Journal Vol – 10 No -2, January 2016

Abstract:

In the present numerical study,laminar free-convection flow and heat transfer over avertical plate with constant heat flux is presented. By means of similarity transformation, the original nonlinear coupled partial differential equations of flow are transformed to a pair of simultaneous nonlinear ordinary differential equations. Then, they are reduced to first order system. Finally, Newton-Raphson method and adaptive Runge-Kutta methodare use dfortheir integration.The computer codes are developed for this numerical analysis in Matlab environment. Velocity and temperature profiles for various Prandtl number are illustrated graphically. Flow and heat transfer parameters are derived as functionsof  Prandtl  numberalone. The results of the present simulation are then compared with experimental data published in literature and find a good agreement.

Keywords:

Constant Heat Flux,Free Convection,Heat Transfer,Matlab,Numerical Simulation,Vertical Plate,

Refference:

I. Sparrow, E. M., Gregg, J. L., “Similar Solutions for Laminar Free Convection from aNon isothermal Vertical Plate”,Trans. ASME, Journal of Heat Transfer,80, pp. 379387, 1958.

II. Pohlhausen, E., “Der Wنrmeaustausch zwischen festen Kِrpern und Flüssigkeiten mitKleiner Reibung und kleiner Wنrmeleitung“,Z. Angew. Math.Mech.1, 235-252,1921.

III. Dotson, J. P., “Heat Transfer from a Vertical Plate by Free Convection” MS Thesis,Purdue University, W. Lafayette. Ind., May 1954.

IV. Goldstien R. J., Eckert E. R. G., “The Steady and Transient Free Convection BoundaryLayerson a Uniformly Heated Vertical Plate,” Int. Journal of Heat and Mass Transfer,1, 208 218, 1960.

V. Fujii T., Fuji M., “The Dependence of Local Nusselt number on Prandtl number incase of Free Convection along a VerticalSurface with Uniform Heat Flux, ”Int.Journal ofHeat andMass Transfer, 19, 121-122, 1976.

VI. Pittman J. F. T., Richardson J. F., Sherrad C. P., “An Experimental Study of HeatTransfer by Laminar Natural Convection between an Electrically-Heated Vertical Plateand both Newtonian and Non-Newtonian Fluids,” Int. Journal of Heat and MassTransfer, 42, 657-671, 1999.

VII. Aydin O., Guessous L., “Fundamental Correlations for Laminar and Turbulent FreeConvection from an uniformly Heated Vertical Plate,”Int. Journal of Heat and MassTransfer, 44, 4605-4611, 2001.

VIII. Bejan, A.,Heat Transfer, John Wiley, New York, 1993.

IX. Incropera, F. P., DeWitt D. P.,Introduction to Heat Transfer, Fourth edition, JohnWiley, New York, 2002.

X. Çengel, Y. A.,Heat Transfer,Second edition, McGraw–Hill, New York, 2003.

XI. Lienhard IV, J. H., Lienhard V, J. H.,A Heat Transfer Textbook, Phlogiston Press,Cambridge, MA, 2003.

XII. Nellis, G., Klein, S.,Heat Transfer, Cambridge UniversityPress, London, UK, 2008.

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Abstract:

Bangladesh bureau of statistics (BBS) publish a statistical year book in every year where comprehensive and systematic summary of basic statistical information of Bangladesh covering wide range of fields. BBS also forecast different sectors such aseconomics, weather, agriculture etc in different time in this country. In this paper we mainly concern on the wheat, rice and maize food grain which plays a vital role in economic development of Bangladesh. The main purposes of this paper as to compare which techniques are better BBS’s or statistical techniques for forecasting. There are different forecasting models are available in statistics among these we used Auto regressive (AR), Moving Average(MA), Auto regressive Moving Average (ARMA)and Auto regressive Integrated Moving Average (ARIMA) models. For this reason, we clarify the stationary and non-stationary series by graphical method. On the basis of that,the stationary model is being set up asthe forecasting purpose. After analyze, we compare the forecasting result of our selective foodgrain and find that for ecasted valu esusing statistical techniques are nearest to the actual values compare to BBS’s project edvalues.

