Mechanical Engineering, Priyadarshani College of Engineering,
Nagpur, Maharashtra, India
J. P. Modak
Mechanical Engineering, Priyadarshani College of Engineering, Nagpur, Maharashtra, India
A. V. Vanalkar
Mechanical Engineering, KDK College of Engineering, Nagpur, Maharashtra, India
ABSTRACT
In power starved India, millions of diesel generator (DG) sets working to meet the shortage of industrial and commercial units now add up to
cumulative capacity of 90000 MW. This figure is nearly equal to India’s total installed power capacity just before a decade and about 36% of installed total
generator set capacity. The typical generation cost is about 15 Rs. Per unit (Kwhr) for midsize genset with diesel cost about 50Rs per liter. It is observed that diesel generator sets are noisy and cause health hazards such as
permanent hearing loss, physiological traumas, stress etc. In order to avoid health risks, Central Pollution control Board (CPCB) the maximum
permissible sound pressure level for new DG set with rated capacity up to 1000KVA, should be less than 75dB(A) at a one meter from enclosure surface. For noise control in DG set, passive noise control method with an
acoustic enclosure with inner surface covered with sound absorbing material is used. The canopy absorbs noise and reduced the noise level to a permissible
limit.
This paper presents the formulation of a Field Data Based Multivariate (FDBM) regression model and an ANN model to estimate noise level outside
canopy/acoustic enclosure. This model predicts the noise of a DG set with canopy on the basis of various independent parameters such as engine load,
canopy thickness, foam thickness and foam density of the system.
Key words: Field Data Based Model, Optimization, Diesel Engine Generator Set Acoustic Enclosure Design, ANN
Cite this Article: R. R. Askhedkar, J. P. Modak and A. V. Vanalkar, Formulation of A Field Data Based Model To Estimate The Noise Level In A
Diesel Generator Set with Acoustic Enclosure. International Journal of Mechanical Engineering and Technology, 7(3), 2016, pp. 368–386. http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=3
1. INTRODUCTION
In today’s world Diesel Generator (DG) set is used widely in industries, hospitals, malls, airports, and many other places as the main or standby source of power generation. The noise levels generated by diesel engines are high and can cause
health hazards like permanent hearing loss, psychological traumas, stress etc. In order to avoid health risks ,Central Pollution Control Board (CPCB) has specified that the
maximum permissible sound pressure level for new DG set with rated capacity up to 1000KVA, shall be less than 75dB(A) at one meter distance from enclosure surface.
The major sources of noise and noise levels generated by DG set [1] at distance of
one meter from the surface are:
Diesel Engine Noise-This noise is caused by combustion forces and mechanical friction like tappet noise, piston slap noise etc. and ranges from 100dB (A) to 121dB (A) depending on the size of the engine.
Radiator Fan Noise: This noise is caused by movement of air at high speed across engine and radiator. The noise level ranges from 100dB (A) to 105dB (A) depending on the speed and size of fan.
Radiator Fan Noise: This noise is caused by movement of air at high speed across engine and radiator. The noise level ranges from 100dB (A) to 105dB (A) depending on the speed and size of fan.
Alternator Noise: This noise is caused by cooling air and brush friction and ranges from 80dB (A) to 90 dB (A)
Induction Noise: This noise is caused by fluctuations in current in alternator winding and ranges from 80dB (A) to 90 dB (A)
Engine Exhaust: Silencers are used to control engine exhaust noise. Silencer reduces the noise by 15-25 dB (A) depending on the class or Grade of Silencer. The Hospital Grade Silencers give a maximum of 25-30 dB (A) insertion loss.
Structural/Mechanical Noise: This noise is generated by mechanical vibration of various engine and alternator parts and components.
A canopy reduces the noise of the DG set. The canopy is nothing else but an
acoustic enclosure with inner surface covered by sound absorbing material. The sound absorbing material absorbs noise and reduces the noise level (outside the canopy) to a permissible limit.
2. LITERATURE REVIEW
Munjal [2] proposed an elementary theoretical model to design acoustic enclosures.
