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Priyabrata Adhikary, Pankaj Kr Roy, Asis Mazumdar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.426-430Selection of Hydro-Turbine Blade Material: Application of Fuzzy Logic (MCDA) Priyabrata Adhikary*, Pankaj Kr Roy**, Asis Mazumdar*** *Asst. Professor - Mechanical, S.V.I.S.T. (WBUT), Kolkata-145, **Asst. Professor, S.W.R.E., Jadavpur University, Kolkata-32, ***Director & Professor, S.W.R.E., Jadavpur University, Kolkata-32,ABSTRACT The primary aim of this paper is to properties at a level that allows composition andprovide background information, motivation for processing parameters to be selected to create alloysapplications and an exposition to the or composite materials with required properties formethodologies employed in the development of any specific applications. Such relationships can befuzzy logic based decision making in hydro or discerned by empirical experiments or by the use ofwater power engineering by optimum selection of mathematical and/or computational models. Varioushydro turbine blade material. All the works on approaches had been proposed to help address thethe application of multi criteria decision analysis issue of material selection till date. However, all(MCDA) or fuzzy logic to material science and these systems and methods are complex andengineering for selection of proper material have knowledge intensive.reported encouraging results till date. In ourviews, the lack of negative results might be due to Modern engineering or commercialthe simplification of engineering or commercial materials, from those used to make simple thingsmaterial problems to manageable and predictable such as plastic carry bags to complex things such assituations. Our appraisal of the literature airplane or electronic chips, do not occur naturally.suggests that the interface between material The need to find optimum material that meetsscience and engineering and artificial intelligent specific requirement then arises. To achieve this,systems or multi criteria decision analysis or engineering or commercial materials experts applyfuzzy logic technique, is still blur. The need to the knowledge generated in engineering orformalize the computational and intelligent commercial materials. Production engineering assystems engineering methodology used in well as material science and engineering [2, 3]materials engineering, therefore, arises. Although includes the expertise involved in the selection,our study focuses on hydro power related design, analysis, fabrication or manufacturing andengineering or commercial materials in evaluation of specific engineering or commercialparticular, we think that our finding applies to materials in their various forms and in differentother areas of engineering as well. To the best of operating conditions with the aim of developingthe authors knowledge this novel multi criteria target property for an application.decision analysis or fuzzy logic approach ofoptimized selection of hydro turbine blade In water power generation “Water-thematerial for small hydro power generation is white coal” is used non-destructively by the force ofabsent in material science for renewable energy gravity, which is a totally carbon-free andliteratures due to its assessment complexity. inexhaustible resource to generate power [7, 10]. The function of the turbine blades in the K.E.KEYWORDS: Fuzzy logic; hydro power plant; conversion system is to convert the linearengineering material selection; Multi Criteria momentum of water jet into rotational motion thatDecision Analysis; MCDA can then be transformed into electrical energy by the generator or alternator. Naturally flowing rivers andINTRODUCTION: streams, flow towards lesser elevation and thus We know that all engineering or provide suitable site for hydropower generation [1,commercial materials have physical properties, 8]. The falling water of waterfalls can be usedelectrical properties, magnetic properties, chemical directly to drive turbines due to its sharp elevation. Ifproperties, manufacturing properties, material cost, the natural fall is not steep, a head is createdproduct shape, material impact on environment, artificially by damming the river or stream, making aavailability, cultural aspects, aesthetics, recycling, reservoir, and diverting its water to a nearby locationetc and mechanical properties that have been already with a penstock where the water is made to fallstudied and accepted in the material science and under gravity, driving a turbine for power generation.engineering literature including knowledge of thevarious relationships between engineering or Initially hydro power became increasinglycommercial materials constituents, structure and popular as an advantageous clean – green – friendly 426 | P a g e
