Purpose This study aims to determine the powdered drug penetration depth delivered by means of a micro-shock tube-based needle-free drug delivery device based on a literature-derived modified penetration model. Design/methodology/approach A two-dimensional transient compressible particle-gas flow simulation was done using ANSYS Fluent. The discrete phase model simulates drug particle behaviour in a continuous gas phase. Several pressure ratios (PRs) were used in the quantitative study. The nozzle exit pressure and particle velocity were validated against experimental results. Using modified mathematical formulae from the literature, particle or powder penetration depth, Mach number, vorticity and the effect of PRs on flow are examined to understand penetration depth changes. Findings By changing the PRs from 9 to 60, the penetration depth was investigated on the basis of particle velocity that was varied from 47 to 179 m/s obtained at different PRs. Using a modified model found in the literature, this penetration depth result has been used to examine the impact of the PRs on the penetration depth. After careful computation, the authors discovered that the penetration depth was marginally impacted between PR = 40 and 60. Penetration depth at PR = 40 and 60 was around 0.053 and 0.057 mm. Originality/value Considering the particle velocity at the stratum corneum–viable epidermis boundary, the particle penetration depth at various PRs for drug delivery using a needle-free device is crucial but rarely studied. Some helpful information for drug distribution using a needle-free device is provided by the current numerical workflow.
Purpose This paper aims to derive a third-order approximate analytical solution using the asymptotic expansion method for the non-self-similar problem of laminar fluid flow and heat transfer in a narrow conical gap between a stationary disk and a rotating cone at small conicity angles (up to 4°). Design/methodology/approach The mathematical model incorporates a third-order series expansion in terms of a small parameter Reynolds number Re for both the three velocity components and the static pressure. Approximate analytical solutions for temperature profiles and Nusselt numbers are developed for moderate Prandtl numbers Pr = 10 in the regime prior to the formation of a separate thermal boundary layer on the stationary disk. Findings The analysis determines how non-self-similar effects influence the profiles of velocity, static pressure and shear stress, as well as the tangent of the flow swirl angle at the stationary disk. Specific conditions are identified under which the derivatives of the radial and tangential velocity components (and the shear stresses) at the disk vanish. Furthermore, results show that the flow swirl angle at the disk is fully consistent with the self-similar solution at low Reynolds numbers. Research limitations/implications The thermal solutions depend on a previously derived similarity parameter and are strictly valid within a limited range of the parameter Sv < 0.8. Practical implications The obtained analytical solutions provide a valuable tool for analyzing and verifying experimental data on fluid flow and heat transfer in gases, water and aqueous solutions. Originality/value For the first time, a third-order approximate analytical solution for this specific non-self-similar conical flow configuration is presented, uniquely expanding the static pressure fields alongside velocity fields within the asymptotic framework.
Purpose This study aims to investigate the hemodynamics characteristics of nanofluid and hybrid nanofluid over a curved surface. The analysis focuses on momentum and energy transport in the presence of nanoparticles and compares the numerical results with artificial neural network (ANN) results. Design/methodology/approach A Casson blood-based nanofluid (nf) and hybrid nanofluid (hnf) flow model over a curved region is considered. The nf consists of 4% MXene (Ti3C2) nanoparticles dispersed in blood, while the hnf contains 4% MXene (Ti3C2) and 4% graphene oxide (GO) nanoparticles dispersed in blood. The flow is influenced by magnetic forces and Darcy–Forchheimer porous effects, whereas the energy field is affected by radiant heat, ohmic heating and uneven heat-source/sink conditions. The mathematical model is solved numerically by the MATLAB bvp5c problem solver. In addition, an ANN model accomplished with the Levenberg–Marquardt backpropagation procedure is used. The random data split is 70% for training, 15% for validation and 15% for testing to predict the heat transfer rate, and the predictions are validated against numerical results. Findings Results show that hnf significantly improves the heat transfer rate compared with nf taken in this study. Also, the relative change in the magnitude of skin friction is approximately 10%–12% for nf and 22%–25% for hnf when compared with pure blood’s skin friction (with baseline ∼ −7.14). For a total sample size of 1,458 and 11 input parameters, the ANN has given the results with root mean squared error (RMSE) = 0.01303894 for nf and RMSE = 0.00066093 for hnf. It is observed that the heat transfer rates predicted by the ANN closely match the numerical results found using the MATLAB bvp5c solver. Practical implications The improved heat transfer rate and drag magnitude are important in many biomedical applications, particularly thermal therapies and drug delivery. The ANN-predicted outcomes validate the heat transfer values across various combinations of physical parameters. Originality/value This novel work investigates the flow of nf (Ti3C2) and hnf (Ti3C2-GO) over a curved-shape geometry under the combined effects. In addition, the ANN is used to guess the heat transmission rate in nf and hnf, and the predictions are verified using the bvp5c method.
