Packed bed latent heat storage systems using phase change materials enable efficient thermal energy storage and recovery, motivating investigation of their coupled flow and heat transfer behavior during charging. This study develops a theoretical and numerical model of an axisymmetric storage tank filled with paraffin and traversed by downward hot-water flow to analyze system dynamics under realistic conditions. Transient momentum and heat transport were solved using a finite element approach with separate energy equations for the heat transfer fluid and storage medium, and the model was validated against experimental data with good agreement. Simulations conducted at a mass flow rate of 0.2 L/min, inlet temperature of 90 °C, and porosity of 0.49 show that fluid velocity decreases markedly inside the porous region by 93.75% (compared to the clear region) due to pressure losses and viscous resistance, resulting in the successful maintenance of a sharp and highly stratified thermocline front along the bed. Thermal results indicate faster temperature diffusion in the fluid than in the phase change material, producing distinct thermocline thicknesses. Melting onset and duration vary along the tank height, occurring earlier near the inlet and later downstream. The local liquid fraction follows the temperature distribution of the storage medium, rising significantly at each level until it reaches 52%, after which it increases slowly by about 2% before the tank reaches full saturation (θ2 = 100%) within a reasonable time. These findings clarify the interplay between flow resistance, heat transfer, and phase change, providing quantitative insight for design and optimization of packed bed thermal storage systems.
In a dynamic business environment, firms face numerous uncertainties that significantly impact operational effectiveness. Recently, artificial intelligence (AI) has become a powerful toolkit for identifying and managing known and unknown uncertainties. This study examines how AI technology enhances operational performance amid uncertainty, a key concern for firms seeking to gain a competitive edge through advanced technology. Grounded in dynamic capabilities theory (DCT), we developed a construct-based model that includes mediating, moderating, and direct relationships. An empirical analysis was conducted on a sample of 811 leading logistics firms, focusing on the implementation of AI in their operations. We employed partial least squares structural equation modeling (PLS-SEM) to test the proposed hypotheses among latent variables and constructs. The results demonstrate that both known and unknown uncertainties positively influence operational performance. Furthermore, AI provides stability and improvement in logistics operations, thereby enhancing competitive positioning. AI has significantly advanced the management of supply chain uncertainties, reducing operational errors and mitigating risks. The findings suggest that policymakers should consider adopting AI technologies in logistics operations to effectively address and navigate uncertainties.
In recent years, economic policy uncertainty has initiated a new discussion in environmental economics on the main drivers of environmental degradation. The main goal is to determine whether economic policy uncertainty leads to environmental damage or contributes to environmental quality. For this purpose, researchers commonly employ panel data analyses based on group estimation and use carbon emissions as a proxy for environmental indicators. However, by doing so they overlook country-specific estimations as well as underrepresent the ecological balance. To overcome these shortcomings, in this paper we employ two new approaches. First, we apply novel Fourier bootstrap autoregressive distributed lag estimation, which is the stronger estimation procedure in time-series analysis, to detect individual outcomes. Second, we use the ecological footprint as a proxy for environmental degradation, which reflects the natural balance more holistically and comprehensively than pollution indicators. In this context, our paper examines the impact of economic policy uncertainty on ecological footprint by using some control variables, such as economic growth and energy consumption. Our sample consists of seven emerging countries from 1965 to 2022. Fourier ARDL test results reveal a strong long-run relationship between ecological footprint and economic policy uncertainty, economic growth, and energy consumption for four emerging countries: India, Indonesia, Russia, and Türkiye. The estimations reveal that economic policy uncertainty in these countries contributes to environmental quality in the long run. In this context, it is important for policymakers to implement environmentally friendly growth strategies far from any uncertainty for the sake of sustainable economic development.
Mathematical and numerical models for Packed Bed Thermal Energy Storage (PBTES) systems are essential to predict the different parameters that influence their thermodynamic behavior and then optimize their performance and efficiency. In this research paper, an industrial-scale sensible thermocline Packed Bed Thermal Energy Storage system (9.17 m high and 4.72 m in diameter) was modeled and simulated during the heat charging process, based on FEM, CFD one-dimensional, and two-phase analysis. The model rigorously couples the Local Thermal Non-Equilibrium (LTNE) energy formulation with Darcy-Forchheimer hydrodynamics. The developed model was verified and validated using experimental data from the literature. The model was in close agreement with the experiment, with a global mean relative error of 3.62%. The two-dimensional velocity and temperature fields were presented to describe flow and temperature distributions in the hybrid medium (free and porous). The effect of varying flow rates (8-15 kg/s), porosities (0.35-0.55), and particle diameters (5-20 cm) on the thermal behavior of the heat storage system, temperature fields for solid and fluid, thermocline behavior, and charge efficiency were evaluated and presented. The simulation results demonstrate that the system achieves a high charge efficiency of 92.3% at a nominal charging rate of 15 kg/s. Increasing mass flow rate accelerates charging but widens the thermocline thickness and thermal stratification. Furthermore, increasing the porosity from 0.35 to 0.55 reduced charging time, decreased the temperature difference between the HTF and the storage medium by 10 degrees C, and increased the final heat charging efficiency by 8%. On the contrary, an increase in particle size from 5 to 20 cm leads to a slower rise in temperature within the solid phase, creating an important LTNE lag of approximate to 34 degrees C, thereby reducing the final heat charge efficiency by 16%, and prolonging the time required to charge the tank.
This study consisted of the physicochemical and structural characterization (FTIR, XRD and SEM) of Capparis spinosa fruits with the study of the anticorrosive activity of their extracts. C. spinosa fruit extracts were obtained using the soxhlet technique and various solvents (hexane, ethanol and distilled water). The gravimetric technique was used to evaluate these extracts as oil and gas pipeline steel corrosion inhibitors in a 1 M HCl acid media. The influence of temperature on the corrosion behavior of mild steel in 1 M HCl in the absence and presence of 1 g/L extracts was investigated at 308 to 338 K. The analysis of C. spinosa fruit powder showed it to be rich in mineral elements (P, Na, Mg, H, Fe, and Ca), and present OH (3400–3200 cm−1), C = C (1730 cm−1) as well as C–O (2935 and 2850 cm−1) groups. As for anticorrosion activity, the usage of the extracts decreased the rate of mild steel corrosion. However, inhibition efficiency increased with increasing inhibitor concentration, reaching 90
