MATERIAL SELECTION FOR FIN BASED ON THERMAL ANALYSIS USING ANSYS AND ANN

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1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 9, Issue 11, November 2018, pp , Article ID: IJMET_09_11_055 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed MATERIAL SELECTION FOR FIN BASED ON THERMAL ANALYSIS USING ANSYS AND ANN S.Karthik Assistant Professor of PSNA College of Engineering & Technology, INDIA K.Muralidharan Professor of PSNA College of Engineering & Technology, INDIA B.Anbarasan Assistant Professor of PSNA College of Engineering & Technology, INDIA ABSTRACT The present study summaries the Selection of fin materials for different applications. Fins are having different applications such as Economizers, Heat Exchangers etc. In internal combustion engines cylinder part is the heart of the engine and this cylinder block forms the wall of a combustion chamber where air fuel mixture burns. Due to the continuous combustion process the cylinder wall subjected to high temperature and heat transfer takes place through the cylinder fins. If the heat is not dissipated properly then it decreases the working efficiency of the engine. Mostly the heat transfer rate through the fin material is depends on the thermal conductivity and other properties of the chosen material. For analysis purpose the standard specimen of Pin-Fin is considered. By validate the procedure in ANSYS 16.1, we can obtain the best suitable material among the materials chosen. By providing the output of analysis as the input for Artificial Neural Network, we can obtain the required material properties of metals. This scheme is very useful in choosing the fin materials for different applications. Keywords: thermal conductivity; composite materials; ANSYS; Thermal analysis; pinfin; ANN. Cite this Article S.Karthik, K.Muralidharan and B.Anbarasan, Material Selection for Fin Based on Thermal Analysis Using Ansys and Ann, International Journal of Mechanical Engineering and Technology, 9(11), 2018, pp INTRODUCTION Generally the performance of vehicles mainly depends on the performance of engine. The selection of best engine design and manufacturing is mainly depends on the selection of materials because internal combustion engines performance directly related to thermal behavior of the materials. Thermal analysis is the branch of material science that investigates the properties of materials that are involved in thermal analysis and also subjected to change with change in editor@iaeme.com

2 S.Karthik, K.Muralidharan and B.Anbarasan temperature. Thermal analysis is also often used for studying of heat transfer through the structures like internal combustion engines, molding blocks and in many more applications where ever heat transfer takes place with conduction and convection modes. Heat transfer simulations were carried out by using ANSYS software. The work is further investigated in ANN. 2. LITERATURE REVIEW The cylinder of an IC engine is the basis and supporting portion of the engine power unit. The function of cylinder is to provide space in which the piston can operate four strokes and thus generate power. In this case the cylinder block was subjected to high temperature during the combustion of fuel, hence the best performance of engine is greatly depends on the best selection of cylinder block material [1]. The cylinder block should be made from the materials which has the following desirable properties [1, 2]. It should be relatively cheap and readily produce castings with good impressions, It should be easily machined, It should be rigid and strong enough in both bending and torsion, It should have good abrasion resistance and corrosion resistance, It should have low thermal expansion and low density, It should have a high thermal conductivity, It should retain its strength at high operating temperatures, Cast iron is the one of the best material that satisfies the above desirable properties except that it has a low thermal conductivity and it is a comparatively heavy material. Because of this unsatisfactory with cast iron, there has been a trend for petrol engines to adopt light aluminum alloys but these alloys have less strength than cast iron [3,4]. To compensate the lower strength of aluminum alloys, the alloy bricks are cast with thicker sections and with some additional supports ribs, which bring their relative weight to about half that of the equivalent cast iron blocks. A typical cast iron would be a grey cast iron containing 3.5% carbon, 2.25% silicon, and 0.65 Manganese. The carbon content gives graphite to improve lubrication, with the silicon, controlling the formation of a laminated structure known as pearlite which is mainly responsible for good wear resistance, while the manganese helps to strengthen and toughen the iron. A common aluminum-alloy composition would be 11.5% silicon, 0.5% manganese, and 0.4% magnesium, and the left part is aluminium. The presence of high silicon reduces the expansion and improves castability, strength, and abrasion resistance, while the other two elements strengthen the aluminum structure. This alloy has good corrosion resistance, but it can absorb only moderate shock loads [5-8] 3. EXPERIMENTAL ANALYSIS 3.1. Material Selection A typical cast iron would be a grey cast iron containing 3.5% carbon, 2.25% silicon and 0.65 Manganese. The carbon content gives graphite to improve lubrication, with the silicon, controlling the formation of a laminated structure known as pearlite which is mainly responsible for good wear resistance, while the manganese helps to strengthen and toughen the iron. A common aluminum-alloy composition would be 11.5% silicon, 0.5% manganese, 0.4% magnesium and the left part is aluminum. The presence of high silicon reduces the expansion and improves castability, strength, and abrasion resistance, while the other two elements strengthen the aluminum structure. This alloy has good corrosion resistance, but it can absorb editor@iaeme.com