Keywords:

ARIMA,ARMA,Forecast,Foodgrain,

Refference:

I.  Abdullah, L. (2012),ARIMA Model for Gold Bullion Coin Selling PricesForecasting,International Journal of Advances in Applied Sciences. Vol. 1, No.4, pp. 153-158.

II. Anderson, T.W. (1984),An Introduction to Multivariate Statistical Analysis, 2nded.New York:John Wiley and Sons Inc.

III. Arumugan, P. and Anithakumari, V. (2013),Fuzzy Time SeriesMethod forForecastingTaiwanExport Data,International Journal of Engineering trendsandTechnology. Vol.8,pp. 3342-3347.

IV. Box, G. E. P. and Jenkins, G. M. (1976),Time Series Analysis: Forecasting andControl,SanFrancisco: Holden-Day.

V. Brokwell, P.J. and Davis, R.A. (1997),Introduction to Time Series andForecasting, Springer,New York

VI.Clements, M. and Hendry, D. (1998),Forecasting Economic Time Series,United UniversityPress, Cambridge.

VII. Deepak, P., et al. (2015), A Comparison of forecasting methods: Fundamentals,Polling, Prediction Markets, and Experts,A Journal of Prediction Markets,Vol.23, No.2, pp.1-31.

VIII. Diebold, F. (2004),Elements of Forecasting, 3rded. Thomsos sourth-westrn,India.

IX. Ediger, S.A, (2006),ARIMAForecasting of Primary Energy Demand by Fuel inTurkey,Energypolicy, Vol. 35, pp.1-8.

X. Gouriroux, C. and Monfort, A. (1997),Time Series and Dynammic Models,Giampiero,M.Gallo Cambridge.

XI. Gujarati, D.N. (2004),Basic Econometrics, 4thed.,McGraw Hill, New York.

XII. Hannan, E.J. (1994),Multiple Time Series,New York: John Wiley & Sons Inc.

XIII. Kumar, et al. (2009),Surface flux modelingusing ARIMA technique in humansubtropicalmonsoon area,Journal of Atmospheric and Solar-TerrestrialPhysics. Vol. 71, pp. 1293-1298.

XIV. Lloret, et al. (2000),Time Series Modeling of Landings in NorthMediterranean Sea,ICESJournal of Marine Science: Journal du Conseil. Vol.57, pp. 171-184.

XV. Mitrea, C. A., Lee, C. K.M. and Wu,Z. (2009), A Comparison betweenNeural Networks and Traditional Forecasting Methods: A Case Study,International Journal of Engineering Business Management, Vol. 1, No. 2, pp.19-24.

XVI. Mucuk, M. and Uysal, D. (2009),Turkey’s Energy Demand,Current ResearchJournal of SocialSciences, Vol.1(3), pp. 123-128.

XVII.Pingfan, H. and Zhibo, T. (2014), A comparison study of the forecasingperformance of three international organizations,JEL codes: C30, C80.

XVIII. Prindyck,R.S. and Rubinfeld, D.L. (1981),Economic Models and EconomicForecasts,3rded.McGraw-Hill, Inc.

XIX. Slvanathan, E. A. (1991),A Note on the Accuracy of Business Economists GoldPrice Forecasts,Australian Journal of Management. Vol. 16, pp. 91-94.

XX. Tseng, et al. (2001),Fuzzy ARIMA model for forecasting the foreign exchangemarket,FuzzySets and Systems. Vol. 118, pp. 1-11.

XXI. Wood, et al. (1996), Classifying Trend Movements in the MSCI U.S.A.Capitalmarket Index-A,Comparison of Regressions, ARIMA and Neural Network Method.Computers &Operation Research. Vol. 23, pp. 611-622.

