As transistor densities surge and boost frequencies intensify transient CPU heat fluxes, passive thermal management with phase change materials (PCMs) offers a zero-power alternative. However, the low thermal conductivity of PCMs demands high-conductivity fins, and no existing study has employed topology optimization to design a multi-layer stacked PCM heat sink for long-duration cooling. The present study's novelty is a four-row PCM heat sink with aluminum fins generated by density-based topology optimization (SIMP, 20% solid fraction, Helmholtz filtering) under steady operating conditions. The optimized geometry is simulated via a transient enthalpy-porosity CFD model resolving conjugate heat transfer, laminar natural convection, and phase change, validated against experimental data (RMSE=2.44 K, NRMSE = 3.26%). Over eight hours of continuous CPU load, sequential melting occurs: the lowest row fully melts in 66 min, the second in 275 min, while the third reaches a liquid fraction of 0.54 and the top remains solid. The heat-sink base temperature stabilizes at 355.72 K (≈82.6 °C) after 8 h, which lies near the upper limit of typical commercial CPU operating temperatures, with a difference of only 3.51 K, compared to 388.36 K for a conventional single-layer radial-fin heat sink after just 1 h—a 42.4 K reduction, reflecting the combined effect of the multi-layer configuration and the topology-optimized fin geometry. Latent heat (2.6 kJ per row) dominates the lower blocks, while natural convection is suppressed to micrometre-per-second velocities.
The rapid advancement of low-power microelectronic technologies has stimulated growing interest in vibration-based energy harvesting as a promising alternative to conventional batteries. While batteries are widely used at these scales, their replacement becomes highly challenging for systems deployed within inaccessible or remote locations. The major drawback of linear harvesters is their limited frequency bandwidth. This limitation, together with the wide frequency spectrum of ambient excitations, significantly diminishes the energy conversion efficiency of linear harvesters. To address this challenge and improve the performance of such systems in practical environments, it is essential to broaden their frequency bandwidth. Among the various approaches for broadening the frequency bandwidth, employing nonlinear techniques has proven to be highly effective in enhancing the efficiency of vibration energy harvesters. This study proves the theoretical modeling of frequency bandwidth analysis in a piezoelectric energy harvester for a series of experimental cases considering magnetic nonlinearity and impact-induced nonlinearity. Applying both approaches together increases the system efficiency by about 80 % compared to using either method separately. Furthermore, the influence of key system parameters—including magnetic strength, input acceleration amplitude, electrical resistance, and initial gap—on the system's dynamic behavior is investigated. Finally, an efficiency index is introduced to assess the harvester's efficiency relative to the conventional design.
Gas hydrate formation is a complex physicochemical process governed by coupled thermodynamic and kinetic factors, making accurate prediction challenging using conventional models. In this study, a machine learning (ML)-based framework was developed to predict and analyze the formation kinetics of CO2 and CH4 hydrates using differential scanning calorimetry (DSC) data. A Random Forest regression model was trained on experimental datasets covering a range of pressures and additive concentrations, including sodium dodecyl sulfate (SDS), lauryl alcohol ethoxylate-2 (LAE2), and tetrahydrofuran (THF). The model demonstrated excellent predictive performance, achieving an R2 of 0.972 and low mean squared error across independent condition-level validation folds. Comparative analysis with design of experiments (DoE) results showed that ML predictions more closely matched experimental data, particularly for heat-flow ratio and relaxation time of the hydrate formation, with significantly lower deviations. The study further identified optimal operating conditions for hydrate formation, highlighting the strong influence of pressure and additive concentration. While DoE provided general trends, ML captured nonlinear interactions more effectively, enabling improved prediction accuracy. Overall, the data consistently show that increasing pressure enhances all aspects of hydrate formation, while the type and concentration of additive determine the efficiency, with LAE2 (for CO2) and THF (for CH4) providing the most favorable conditions.
Estimating the suspended sediment load (SSL) in watersheds is a fundamental challenge in surface water resource management and hydraulic structure design. This study utilized data from the Haraz, Babol Rud, Talar, and Neka Rud watersheds in Mazandaran province, Iran, encompassing discharge, daily sediment load, and key physical characteristics. These data were organized in three different combinations as inputs to several machine learning models. Among these, the input scenario M3, which combines streamflow data with physical watershed characteristics, demonstrated the best performance across all models. The machine learning models employed were Support Vector Regression (SVR), Long-Short Term Memory (LSTM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), the performance of which was enhanced using two optimization algorithms: Particle Swarm Optimization (PSO), and the Flow Direction Algorithm (FDA). Results indicated that FDA-optimized models consistently outperformed their PSO counterparts. Specifically, the XGBoost-FDA hybrid model exhibited superior performance, achieving high accuracy in both training (RMSE = 2.86 ton/day, MAE = 2.20 ton/day, Pearson R = 0.93, KGE = 0.88, NSE = 0.86) and testing phases (RMSE = 3.05 ton/day, MAE = 2.31 ton/day, R = 0.93, KGE = 0.89, NSE = 0.86). This performance represents a substantial improvement over a baseline simple linear regression model (R = 0.74). The findings of this study have significant implications for engineers and policymakers in the design of hydraulic structures and water resources management.
The release rate of curcumin (CU) was controlled by developing a new and suitable formulation using an anti-solvent co-precipitation method. Layer-by-layer nanoparticles of xanthan (XA) and cholesterol (CHO) were successfully fabricated as a composite drug co-delivery system of curcumin (CU) and sodium diclofenac (DI) with a lipophilic core of CHO and an outer hydrophilic biopolymer coating of XA. The Box-Behnken experimental design was used for drug formulation and optimization to achieve maximum loading capacity (LC) and encapsulation efficiency (EE). According to the zeta potential results, the surface charge of nanoparticles was close to that of xanthan (-18.8 mV), which can approve that the nanoparticles were properly coated with xanthan, while cholesterol precursor formed the core cavity of composite drug. The results showed that EE and LC were 68.07
