<p>Harnessing solar energy through solar air heaters (SAHs) offers a sustainable solution for heating and drying applications. This study presents a computational investigation of SAHs with novel inclined offset ribs aimed at enhancing thermal performance. Unlike most studies which focused on first law efficiency, the present work emphasizes second law optimization through evaluation of the energy devaluation number (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(N_{{{\text{dev}}}}^\text{en}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>dev</mtext> </mrow> <mtext>en</mtext> </msubsup> </math></EquationSource> </InlineEquation>) and exergy destruction number (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(N_{{{\text{des}}}}^{\text{ex}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>des</mtext> </mrow> <mtext>ex</mtext> </msubsup> </math></EquationSource> </InlineEquation>). The Reynolds-averaged Navier–Stokes equations, coupled with the <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(RNG k - \varepsilon\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>R</mi> <mi>N</mi> <mi>G</mi> <mi>k</mi> <mo>-</mo> <mi>ε</mi> </mrow> </math></EquationSource> </InlineEquation> turbulence model, are solved for rib inclination angle <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(\emptyset\)</EquationSource> <EquationSource Format="MATHML"><math> <mi mathvariant="normal">∅</mi> </math></EquationSource> </InlineEquation> (= 45° to 150°), with corresponding relative angle (<InlineEquation ID="IEq5"> <EquationSource Format="TEX">\(\alpha = \frac{\varphi }{{90^{0} }}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>α</mi> <mo>=</mo> <mfrac> <mi>φ</mi> <msup> <mn>90</mn> <mn>0</mn> </msup> </mfrac> </mrow> </math></EquationSource> </InlineEquation> = 0.5 to 1.66). Additionally, the vertical length of the ribs (<InlineEquation ID="IEq6"> <EquationSource Format="TEX">\(l_{{\text{r}}}\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>l</mi> <mtext>r</mtext> </msub> </math></EquationSource> </InlineEquation>) is varied from 0.25 to 2.00&#xa0;mm, with corresponding blockage ratio <InlineEquation ID="IEq7"> <EquationSource Format="TEX">\(\beta\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation> ranging from <InlineEquation ID="IEq8"> <EquationSource Format="TEX">\(0.0125\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>0.0125</mn> </mrow> </math></EquationSource> </InlineEquation> to <InlineEquation ID="IEq9"> <EquationSource Format="TEX">\(0.1\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>0.1</mn> </mrow> </math></EquationSource> </InlineEquation>. Reynolds numbers <InlineEquation ID="IEq10"> <EquationSource Format="TEX">\(\left( {Re} \right)\)</EquationSource> <EquationSource Format="MATHML"><math> <mfenced close=")" open="("> <mrow> <mi mathvariant="italic">Re</mi> </mrow> </mfenced> </math></EquationSource> </InlineEquation> ranges from 6000 to 21,000. The entropy generation due to viscous dissipation <InlineEquation ID="IEq11"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},\text{D}}}^{{\prime \prime \prime }}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <mtext>D</mtext> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation> and heat transfer (conduction) with finite temperature difference <InlineEquation ID="IEq12"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},{\text{C}}}}^{\prime \prime \prime }\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <mtext>C</mtext> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation> are studied along with Nusselt number <InlineEquation ID="IEq13"> <EquationSource Format="TEX">\(\left( {Nu} \right),\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mfenced close=")" open="("> <mrow> <mi mathvariant="italic">Nu</mi> </mrow> </mfenced> <mo>,</mo> </mrow> </math></EquationSource> </InlineEquation> friction factor <InlineEquation ID="IEq14"> <EquationSource Format="TEX">\(\left( f \right)\)</EquationSource> <EquationSource Format="MATHML"><math> <mfenced close=")" open="("> <mi>f</mi> </mfenced> </math></EquationSource> </InlineEquation>, and