Innovative mathematical correlations for estimating mono-nanofluids' density: Insights from white-box machine learning - Omid Deymi; Fahimeh Hadavimoghaddam; Saeid Atashrouz; Saptarshi Kar; Ali Abedi; Ahmad Mohaddespour; Mehdi Ostadhassan; Abdolhossein Hemmati-Sarapardeh (Results in Physics)
The current research offers credible mathematical models solely for estimating mono-nanofluids' density (ρnf), which can be useful for thermal engineering calculations required by various industries and applications. Accordingly, a comprehensive data bank encompassing 4004 experimental data-points was utilized to execute two rigorous machine-learning techniques: Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP). Subsequently, two high-accuracy correlations were fine-tuned based on the four independent variables: average nanoparticle diameter (dnp), nanoparticle mass concentration (ϕm), nanoparticle density (ρnp), and base-fluid density (ρbf). Two variables pressure (P) and temperature (T), with rather minor impacts on the density of the mono-nanofluids under investigation, were excluded in the final correlations as a result of the modeling process and the intelligent operation of the machine-learning techniques. By performing multiple statistical and graphical analyses, comparative evaluations highlighted the superior performance and outstanding accuracy of the GEP-based correlation (with AAPRE=0.6614% and R2=0.9671). Moreover, sensitivity analysis and parametric trend assessments revealed that ϕm and ρbf were the most crucial variables affecting ρnf values, with relevancy factors of approximately 0.72 and 0.71, respectively. By considering the GEP-based correlation's outputs and applying the leverage statistical approach, a considerable portion (96.33%) of the total data-points was identified as valid data.