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The Joule–Thomson (JT) effect is well characterized at engineered throttling devices such as valves, regulators, and leak orifices, but its occurrence at internal weld-joint geometry, an unintentional, workmanship-tolerance-scale feature present at every girth weld, has not previously been established experimentally; existing JT-cooling studies assume an intentional, externally specified throttling geometry, while weld-integrity studies address the erosion, corrosion, and stress consequences of internal weld irregularities without examining their thermal effect. This study addresses that gap by providing controlled experimental evidence of weld-induced JT cooling as a distinct micro-throttling mechanism. The objective was to determine whether internal weld-joint geometry (weld reinforcement and hi-lo misalignment) constitutes a measurable, thermodynamically consistent JT throttling mechanism under representative natural gas transmission conditions. A natural gas pipeline test section incorporating a smooth-pipe baseline and four internal weld geometries (low, medium, and high reinforcement, and hi-lo misalignment) was tested at upstream pressures of 40–70 bar and flow velocities of 5–15 m/s, with the measured response validated against Peng-Robinson-based isenthalpic predictions (ΔT = μJTΔPw) and independent CFD simulation. The CFD simulation reproduced the experimental pressure loss to within an average of 3.5%. Weld-induced pressure loss and the corresponding corrected temperature response increased systematically with geometric severity and flow velocity, reaching a maximum additional pressure loss of 0.199 bar and a maximum corrected temperature reduction of 0.0404 K for the hi-lo geometry at 60 bar and 15 m/s; flow velocity accounted for 55.96% of the variance in the response, followed by weld geometry (26.62%) and their interaction (13.84%), and a regression model linking temperature response to weld severity, velocity, and pressure achieved R² = 0.9642. These results confirm that internal weld geometry constitutes a genuine, thermodynamically consistent local JT throttling mechanism, one that is two to three orders of magnitude smaller than the cooling associated with engineered throttling devices and unlikely to present a significant hydrate, embrittlement, or corrosion hazard at an isolated girth weld. Copyright © 2027 by Authors, Published by Universitas Diponegoro and BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).
High entropy alloys (HEAs) have no less than four principal elements in (near) equal atomic percentage, exhibiting a solid solution state with fine precipitates, as well as distinct material properties. HEAs material design method influences its mechanical and corrosion behaviors significantly, but the underlying mechanism needs further investigation. It has been reported that the second-phase precipitates, work hardening behavior, and corrosion resistance of HEAs are vary depending on the amount of alloying element and heat treatment. The research progress of HEAs and their unique characteristics are reviewed, including microstructural evolution, mechanical properties, and corrosion behaviors. Also the description of HEA’s solid solution and precipitates strengthening will be highlighted. Furthermore, the effects of alloying and heat treatment, on the above-mentioned properties, are discussed. In addition, the challenges, prospects, and industrial applications of HEAs will be discussed. Furthermore, this work points out the future developments of HEAs, fulfilling the strict requirement from industries.
Groundwater vulnerability assessments are crucial for sustainable water resource management, especially in regions experiencing anthropogenic pressures. This study integrated geo-electrostratigraphic data with SINTACS, aquifer vulnerability index (AVI), and Groundwater occurrence - Overlying lithology - Depth (GOD) models to enhance vulnerability mapping within a major coastal aquifer impacted by saline intrusion. SINTACS incorporates depth of groundwater (S), infiltration (I), nature of vadose zone (N), type of soil (T), aquifer media (A), hydraulic conductivity (C), and slope/topography (S). The AVI evaluates aquifer susceptibility based on hydraulic resistance, while the GOD model is derived from groundwater occurrence, overlying lithology, and depth to groundwater. This hybrid, multimodel assessment approach was necessary to reduce model-specific bias, improve spatial reliability, and increase confidence in the results. SINTACS scores (154-188) classified 90% of the area as having moderate-high vulnerability, and 10% as having high vulnerability to contamination. This was corroborated by GOD scores (0.35-0.63) and AVI scores (1.23-2.59), with the most vulnerable zones concentrated in areas with thin overburden. Longitudinal conductance was used to corroborate results obtained from the vulnerability models. This work provides a scientifically robust framework for assessing aquifer vulnerability within heterogeneous aquifer systems, and the methodologies employed are replicable in other regions worldwide.
Aquifer transmissibility indices influence interactions between the rock matrix and pore water and, consequently, fluid flow within aquifer systems. This study quantified multiple transmissibility indices within a complex, saline-vulnerable aquifer system to improve the accuracy of fluid-flow modeling. Geo-electrostratigraphic data obtained from vertical electrical soundings (VES) were integrated to derive aquifer transmissibility indices, including bulk aquifer resistivity, formation factor, porosity, intrinsic permeability, hydraulic conductivity, transmissivity, tortuosity, surface capillary radius, surface area per unit pore volume, and specific surface area. Permeability ranged from 4303.4 mD to 48049.7 mD, and hydraulic conductivity varied from 2.6 to 28.7 m/day, indicating good transmissibility. Large values of porosity, permeability, hydraulic conductivity, surface capillary radius, and specific surface area indicated good transmissibility, while high surface area per pore volume indicated reduced transmissibility. Groundwater yield potential ranged from 1075.6 to 20664.3, indicating a prolific aquifer, and longitudinal conductance values indicated that 50
We present an advanced quantum computational framework using Gaussian spherical quantum dots (GSQDs) embedded in GaAs, integrating both theoretical and AI-driven computational methodologies. By employing Nikiforov–Uvarov Functional Analysis (NUFA) alongside machine learning-assisted modeling, we derived eigenvalues and wave functions for the GSQD system. Our study further explored the energy spectra and Rényi entanglement entropy for various quantum states, revealing a highly ordered and stable system characterized by consistently low entropy values and minimal thermal excitation of higher states. This intrinsic stability highlights the viability of GaAs and Kagome lattice materials for robust quantum computing applications. Additionally, we analyzed the quantum properties of donor impurity states within GSQDs, recalculating eigen energies in effective atomic units using donor effective Rydberg ( R_D ) and donor effective Bohr radius a_D . A comparative evaluation across multiple computational approaches provided a unified assessment of energy predictions and entropy calculations, reinforcing the computational accuracy and consistency of our methodology. By synthesizing results from diverse theoretical and numerical techniques, we established a robust framework for optimizing GSQD-based architectures in quantum information processing. Our findings demonstrate the efficiency of AI-driven computational techniques and NUFA in solving the Class Yukawa and Hellmann Perturbations relevant to quantum computing. Furthermore, we emphasize the significance of Rényi entropy as a key metric for analyzing quantum coherence, entanglement, and uncertainty in low-dimensional semiconductor structures. These insights contribute to the design and optimization of next-generation quantum dot architectures, reinforcing their potential for scalable quantum information technologies.