thermal enhancement factor <InlineEquation ID="IEq15"> <EquationSource Format="TEX">\(\left( {TEF} \right)\)</EquationSource> <EquationSource Format="MATHML"><math> <mfenced close=")" open="("> <mrow> <mi mathvariant="italic">TEF</mi> </mrow> </mfenced> </math></EquationSource> </InlineEquation>. The results show that <InlineEquation ID="IEq16"> <EquationSource Format="TEX">\(N_{{{\text{des}}}}^\text{ex}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>des</mtext> </mrow> <mtext>ex</mtext> </msubsup> </math></EquationSource> </InlineEquation> increases, while <InlineEquation ID="IEq17"> <EquationSource Format="TEX">\(N_{{{\text{dev}}}}^\text{en}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>dev</mtext> </mrow> <mtext>en</mtext> </msubsup> </math></EquationSource> </InlineEquation> decreases with rising <InlineEquation ID="IEq18"> <EquationSource Format="TEX">\(Re\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">Re</mi> </mrow> </math></EquationSource> </InlineEquation>, due to turbulence and convective heat transfer, which intensify temperature gradients and viscous dissipation, leading to greater entropy generation and more effective energy utilization. However, both <InlineEquation ID="IEq19"> <EquationSource Format="TEX">\(N_{{{\text{dev}}}}^\text{en}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>dev</mtext> </mrow> <mtext>en</mtext> </msubsup> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq20"> <EquationSource Format="TEX">\(N_{{{\text{des}}}}^\text{ex}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>des</mtext> </mrow> <mtext>ex</mtext> </msubsup> </math></EquationSource> </InlineEquation> gradually decline reaching a minimum at <InlineEquation ID="IEq21"> <EquationSource Format="TEX">\(\alpha = 1.33\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.33</mn> </mrow> </math></EquationSource> </InlineEquation> and 1.50. The near-wall regions are dominated by direct (mean) dissipation <InlineEquation ID="IEq22"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},{\overline{\text{D}}}}}^{\prime \prime \prime }\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <mover> <mtext>D</mtext> <mo>¯</mo> </mover> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation> due to molecular viscosity, whereas indirect (turbulent) dissipation <InlineEquation ID="IEq23"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},{\text{D}}^{\prime } }}^{\prime \prime \prime }\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <msup> <mrow> <mtext>D</mtext> </mrow> <mo>′</mo> </msup> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation> dominates the regions around the ribs, particularly in the wake. Total entropy generation <InlineEquation ID="IEq24"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},{\text{T}}}}^{\prime \prime \prime }\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <mtext>T</mtext> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation> follows a pattern similar to <InlineEquation ID="IEq25"> <EquationSource Format="TEX">\(\dot{S}_{{{\text{gen}},{\text{C}}}}^{\prime \prime \prime }\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mover accent="true"> <mi>S</mi> <mo>˙</mo> </mover> <mrow> <mrow> <mtext>gen</mtext> <mo>,</mo> <mtext>C</mtext> </mrow> </mrow> <mrow> <mo>″</mo> <mo>′</mo> </mrow> </msubsup> </math></EquationSource> </InlineEquation>​, confirming that thermal irreversibilities predominantly govern overall entropy generation. An optimal blockage ratio of <InlineEquation ID="IEq26"> <EquationSource Format="TEX">\(\beta\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>β</mi> </math></EquationSource> </InlineEquation> = 0.05 yields minimum values of both <InlineEquation ID="IEq27"> <EquationSource Format="TEX">\(N_{{{\text{dev}}}}^\text{en}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>dev</mtext> </mrow> <mtext>en</mtext> </msubsup> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq28"> <EquationSource Format="TEX">\(N_{{{\text{des}}}}^\text{ex}\)</EquationSource> <EquationSource