A High-Dimensional Image Cryptosystem Integrating IEEE-754 Mantissa Folding and Galois Field Diffusion
DOI:
https://doi.org/10.62431/hjsd0m30Keywords:
Image Encryption, Hyper-chaos, IEEE-754 Mantissa Extraction, Fisher-Yates Permutation, Cryptographic Nonce, Galois Field , Deep Learning Cryptanalysis, IoMT SecurityAbstract
Access to clean water is an essential need of human life, but at present, around ~4 billion people are being deprived of easily available drinking water due to various factors like urbanization, rising population, industrial growth, and pollution. Solar desalination provides an environmentally friendly and economical alternative. Low productivity is a major limitation of passive solar stills. This paper presents a detailed mathematical model designed to study a solar still enhanced with Al₂O₃ nanoparticles. A numerical model is developed on energy balance equations for basin water, glass cover, and absorber and solves them numerically through the fourth-order Runge-Kutta (RK4) method in MATLAB, ensuring high computational accuracy. Nanofluid's optical behavior is captured by using an extinction coefficient through Beer-Lambert's law, which allows better solar radiation absorption estimation. Thermal conductivity is integrated with Hamilton Crosser model. Thorough parametric study is conducted to quantify the effect of nanoparticle concentration (0-2 wt%) and water depth (1-5 cm) on productivity. The nanofluid-based solar still achieves its highest freshwater production rate of 5.202 L/m² per day at a water depth of 1 cm and Al₂O₃ concentration of 2 wt%, which exceeds the production rate of conventional stills that operate without nanoparticles, which reaches 4.633 L/m². The stability of the numerical is verified with Monte Carlo analysis. Simulation results are verified with literature-based data. The study finds that using Al₂O₃ nanoparticles raises basin water temperature and freshwater production relative to the conventional model.
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