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Priyabrata Adhikary, Pankaj Kr Roy, Asis Mazumdar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.426-430renewable energy resource [8, 14]. Unlike thermal material science and engineering needs excellentpower plants there is no pollution of gaseous expert knowledge about the assessment and theemissions in-case of hydropower. Again in nuclear situations of the hydro power project. Expert viewspower plants, there are radioactive wastes. The water aimed to find out the input variables that affectused in hydro power generation remains fully intact output variable, using fuzzy based “Delphi Method”and utilizable or reusable afterwards. Setting up of [12], and give a perfect evaluation in order to helpreservoirs by damming rivers had also appeared to material science and engineering experts tobe a safe and wise strategy because it promised to determine the critical areas as well as theirenable utilizing the river-flow to a maximum extent importance.by flood control [9], ensure year round availabilityof water for irrigation-cultivation, navigation, The function of the turbine blades in theentertainment, fish culture etc [11]. This paper kinetic energy conversion system is to convert theprovides a simple-to-use multi criteria decision linear momentum of water jet into rotational motionanalysis or fuzzy logic based optimum material that can then be transformed into electrical energyselection tool or model for hydraulic turbine blades by the generator or alternator [17]. Hence theyused in small hydro power generation establishing a constantly experience dynamic loading. So strengthmore meaningful material performance evaluation and toughness are considered as fuzzy selectionsystem. material properties from the operation point of view. Again corrosion resistance and weld-ability are both Among the numerous MCDA methods important attributes to be considered when selectingavailable for material science and engineering an alloy for turbine blades from the maintenanceanalysis, the most prevalent are Macbeth, AHP, point of view. The final material property chosen forPromethee, Electre, and Fuzzy logic as observed. It the turbine blade is density. A low density isuses a subjective assessments of relative importance extremely vital for better running performance asconverted to a set of overall scores (weights), well as increased efficiency of the system as lighterarranging in this way the structure of the problem in blades will rotate more easily. The materials widelya hierarchy way. Fuzzy logic can be one of the most used for Kaplan Turbine blade material arepowerful decision analysis methods for the same. In SS(16Cr5Ni), SS(13Cr4Ni), SS(13Cr1Ni) etc.this paper, an applied material science andengineering problem is solved using multi criteria Generally speaking, toughness (F1)decision analysis (MCDA) or fuzzy logic. indicates how much energy a material can absorb before rupturing while strength (F2) indicates howMATERIALS AND METHODS: much force the material can support. Material The data provided in engineering material toughness is defined as the amount of energy perliterature must, be validated with material behaviour volume that a material can absorb before rupturing.information which agrees closely with experimental It is also defined as the resistance to fracture of aresults, for them to be useful in industrial or material when stressed. Toughness requires acommercial use. These observations are expressed balance of strength and ductility. Strength andlinguistically and the complexity of the problem toughness are related. A material may be strong andrequires simplification, resulting in a manageable tough if it ruptures under high forces, exhibitingbut less accurate models. Analytical models for high strains, while brittle materials may be strongsimulating the macro-mechanical behaviour of but with limited strain values so that they are notcomposite materials are difficult, and results in the tough.use of simplifying assumptions which compromisedaccuracy. The design approach employed by Corrosion (F3) is the gradual destruction ofengineering or commercial materials experts in material, usually metals, by chemical reaction withindustry involves conception and reasoning about its environment. In the most common use of theabstract objects using their cognitive ability and word, this means electro-chemical oxidation ofexperience. Hence these processes are best modelled metals in reaction with an oxidant such as oxygen.linguistically having the uncertainty, imprecision Corrosion can be concentrated locally to form a pitand vagueness of material properties. Keeping in or crack, or it can extend across a wide area more orview of the above research works on hydro turbine less uniformly corroding the surface. Becausematerial selection, a novel decision making method corrosion is a diffusion controlled process, it occursis proposed in this paper for material selection for a on exposed surfaces. Often it is possible togiven engineering design as well as for chemically remove the products of corrosion to givemanufacturing it [4]. The aim of the present paper is a clean surface but with pitting.to propose a novel multi criteria decision analysis orfuzzy logic method to deal with the material The weld-ability (F4), also known as join-selection problems considering both qualitative and ability, of a material refers to its ability to be welded.quantitative attributes. The use of the fuzzy logic in Many metals and thermoplastics can be welded, but 427 | P a g e