His model is based on Insertion loss, defined as reduction of Sound Pressure Level (SPL) at the receiver due to location of the source (machine) in an acoustic enclosure. He also provided data for random incidence transmission loss of typical partition
Mirowska and Marianna [3] analysed the relationships between sound absorption coefficients, air flow resistivity of material and the thickness of the layer. He also
prepared monograms, which could be used to estimate sound absorption coefficients if air flow resistivity and the thickness of PU foam material are known.
MP Joshi et all [4] observed that flow resistivity of a standard sample of melamine foam increases with density of material. Sound absorption and NRC showed higher values with increase in flow resistivity of the material.
Joseph E. Blanks [5] presented an effective design of an enclosure for portable generator to reduce the radiated noise. The important considerations for design were
1) Acoustic effects of enclosure 2) Heat transfer considerations and 3) Optimisation of enclosure by hook and jooves method or pattern search method.
I. J. Prager [6] presented different aspects of sound propagation inside a partially open
enclosure densely packed with active and passive installations. Depending on the relation between wavelength and the geometrical dimension of the system, the sound
field structure inside the enclosure was found to vary with frequency.
A K Gupta et al [7] presented the study in which passenger car diesel engine was converted to 30 KVA DG set by using 3000 rpm constant speed fuel injection pump.
The objective of the study was to design and optimize a canopy for DG set to reduce noise level along with adequate cooling requirements. Provision of adequate cooling
resulted in significant reduction of noise level of 24.6dB.
Paresh Shravage [8] concluded on the basis of his study that intrinsic parameters can be used to predict acoustic behavior of sound package material. Simulation gives
better understanding of noise insulation in terms of sound absorption.
Literature review indicates that though theoretical models are available to a
Correlate reduction of noise level with canopy wall thickness and thickness and density of noise absorbing material to predict the reduction of noise level of DG generator set with canopy, it is not possible to use these models for canopy design
because of oversimplified assumptions used in developing these models.
3. PLANNING OF EXPERIMENTATION TO GENERATE
DESIGN DATA FOR CANOPY OF DG GENERATOR SET
The steps in planning of experimentation are:
3.1. Study of the System
The present research work is an attempt to reduce the noise of diesel generator set
noise by using passive noise reduction technique. To reduce noise by passive method, DG set is enclosed in the canopy with inlet and outlet openings for ventilation. The
canopy is made up of MS sheet of different thicknesses. A layer of absorption material is pasted inside the enclosure. This acoustic absorption material absorbs the noise generated by the engine and alternator.
3.2 Identification of Performance (dependent) Variables and Physical Quantities
(Independent Variables) Affecting Performance of System
The term variable is used in a very general sense to apply to any physical quantity that undergoes change. If a physical quantity can be changed independent of the other quantities, then it is an independent variable. If a physical quantity changes in
response to variation of one or more independent variables, it is termed as dependent or response variable. The independent variables, dependent variables and
corresponding ∏terms in diesel generator (with canopy) system are given in Table 2.
3.3. Reduction of Variable by Using Dimensional Analysis
Dimensional analysis is carried out to established dimensional equations, exhibiting relationships between dependent ∏ terms and independent ∏ terms using
Buckingham ∏ theorem. As the number of independent variables are too large, they are reduced to few using dimensional analysis by applying Buckingham’s ∏ theorem
[9] .When this theorem is applied to a system having “n” independent variables and “m” primary dimensions,(n-m) number of ∏ terms are formed. Three primary dimensions used are L, M, T. Dimensional analysis can be used primarily as an
experimental tool to combine many experimental variables into one.
3.4. Selection of Experimentation Method
The objective of planning the experimentation is to obtain reliable and accurate results with the execution of minimum number of trials. Experimentation methods
predominantly used in planning experimentations in engineering field are:
Taguchi Method of Experimentation
Factorial Experimentation
Classical Plan of Experimentation
Field Data Based Mathematical Modeling
For conducting experimentation using the first three methods, large number of canopies, as per the design of experimentation, is required and the cost is huge.
