Mechanical Engineering, Shahrekord
Iran
ahmadi@eng.sku.ac.ir


Morteza
Bayareh
Department of Mechanical Engineering, Shahrekord University, Shahrekord,Iran
Department of Mechanical Engineering, Shahrekord
Iran
m.bayareh@eng.sku.ac.ir
Cogeneration Cycle
Exergy
Solar energy
Ejector
Cascade Condenser
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Economic optimization of solar systems in uncertain economic conditions using the Monte Carlo method
2
2
Solar energy is an environmentally sustainable energy source as it is clean and inexhaustible. Solar systems are very common and costeffective, thus, can be used for many home applications. In this paper, a new method is presented to optimize solar systems economically, regarding to energy cost fluctuations. In spite of conventional analyses, in which the inflation is considered constant, this method considers a probability distribution for inflation. The probability function of the life cycle solar saving (LCS) is then estimated by the Monte Carlo method. The expected value of LCS is used as the objective function. The standard domestic solar system is considered as a benchmark to show capability of the method. Three most important parameters of a solar water heating system are considered as manipulated variables. The optimal value of each parameter was found based on the proposed procedure, and employing the particle swarm optimization (PSO) algorithm as the optimization method. The results show that the collector area of 17 m2, collector angle of 42o, and storage tank of 100 l/m2 maximize LCS to the mean value of 9930 USD for the selected case study. Also, the probability distribution of LCS shows that the mean value of the payback time is 4.1138 years with standard deviation of 1.3182.
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Samaneh
Kasiri
Department of Mechanical Engineering, K. N. Toosi University of Technology
Tehran, Iran
Department of Mechanical Engineering, K.
Iran
samaneh.kasiri@gmail.com


Mahyar
Momen
Department of Mechanical Engineering, K. N. Toosi University of Technology
Tehran, Iran
Department of Mechanical Engineering, K.
Iran
mahyar.momen90@gmail.com


Ali
Behbahaninia
Department of Mechanical Engineering, K. N. Toosi University of Technology
Tehran, Iran
Department of Mechanical Engineering, K.
Iran
Economic Analysis
Monte Carlo
optimization
Solar energy
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