Technobius Physics

Heat transfer-based analysis of building energy demand and energy systems

Aida Nazarova, Gulzhan Tulekenova, Yer-Targyn Tleukenov — Tue, 23 Jun 2026 00:00:00 +0500

This study examines the thermal performance of buildings and the efficiency of alternative energy supply systems under cold climatic conditions, with a focus on Eastern Kazakhstan. The objective is to quantify the impact of building envelope insulation and energy system configuration on heating demand, primary energy consumption, carbon emissions, and economic performance. An analytical methodology based on standardized building physics approaches was applied to determine heat demand, accounting for transmission and ventilation losses as well as internal and solar heat gains. Climatic conditions were incorporated using the heating degree-day method. Several energy supply configurations, including conventional fossil-based systems and renewable-integrated solutions, were evaluated in terms of energy, environmental, and economic indicators. The results indicate that improving the insulation standard reduces the specific heating demand from approximately 185 kWh/m²·a to 63 kWh/m²·a, corresponding to a reduction exceeding 60%. Transmission losses represent about 70% of total heat losses. Primary energy consumption decreases from approximately 280 kWh/m²·a for coal-based systems to about 110 kWh/m²·a for renewable-based configurations. Similarly, carbon emissions are reduced from 0.34 kg/kWh to 0.04 kg/kWh. Systems with higher initial investment demonstrate improved life-cycle economic performance due to lower operating costs. The findings confirm that enhanced thermal insulation combined with efficient and low-carbon energy systems constitutes a key strategy for reducing energy demand and environmental impact in cold climate regions. The proposed analytical framework provides a consistent and reproducible basis for evaluating building energy performance and supporting energy planning decisions.

Machine-learning-assisted modeling of defect-mediated charge transport in anisotropic 2D semiconductors

Zhang Wei — Thu, 25 Jun 2026 00:00:00 +0500

This study investigates defect-mediated charge transport in anisotropic two-dimensional semiconductors using combined transport modeling and machine-learning analysis. The objective of the work was to determine how structural defects and lattice anisotropy influence electrical conductivity and carrier mobility in low-dimensional quantum materials. Monolayer transition-metal dichalcogenides, black phosphorus, and ReS₂-based systems with various defect configurations were analyzed. Transport behavior was modeled within a semiclassical framework incorporating anisotropic carrier mobility and defect-assisted scattering mechanisms. Machine-learning models, including random forest regression, gradient boosting regression, and artificial neural networks, were applied to predict conductivity variations using structural and electronic descriptors. The results demonstrated that conductivity decreases nonlinearly with increasing defect concentration, particularly above 10¹³ cm⁻², where localization effects become dominant. Strongly anisotropic materials exhibited enhanced sensitivity to vacancy-induced disorder due to directional carrier confinement. Among the evaluated algorithms, the artificial neural network achieved the highest predictive accuracy with an R² value of 0.972. Feature-importance analysis revealed that defect concentration, effective mass, and lattice anisotropy ratio are the primary factors governing transport degradation. The study confirms that accurate prediction of transport properties in anisotropic quantum materials requires simultaneous consideration of defect physics and directional electronic transport. The proposed approach may be useful for the design and optimization of next-generation nanoelectronic and flexible semiconductor devices.

Physics-Informed Neural Networks for modeling heat diffusion in anisotropic solids

Saira Sakhabayeva — Fri, 26 Jun 2026 00:00:00 +0500

Accurate simulation of heat diffusion in anisotropic solids is essential for the design and optimization of advanced engineering materials and thermal management systems. However, conventional numerical approaches often require computationally intensive mesh generation and repeated solution procedures, particularly for transient problems involving directional heat transport. This study investigates the application of Physics-Informed Neural Networks for modeling transient heat diffusion in anisotropic solid materials. The proposed framework incorporates the governing heat conduction equation, initial conditions, and boundary conditions directly into the neural network training process, enabling physically consistent predictions without extensive labeled datasets. The model was trained using a combination of Adam and L-BFGS optimization algorithms and validated against finite element method simulations. The influence of thermal anisotropy was evaluated for conductivity ratios ranging from isotropic conditions to strongly anisotropic cases. The developed Physics-Informed Neural Networks demonstrated excellent agreement with finite element method solutions, achieving a root mean square error of 0.014 K, a mean absolute error of 0.010 K, a maximum absolute error of 0.061 K, and a coefficient of determination of 0.9997. Although prediction errors increased slightly with increasing anisotropy, the model maintained high accuracy and stable convergence across all investigated scenarios. The results confirm that Physics-Informed Neural Networks provide an accurate and physically consistent alternative to traditional numerical methods for anisotropic heat transfer analysis. The proposed approach offers significant potential for rapid thermal simulations, inverse heat transfer problems, digital twin development, and real-time engineering applications involving complex anisotropic materials.