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Abstract:

The aim of this paper is to investigate the distribution of stresses due to expansion of a spherical cavity at the centre of a non-homogeneous  metallic sphere of finite radius for an elasto-plastic solid with effect of work-hardening under an increasing internal pressure, the external pressure remaining constant. The non-homogenecity of the elastic material is characterised by supposing that the Lame constrants very exponentially as the function of radial distance. The case of ideal plastic solid has also been deduced from this general case.  

Keywords:

Refference:

1. R. Hill (1950), : Theory of Plasticity, Oxford University Press, p-317

2. A.E.H.Love (1952),: The Mathematical Theory of Elasticity, Dover
Publication, P-164, Landon.

3. Saint-Venant (1865), : Jour, De-Math, Primes at appl, (Lonvilla) t-10.

4. S.G.Lekhnitskii (1963), : Theory of Elasticity of an Anisotropic Elastic
Body, Holden-Day, INC, p-390.

5. H.G.Hopkins (1960), : Progress in solid Mechanics, Vol-I, p-80, Edited by I.N.Sneddon and R.Hill, North Holland Publishing Company, Amsterdem.

6. P. R. Sengupta (1969), Ind. Jour, Mech and Math., Special issue, Part-II,
p-80, Prof. B. Sen, D.Sc., F.N.I., 70th’Birth Anniversary Volume
7. L. K. Roy (1992), Proc. Nat. Acad. Sci. India, 62(A), III p-445

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Abstract:

A numerical algorithm is presented for studyinglaminar convection flow andheat transfer over an isothermal horizontal plate. By means of similarity transformation,the original nonlinear coupled partial differential equations of flow are transformed to apair of simultaneous nonlinear ordinary differential equations. Subsequently they arereduced to a first order system and integrated using Newton RaphsonandadaptiveRunge-Kutta methods.The computer codes are developed for this numerical analysis inMatlab environment. Velocity, and temperature profiles for various Prandtl number areillustrated graphically. Flow, and heat transfer parameters are derivedas functions of Prandtl number alone. The results of the present simulation are then compared withexperimental data in literature with good agreement.

Keywords:

Free Convection, Heat Transfer ,Isothermal Horizontal Plate, Matlab,Numerical Simulation,

Refference:

I.Blasius, H., “Grenzschichten in Flussikeiten mitkleiner Reibung”, Z. Angew,Math.Phys., vol. 56, pp. 1-37, 1908 [English translation in NACA Technical Memo. 1256].

II.Meksyn, D., “New Methods in Laminar Boundary Layer Theory”, Pergamon,London, 1961.

III.Rosenhead, L. (ED), “Laminar Boundary Layers”, Oxford Univ. Press, London, 1963.

IV.Hansen, A. G., “Similarity Analysis of BoundaryValue Problems in Engineering”,Prentice-Hall, Englewood Cliffs, N. J., 1964.

V.Bejan, A.,Heat Transfer, John Wiley, New York, 1993.

VI.Sachdev, P. L., “Self-Similarity and Beyond: Exact Solutions of NonlinearProblems”, CRC Press, Boca Ratton, Fla, 2000

VII.Incropera, F.P. and DeWitt D.P.,Introduction to Heat Transfer, Fourthedition, JohnWiley, New York, 2002.

VIII.Çengel, Y.A.,Heat Transfer,Second edition, McGraw–Hill, New York, 2003.

IX.Lienhard IV, J.H. and Lienhard V, J.H.,A Heat Transfer Textbook, Phlogiston Press,Cambridge, MA, 2003.

X.Nellis, G. and Klein, S.,Heat Transfer, Cambridge University Press, London, UK,2008.

XI.Mitra A., “Numerical Simulation on Laminar Free-Convection Flow and HeatTransfer OveranIsothermal Vertical Plate,”International Journal of Research inEngineering & Technology, 04, 2015, pp 488-494.

XII.Leipmann, H. W., “Investigation on Laminar Boundary-Layer Stabilty andTransition on Carved Boundaries,“ NACAWartime Report W107 (ACR3H30),1943 [see also NACA Technical Memo. 1196 (1947) and NACA Report 890 (1947)].

