In a paper published in the journal Scientific Reports, researchers investigated the effects of nanoparticle addition on engine coolants. They tested coolants with varying nanoparticle concentrations and engine loads, observing significant improvements in heat transfer. An artificial neural network (ANN) model identified the optimal nanoparticle concentration, and an economic analysis showed a payback period of under 4 years for the coolant's viability.
Calculated UxA values by using ANN model. Image Credit: https://www.nature.com/articles/s41598-024-68701-3
Related Work
Past work has shown that nanoparticles significantly enhance engine cooling systems by improving heat transfer and thermal efficiency. Research has shown that incorporating nanoparticles into coolants, such as graphene, aluminum oxide (AlO₃), and titanium dioxide (TiO₂), can improve thermal conductivity and vehicle efficiency.
Research also indicated that the optimal nanoparticle concentration varies but generally leads to better cooling efficiency and reduced engine wear. Experimental results demonstrated that these additives improve dynamic performance and engine life by increasing heat transfer and creating a protective film on engine surfaces.
Experimental and Analytical
TiO₂ nanoparticles were first analyzed using scanning electron microscopy (SEM) and x-ray diffraction (XRD) techniques. These nanofluids were stirred and sonicated to ensure stability, and their effects on heat transfer performance were experimentally investigated. An ANN model was employed to determine the optimal TiO₂ concentration by analyzing various experimental results. The final stage involved conducting techno-economic analyses to evaluate the optimized coolant's payback period and cumulative net present value.
The experimental setup featured an Erin Motor brand, 4-stroke, single-cylinder engine operating on natural gas. The system included an engine, heat exchanger, dynamometer, cooling system, and measurement equipment. The motor's load was adjusted using an alternating current (AC) generator and an adjustable resistance load bank controlled by a programmable logic controller (PLC), allowing precise load control.
Data collection was managed through National Instruments (NI's) labview software, with measurements taken from various sensors and instruments, including an incremental encoder, turbine-type flow meter, and thermal camera.
TiO₂ nanoparticles were obtained from Nanografi, with a diameter of 20 nm. SEM and XRD characterized the nanoparticles, and the nanofluids were prepared by dispersing them in water and subjecting them to stirring and sonication.
The stability of the nanofluids was monitored over five hours. Heat transfer performance was evaluated using the UxA value, which combines the overall heat transfer coefficient and heat transfer surface area. Levenberg-Marquardt optimization was used to train the artificial neural network (ANN), which has an output layer with one neuron and a hidden layer with five neurons. An economic analysis of the nanoparticle-doped coolant was conducted using standard financial metrics, such as net present value (NPV) and internal rate of return (IRR).
Nanoparticle Cooling Evaluation
The TiO₂ nanoparticles were analyzed using SEM and XRD to determine their size and crystallinity. SEM revealed that the nanoparticles were spherical with sizes between 20 and 32 nm, and they were well-distributed, which minimized aggregation. XRD confirmed that the nanoparticles were in the anatase form, with clear diffraction peaks indicating their purity.
Nanofluids with varying concentrations of TiO₂ (0.15%, 0.3%, 0.5%, and 0.6% by weight) were prepared, and their stability was monitored over five hours, showing that the nanoparticles remained well-dispersed without significant aggregation.
The heat transfer performance was assessed through the UxA value, which combines the overall heat transfer coefficient and surface area. Results showed that a 0.6% TiO₂ concentration improved heat transfer significantly across all tested engine loads, with enhancements ranging from 30.7% to 40.8%. The lowest concentration, 0.15%, showed minimal improvement. An ANN model predicted that a 0.26% TiO₂ concentration was optimal, as higher concentrations did not yield additional benefits.
Economic analysis of the optimized coolant (0.26% TiO₂) demonstrated significant potential benefits. Two cost-effective cooling system modification options were evaluated: minor revisions to the existing system or complete replacement of components.
The first option, involving minor adjustments, was cost-effective with a low investment required, while the second option was more expensive. The analysis revealed that reducing pump speed could lower fuel consumption and emissions, aligning with sustainability goals. Economic indicators showed a favorable investment with a payback period of 3 years and 7 months, making the revised cooling system a viable and economically beneficial solution.
Conclusion
To sum up, SEM and XRD analyses revealed the size and crystallinity of TiO₂ nanoparticles. Nanofluids with various TiO₂ concentrations (0%, 0.15%, 0.3%, 0.5%, and 0.6%) were tested for heat transfer in different engine loads, with 0.6% TiO₂ showing the greatest improvement in heat transfer, up to 40.8% at 3.8 kW.
An ANN model identified 0.26% TiO₂ as optimal for heat transfer, and economic analysis demonstrated a payback period of 3 years and 7 months with a net present value of $955. Future studies were suggested to explore the long-term stability of different nanoparticle ratios, test larger engines, and further assess the economic impact and ANN applicability.
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