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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
Selection 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 and
provide background information, motivation for processing parameters to be selected to create alloys
applications and an exposition to the or composite materials with required properties for
methodologies employed in the development of any specific applications. Such relationships can be
fuzzy logic based decision making in hydro or discerned by empirical experiments or by the use of
water power engineering by optimum selection of mathematical and/or computational models. Various
hydro turbine blade material. All the works on approaches had been proposed to help address the
the 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 and
engineering for selection of proper material have knowledge intensive.
reported encouraging results till date. In our
views, the lack of negative results might be due to Modern engineering or commercial
the simplification of engineering or commercial materials, from those used to make simple things
material problems to manageable and predictable such as plastic carry bags to complex things such as
situations. 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 meets
science and engineering and artificial intelligent specific requirement then arises. To achieve this,
systems or multi criteria decision analysis or engineering or commercial materials experts apply
fuzzy logic technique, is still blur. The need to the knowledge generated in engineering or
formalize the computational and intelligent commercial materials. Production engineering as
systems 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 and
engineering or commercial materials in evaluation of specific engineering or commercial
particular, we think that our finding applies to materials in their various forms and in different
other areas of engineering as well. To the best of operating conditions with the aim of developing
the authors knowledge this novel multi criteria target property for an application.
decision analysis or fuzzy logic approach of
optimized selection of hydro turbine blade In water power generation “Water-the
material for small hydro power generation is white coal” is used non-destructively by the force of
absent in material science for renewable energy gravity, which is a totally carbon-free and
literatures 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 linear
engineering material selection; Multi Criteria momentum of water jet into rotational motion that
Decision Analysis; MCDA can then be transformed into electrical energy by the
generator or alternator. Naturally flowing rivers and
INTRODUCTION: 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 used
electrical properties, magnetic properties, chemical directly to drive turbines due to its sharp elevation. If
properties, manufacturing properties, material cost, the natural fall is not steep, a head is created
product shape, material impact on environment, artificially by damming the river or stream, making a
availability, cultural aspects, aesthetics, recycling, reservoir, and diverting its water to a nearby location
etc and mechanical properties that have been already with a penstock where the water is made to fall
studied and accepted in the material science and under gravity, driving a turbine for power generation.
engineering literature including knowledge of the
various relationships between engineering or Initially hydro power became increasingly
commercial materials constituents, structure and popular as an advantageous clean – green – friendly

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renewable energy resource [8, 14]. Unlike thermal material science and engineering needs excellent
power plants there is no pollution of gaseous expert knowledge about the assessment and the
emissions in-case of hydropower. Again in nuclear situations of the hydro power project. Expert views
power plants, there are radioactive wastes. The water aimed to find out the input variables that affect
used 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 help
reservoirs by damming rivers had also appeared to material science and engineering experts to
be a safe and wise strategy because it promised to determine the critical areas as well as their
enable utilizing the river-flow to a maximum extent importance.
by flood control [9], ensure year round availability
of water for irrigation-cultivation, navigation, The function of the turbine blades in the
entertainment, fish culture etc [11]. This paper kinetic energy conversion system is to convert the
provides a simple-to-use multi criteria decision linear momentum of water jet into rotational motion
analysis or fuzzy logic based optimum material that can then be transformed into electrical energy
selection tool or model for hydraulic turbine blades by the generator or alternator [17]. Hence they
used in small hydro power generation establishing a constantly experience dynamic loading. So strength
more meaningful material performance evaluation and toughness are considered as fuzzy selection
system. 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 selecting
available for material science and engineering an alloy for turbine blades from the maintenance
analysis, the most prevalent are Macbeth, AHP, point of view. The final material property chosen for
Promethee, Electre, and Fuzzy logic as observed. It the turbine blade is density. A low density is
uses a subjective assessments of relative importance extremely vital for better running performance as
converted to a set of overall scores (weights), well as increased efficiency of the system as lighter
arranging in this way the structure of the problem in blades will rotate more easily. The materials widely
a hierarchy way. Fuzzy logic can be one of the most used for Kaplan Turbine blade material are
powerful decision analysis methods for the same. In SS(16Cr5Ni), SS(13Cr4Ni), SS(13Cr1Ni) etc.
this paper, an applied material science and
engineering 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 how
MATERIALS AND METHODS: much force the material can support. Material
The data provided in engineering material toughness is defined as the amount of energy per
literature 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 a
results, for them to be useful in industrial or material when stressed. Toughness requires a
commercial use. These observations are expressed balance of strength and ductility. Strength and
linguistically and the complexity of the problem toughness are related. A material may be strong and
requires simplification, resulting in a manageable tough if it ruptures under high forces, exhibiting
but less accurate models. Analytical models for high strains, while brittle materials may be strong
simulating the macro-mechanical behaviour of but with limited strain values so that they are not
composite materials are difficult, and results in the tough.
use of simplifying assumptions which compromised
accuracy. The design approach employed by Corrosion (F3) is the gradual destruction of
engineering or commercial materials experts in material, usually metals, by chemical reaction with
industry involves conception and reasoning about its environment. In the most common use of the
abstract objects using their cognitive ability and word, this means electro-chemical oxidation of
experience. 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 pit
and vagueness of material properties. Keeping in or crack, or it can extend across a wide area more or
view of the above research works on hydro turbine less uniformly corroding the surface. Because
material selection, a novel decision making method corrosion is a diffusion controlled process, it occurs
is proposed in this paper for material selection for a on exposed surfaces. Often it is possible to
given engineering design as well as for chemically remove the products of corrosion to give
manufacturing it [4]. The aim of the present paper is a clean surface but with pitting.
to propose a novel multi criteria decision analysis or
fuzzy 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