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Abstract:

In this paper, we have developed an inventory model for deteriorating items withprice and time dependentdemand considering inflation effect on the system. Shortages ifany are allowed and partially backlogged with a variable rate dependent on the durationof waiting time up to the arrival of next lot. The corresponding problem has beenformulated as a nonlinear constrained optimization problem, all the cost parameters arecrisp valued and solved. A numerical example has been considered to illustrate the modeland the significant features of the results are discussed. Finally, based on these examples,a sensitivity analyses have been studied by taking one parameter at a time keeping theother parameters as same.

Keywords:

Inventory,deterioration,partially backlogged shortages ,permissible delay in payment,

Refference:

I.C.W. Haley, H.C. Higgins, Inventory policy and trade credit financing, Manage.Sci. 20 (1973) 464-471.

II.S.K. Goyal, Economic order quantity under conditions of permissible delay inpayments, J. Oper. Res. Soc. 36 (1985) 35–38.

III.S.P. Aggarwal, C.K. Jaggi, Ordering policies of deteriorating items underpermissible delay in payments, J. Oper. Res. Soc. 46 (1995) 658–662.

IV.A.M.M. Jamal, B.R. Sarker, S. Wang, An orderingpolicy for deteriorating itemswith allowable shortages and permissible delay inpayment, J. Oper. Res. Soc. 48(1997) 826-833.

V.H. Hwang, S.W. Shinn, Retailer’s pricing and lotsizing policy for exponentiallydeteriorating products under the condition ofpermissible delay in payments,Comp. Oper. Res. 24 (1997) 539–547.

VI.C.T. Chang, L.Y. Ouyang, J.T. Teng, An EOQ model for deteriorating items undersupplier credits linked to ordering quantity, Appl. Math. Model. 27 (2003) 983–996.

VII.P.L. Abad, C.K.Jaggi, A joint approach for setting unit price and the length of thecredit period for a seller when end demand is price sensitive, Int. J. Prod. Econ. 83(2003) 115–122.

VIII.L.Y. Ouyang, K.S. Wu, C.T. Yang, A study on an inventory model for noninstantaneous deteriorating items with permissible delay in payments, Comp. Ind.Eng. 51 (2006) 637–651.

IX.Y.F. Huang, An inventory model under two levels of trade credit and limitedstorage space derived without derivatives, Appl. Math. Model. 30 (2006) 418–436.

X.Y.F. Huang, Economic order quantity under conditionally permissible delay inpayments, Euro.J.Oper.Res.176 (2007) 911–924.

XI.Y.F. Huang, Optimal retailer’s replenishment decisions in the EPQ model undertwolevels of trade credit policy, Euro.J.Oper.Res.176 (2007) 1577–1591.

XII.B. Das, K. Maity, M. Maiti, A two warehouse supply-chain model underpossibility/necessity/credibility measures, Math. Comp. Model. 46 (2007) 398–409.

XIII.B. Niu, J.X. Xie, A note on Two-warehouse inventory model with deteriorationunder FIFO dispatch policy, Euro. J. Oper. Res. 190 (2008) 571-577.

XIV.M. Rong, N.K. Mahapatra, M. Maiti, A twowarehouse inventory model for adeteriorating item with partially/fully backlogged shortage and fuzzy lead time,Euro. J. Oper. Res. 189 (2008) 59–75.

XV.J.K. Dey, S.K. Mondal, M. Maiti, Two storage inventory problem with dynamicdemandand interval valued lead-time over finite time horizon under inflation andtime-value of money, Euro. J. Oper. Res. 185 (2008) 170–194.

XIV.T.P. Hsieh, C.Y. Dye, L.Y. Ouyang, Determining optimal lot size for a twowarehouse system with deterioration and shortagesusing net present value, Euro.J. Oper. Res. 191 (2008) 182-192.

