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Performance analysis of multi-gap V-roughness with staggered elements of solar air heater based on artificial neural network and experimental investigations

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Abstract

Among all renewable energy sources, solar power is one of the major sources which contributes for pollution control and protection of environment. For a number of decades, technologies for utilizing the solar power have been the area of research and development. In the current research, thermal performance parameters of multi-gap V-roughness with staggered elements of a solar air heater (SAH) are experimentally investigated. The artificial neural network (ANN) is also utilized for predicting the thermal performance parameters of SAH. Experiments were executed in a rectangular channel with one roughened side at the top exposed to a uniform heat flux. A significant rise in thermal efficiency performance was reported under a predefined range of Reynolds number (Re) from 3000 to 14000 with an optimized value of relative roughness pitch ratio (P/e) and relative staggered rib length (w/g) as 12 and 1, respectively. The maximum thermal efficiency was attained in the range from 42.15 to 87.02% under considered Reynolds numbers for optimum value of P/e as 12 and w/g as 1. A multilayered perceptron (MLP) feed-forward ANN trained by the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was utilized to predict the thermal efficiency (ηth), friction (f), and Nusselt number (Nu). The thermal performance parameters such as P/e, w/g, Re, and temperature at the inlet, outlet, and plate were the critical input parameters/signals used in the ANN method. The optimum ANN arrangement/structure to predict the Nu, f, and ηth demonstrate higher accurateness in assessing the performance characteristics of SAH by attaining the root mean squared error (RMSE) in prediction and the Pearson coefficient of association (R2) of 1.591 and 0.994; 0.0012 and 0.851; and 0.025 and 0.981, respectively. The prediction profile plots of the ANN demonstrate the influence of various input parameters on the thermal performance parameters.

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Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

A :

Cross-sectional area (WH)

a :

Weighted value of neuron

A c :

Cross-sectional area of duct

A o :

Area of orifice plate, m2

ANN:

Artificial neural network

A P :

Surface area of heated plate, m2

BFGS:

Broyden–Fletcher–Goldfarb–Shanno

C d :

Coefficient of discharge for orifice meter

C p :

Specific heat of air, J/kg–K

D h :

Hydraulic diameter of duct, m

H :

Number of hidden layers

H′:

Height of duct

h :

Heat transfer coefficient, W/m2 K

I :

Heat flux, W/m2

k a :

Thermal conductivity of air, W/m K

i, j, k, v, h, m :

Symbols for mathematical processing of data

L :

Length of duct across the point of pressure drop

m :

Mass flow rate of air, kg/s

MLP:

Multilayered perceptron

N g :

Number of gaps

N :

Number of input signals

n :

Kinematic viscosity of air, m2/s

P :

Rib pitch, m

P′:

Staggered rib position, m

Q u :

Useful heat gain, W

R 2 :

Pearson’s coefficient of association

r :

Staggered rib size, m

RMSE:

Root mean square error

SAH:

Solar air heater

THP:

Thermo-hydraulic performance

T a :

Ambient temperature, K

T i :

Fluid inlet temperature, K

T o :

Fluid outlet temperature, K

T f :

Mean air temperature, K

T p :

Mean plate temperature, K

V :

Velocity of air, m/s

W :

Width of duct

w :

Weight assigned to neuron

x :

Input signal

y :

Output signal

y Expected :

Expected value of output

y Predicted :

Predicted value of output

z :

Hidden layer signal values

d/w :

Gap position

e/D h :

Relative roughness height

f or f r :

Friction factor for roughened duct

f s :

Friction factor for smooth duct

g/e :

Relative gap width

Nu or Nur :

Nusselt number of roughened duct

Nus :

Nusselt number for smooth duct

Pr:

Prandtl number

P/e :

Relative roughness pitch

P′/P :

Relative staggered rib pitch

Re:

Reynolds number

w/g :

Relative staggered rib length

s′/s :

Relative roughness segment ration

W/w :

Relative roughness width

W/H :

Duct aspect ratio

α :

Angle of attack

β :

Ratio of orifice diameter to pipe diameter, do/dp

ω :

Final weightage at the last hidden layer

ρ :

Density of air, kg/m3

η eff :

Thermal efficiency

φ(v):

Symbols for transfer functions for processing data at hidden and output layers

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Authors and Affiliations

  1. Department of Mechanical Engineering, Rajasthan Technical University, Kota, 324010, India

    Piyush Kumar Jain, Rahul Jain & Kunj Bihari Rana

  2. Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, India

    Piyush Kumar Jain & Atul Lanjewar

Authors
  1. Piyush Kumar Jain
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  2. Atul Lanjewar
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  3. Rahul Jain
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  4. Kunj Bihari Rana
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Contributions

Piyush Kumar Jain and Rahul Jain conceptualized the study and performed formal experimental data collection and analysis. Kunj Bihari Rana and Atul Lanjewar conceptualized and supervised the whole study. All authors read and approved the final manuscript

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Correspondence to Kunj Bihari Rana.

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Jain, P.K., Lanjewar, A., Jain, R. et al. Performance analysis of multi-gap V-roughness with staggered elements of solar air heater based on artificial neural network and experimental investigations. Environ Sci Pollut Res 28, 32905–32920 (2021). https://doi.org/10.1007/s11356-021-12875-0

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