Aspen Plus Preliminary Simulation of Nanofluids. Eman Abdel-Hakim Tora *

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1 htt:// Asen Plus Preliminary Simulation of Nanofluids Eman Abdel-Hakim Tora * Deartment of hemical Engineering and Pilot Plant, National Research entre, El Dokki, airo12311, Egyt emantora@gmail.com Abstract: A rising trend exists towards using nanofluids as heat transfer agent in rocess engineering. Hence there is a consequent justified need for rocess simulation softwares caable of treating nanofluids and recognizing their enhanced thermal roerties and the resulting imact on the whole simulated rocess. Thus, this aer examines the caacity of one of the most known rocess simulation softwares, Asen lus, to simulate nanofluids. Preliminary investigation indicates that rocess simulation software could have solid articles within default range down to microns- not nanoarticles, thus simulation of nanofluids otentially may give misleading results in terms of rocess energy consumtion. This shortage can be omitted by the roosed aroach, which aves the way for nanofluids broad alications in industrial lants to be examined easily and shortly via rocess simulation software ackages. A two - ste aroach is roosed to introduce nanofluids and their altered thermohysical roerties and consequent heat duty change into rocess simulation software: a roer mathematical model is chosen, and then introduced into Asen lus alculator block as FORTRAN code. A simulation examle of a heat exchanger heats Water-Al 2 O 3 nanofluid over a range of nanoarticles volumetric concentration via the roosed aroach is executed. Simulation results rove the alicability and validity of the aroach whereby the enhanced thermohysical roerties and the energy conservation due to emloying nanofluids have been recognized by the simulation software. [Tora, EA. Asen lus Preliminary Simulation of Nanofluids. J Am Sci 2012;8(12): ]. (ISSN: ). htt:// 54 Keywords: Nanofluids; rocess simulation; Asen lus; heat transfer 1. Introduction Recently, emloying nanofluids as heat transfer agent becomes an uward trend and a considerable alternative to markedly enhance the heat transfer rocess (Das et al., 2008). Effective heat transfer during heating and cooling streams is an urgent demand in chemicals, etrochemicals, and harmaceuticals industries (Tora and El-Halwagi, 2010). Obviously, industry relies on comuter simulation as a quick and effective tool to test, monitor, analyze, and modify the individual units (e.g. heat exchangers) and the entire rocess erformance as well. Available rocess simulation software ackages address in detail the conventional methods to enhance heat transfer like increasing the area of the heat exchangers, but that may not be the case considering nanofluids. These simulation software ackages include their own build in modules library and comonents databanks. Furthermore, user defined comonents and equiments can be introduced into the simulation software ackages (Asen lus, 2012), but all rely on conventional thermohysical models which almost not alicable with nanofluids. Therefore, commercially available rocess simulation softwares may give misleading results when simulating energy systems emloying nanofluids in terms of heat duty and um ower (Tora, 2012)! Nanofluid refers to ' nanoarticles fluid susension' according to Stehen hoi (hoi, 1995), who was the first to susend nanoarticles (at least one of the main dimensions has length less than 100 nm) into a base fluid to get a susension with heat transfer roerties better than the original fluid (Abouali and Ahmadi, 2012). However, this concet dates back to Maxwell work (1873s), larger size solid articles had been used (Wong and astillo, 2010). Both the conduction and convection heat transfer may be enhanced via using nanofluids. Nevertheless, the thermal conductivity enhancement is significant; more research is needed to investigate the augmentation of the convective heat transfer (Kim et al., 2004). onvection heat transfer deends on the flow tye: laminar flow, turbulent flow, and ool boiling. Yet thermal conductivity is iluenced by nanoarticles (material, shae, size, volumetric concentration), base fluid material, H, additives, and the ratio ( k k ) between the nanoarticles and the base fluid. Disersing volume ercentage of nanoarticles into a base fluid increases its thermal conductivity by %. However, that enhancement is limited to certain volumetric concentration (Palm et al., 2006), and can be offset by other arameters. Metals (Al, u, Au), metal oxides (Al 2 O 3, uo), and nonmetals (TiO 2, NT, Si, Ti) can act as aroriate nanoarticles [18]. Likewise, the base n 391