XVII.M.K. Maiti, Fuzzy inventory model with two warehouses under possibilitymeasure on fuzzy goal, Euro. J. Oper. Res. 188 (2008) 746–774.

XVIII.C. K. Jaggi, and P. Verma,Joint optimization of price and order quantity withshortages for a two-warehouse system,Top (Spain),16 (2008) 195-213.

XIX.S.S. Sana, K.S. Chaudhuri, A deterministic EOQ model with delays in paymentsand price-discountoffers, Euro. J. Oper. Res.184 (2008) 509–533.

XX.Y.F. Huang, K.H. Hsu, An EOQ model under retailerpartial trade credit policy insupply chain, Int. J. Prod. Econ. 112 (2008) 655–664.

XXI.C.H. Ho, L.Y. Ouyang, C.H. Su, Optimal pricing, shipmentand payment policyfor an integrated supplier–buyer inventory model with two-part trade credit, Euro.J. Oper. Res. 187 (2008) 496–510.

XXII.C.C. Lee, S.L. Hsu, A two-warehouse production model for deterioratinginventory items withtime-dependent demands,Euro. J. Oper. Res. 194 (2009)700–710.

XXIII.C.K. Jaggi, K.K. Aggarwal, P. Verma, Inventory and pricing strategies fordeteriorating items with limited capacity and timeproportional backlogging rate,Int. J. Oper. Res. 8(3) (2010) 331-354.

XXIV.C.K. Jaggi, A. Khanna, Supply chain models fordeteriorating items with stockdependent consumption rate and shortages underinflation and permissible delayin payment,Int. J. Math. Opera. Res. 2(4) (2010) 491-514.

XXV.C.K. Jaggi, A. Kausar, Retailer’s ordering policy in a supply chain when demandis price and credit period dependent,Int. J. Strat. Dec. Sci. 2(4) (2011) 61-74.

XXVI.A.K. Bhunia, A.A. Shaikh,A two warehouse inventory model for deteriorating itemswith time dependent partial backlogging and variable demand dependent on marketingstrategy and time,International Journal of Inventory Control and Management, 1 (2011),95-110.

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Abstract:

The position control system is one of the interesting term in control systemengineering. Now a days several control system algorithm have been applied in thatapplication. PID controller is a well known controller and widely used infeedbackcontrol in industrial processes. For position control system sometime pid controller is notaccurate for this application because of non linear properties. Therefore e in thisresearch the fuzzy logic controller is proposed to overcome the problemof pid controller.Fuzzy logic controller has a ability to overcome the problem of pid controller. Fuzzylogic controller has ability to control the non linear systems also because the algorithmused is concentrated by emulating the expert and implementedin language based on theexperimental result , the fuzzy logic controller designed, is able to improve theperformance of the position control system compare to the pid controller, in terms of risetime (Tr) is 50%, settling time Ts is 80% and maximum overshoot (M%) is 98%, and thatcan be reduced.

Keywords:

uzzy logic controller,PID controller, position control,DC servo motor,

Refference:

I. Nagrath, I.J. and M. Gopal,Control Systems Engineering, 3rdedition, New AgePublishers, 2000

II.Driankov, D., H. Hellendoorn and M. Reinfrank,An Introduction to FuzzyControl,2ndedition, Springer International Student Edition, 2001

III.Klir, G.J. and Bo Yuan,FuzzySets and Fuzzy Logic: Theory and Applications,Prentice Hall International, 1995

IV.Chen, G. and T.T. Pham,Introduction to, Fuzzy Sets, Fuzzy Logic and Fuzzy]Control System,CRC Press, 2001

V.Beucher, O. and M. Weeks,INTRODUCTION TO MATLAB® & SIMULINK AProject Approach,3rdedition, Infinity Science Press LLC, 2008

VI.Rajoriya, A. and H. Ahmed(2014)Performance Assessment of Tuning Methodsfor PID Controller Parameter used for Position Control of DC Motor,InternationalJournal , Electrical Engg., 9, 139-148

VII. http://www.mathworks.com/matlabcentral

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