Therefore, it is not possible to conduct experimentation using the first three methods. So, Field Data Based Modeling technique is selected for conducting experimentation.
The data is generated by measuring the noise level (outside the canopy) of DG set as per ISO 8528 Part: 10 using available canopies of different designs.
3.5. Selection and Calibration of Instruments
The canopies used for testing are available and not specially fabricated for this
research work. The tolerances on various parameters are specified in the drawings of canopies. The tolerances on various parts of canopy are specified in Table 1.
Table 1 Tolerance on various parameters
Sr. No. Parameter Tolerance
1 Canopy thickness ±0.2mm
2 PU foam thickness ±2mm
3 PU foam density ±5Kg/m3
On receipt material is inspected as per specifications. For noise and vibration data
acquisition, B & K Pulse system is used. The accelerometer is used for vibration measurement and microphone for noise measurement. The equipment is calibrated
before testing using standard calibration process.
3.6. Deciding Test Envelopes and Test Points
Table 3 shows the test envelopes and test points for various parameters. These values are taken from the dimensions of available canopies.
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
Table 3 shows the test envelopes and test points for various parameters. These values are taken from the dimensions of available canopies.
4. CONDUCTION OF EXPERIMENTS
Figure. 1 shows the DG set used in experimentation. The specifications of DG Set are
given in Table 4.The noise generated by DG generated set was measured at a distance of 1m at 12 locations as specified in ISO8528 Part: 10. [10].The noise is measured using microphone at all locations and overall noise is calculated as per the formulae
given in ISO8528 Part:10. In order to have accurate data, average of 5set of readings is taken at each measurement point and recorded.
Figure.1 Diesel Generator Set used in Experimentation
Table 4 Specification of Diesel Generator Set
Silencer
Radiator
Baseplate
Diesel Engine
Alternator
Canopy/Acoustic Enclosure
Parameter Details
Engine 2 Cylinder
Aspiration Natural
Number of Cylinders 2
HP 25.5
Bore X stroke (mm) 105 X 120
Displacement (CC ) 2080
Connecting Rod Length (mm) 216
Gas flow ( kg/hr) 99
Exhaust gas temp. (deg. C) 570
Gas Velocity (m/s) 19.5
Back Pressure limit 4.9 kPa
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
4.1. Data Generated Through the Measurement of Noise Level of DG Set
with Canopy
The data of 660 points is generated by using canopies of different combinations of sheet metal thickness (∏21), PU foam material Density (∏28) and thickness (∏29).
Sample data of 20 readings is given in Table 5.
4.2 Test Data Checking and Rejection
The noise measurement system is very precise. The difference in maximum and minimum readings in a set of five readings is less than 0.2 dB (A). Therefore if
variation is more than 0.2 dB(A) , it is assumed that there is some assignable cause for the variation and the set is rejected. After removing the assignable cause, the noise
5. MODEL FORMULATION AND ESTIMATION OF RELIABILITY
The independent∏ terms in the model are ∏8 (Engine Load), ∏21 (Canopy Thickness), ∏28 (Foam Thickness) and ∏29 (Foam Density). The dependent ∏ term
in model is noise level outside canopy (∏27). All the other independent ∏ terms related to Diesel engine - alternator system remain constant during experimentation
and are represented by a constant (term) in the model.
Figure 2: Model to predict noise level of Diesel Generator set.
Figure. 2 shows the model co-relating noise level outside canopy (dependent variable∏27) with independent variables ∏8 (Engine Load), ∏21 (Canopy
Thickness), ∏28 (Foam Thickness) and ∏29( Foam density). The model is formulated is as under:
(1)
The noise level outside the canopy (∏27) is a function of . The function may be linear, polynomial, exponential or any other function. Linear,
Polynomial or exponential models can be formulated by assuming the function to be linear polynomial or exponential respectively.
5.1 Formulation of Polynomial Model
The Procedure used for formulating Polynomial model for noise level outside canopy
(∏27) is discussed below.
The polynomial model will be of the form given below.