2 htt:// fluid can be water, oil, and ethylene glycol (Pang and Kang, 2007). The thermal conductivities of some known nanoarticle solids and base fluids are given (Table 1) whereby the significant difference between the oor heat transfer roerties of the base fluids and that of the nanoarticles is clear. Table 1. Thermal conductivities of different solid nanoarticles and base fluids [Eastman et al., 1997; Kakaç and Pramuanjaroenkij, 2009; Linhard, 2005]. Nanoarticles: (i) (ii) (iii) Metal Nonmetal Oxides Material Silver (Ag) oer (u) Aluminum (Al) Diamond arbon nanotubes Silicone Alumina (Al 2O 3) Thermal conductivity W/K m Available rocess simulation software mostly has these re-mentioned base fluids as conventional fluids in their build in databanks; hitherto those databanks lack the corresonding nanofluids. Therefore, this work tackles introducing an aroach to overcome this shortage, and to ave the way in front of extending these software ackages' caability to simulate thermal rocesses emloying nanofluids. To accomlish this target, taking advantage of the develoed models reresenting the heat - transfer enhancement due to relacing conventional fluids with corresonding nanofluids, treating them to comiled into simulation software ackages, and adoting simulation software to consider those nanofluids' models when erforming energy calculations are the core stes of the roosed aroach. 1. Heat transfer enhancement via using nanofluids Emloying nanofluids as heat transfer agents in thermal energy systems has high otential to enhance the systems' efficiency, since susending nanoarticles with high thermal conductivity into a base fluid () roduces a nanofuid () with higher thermal conductivity ( k ), density ( ), and viscosity but lower secific heat. Thoose changes significantly imact the heat exchanger areas and the um ower which in turn iluences on the caital cost and oerating cost as well (Tora, 2012) Modeling nanofluids thermohysical roerties The enhanced secific heat of the nanofluid can be anticiated via two mathematical models [Maré et al., 2012]. First is known as equation of Xuan and Roetzel (2000); workability of the ideal mixture theory ( n i1 i i ) is assumed and is given by be,, ( 1 ),, 1 where is any thermo hysical roerty of an ideal mixture, is the corresonding value of ure comonents ( i : n i ) forming the mixture and i is the fraction of the comonent in the mixture. Other model is based on roosing a thermal equilibrium between the nanoarticles and the base fluid. That model (model II) has been selected for this work due to its justification; it can estimate the nanofluid secific heat as given as, ( ( 1 ),,, 2 ) is the nanofluid enhanced density and V n V n V ( 1 ) 3 n n n n 4 m m m Thermo hysical roerties of ure water as a base fluid and ure alumina as nanoarticles [Maiga et al., 2005; Kakaç and Pramuanjaroenkij, 2012] are listed (Table 2). Both the resulting nanofluids' secific heat and density have been calculated considering different nanoarticles volumetric fraction (0-7) %. Using those models, the augmentation of nanofluid density and decrease in the secific heat are listed (Table 3). 392

3 htt:// Table 2. omarison between roerties of ure water as a base fluid and 30 nm alumina nanoarticles (Palm et al., 2012; Wong and astillo, 2010]. Proerty Base fluid (water) Nanoarticles (Al 2O 3) Secific heat,, (kj/kg K) Density,, (kg/m 3 ) Thermal conductivity, k, (W/m K) Thermal diffusivity,, m 2 /s *10-9 Table 3. Al 2 O 3 -Water nanofluid roerties with different nanoarticles volumetric concentration Density,, Nanoarticles volumetric concentration ( ) Model II Secific heat,, kg/m kj/kg K 2.2. Heat transfer rate For constant volume system, heat transfer rate, Q, can be given as: du dt Q m, 5 v dt dt Likewise, rate of heat transfer in constant ressure system can be calculated as: dh d( U PV ) dt Q m, 6 dt dt dt Liquid and solid systems can be considered as incomressible materials (Linhard, 2005). Therefore, nanofluid is treated as incomressible fluid in this study, since nanofluid is a mixture of solid and liquid. Additively, for constant ressure and constant volume systems, the two secific heats are similar, thus can be used generally to refer to the nanofluid secific heat. Therefore, heat flow can be written as Q m T, 7 In the next section, simulation will be conducted on the basis of using equation 7 combined with the mentioned model of the secific heat. 2. Simulation Process simulation softwares like ASPEN PLUS and HYSYS, etc. are widely used in industrial facilities and energy lants as quick and efficient tool to monitor and evaluate the system erformance. Thus incororating nanofluids into rocess simulation softwares - articularly Asen lus - is tackled herein aiming to enable the simulation software to recognize the occurring difference due to nanofluids usage in the simulated rocess. That may facilitate investigating the imact of emloying nanofluids into industrial rocesses considering thermal energy systems and alications, and furthermore ave the way to wide and large scale alications of nanofluids. Simulation rograms mostly have built in databanks containing conventional solids such as metals and conventional fluids like water. They may have articles size distribution but in the ranges of microns (Asen lus, 2012); they lack the corresonding nanoarticles. Therefore, simulation of energy systems or rocesses emloying nanofluids may be unworkable or even gives misleading results. Nanoarticles can be Al 2 O 3, TiO 2, and u, however, the base fluid can be water, oil, and lubricant which are just a few to name. Available rocess simulation softwares mostly have them as conventional solids and fluids in their build in databanks (Palm et al., 2006), but so far those databanks lack the corresonding nanofluids (Tora, 2012). The feasibility of simulating nanofluids using Asen lus needs to be investigated seeking to treat with its default caacity to coe with the rising alied research in the field of nanofluids alications in rocesses and energy systems Process simulation software: Asen lus One of the rocess simulation softwares, Asen lus, is used to investigate the feasibility of simulation of nanofluids with assuming that the nanofluids are well reared and have a stable roerties during the entire rocess, with considering nanofluid a single hase [Maiga et al., 2005; Pang and Kang, 2012). A simle flow sheet given in Fig. 1 was built using Asen lus; it consists of a heat exchanger (HX-BF) used to heat conventional fluid which is water in this case; another heat exchanger (HX-NF) used to heat nanofluid that is Al 2 O 3 - water nanofluid. The heat exchangers task is to raise the temerature of the working fluid by 50 degrees elsius. The target is to 393

