This investigation reports on financial metrics related to passive heat exchangers with AI-enhanced nanoparticle usage, suggesting a balance between costs and thermal performance.
This study investigates the economic assessments for designing passive heat exchangers using a hybrid nanofluid of multi-walled carbon nanotub (MWCNT) and silica nanoparticles in water. By conducting computational fluid dynamics simulations and applying machine learning, we generated high-resolution data to analyze key input parameters affecting financial metrics like net present value (NPV) and internal rate of return. Results reveal that while high nanoparticle costs can reduce NPV, their use significantly improves thermal energy transfer, particularly at higher volume fractions (φ) and specific Rayleigh numbers (Ra) when volume fraction ratios are below 0.75 and 0.9.
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Obaid et al. (2025) studied this question.
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