3 Material Selection for Fin Based on Thermal Analysis Using Ansys and Ann only moderate shock loads. In this work we are going to compare four different materials, they are Grey cast iron, Mg alloy, brass and Al alloys. Materials Composition Table 1 Material Properties Youngs Modulus (gpa) Density (kg/m 3 ) Specific Heat (j/kg.k) Thermal conductivity (w/m.k) Brass Zn , Cu Grey Cast Iron C , Si , Mn Mg Alloy Al , Zn , Mn A380 A360 A356 Al-MMC Al Alloy 6061 Si ,Fe 1.3 Max,Cu ,Mn 0.50 Max,Mg 0.10 Max, Balance Al Si , Cu-0.6, Mg , Fe-2.0 Si , Fe 0.2 Max, Cu 0.2 Max, Mn 0.2 Max, Mg , Balance Al Si , Cu , Mg , Fe Al , Mg , Si , other each Max0.005, balance Al 3.2. Experimental Reading Table 2 Reading for Brass Material S.NO T 1 T 2 T 3 T 4 T mean T 5 T 6 T atm Clculation: Voltmeter =70volts Current =0.28 ampere Length of fin (L) = m Diameter of fin (D) = m Mean surface temperature (Tm) = C (Steady state reading from table) Atmosphere temperature (Ta) = 40 C (Steady state reading from table) Film temperature (Tf) = C (From HMT DDB for Tf= C ) Kinematic viscosity (Γ = ^-6 m²/s (from data book) Prandtl number (pr =0.698 (from data book) Thermal conductivity (K) = w/mk (from data book) Grashof Number (Gr) = (gβl³δt)/γ² = Raleigh Number (Ra) = Gr Pr editor@iaeme.com

4 S.Karthik, K.Muralidharan and B.Anbarasan Nusselt Number (Nu) = 0.6 (Ra) ^0.2 = Perimeter (P = = m Area of cross section (A = π/4 d² = m² Mass (M = (hp/ka) ^½ = 8.11 Kg Heat transfer co-efficient (h) = (Nu K)/D = W/m²k Steady state heat (Q) = m cp (T2-T1) = ANALYSIS OF FIN USING ANSYS ANSYS develops and markets finite element analysis software used to simulate engineering problems. The software creates simulated computer models of structures, electronics or machine components to simulate strength, toughness, elasticity, temperature distribution, electromagnetism, fluid flow and other attributes. ANSYS is used to determine how a product will function with different specifications, without building test products or conducting crash tests. For example: ANSYS software may simulate how a bridge will hold up after years of traffic, how to best process salmon in a cannery to reduce waste, or how to design a slide that uses less material without sacrificing safety. Most ANSYS simulations are performed using the ANSYS Workbench software, which is one of the company's main products. Typically ANSYS users break down larger structures into small components that are each modeled and tested individually. A user may start by defining the dimensions of an object, and then adding weight, pressure, temperature and other physical properties. Finally, the ANSYS software simulates and analyzes movement, fatigue, fractures, fluid flow, temperature distribution, electromagnetic efficiency and other effects over time. Brass Grey cast iron Mg alloy A editor@iaeme.com

