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Open research questions in Nanofluid Flow and Heat Transfer

43 unresolved questions extracted from the limitations and future-work sections of 468 Nanofluid Flow and Heat Transfer papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • • Include temperature- and concentration-dependent thermophysical and rheological properties of the Cas- son hybrid nanofluid. • Develop non-homogeneous two-phase or Buongiorno- type models to account for Brownian motion, thermopho- resis, and particle migration in curved porous geometries. • Extend the analysis to unsteady and three-dimensional curved configurations, and to transitional/turbulent flow regimes relevant to real devices. • Validate the present numerical trends with high-fidelity CFD simulations and experimental studies for CNT- based hybrid nanofluids in porous curved channels and heat exchangers. Author contributions Pawan Kumar Jangir was contributed writing– review and editing, writing–original draft, data curation, conceptualiza- tion, software, visualization, methodology, investigation, and formal analysis. Ruchika Mehta was involved in supervision, investigation, conceptualization, formal analysis, and review and editing. Shilpa Choudhary was performed investigation, software, data curation, and review and editing. Tripti Mehta was done review and editing, soft- ware, investigation, and conceptualization. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. P. K. Jangir et al. Data availability No datasets were generated or analysed during the current study.

    Thermal analysis of enhanced heat transfer radiative MHD Darcy–Forchheimer Casson SWCNT-MWCNT hybrid nanofluid flow over a curved stretching/shrinking surface · 2026 · DOI

  • Future work could focus on experimental validation, optimizing nanoparticle shape and electroosmotic conditions, and extend- ing the model to hybrid nanofluids, complex geometries, and variable magnetic fields to improve practical applications in drug delivery, lab-on-chip devices, and thermal man- agement systems.

    Theoretical aspects of nanofluid peristaltic transport in tapered wavy structure · 2026 · DOI

  • 3 Future research directions Future research could explore turbulent and unsteady nanofluid flows, variable thermo- physical properties, hybrid nanofluids, magnetic or electric field effects, and experimen- tal validation under realistic industrial conditions. 1 Research limitations The research is limited by assumptions of steady, laminar, and idealized nanofluid behav- ior, neglecting turbulence, variable properties, and real-world complexities such as par- ticle agglomeration and surface roughness.

    Comparative boundary-layer analysis of Buongiorno nanofluid flow over flat surfaces: Blasius, Sakiadis, stagnation-point, and stretching-sheet configurations · 2026 · DOI

  • As far as the modeling technique used in this present paper is concerned, a few of its limitations can be identified: 1. Simplified Artery Geometry: Although the research seeks to investigate the flow of fluids in human arteries as part of the human circulatory system. The investigations would be unable to account for the complexity observed in the real system because of the dependency on idealized arterial geometries with a simplified description of the stenosis, the surface roughness, and cilia. 2. Properties of nanofluids and behaviors of microorganisms are studied under consistent circumstances. 3. The study mainly refers to the assumption of a low Reynolds number and extended wavelength. 4. High model complexity due to multiple coupled phenomena. 5. Idealized assumptions (low Reynolds number, long wavelength, simplified artery geometry). 6. Limited quantitative validation; current comparisons are mainly qualitative.

    Peristaltic flow of sutterby nanofluid in a stenosed artery with ciliated endothelium and wall roughness under hall and ion slip effects · 2026 · DOI

  • Based on the results, the following recommendations and future directions are proposed: 1. Optimizing nanofluid composition: Further work should focus on identifying the ideal ratio of MWCNT, Fe3O4, and CeO2 thermal conductivity while keeping viscosity within practical limits for convective systems. to maximize 2.

    Experimental investigations of the thermophysical properties of MWCNT–CeO2–Fe3O4 nanofluids · 2026 · DOI

  • 1 Limitations of the present study The present study on the machine-learning-based predictive assessment of inclined MHD micropolar bioconvection over a curved porous stretching sheet is subject to the following limitations: • The analysis is limited to steady, two-dimensional laminar flow based on similarity transformation assumptions.

    Machine-learning-based predictive assessment of inclined MHD micropolar bioconvection over curved porous stretching sheet · 2026 · DOI

  • Maximum performance evaluation criterion (PEC = 1.83) is achieved at 5% Al₂O₃ concentration, but the paper does not investigate whether PEC continues to improve or plateau beyond 5% concentration, nor does it explore the physical mechanism underlying the observed PEC trend using nanoparticle agglomeration analysis or particle size distribution characterization.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • The Grid Convergence Index (GCI ≈ 0.48%) is reported for CFD discretization uncertainty, but the paper does not specify the turbulence model employed, mesh independence study details, or validation of the nanofluid property correlations (thermal conductivity, viscosity, density) used in the CFD simulations across the 1-5% Al₂O₃ concentration range.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • The paper reports a 5% average deviation between CFD-predicted and experimentally measured outlet temperatures but attributes this solely to steady-state assumptions and heat losses. The specific quantitative contribution of each neglected physical phenomenon (wind speed effects, radiation losses, conduction losses, convection losses) to the CFD-experimental discrepancy has not been decomposed through sensitivity analysis.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • The random forest machine learning model achieved high cross-validation R² values (0.995 for outlet temperature) on the current dataset, but the model's transferability to automotive radiators with different geometric designs, materials, or flow path configurations has not been assessed. Application of this surrogate model to novel radiator architectures requires validation with additional CFD simulations.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • While feature importance analysis (Fig. 15) identifies nanoparticle concentration, inlet temperature, and mass flow rate as input parameters, the paper does not investigate potential nonlinear interaction effects between these variables or their combined influence on radiator performance degradation at extreme nanoparticle concentrations (>5%) where agglomeration and viscosity penalties may dominate thermal gains.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • The experimental validation dataset covers only six coolant compositions (base EG plus 1%, 2%, 3%, 4%, and 5% Al₂O₃ nanoparticle concentrations) with incomplete experimental measurements reported for some conditions (Table 9 shows missing data points). The machine learning model's generalization capability beyond this narrow nanoparticle concentration range and for alternative nanoparticle materials beyond Al₂O₃ remains unvalidated.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • The CFD simulations were conducted under steady-state assumptions that neglect transient environmental factors including wind speed variation and radiative/conductive heat losses from the radiator surface. The machine learning model trained on these steady-state CFD data therefore cannot predict nanoparticle-enhanced radiator performance under dynamic real-world automotive operating conditions with fluctuating ambient temperature and airflow.

    Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning · 2026 · DOI

  • While the paper demonstrates enhanced capability to predict fluid behavior in complex conditions, the practical implementation and optimization of the trade-off between hydrodynamic shear and thermal/mass-transfer performance in real engineering applications (nanofluid cooling, coating technologies, polymer extrusion) requires further investigation.

    Influence of pressure gradient and buoyancy on viscous dissipation and Joule heating in Carreau nanofluid flow · 2026 · DOI

  • • Future research could explore more complex scenar- ios by incorporating additional physical effects such 123 as Joule heating, nanoparticle concentration, porosity, chemical reactions, and more realistic boundary con- ditions.

    Significance of variable thermal conductivity on magnetized Sutterby nanofluid over stretching sheet with viscous dissipation impacts · 2026 · DOI

  • The study's novelty lies in its comprehensive integration of radiation, chemical processes, and MHD effects in analyzing viscoelastic fluid flows—a topic that remains underexplored in the literature.

    Unstationary Viscoelastic MHD Flow of Walters-B Liquid Through a Vertical Porous Plate with Chemical Reactions · 2025 · DOI

  • While previous studies have demonstrated the stabilizing influence of magnetic fields for specific fluid properties, the role of the Prandtl number (Pr) in the transition from an axisymmetric steady state to three-dimensional flow structures has not yet been fully clarified.

    Influence of Prandtl number on three-dimensional instability of magnetohydrodynamic natural convection in an annular enclosure under a toroidal magnetic field · 2026 · DOI

  • Although extensive studies exist on convection in conventional nanofluids, the stability characteristics of hybrid nanofluids saturated in porous media, particularly under combined solutal and nanoparticle effects, remain insufficiently explored in the literature.

    Onset of convection in a hybrid nanofluid porous layer · 2026 · DOI

  • Originality/value This study investigates the unsteady oblique stagnation-point flow of Maxwell fluid over a vertical plate with buoyancy effects, a topic insufficiently explored in existing literature.

    Unsteady oblique stagnation-point flow of Maxwell fluid over an oscillating vertical plate: a physics-informed neural networks and homotopy analysis method comparative study · 2026 · DOI

  • Future Studies: Future studies will examine transport processes in permeable media, which will soon be articulated using many non-Newtonian models, including Maxwell fluid, Jeffrey fluid, Power fluid, Carreau fluid, Williamson fluid, Ellis fluid, Eyring-Pow- ell fluid, and Cross fluid.

    MHD Casson nanofluid flow over a vertical stretchable sheet saturated with a porous medium: a parametric approach for sensitive analysis · 2026 · DOI

  • Scalability of the CME numerical method to three-dimensional arterial networks and branching vessels requires investigation.

    Semi-Analytical Study of Pulsatile Nanofluid Flow in Porous Stenosed Arteries Under Magnetic and Thermal Effects · 2026 · DOI

  • The model analysis is limited to unsteady Maxwell nanofluid flow through a stenosed artery; extension to other arterial geometries and complex vascular networks remains unexplored.

    Thermal Transport Characteristics of Fractional Maxwell Fluid Model for Blood Flow in a Stenosed Artery · 2026 · DOI

  • The findings are noted as relevant to hyperthermia treatment, magnetic drug targeting, and thermal regulation in cardiovascular disorders, but no experimental validation in these biomedical applications is presented.

    Thermal Transport Characteristics of Fractional Maxwell Fluid Model for Blood Flow in a Stenosed Artery · 2026 · DOI

  • Extension of the model to include additional nanofluid types beyond Carreau fluids and validation across varying nanoparticle concentrations and types is needed.

    Influence of pressure gradient and buoyancy on viscous dissipation and Joule heating in Carreau nanofluid flow · 2026 · DOI

  • The analysis is restricted to stretching-disk geometries; applicability to other flow configurations and geometries (e.g., cylinder, sphere, channel flows) remains unexplored.

    Influence of pressure gradient and buoyancy on viscous dissipation and Joule heating in Carreau nanofluid flow · 2026 · DOI

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43 open questions have been extracted from the limitations and future-work passages of 468 Nanofluid Flow and Heat Transfer papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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