Format="MATHML"><math> <msubsup> <mi>N</mi> <mrow> <mtext>des</mtext> </mrow> <mtext>ex</mtext> </msubsup> </math></EquationSource> </InlineEquation>. Therefore, the configuration with <InlineEquation ID="IEq29"> <EquationSource Format="TEX">\(\alpha = 1.5, \beta = 0.05,\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>α</mi> <mo>=</mo> <mn>1.5</mn> <mo>,</mo> <mi>β</mi> <mo>=</mo> <mn>0.05</mn> <mo>,</mo> </mrow> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq30"> <EquationSource Format="TEX">\(Re = 15000\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>R</mi> <mi>e</mi> <mo>=</mo> <mn>15000</mn> </mrow> </math></EquationSource> </InlineEquation> yields the optimal overall thermo-hydraulic performance based on both first and second law assessments.</p>

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Second law analysis of turbulent flow over inclined offset ribs in a solar duct for enhanced performance

  • Jay Shankar Prasad,
  • Anupam Dewan,
  • Aparesh Datta,
  • Sirshendu Mondal

摘要

Harnessing solar energy through solar air heaters (SAHs) offers a sustainable solution for heating and drying applications. This study presents a computational investigation of SAHs with novel inclined offset ribs aimed at enhancing thermal performance. Unlike most studies which focused on first law efficiency, the present work emphasizes second law optimization through evaluation of the energy devaluation number ( \(N_{{{\text{dev}}}}^\text{en}\) N dev en ) and exergy destruction number ( \(N_{{{\text{des}}}}^{\text{ex}}\) N des ex ). The Reynolds-averaged Navier–Stokes equations, coupled with the \(RNG k - \varepsilon\) R N G k - ε turbulence model, are solved for rib inclination angle \(\emptyset\) (= 45° to 150°), with corresponding relative angle ( \(\alpha = \frac{\varphi }{{90^{0} }}\) α = φ 90 0  = 0.5 to 1.66). Additionally, the vertical length of the ribs ( \(l_{{\text{r}}}\) l r ) is varied from 0.25 to 2.00 mm, with corresponding blockage ratio \(\beta\) β ranging from \(0.0125\) 0.0125 to \(0.1\) 0.1 . Reynolds numbers \(\left( {Re} \right)\) Re ranges from 6000 to 21,000. The entropy generation due to viscous dissipation \(\dot{S}_{{{\text{gen}},\text{D}}}^{{\prime \prime \prime }}\) S ˙ gen , D and heat transfer (conduction) with finite temperature difference \(\dot{S}_{{{\text{gen}},{\text{C}}}}^{\prime \prime \prime }\) S ˙ gen , C are studied along with Nusselt number \(\left( {Nu} \right),\) Nu , friction factor \(\left( f \right)\) f , and thermal enhancement factor \(\left( {TEF} \right)\) TEF . The results show that \(N_{{{\text{des}}}}^\text{ex}\) N des ex increases, while \(N_{{{\text{dev}}}}^\text{en}\) N dev en decreases with rising \(Re\) Re , due to turbulence and convective heat transfer, which intensify temperature gradients and viscous dissipation, leading to greater entropy generation and more effective energy utilization. However, both \(N_{{{\text{dev}}}}^\text{en}\) N dev en and \(N_{{{\text{des}}}}^\text{ex}\) N des ex gradually decline reaching a minimum at \(\alpha = 1.33\) α = 1.33 and 1.50. The near-wall regions are dominated by direct (mean) dissipation \(\dot{S}_{{{\text{gen}},{\overline{\text{D}}}}}^{\prime \prime \prime }\) S ˙ gen , D ¯ due to molecular viscosity, whereas indirect (turbulent) dissipation \(\dot{S}_{{{\text{gen}},{\text{D}}^{\prime } }}^{\prime \prime \prime }\) S ˙ gen , D dominates the regions around the ribs, particularly in the wake. Total entropy generation \(\dot{S}_{{{\text{gen}},{\text{T}}}}^{\prime \prime \prime }\) S ˙ gen , T follows a pattern similar to \(\dot{S}_{{{\text{gen}},{\text{C}}}}^{\prime \prime \prime }\) S ˙ gen , C ​, confirming that thermal irreversibilities predominantly govern overall entropy generation. An optimal blockage ratio of \(\beta\) β  = 0.05 yields minimum values of both \(N_{{{\text{dev}}}}^\text{en}\) N dev en and \(N_{{{\text{des}}}}^\text{ex}\) N des ex . Therefore, the configuration with \(\alpha = 1.5, \beta = 0.05,\) α = 1.5 , β = 0.05 , and \(Re = 15000\) R e = 15000 yields the optimal overall thermo-hydraulic performance based on both first and second law assessments.