Quantitative analysis of the electromagnetic hybrid nanofluid flow within the gap of two tubes using deep learning neural networks

Abstract

Purpose(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO2) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO2) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.Design/methodology/approachThe two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).Findings(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.Research limitations/implications(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.Practical implications(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO2) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO2) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.Social implicationsThe drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.Originality/valueAll the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.