∏21= K +
(2)
The Value of , , , are
taken from Polynomial graph between ∏27and ∏8,∏21,∏28 and ∏29 respectively.
For formulating the polynomial model, a graph is plotted between engine load (∏8) and noise after canopy (∏27) using the following procedure.
In experimentation, the data is collected for 6 values of ∏8. i.e. 0.2, 2.6, 5.2, 7.81, 10.37 and 11.38. It is observed that for each value of ∏8, the observed value of ∏27
is different and varying. So to plot a graph between ∏8 & ∏27, for each value of ∏8 average value of ∏27 is calculated. The values of engine load (∏8) and corresponding average values of noise outside canopy (∏27) are shown in Table 6.
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
Fig.3 shows the graph between engine load (∏8) and corresponding average values of noise outside canopy (∏27).
Table 6 Values of Engine Load (∏8) and Average Noise outside Canopy (∏27)
∏8 ∏27
0.20 71.41
2.60 71.04
5.20 71.23
7.81 72.02
10.37 72.64
11.38 72.93
Figure.3 Graph of engine load (∏8) and noise level outside canopy ( ∏27)
The best fit polynomial graph is shown in fig.3. The equation for polynomial is (3).
(3)
The values of canopy sheet thickness (∏21) and corresponding average values of
noise outside canopy (∏27) are shown in Table 7. Fig.4 shows the graph between canopy sheet thickness (∏21) and corresponding average values of noise outside canopy (∏27).
Table 7 Values of Canopy Sheet Thickness ∏21 and Noise outside Canopy ∏27
Figure 4 Graph of Canopy Sheet Thickness (∏21) and Noise Level Outside Canopy ( ∏27)
The best fit polynomial graph is shown in fig.4. The equation for polynomial is (4)
(4)
The values of PU foam thickness (∏28) and corresponding average values of noise outside canopy (∏27) are shown in Table 8. Fig.5 shows the graph between PU foam thickness (∏28) and corresponding average values of noise outside canopy
(∏27).
The best fit polynomial graph is shown in fig.5. The equation for polynomial is (5).
(5)
Table 8 Values of PU Foam Thickness (∏28) and Noise outside Canopy (∏27)
Figure 5: Graph of PU Foam Thickness (∏28) and Noise Level outside Canopy (∏27)
∏28 ∏27
0.002886751 72.81
0.004330127 72.40
0.005773503 72.13
0.007216878 71.75
0.014433757 71.67
0.021650635 71.32
0.028867513 71.07
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
The values of PU foam density (∏29) and corresponding average values of noise outside canopy (∏27) are shown in Table 9. Fig.6 shows the graph between PU foam
density (∏29) and corresponding average values of noise outside canopy (∏27).
Table 9 Values of PU foam density (∏29) and noise outside canopy (∏27)
.
Figure 6 Graph of PU Foam Density (∏29) and Noise Level outside Canopy ( ∏27)
The best fit polynomial graph is shown in fig.6. The equation for polynomial is (6).
(6)
Therefore the Final Polynomial Equation is (7)
(7)
The value of Ki constant is calculated by equating the measured value of noise ∏27 with the predicted value for ith setting of canopy. Table 10 shows the Ki values for each of the measurement.
The average value of, Ki is taken as K. The value of K comes out to be 83.23. So the polynomial model for Diesel Engine Alternator Canopy System is (8)
To establish the accuracy of the model, the error i.e. the difference between actual
and the predicted values of dependent variable, obtained by substituting the values of independent terms in polynomial model for different 660 experimental settings
are evaluated. On the basis of error the coefficient of determination (R2) is evaluated. Coefficient of determination provides a measure of how well future outcomes are
likely to be predicted by this model .The value of R2 is evaluated by using equation 9.
Table 10 Ki Values and Average K from Polynomial (7)
R2 = 1-
(9)
Where yi = observed value of dependent variable for ith experimental set up
fi = predicted value of dependent variable for ith experimental set up
And = mean of yi
R2 = Coefficient of Determination.
The predicted sample values are shown in Table 11.