4 htt:// check the default caacity of the software to simulate nanofluid, and to accurately recognize the difference between nanofluid and conventional fluid. Figure 1. Asen lus simulation of base fluid and corresonding nanofluid (1vol.% Al 2 O 3 ) in thermal energy system. The simulation run results show that the base fluid heat exchanger heat rate is W, while the nanofluid heat exchanger heat rate is 5664 W. Thus according to equation 7, secific heat of the base fluid (water) is J/kg K and that of the nanofluid, Al 2 O 3 -water, is 4091 J/ kg K. Therefore, the results are accurate with the base fluid, but inaccurate with the nanofluid according to eq. 2 and Table 3 that reresent the redicted values of nanofluids roerties. Simulation is executed using different nanoarticle volume fraction; the runs results are comared with the model values, whereby the simulation results indicate that: (i) The accuracy of the simulation software decreases linearly with increasing the susended nanoarticles volumetric concentration. (ii) The difference between the simulation and the model results increases linearly with increasing the nanoarticles volumetric fraction, which can be described as Asen _ Asen NF ( ) _ NF ( ) and for the density and the secific heat. (iii) The simulation redicts that the density decreases and the secific heat increases with the increase of the nanoarticles volumtric fraction, which is in contrast to the real case. To get Asen lus fully adoted for nanofluids' models, a alculator block is roosed herein Incororating nanofluids into rocess simulation software A roosed method, Asen lus alculator block, to incororate nanofluids into rocess simulation software is roosed here. 'alculator block' is a tool in Asen lus emowers the user to introduce mathematical models written in Fortran language using build in Fortran inside Asen lus or via linking Asen lus with Excel sheets, then the simulation calculations are erformed based on those entered model, and overwrite the rogram default models. Fortran code is selected as its caacity to enter models into Asen directly is significant. As given in Fig.2 first the flow sheet variables included in the nanofluids model (nanofluid secific heat, nanofluid density, etc.) are identified with setting values for the arameters (e.g. nanoarticle volumetric fraction) and the oerating conditions (temerature, mass flow rate). Then, Fortran code of the model redicting nanofluids roerties is written in the alculator block, and finally the alculator is executed. In the alculator block Fortran code, PBF is the secific heat of the base fluid, PP is the secific heat of the nanoarticles, DBF is the density of the base fluid, DP is the density of the nanoarticle, TIN is the temerature of the stream entering the heater, TOUT the temerature of the stream exiting the heater, VNP is the volumetric concentration of the nanoarticles, DNF is the density of the nanofluid, PNF is the secific heat of the nanofluid, and QNF is the heat duty of the heat exchanger needed to heat the nanofluid. Results show that Asen lus calculator blcok successfully calculate nanofluid secific heat and the consequently the heat duty. Different nanoarticle volumtric fractions have been used as given in Fig.3. Therefore, rocess simulator software can be used to accuratly handle nanofluids rocesses under different conditions. 394

5 htt:// Figure 2. Asen lus alculator block to introduce nanofluids enhanced roerties and erform resultant heating duty. (a) 1vol.% (b) 3vol.% (c) 5vol.% (d) 7vol.% Figure 3. alculator block results at different nanoarticles volume fractions ( ) % 395

