Modeling the temporal and spatial dynamics of heat pollution within environmental systems is essential to accurately predict thermal pollution within the environment. Therefore, accurate predictions of thermal pollution dynamics require an understanding of the multiscale, nonlinear interactions that establish the mechanisms of thermal transport, dispersion, and ecological response. In this paper, we provide an integrated set of methodologies that applies both three-dimensional computational fluid dynamics (CFD) and cutting-edge machine learning (ML) and deep learning (DL) methodologies for the environmental thermal assessment. We assess the performance of six types of predictive ML models-linear regression, random forest, gradient boosting, multilayer neural networks (MNNs), support vector regression (SVRs), and long short-term memory (LSTM) networks-against benchmark datasets for predicting thermal plumes, predicting cooling tower dynamics, and forecasting river temperatures using CFD. Among these models, the LSTM networks performed by far the best for predicting thermal activities over time (R² = 0.95, RMSE = 1.5°C). Conversely, the best performing model for identifying spatial thermal patterns was gradient boosting.
U. Khusankhodzhaev, Laziz Qayimov, Shakhodat Kobilova et al.· EPJ Web of Conferences· 0 citations
This study presents a computationally efficient quasi-one-dimensional mathematical model based on the traveling wave method to investigate hydraulic shock attenuation using a gas-hydraulic damper in pipeline systems. Unlike conventional models, this formulation accounts for fluid compressibility and incorporates a non-linear boundary condition strictly satisfying gas mass conservation within the damper. The model was successfully validated against a MATLAB 2024 Simulink benchmark, demonstrating a maximum pressure amplitude discrepancy of only 5–8%. A parametric analysis evaluated the effects of damper volume, initial gas pressure, and pipe diameter on surge suppression. Results show that insufficient damper volume causes extreme negative pressure drops, risking severe cavitation and fluid column separation. Conversely, excessive volume induces “over-damping,” undesirably increasing system inertia and delaying steady-state recovery. Crucially, scaling analysis reveals that a damper optimized for a specific pipe diameter loses efficacy in larger pipes, as the flow’s kinetic energy scales with the diameter’s square. This model provides a robust, precise computational tool for the optimal and safe design of pipeline networks.
B. Bakhtiyorov, K. Mamadaliev, Khayotjon Aminov et al.· Fluids· 0 citations
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