7. ANALYSIS BY ARTIFICIAL NEURAL NETWORK (ANN) MODEL
Artificial neural networks have emerged as attractive tools for nonlinear process modeling, especially in situations where conventional regression models become
impractical and cumbersome with coefficient of determination (R2) relatively very small, resulting in unreliable prediction of the output. Therefore, ANN appears to be
more appropriate for solving nonlinear problems at industrial levels. An attempt is made to predict the noise level of DG Set outside canopy by ANN and compares the results with Field Data Based Multivariate Model (FDBM).
7.1. A Procedure for Model Formulation in ANN
A neural network is used to map a data set of inputs and targets. Different software’s / tools have been developed to construct ANN. MATLAB being an internationally accepted tool, has been selected for developing ANN model. The procedure followed
is given below:
The Neural Network Fitting Tool is used to select data, create and train a network, and evaluate its performance using mean square error and regression analysis.
A two-layer feed-forward network with sigmoid hidden neurons and linear output is selected.
The observed data from the experimentation is separated into two parts viz. input data or the data of independent pi terms and the target data or dependent pi terms.
The input and target data samples are randomly divided into three categories training, Validation and testing. The neuron size for the hidden layer is chosen as 20.
The network is typically trained with Levenberg-Marquardt back propagation algorithm and performance parameters are evaluated.
7.2. Results by ANN
The ANN model is formulated and processed in MATLAB.The input for neural network for four independent variables engine load (∏8), Canopy thickness (∏21),
PU foam Thickness (∏28) and PU foam density (∏29) and the target noise outside canopy (∏27) are given as input and target to neural network respectively.
The Sample Input data and Target data is given in the Table 12.
The neural Network diagram for four input variable, 1 hidden layer with 10
neurons and one output target is shown in Figure.7.
Figure 7 Neural Network diagram for Input and Target
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
The correlation between Target and the output data by ANN is shown in the Figure.9.
Figure 9 Correlation between Target and Output Data
8. OPTIMIZATION OF SYSTEM
The optimum values for independent ∏ terms, within their ranges in experimentation
are decided by plotting the graph between product of ∏8, ∏28, ∏21and ∏29 on X axis and ∏27 on Y axis for 660 settings of experimentation as shown in Fig10. The value of product ∏8*∏21*∏28*∏29 corresponding to minimum value of ∏27 i.e
69.58 gives the optimum value of product and corresponding values of independent ∏ terms indicates optimal setting. The optimal values of independent Pie terms are as
under.
∏8: 2.60 ∏28:0.0289 ∏29: 0.075 and ∏21:0.0036
Figure 10 The Graph between Product of ∏8, ∏28, ∏21& ∏29 and ∏27
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
The sensitivity varies from minimum of 0.0099 at ∏28 = 0.002887 to maximum of 0.0513 at ∏28 = 0.028868.
Figure 13 Sensitivity for PU foam thickness (∏28)
Figure.14 shows a graph indicating the sensitivity of PU foam density (∏29) for
various values of ∏29.
The sensitivity varies from minimum of 0.0034 at ∏29 = 0.075 to maximum of
0.0097 at ∏29 = 0.04.
Figure 14 Sensitivity for PU foam Density (∏29)
10. CONCLUSIONS
The important conclusions based on above work are:
This paper presents a detailed approach for formulating Field Data Based Mathematical Model for a diesel generator set with canopy system for the prediction of noise level outside canopy. Noise level after Canopy is predicted by multivariable regression mode land compared with the prediction by ANN modeling. It is observed that the coefficient of determination is higher for ANN model indicating that ANN modeling produces more reliable results than multivariate regression model.
Formulation of A Field Data Based Model To Estimate The Noise Level In A Diesel Generator Set with Acoustic Enclosure
The model is optimized and the optimal values of independent parameters for canopy are evaluated.
The sensitivity analysis of model indicates that the system is not sensitive to permissible variation in independent variables of the system.
Multivariable regression modeling can be used to model any system to predict the performance. If this model has low value of coefficient of determination, ANN model should be formulated to obtain more reliable results.
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