5 Material Selection for Fin Based on Thermal Analysis Using Ansys and Ann A360 A380 Al-MMC Al alloy ARTIFICIAL NEURAL NETWORK Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" (i.e. progressively improve performance on) tasks by considering examples, generally without task-specific programming. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any a priori knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they evolve their own set of relevant characteristics from the learning material that they process Regression analysis: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed editor@iaeme.com

6 S.Karthik, K.Muralidharan and B.Anbarasan Trained values 6. RESULT INTERPRETATION Thermal analysis of cylinder block was analyzed with ANSYS software. In these simulations different alloys has been considered by taking the conductivity, density and specific heat as major material properties for thermal analysis. Materials that are used for present analysis are aluminium, Magnesium and cast iron alloys. The properties of these materials are listed below [3, 8],

7 Material Selection for Fin Based on Thermal Analysis Using Ansys and Ann In the above analysis of fins, the results are summarized below: The Cast Iron, Mg Alloy and A380 having the minimum steady state heat value and Thermal flux value. But Cast Iron due to more density and low thermal conductivity won t give the high thermal efficiency. And Mg Alloy also having the low thermal conductivity, it may not give high thermal efficiency. In A380 having the high thermal conductivity than Cast iron and Mg Alloy, it may be considered as best suitable material for fins among the materials chosen. 7. CONCLUSION In this present work, thermal analysis of fin were performed with various alloys to find out the best material which gives the best heat transfer rate through it and keep the engine in safe working condition and also consists of high strength with light weight. From the above thermal analysis results it is to be identified that, both grey cast iron and Magnesium alloys are the best two composite materials that gives the better heat transfer rate due to its more density. Coming to practical applications most of the heavy vehicles cylinder blocks are manufactured with these materials. However these materials are not that much suitable for light vehicles due to its more weight, hence there is a development of light Aluminium alloys, hence in this paper some of aluminium alloys are also considered for thermal analysis and compared all the results for best one. From all the above nodal temperature contours and from column charts, it is to be concluded that A380 had the better heat transfer rate along with more strength as compared with other considered alloys. In our project, the experimental reading of pin-fin apparatus is validating with the ANSYS software. The same procedure is repeated for different alloys. The result of steady state heat value obtained from analysis is given as input for Artificial Neural Network and the material properties are given as output. After training whatever the steady state temperature required, we can obtain the material properties editor@iaeme.com

8 S.Karthik, K.Muralidharan and B.Anbarasan REFERENCES [1] G. Babu, M. Lavakumar,Heat Transfer Analysis and Optimization of Engine Cylinder Fins of Varying Geometry and Material, Journal of Mechanical and Civil Engineering (IOSR- JMCE, Volume 7, Issue 4 (Jul. - Aug. 2013), PP [2] P. Sai Chaitanya, B. Suneela Rani, K. Vijaya Kumar, Thermal Analysis of Engine Cylinder Fin by Varying Its Geometry and Material,, Journal of Mechanical and Civil Engineering, Volume 11, Issue 6 Ver. I (Nov- Dec. 2014), PP [3] Angus, H. T. Cast Iron: Physical and Engineering Properties. BCIRA, 1960, pp [4] M. Azadi, M. Shariyat, Nonlinear transient transfinite element thermal analysis of thickwalled FGM cylinders with temperature-dependent material properties, Meccanica, Vol. 45, No. 3, 2010, pp [5] Thornhill D. and May A., An Experimental Investigation into the Cooling of Finned MetalCylinders in a free Air Stream, SAE Paper (1999) [6] Biermann E. and Pinkel B., Heat Transfer from Finned Metal Cylinders in an Air Stream, NACA Report No. 488 (1935) [7] Zakirhusen, Memon K. and Sundararajan T., Indian Institute of Technology Madras, V. Lakshminarasimhan, Y.R. Babu and Vinay Harne, TVS Motor Company Limited, Simulation and Experimental Evaluation of Air Cooling for Motorcycle Engine, / (2006) [8] Gundlach, R. B. The effects of alloying elements on the elevated temperature properties of gray irons. AFS Transactions, 1983, p editor@iaeme.com