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Shannon Entropy in Uncertainty Quantification for the Physical Effective Parameter Computations of Some Nanofluids
Marcin Kamiński1, Rafał Leszek Ossowski1
1Faculty of Civil Engineering, Architecture and Environmental Engineering, Lodz University of Technology, 93-590 Łódź, Poland.
Nanomaterials (Basel, Switzerland)
|February 13, 2025
Summary
This study uses Monte Carlo simulations to predict nanofluid properties like density and viscosity. It introduces Shannon entropy to quantify uncertainty in these physical parameters for better reliability.
Area of Science:
- Nanofluids and Microfluidics
- Computational Physics
- Thermodynamics
Background:
- Accurate prediction of thermophysical properties is crucial for nanofluid applications.
- Existing deterministic models often lack robust uncertainty quantification.
- Nanoparticle volume fraction introduces inherent variability in fluid properties.
Purpose of the Study:
- To probabilistically simulate effective physical parameters of nanofluids.
- To estimate Shannon entropy for key thermophysical properties.
- To analyze uncertainty in nanofluidic systems using computational methods.
Main Methods:
- Utilized a deterministic model based on the rule of mixtures and semi-empirical formulas.
- Employed Monte Carlo simulation to randomize the deterministic model.
- Treated nanoparticle volume fraction as a Gaussian uncertainty source.
Main Results:
- Calculated effective density, heat conductivity, heat capacity, and viscosity.
- Quantified Shannon entropy for these homogenized parameters.
- Validated entropy variations against statistical disorder of nanoparticle fraction.
Conclusions:
- The study provides a novel method for uncertainty analysis in nanofluids.
- Probabilistic simulation enhances the reliability of physical parameter predictions.
- Findings advance nanofluidic and microfluidic research for precision applications.
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