6 htt:// 4. onclusion Simulation of nanofluids as heat transfer agents via existing rocess simulation software (Asen lus) is investigated. The simulation default models redict the thermohysical roerties inaccurately esecially with increasing the nanoarticles volumetric concentration. The simulation redicts increasing the secific heat and decreasing the density of the nanofluids with increasing the nanoarticles volumetric concentration, which is the oosite to what ractically takes lace. To adot the simulation software to fully recognize the existence of nanofluid, instead of the conventional fluid, a calculator block with a mathematical model redicting the considered nanofluids altered roerties and consequent changes is incororated into the software. This aroach succeeds to emower the widely used rocess simulation software to treat with nanofluids erfectly. This aves the way for industrial lants as well as academies to easily and shortly investigate the imact of using nanofluids as working fluid on the entire rocess. However the given examle considers only the secific heat and the density, that sounds enough to adequately illustrate the assumed aroach to simulate nanofluids via rocess simulation software (Asen lus), extra mathematical exressions of other thermohysical roerties can be similarly added to the alculator block. The roosed aroach sounds alicable articularly with the investigated simulation software, yet further work is required to develo a global method. orresonding Author: Dr. Eman Abdel-Hakeem Tora Deartment of hemical Engineering and Pilot Plant National Research entre El Buhoos St. - El Dokki airo Egyt emantora@gmail.com References 1. Asen lus solids (2012); accessed on 26 Setember (2012); available at htt:// ussolidsv7_2-start.df 2. Abouali O, Ahmadi G. omuter Simulations of Natural onvection Of Single Phase Nanofluids in Simle Enclosures: a ritical Review. Journal of Alied Thermal Engineering 2012; 36: hoi S.U.S. Enhancing Thermal onductivity of Fluids with Nanoarticles, Develoments And Alications of Non- Newtonian Flows. FED 1995; 231: MD Das SK, hoi SUS, Yu W, Pradee T. Nanofluids: Science and Technology. John Wiley & Sons, Inc. Hoboken, NJ, USA, Eastman J A, hoi SUS, Li S, Thomson J, Lee S, Enhancement Thermal onductivity Through the Develoment of Nanofluids. In 1996 Fall Meeting of the Materials Research Society (MRS), Boston, USA, Khanafer K, Vafai, Lightstone M. Buoyancydriven heat transfer enhancement in a twodimensional enclosure utilizing nanofluids. International Journal of Heat and Mass Transfer 2003; 46: Kim J, Kang YT, hoi K. Analysis of onvective Instability and Heat Transfer haracteristics of Nanofluids. Physics of Fluids 2004; 16: Kakaç S, Pramuanjaroenkij A. Review of onvective Heat Transfer Enhancement with Nanofluids. International Journal of Heat and Mass Transfer 2009;52: Linhard J H. Transfer Textbook. Phlogiston Press. Massachusetts, USA, Manca O, Nardini S. Numerical Investigation on Mixed onvection in Triangular ross-section Ducts with Nanofluids. Advanced Mechanical Engineering 2012; ID Maré T, Sow O, Halelfadlm S, Lebourlout S, Nguyen.T. Exerimental Study of the Freezing Point of -Al 2 O 3 /Water Nanofluid. Advanced Mechanical Engineering 2012; ID Maiga SB, Palm SJ, Roy T, Galanis N. Heat Transfer Enhancement by Using Nanofluids in Forced onvection Flows. International Journal of Heat and Fluid Flow 2005; 26: Tora EA, El-Halwagi M. Integration of Solar Energy into Absortion Refrigerators and Industrial Processes. Journal of hemical Engineering Technology 2010; 33: Tora EA, El-Halwagi M. Integrated oncetual Design of Solar-Assisted Trigeneration Systems. omuters and hemical Engineering 2011; 35: Tora EA. omuter Simulation of Nanofluids Alications in Processes Energy Systems. 1 st International oerence of Advanced Basic and Alied Science (ABAS), 6-8 November (2012), Hurghada, Egyt. 16. Palm SJ, Roy G, Nguyen T. Heat Transfer Enhancement with the Use of Nanofluids in Raid Flow ooling Systems onsidering Temerature-Deendent Proerties. Alied Thermal Engineering 2006; 26: Pang, Kang Y. Stability and Thermal onductivity haracteristics of Nanofluids (H 2 O/H 3 OH+Nal+Al 2 O 3 nanoarticles) for O 2 Absortion Alication. International Refrigeration and Air conditioning oerence. Purdue, July 16-19,

7 htt:// 18. Wong KV, astillo MJ. Heat Transfer Mechanisms and lustering in Nanofluids. Advanced Mechanical Engineering 2010; ID Wen D, Ding Y. Formulation of Nanofluids for Natural onvective Heat Transfer Alications. International Journal Heat and Fluid Flow 2005; 26: Xuan Y, Roetzel W. oncetions for Heat Transfer orrelation of Nanofluids. International Journal of Heat and Mass Transfer 2000; 43: Yu W, hoi SUS. The Role of Interfacial Layers in the Enhanced Thermal onductivity of Nanofluids: A Renovated Maxwell Model", Journal of Nanoarticle Resources 2003: Nomenclature k n thermal conductivity (W/m K) secific heat of nanoarticles (kj/kg K), secific heat of nanofluid (kj/kg K) V Q volume of the nanoarticles (m 3 ) heat transfer rate (W) secific heat (kj/kg K) Greet letters density (Kg/m3) volumetric fraction of nanoarticles Subscrits n nanofluid base fluid n nanoarticle 11/5/