Kazakh-British Technical University

The study of conjugate convection plays a key role in heat transfer systems such as collectors, heat exchangers, electrical cooling, and thermal insulation. In this paper, we investigated conjugate natural convection in a square inclined cavity with a heat-conducting baffle using computational fluid dynamics and machine learning. The effect of inclination angles on conjugate natural convection was examined, ranging from 0 to 330°. The goal of the study was to improve the accuracy and efficiency of predicting temperature changes in the computational domain. To achieve this goal, three regression models were developed and tested: gradient boosting, random forest, and decision tree. The Computational fluid dynamics (CFD) model solved the Navier-Stokes governing equations using the control volume method. Analysis of the results showed that the gradient boosting model demonstrated the best performance, with high values of the coefficient of determination (best case 0.988) and minimal prediction errors. The random forest model also demonstrated good results, although slightly inferior to the gradient boosting model. The tree model demonstrated the least effective solution among all the models considered, but still achieved acceptable accuracy. It should be noted that the gradient boosting model yielded an R2 of 0.987, a Mean Squared Error (MSE) of 0.033, a Mean Absolute Error (MAE) of 0.13, and a Mean Absolute Percentage Error (MAPE) of 0.00044. Thus, the obtained error values are the lowest compared to other machine learning models. The obtained results confirm the potential of machine learning methods for quickly obtaining solutions to fluid dynamics and heat transfer modeling problems.

Secure long-term storage of carbon dioxide (CO2) in saline aquifers requires a clear understanding of how reservoir conditions and geochemical reactions influence rock properties and trapping mechanisms. While the individual effects of wettability, temperature, and salinity have been examined, their combined impact, particularly under varying wettability states where hysteresis controls phase behavior, remains insufficiently quantified. This study employs reactive transport modeling to assess the effects of temperature (50-90 degrees C), salinity (70,000-210,000 ppm NaCl), and wettability on CO2 mineralization, dissolution, and capillary trapping in carbonate-rich formations. Geochemical reactions involving calcite, kaolinite, and anorthite are modeled using transition state theory, while water-wet and mixed-wet conditions are represented through relative permeability and capillary pressure curves that capture wettability-dependent flow behavior. Mineralization increases from similar to 4.4 x 10(6) to similar to 1.3 x 10(7) mol in mixed-wet systems from 50 degrees C to 90 degrees C, while capillary trapping decreases from similar to 67 % to similar to 54 % (water-wet) and similar to 48 % to similar to 36 % (mixed-wet), and dissolution rises to > 30 %. At 90 degrees C, increasing salinity shifts CO2 plumes from vertically elongated, dissolution-dominant (similar to 36 %) in mixed-wet to laterally confined, capillary-dominant (similar to 82 %) in water-wet systems, governed by hysteresis (0.2 vs. 0.35). Calcite precipitation declines by similar to 22 % (water-wet) with temperature and by similar to 14 % with salinity, while kaolinite precipitation triples and anorthite dissolution varies from - 25 % to + 90 % depending on wettability. Over 60 years, porosity and permeability increase modestly by similar to 0.36 % and similar to 1.22 %. These findings clarify how wettability-driven interfacial dynamics influence mineralization and phase trapping, providing mechanistic guidance for optimizing CO2 storage performance across diverse reservoir settings.

In this paper, we investigate the behavior of different viscosity models under conditions of conjugate natural convection in a square cavity divided by a heat-conducting partition with a changing inclination angle and differential heating of the side walls. Numerical modeling is performed using the finite volume method for a fluid with a viscosity similar to the properties of blood. The main parameters of the study were the cavity inclination angle (0 degrees, 30 degrees, 45 degrees, 60 degrees, and 90 degrees) and the choice of the viscosity model of the non-Newtonian fluid. The results showed that inclination angles of 30 degrees and 60 degrees contribute to the enhancement of convection flows. Among the studied non-Newtonian viscosity models and the power law model demonstrated the highest flow velocity, whereas for the Herschel-Bulkley model, heat transfer due to conduction prevailed, with minimal mixing process.

Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for various applications. This paper presents a review of color models and spaces, analyzing their theoretical foundations, computational properties, and practical applications. We explore traditional models such as RGB, CMYK, and YUV, perceptually uniform spaces like CIELAB and CIELUV, and fuzzy-based approaches as well. Additionally, we conduct a series of experiments to evaluate color models from various perspectives, like device dependency, chromatic consistency, and computational complexity. Our experimental results reveal gaps in existing color models and show that the HS* family is the most aligned with human perception. The review also identifies key strengths and limitations of different models and outlines open challenges and future directions. This study provides a reference for researchers in image processing, perceptual computing, digital media, and any other color-related field.

Polymer microspheres are crucial for oilfield profile control, but face higher strength demands due to fluid pressure and pore-throat shear. A dual-network polymer microsphere aimed at high strength and maintained expansibility was synthesized via inverse emulsion polymerization from acrylamide (AM), diallyldimethylammonium chloride (DMDAAC), hexadecyl dimethyl allyl ammonium chloride (C16), and octadecyl acrylate (ODA), which was named P(AM/DMDAAC/C16/ODA) (abbreviated as (PADCO)). Its expansibility, suspension rheology, adsorption, and plugging effectiveness were investigated using mass difference, oscillatory frequency sweeps, UV spectrophotometry, and core plugging tests. The strength mechanism was studied via elastic testing, cryo-SEM, and tensile/compressive strain analysis. PADCO with varying hydrophobic monomer content showed consistent trends, but the 1.0 mol% ODA variant (1.0PADCO) performed optimally due to ideal hydrophobic association density and network matching. DMDAAC imparted positive charge, enabling strong electrostatic adsorption onto sandstone, while surface ODA groups enhanced adsorption onto sandstone via polarity similarity. The strength enhancement mechanism of the PADCO microsphere is summarized as "covalent/non-covalent double-network structure reinforcement." The incorporation of hydrophobic association monomers into the PADCO structure provides hydrophobic association interactions for the polymer network. This association acts as additional physical crosslinking points, increasing the crosslink density. Following hydration and swelling, these non-covalent interactions serve a sacrificial role in energy dissipation, significantly enhancing the material strength. This study provides valuable insights for the application of polymer micro-sphere blocking agents and enhanced oil recovery in high-water-cut reservoirs, offering an innovative perspective for improving the intrinsic strength of polymer microsphere while preserving their hydration and expansion capabilities.