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Priyabrata Adhikary, Pankaj Kr Roy, Asis Mazumdar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.426-430some are easier to weld than others. A materials be as given in Table-1a, 2a, 3a. In these tablesweld-ability is used to determine the welding elements (eij) represents the degree of membershipprocess and to compare the final weld quality to value of effect (Ej) for the factor of (Fi). In thisother materials. Weld-ability is often hard to define section our aim is to assess the membership value ofquantitatively, so most standards define it each factor (Fi) against its each possible effect (Ej).qualitatively. There is no factor alone which has maximum membership value for its all possible effects rather The density (F5) of an engineering material the degree of membership value [5, 6] may vary oris defined as its mass per unit volume. In some cases equal with other factors.density is also defined as its weight per unit volume,although this quantity is more properly called Developing weighted matrix:specific weight. Density is an intensive property in It is also a (N x M) matrix (mij), constructedthat increasing the amount of a substance does not from above. The element (mij) of this matrix denotesincrease its density; rather it increases its mass. The the weighted value of effect (Ej) for the “Materialdensity at any point of a homogeneous object equals Property” (Fi). If the weight of factor (Fi) is (wij) forits total mass divided by its total volume. the “Impact or Effect” of (Ej), then weighted value of (mij) = (eij x wij) where (wi) = Wt. of factor of (Fi) There is a huge and growing amount of for i = 1,2,..,n. The weighted matrix will be as givenengineering or commercial materials available to in Table-1b, 2b, 3b.modern materials experts. The many features used todescribe these commercial materials, together with Developing weighted average matrix:their limitations of the interaction between these It is also a (N x M) matrix (Wavg)ij,features, makes material selection process complex. constructed from above weighted matrix [5, 6] of allThese complexities, and the accompanying rapid engineering or commercial materials which are takenrate of change in the demand for new engineering or into consideration in the process of materialcommercial materials, offer new challenges to selection for the hydraulic turbine blade material.materials experts. This motivates them to the The element (Wavg)ij of the matrix denotes thedevelopment and application of more versatile average weighted value of “Impact or Effect” (E j)material selection techniques or tools. At the for the “Material Properties” (Fi) of all engineeringmoment, multi criteria decision analysis or fuzzy or commercial materials A,B,C…. which could belogic methods are becoming widely accepted as they evaluated by:are gaining prominence in various areas ofengineering. (Wavg)ij = [(mij)A + (mij)B + (mij)C] / [(wij)A + (wij)B + (wij)C]THEORY AND CALCULATIONS:Fuzzy logic preliminaries: Optimal selection of fuzzy turbine blade material: The fuzzy linguistic variable forengineering material selection [13] can be easily Optimal selection with degree of index = Maximumcharacterized by common terms as: “Safe- Critical- value of ∑Ei of the “Weighted Average Matrix”.Unsafe; Strong – Average – Weak; Good –Moderate – Bad; High-Medium-Low” etc. Each CASE STUDY:term is called a linguistic modifier. Hence a fuzzy Considering a project “Selection ofset is formed when a linguistic variable is combined hydraulic turbine blade material for small hydrowith a linguistic modifier. Fuzzy arithmetic can be power generation project in Himalayan region insolved by widely accepted “Weighted Average India”.Matrix Method”. Application of multi criteriadecision analysis or fuzzy logic method [15, 16] in We can calculate fuzzy optimized materialengineering material selection depends on following selection output by “Weighted Average Method” forsteps: blade of a hydro turbine project as presented here. Let us consider 5 Material Properties with 3Developing membership value matrix: categories of there effect (for the sake of simplicity This is a (N x M) matrix (Eij), formulated in presenting our methodology). Assessing thefrom the above fuzzy relation where each element of “Degree of Truth ness” of all effects for all materialthe matrix, is equal to the corresponding value of properties:degree of truth-ness. Here “N” considers the no. of“Material Properties” and “M” is the no. of “Impact Consider 2 Universes X= {F1, F2, F3, F4, F5}or Effects”. Consider the set of collection of all and Y= {E1, E2, E3}. Where, F1=Toughness,“Material Properties”, say X= {F1, F2,.., Fn} and the F2=Strength, F3=Corrosion Resistance, F4=Weld-set of collection of all types of “Impacts or Effects”, ability, F5=Density; and E1=Unacceptable,Y={E1, E2,.., En}. The membership value matrix will E2=Critical, E3=Acceptable. 428 | P a g e
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Priyabrata Adhikary, Pankaj Kr Roy, Asis Mazumdar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.426-430 DISCUSSION:For simplicity in presenting our model we In this paper, we have presented theconsidered only 3 different engineering or applications of multi criteria decision analysis orcommercial materials suitable for hydraulic turbine fuzzy logic techniques focusing on hydro powerblade. Now the membership value or “Degree of related material science and engineering. Although,Truth-ness” matrix for the Fuzzy relation R on (N x this paper is by no means an exhaustive review ofM) could be given by Table-1, 2, 3 along with the literature in all the application of artificialweight (Wi) of respective material property (Fi). intelligence to materials engineering, we hope that we have given an adequate overview of what isFuzzy Weighted Average: