Machine learning specific heat capacities of nanofluids containing CuO and Al 2 O 3

Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches

In the context of gas processing and carbon sequestration, an adequate understanding of the solubility of acid gases in ionic liquids (ILs) under various thermodynamic circumstances is crucial. A poisonous, combustible, and acidic gas that can cause environmental damage is hydrogen sulfide (H₂S). ILs are good choices for appropriate solvents in gas separation procedures. In this work, a variety of machine learning techniques, such as white-box machine learning, deep learning, and ensemble learning, were established to determine the solubility of H₂S in ILs. The white-box models are group method of data handling (GMDH) and genetic programming (GP), the deep learning approach is deep belief network (DBN) and extreme gradient boosting (XGBoost) was selected as an ensemble approach. The models were established utilizing an extensive database with 1516 data points on the H₂S solubility in 37 ILs throughout an extensive pressure and temperature range. Seven input variables, including temperature (T), pressure (P), two critical variables such as temperature (T_c) and pressure (P_c), acentric factor (ω), boiling temperature (T_b), and molecular weight (Mw), were used in these models; the output was the solubility of H₂S. The findings show that the XGBoost model, with statistical parameters such as an average absolute percent relative error (AAPRE) of 1.14%, root mean square error (RMSE) of 0.002, standard deviation (SD) of 0.01, and a determination coefficient (R²) of 0.99, provides more precise calculations for H₂S solubility in ILs. The sensitivity assessment demonstrated that temperature and pressure had the highest negative and highest positive affect on the H₂S solubility in ILs, respectively. The Taylor diagram, cumulative frequency plot, cross-plot, and error bar all demonstrated the high effectiveness, accuracy, and reality of the XGBoost approach for predicting the H₂S solubility in various ILs. The leverage analysis shows that the majority of the data points are experimentally reliable and just a small number of data points are found beyond the application domain of the XGBoost paradigm. Beyond these statistical results, some chemical structure effects were evaluated. First, it was shown that the lengthening of the cation alkyl chain enhances the H₂S solubility in ILs. As another chemical structure effect, it was shown that higher fluorine content in anion leads to higher solubility in ILs. These phenomena were confirmed by experimental data and the model results. Connecting solubility data to the chemical structure of ILs, the results of this study can further assist to find appropriate ILs for specialized processes (based on the process conditions) as solvents for H₂S.

Effect of Hybrid Nanofluids Concentration and Swirling Flow on Jet Impingement Cooling

Nanofluids have become increasingly salient in heat transfer applications due to their promising properties that can be tailored to meet specific needs. The use of nanofluids in jet impingement flows has piqued the interest of numerous researchers owing to the significant heat transfer enhancement, which is vital in the technological dependence era in every aspect of life, particularly in engineering applications and industry. The aim of this current work is to investigate the effect of hybrid nanofluids concentration and swirling flow on jet impingement cooling through experimental approach. The hybrid nanofluids are prepared through a two-step method and the characterization process is carried out to study the stability and morphological structure of the sample prepared. The prepared hybrid nanofluids are then used as a cooling agent to evaluate the heat transfer performance of jet impinging system. The experimental investigation compares the performance of swirling impinging jets (SIJs) with conventional impinging jets (CIJs) under various jet-to-plate distance (H/D) ratios and nanofluid concentrations. The effects of adding surfactant on nanofluids are also examined. The heat transfer performance of ZnO/water and CuO/water mono-nanofluids are used as comparison to ZnO-CuO/water hybrid nanofluid. The results show that the thermal performance of ZnO-CuO/water hybrid nanofluid is better than that of the mono-nanofluids. Furthermore, as the mass fraction increases, the heat transfer rates improve. The effect of heat transmission by swirling impinging jets is better than that of conventional impinging jets under similar operating conditions. At H/D = 4, Re = 20,000 and hybrid nanofluid concentration at 0.1% under SIJ is observed to have the highest overall Nusselt number.

Machine learning potential for interacting dislocations in the presence of free surfaces

Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.