<p>This study presents a comprehensive analysis of Casson-micropolar nanofluid flow over a porous stretching sheet, addressing a critical gap in non-Newtonian fluid dynamics where previous studies have treated yield stress and microrotation effects in isolation. While Casson fluids (modeling yield stress) and micropolar fluids (modeling particle rotation) have been extensively studied separately, their combined behavior in nanofluids remains largely unexplored despite being essential for applications like targeted drug delivery (where blood’s yield stress coexists with cellular rotation) and industrial slurry transport. Our novel hybrid model unifies these effects with thermal radiation and chemical reactions, solved using an optimized fourth-order Runge–Kutta method. Key findings reveal a 32% increase in velocity gradients under strong yield stress (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="56" /> </InlineMediaObject> <EquationSource Format="TEX">$\beta = 0.5$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </math></EquationSource> </InlineEquation>), a 12% temperature enhancement from radiation (<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="57" /> </InlineMediaObject> <EquationSource Format="TEX">$R = 0.5$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>R</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </math></EquationSource> </InlineEquation>), and a 40% concentration reduction due to chemical reactions (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="60" /> </InlineMediaObject> <EquationSource Format="TEX">$K = 1.0$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>K</mi> <mo>=</mo> <mn>1.0</mn> </mrow> </math></EquationSource> </InlineEquation>). The work’s primary innovation lies in introducing the dominance ratio <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq4.gif" Format="GIF" Height="25" Rendition="HTML" Resolution="72" Type="Linedraw" Width="12" /> </InlineMediaObject> <EquationSource Format="TEX">$\frac{d}{\beta }$</EquationSource> <EquationSource Format="MATHML"><math> <mfrac> <mi>d</mi> <mi>β</mi> </mfrac> </math></EquationSource> </InlineEquation> that predicts whether microrotation (<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq5.gif" Format="GIF" Height="25" Rendition="HTML" Resolution="72" Type="Linedraw" Width="42" /> </InlineMediaObject> <EquationSource Format="TEX">$\frac{d}{\beta }> 1$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mfrac> <mi>d</mi> <mi>β</mi> </mfrac> <mo>></mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) or yield stress (<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq6.gif" Format="GIF" Height="25" Rendition="HTML" Resolution="72" Type="Linedraw" Width="42" /> </InlineMediaObject> <EquationSource Format="TEX">$\frac{d}{\beta } < 1$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mfrac> <mi>d</mi> <mi>β</mi> </mfrac> <mo><</mo> <mn>1</mn> </mrow> </math></EquationSource> </InlineEquation>) controls flow behavior—a crucial distinction absent in prior literature. These results provide actionable guidelines for biomedical engineering (optimal <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq7.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="94" /> </InlineMediaObject> <EquationSource Format="TEX">$d = 0.2-0.4$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>d</mi> <mo>=</mo> <mn>0.2</mn> <mo>-</mo> <mn>0.4</mn> </mrow> </math></EquationSource> </InlineEquation> for drug delivery) and industrial processes (<InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44444_2025_54_Article_IEq8.gif" Format="GIF" Height="15" Rendition="HTML" Resolution="72" Type="Linedraw" Width="96" /> </InlineMediaObject> <EquationSource Format="TEX">$\lambda = 0.4-0.6$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>λ</mi> <mo>=</mo> <mn>0.4</mn> <mo>-</mo> <mn>0.6</mn> </mrow> </math></EquationSource> </InlineEquation> for oil recovery), bridging the gap between theoretical models and real-world applications.</p>

Numerical analysis of Casson-micropolar nanofluid flow over a porous stretching sheet with thermal radiation and chemical reactions

期刊论文
出版模式：

开放获取
发表日期：

2025年11月13日

查看全文

Abdullahi Buhari,
M. N. Sarki

摘要

This study presents a comprehensive analysis of Casson-micropolar nanofluid flow over a porous stretching sheet, addressing a critical gap in non-Newtonian fluid dynamics where previous studies have treated yield stress and microrotation effects in isolation. While Casson fluids (modeling yield stress) and micropolar fluids (modeling particle rotation) have been extensively studied separately, their combined behavior in nanofluids remains largely unexplored despite being essential for applications like targeted drug delivery (where blood’s yield stress coexists with cellular rotation) and industrial slurry transport. Our novel hybrid model unifies these effects with thermal radiation and chemical reactions, solved using an optimized fourth-order Runge–Kutta method. Key findings reveal a 32% increase in velocity gradients under strong yield stress ( $\beta = 0.5$ $β = 0.5$ ), a 12% temperature enhancement from radiation ( $R = 0.5$ $R = 0.5$ ), and a 40% concentration reduction due to chemical reactions ( $K = 1.0$ $K = 1.0$ ). The work’s primary innovation lies in introducing the dominance ratio $\frac{d}{\beta }$ $\frac{d}{β}$ that predicts whether microrotation ( $\frac{d}{\beta }> 1$ $\frac{d}{β} > 1$ ) or yield stress ( $\frac{d}{\beta } < 1$ $\frac{d}{β} < 1$ ) controls flow behavior—a crucial distinction absent in prior literature. These results provide actionable guidelines for biomedical engineering (optimal $d = 0.2-0.4$ $d = 0.2 - 0.4$ for drug delivery) and industrial processes ( $\lambda = 0.4-0.6$ $λ = 0.4 - 0.6$ for oil recovery), bridging the gap between theoretical models and real-world applications.