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some are easier to weld than others. A material's be as given in Table-1a, 2a, 3a. In these tables
weld-ability is used to determine the welding elements (eij) represents the degree of membership
process and to compare the final weld quality to value of effect (Ej) for the factor of (Fi). In this
other materials. Weld-ability is often hard to define section our aim is to assess the membership value of
quantitatively, 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 or
is 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), constructed
that increasing the amount of a substance does not from above. The element (mij) of this matrix denotes
increase its density; rather it increases its mass. The the weighted value of effect (Ej) for the “Material
density at any point of a homogeneous object equals Property” (Fi). If the weight of factor (Fi) is (wij) for
its 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 given
engineering or commercial materials available to in Table-1b, 2b, 3b.
modern materials experts. The many features used to
describe 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 all
These complexities, and the accompanying rapid engineering or commercial materials which are taken
rate of change in the demand for new engineering or into consideration in the process of material
commercial 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 the
development 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 engineering
moment, multi criteria decision analysis or fuzzy or commercial materials A,B,C…. which could be
logic methods are becoming widely accepted as they evaluated by:
are gaining prominence in various areas of
engineering. (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 for
engineering material selection [13] can be easily Optimal selection with degree of index = Maximum
characterized 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 of
set is formed when a linguistic variable is combined hydraulic turbine blade material for small hydro
with a linguistic modifier. Fuzzy arithmetic can be power generation project in Himalayan region in
solved by widely accepted “Weighted Average India”.
Matrix Method”. Application of multi criteria
decision analysis or fuzzy logic method [15, 16] in We can calculate fuzzy optimized material
engineering material selection depends on following selection output by “Weighted Average Method” for
steps: blade of a hydro turbine project as presented here.
Let us consider 5 Material Properties with 3
Developing 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 the
from the above fuzzy relation where each element of “Degree of Truth ness” of all effects for all material
the 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.

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DISCUSSION:
For simplicity in presenting our model we In this paper, we have presented the
considered only 3 different engineering or applications of multi criteria decision analysis or
commercial materials suitable for hydraulic turbine fuzzy logic techniques focusing on hydro power
blade. 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 of
M) could be given by Table-1, 2, 3 along with the literature in all the application of artificial
weight (Wi) of respective material property (Fi). intelligence to materials engineering, we hope that
we have given an adequate overview of what is
Fuzzy 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 neural
Now the “Membership Value Matrix” is shown in networks, multi criteria decision analysis or fuzzy
Table- 2a, 3a, 4a and the “Weighted Value Matrix” logic and genetic algorithms. The multi criteria
in Table- 2b, 3b, 4b respectively. decision analysis or fuzzy logic systems seem to be
the most popularly used hybrid of these techniques
RESULTS: 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 computational
Similarly, we can calculate all other values as shown approaches as multi criteria decision analysis or
in 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.

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