Purpose Efficient thermal management remains a critical challenge for modern spacecraft due to increasing power densities and extreme operating conditions. This study aims to investigate the flow, heat and mass transfer characteristics of third-grade nanofluid containing microbial swimmers over a horizontal cylinder under the influence of an inclined magnetic field, incorporating non-Fourier heat flux and non-Fick mass flux models. Design/methodology/approach The highly nonlinear coupled system of ordinary differential equations is solved numerically using the deep Galerkin method (DGM), a robust physics-informed deep learning approach. Findings The effects of key parameters such as thermal relaxation parameter (λ1), concentration relaxation parameter (λt), curvature parameter (γ) and third-grade viscoelastic fluid parameter on velocity, temperature, concentration and motile density profiles are thoroughly analyzed. Quantitative results show that an increase in the thermal relaxation parameter reduces the skin-friction coefficient by approximately 13% (from 3.417 to 2.968) while decreasing the local Nusselt number and increasing the local Sherwood and density numbers. A similar trend is observed with the concentration relaxation parameter. Microbial swimmers significantly enhance micro-convection and the inclined magnetic field together with the relaxation parameters provide effective control over thermal and mass boundary layers. Overall, the third-grade viscoelastic nanofluid with microbial swimmers demonstrates superior and tunable thermal performance compared with conventional fluids. Originality/value The present analysis demonstrates that third grade viscoelastic nanofluids with microbial swimmers offer superior thermal performance compared to conventional fluids, making them promising candidates for advanced bio-inspired spacecraft cooling systems. This work provides valuable insights for the development of next-generation passive and active thermal management technologies for satellites and deep-space missions.
Purpose Accurate prediction and optimization of nonlinear thermofluid transport are essential for the design of advanced thermal engineering systems. This study aims to develop a hybrid computational–optimization–data-driven framework for modeling coupled transport phenomena in magnetized Eyring–Powell fluids with variable thermophysical properties. The optimization analysis investigates coupled thermofluid transport in an unsteady magnetized Eyring–Powell fluid over a vertical surface with temperature-dependent viscosity and thermal conductivity. The primary objective is to analyze, optimize and predict convective heat transfer using response surface methodology (RSM) based on a central composite design. Design/methodology/approach The governing nonlinear partial differential equations are solved numerically using an explicit finite difference method to obtain the flow, thermal, concentration and entropy generation fields. The resulting numerical data are subsequently used to construct linear, quadratic and interaction regression models for the Nusselt number (Nu) as functions of the magnetic parameter (M), Eckert number (Ec), heat source/sink parameter (δ), Dufour number (Du) and the Eyring–Powell fluid parameter (K). The adequacy of the proposed regression model is evaluated through analysis of variance. In addition, an artificial neural network (ANN) based on the Levenberg–Marquardt back-propagation algorithm is developed to predict the transport behavior under various operating conditions. Findings The developed quadratic RSM model demonstrates excellent statistical significance (F = 596,513.10), (p < 0.001) and predicts the Nusselt number with relative errors below 0.156%, while the heat source/sink parameter is identified as the dominant factor governing heat transfer (F = 9.33 × 106). The predictive capability of the ANN is validated through mean squared error, error histogram and absolute error analyses, confirming its high prediction accuracy. Physically, increasing the Eyring–Powell fluid parameter (K) enhances both the fluid velocity and temperature fields, whereas higher dynamic viscosity suppresses the flow by reducing the fluid velocity. Originality/value The proposed numerical–optimization–ANN framework offers an efficient tool for predicting and optimizing convective heat transfer in magnetized non-Newtonian fluid systems, with potential applications in thermal management, energy conversion, polymer extrusion and process engineering.