currently happening in this evolving and dynamic(Wavg)ij = [(mij)A + (mij)B + (mij)C] / [(wij)A + (wij)B + area of research. Three soft computing techniques(wij)C] that are prominently used in engineering or commercial materials engineering: artificial neuralNow the “Membership Value Matrix” is shown in networks, multi criteria decision analysis or fuzzyTable- 2a, 3a, 4a and the “Weighted Value Matrix” logic and genetic algorithms. The multi criteriain Table- 2b, 3b, 4b respectively. decision analysis or fuzzy logic systems seem to be the most popularly used hybrid of these techniquesRESULTS: in engineering or commercial materials engineering.Therefore we can calculate, The tool of the trade is also changing from the(Wavg)11 = [4.5+17.5+33] / [90+70+60] = 0.25 traditional mathematical and analytical approaches to logical modelling, simulation and computationalSimilarly, we can calculate all other values as shown approaches as multi criteria decision analysis orin Table-4. fuzzy logic. CONCLUSION: The predictive accuracy of the fuzzy model is very reasonable as shown. It is well understood that the vague and data scarcity problem in material science for the selection of material can be easily solved using multi criteria decision analysis or fuzzy logic. From the very approximate data, the model is capable of generating reasonably accurate result. Our multi criteria decision analysis or fuzzy logic based computational approach to material science and engineering has the potential of making material science and engineering process more effective and efficient. This will, in effect, facilitates an appropriate management of human efforts as well as natural engineering or commercial materials and resources, particularly those that are susceptible to depletion. ACKNOWLEDGEMENT: The authors declare that there is no conflict of interests. REFERENCE: [1] S. Abbasi, and T. Abbasi: Small hydro and the environmental implications of its extensive utilization, Renewable and sustainable energy reviews 15, issue-4, pp. 2134–2143 (Elsevier), (2011)Then ∑Ei = 2.07 for E3, which reveals that the [2] S.R. Ansari, R. Chandna and P.K. Mittal:material quality or material performance is in grade Multi-criteria decision making using multiof “Acceptable” with “Degree of Index=2.07”for SS criteria decision analysis or fuzzy logicas blade material. approach for evaluating the manufacturing flexibility, Journal of Engineering and Technology Research Vol. 2, No.12, pp. 237-244, (2010) 429 | P a g e
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Priyabrata Adhikary, Pankaj Kr Roy, Asis Mazumdar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 1, January -February 2013, pp.426-430[3] S.R. Ansari and R. Chandna: Comparison reasoning, Information sciences, Vol.8, of Fuzzy and MCDM Approach to Measure No.3, pp. 199-249, (1975) Manufacturing Flexibility, International [16] L.A. Zadeh: Multicriteria decision analysis Journal of Scientific & Engineering or fuzzy logic = computing with words, Research, Vol.3, Issue-5, pp. 1-9, (2012) IEEE Transactions on fuzzy systems Vol.4,[4] A. Azeem and S.K. Paul: Selection of the No.2, pp.103-111, (1996) optimal number of shifts in fuzzy [17] David A. Chin, A. Mazumdar, P.K. Roy: environment: Manufacturing company’s Water-Resources Engineering (3rd Edition), facility application, JIEM, Vol.3, No.1, pp. Pearson Educations Ltd. 54-67, (2011)[5] S. Biswas, P.K. Roy and S. Datta: Selection of most suitable site for an irrigational project: use of vague logic, IJFSRS, Jan- June (08), Vol.1, Issue-1, pp. 41-44, (2008)[6] S. Biswas, P.K. Roy and S. Datta: IWRM: Approach of intuitionist Fuzzy-EIA for selection of best catchments area, Advances in fuzzy mathematics, Vol.3, No.1, pp. 43- 50, (2008)[7] P. Adhikary, P.K. Roy and Asis Mazumdar: Safe and efficient control of hydro power plant by fuzzy logic, IJESAT, Vol.2, Issue- 5, pp. 1270-1277 (2012)[8] P. Adhikary, P.K. Roy and Asis Mazumdar: MCDA of manpower shift scheduling for cost effective hydro power generation, IJETED, Vol.7, Issue-2, pp. 116-127 (2012)[9] R. Arunkumar and V. Jothiprakash: Optimal reservoir operation for hydro power generation using non-linear programming model, J. Inst. Eng. India, Ser. A (Springer), 10.1007/s40030-012-0013-8, Sept. 2012[10] A.C. De-souza: Assessment and statistics of Brazilian hydroelectric power plants: dam areas versus installed and firm power, Renewable and sustainable energy reviews 12, pp. 1843–1863 (Elsevier), (2008)[11] A. Flamos, et al: Hydro energy: techno- economic and social aspects within new climate regime, Int. J. Renewable Energy Technology, Vol.2, No.1, pp. 32-62, (2011)[12] D. Kanama, A. Kondo and Y. Yokoo: Development of technology foresight: integration of technology road mapping and the Delphi method, Int. J. of Technology Intelligence and Planning, Vol.4, No.2, pp. 184 – 200, (2008)[13] B.K.Patel and R.V.Rao: Material selection using a novel multiple attribute decision making method, International Journal of Manufacturing, Engineering or commercial materials, and Mechanical Engineering, Vol.1, No.1,pp. 43-56, (2011)[14] M.N. Rao: Implementation of a small hydro power project in India: issues and lessons, Int. J. Renewable Energy Technology, Vol.2, No.1, pp. 53-66, (2011)[15] L.A. Zadeh: The concept of a linguistic variable and its application to approximate 430 | P a g e
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