Processes

Kombucha, a fermented tea beverage celebrated for its unique flavor and health-promoting properties, is traditionally produced from sugared tea and a symbiotic culture of bacteria and yeast (SCOBY). In this study, Nashi pear (Pyrus pyrifolia) pomace, a nutrient-rich by-product of juice processing, was explored as a novel substrate for kombucha production, combining sustainability with functional innovation. Beverages were prepared using black tea or pear pomace with varying sugar concentrations (3%, 5%, 7% w/v) and fermented for six days at 22 °C. Physicochemical parameters, bioactive compounds, antioxidant activity, color, and microbial populations were systematically analyzed. Pomace-based kombucha exhibited higher initial pH (4.3–4.7) and higher initial titratable acidity compared to tea-based variants (pH 3.4–3.6). These values stabilized at 3.6–3.8 by the end of fermentation, ensuring safety while preserving bioactive stability. While tea kombucha had higher polyphenol content (943.81–967.74 mg GAE/100 mL) and antioxidant activity (52.22–99.87% DPPH scavenging), pear pomace kombucha offered moderate bioactivity (up to 435.13 mg GAE/100 mL and 33.52% DPPH scavenging) and distinctive color (significantly higher b* value reaching 42.7), along with robust microbial growth. The results demonstrate that Nashi pear pomace can serve as a functional, eco-friendly alternative substrate, transforming fruit processing waste into a value-added beverage with enhanced health-promoting properties. This approach highlights a sustainable pathway for circular economy practices in food production and introduces a promising direction for innovative kombucha formulations.

Accurately predicting the ultimate tensile strain of full-scale pipelines with unequal wall thickness containing cracked girth weld joints is essential for strain-based design, structural integrity assessment, and safe operation. However, many existing limit state prediction methods for full-scale girth welds are developed for equal wall thickness configurations or idealized geometries, and their applicability to unequal wall thickness conditions remains limited. To address this gap, this paper develops a limit state prediction model for the ultimate tensile strain of cracked girth welded joints in full-scale pipelines with unequal wall thickness. The model is established using a numerical database generated from finite element simulations, incorporating realistic pipe geometry, material properties, wall thickness mismatch, and representative crack defect characteristics. By considering the stress and strain concentration effects induced by geometric non-uniformity in the weld region, the proposed model provides a practical and efficient tool for limit state evaluation. During pipeline construction, it supports the formulation of quantitative requirements for key design and fabrication parameters, such as the strength matching level. During stable operation, it enables reliable prediction of the strain capacity of existing girth welds in pipelines with unequal wall thickness, thereby supporting integrity management and decision making for safe service.

Gas turbines play a critical role in modern power systems, yet their transient operations (e.g., start-up, load mutation) induce significant thermal inertia in metal components, leading to deviations between simulation results and actual performance. Traditional low-dimensional (1D/0D) simulation models sacrifice detailed flow and temperature field information to reduce computational load, while high-dimensional (3D) computational fluid dynamics (CFD) models are impractical for full-system simulations due to excessive computational costs. This discrepancy creates a critical trade-off between simulation accuracy and efficiency in gas turbine thermal inertia studies. To address this challenge, this study proposes a temperature-gradient-guided dynamic genetic optimization sampling algorithm (TDGA) and integrates it into a multi-dimensional data scaling framework for gas turbines. A fully coupled simulation framework was established, combining 3D CFD models for turbine flow paths (resolving detailed flow and temperature fields) and 1D thermal models for metal components (casing, hub, blades). The TDGA was designed to enable efficient data interoperability between models: it incorporates a dynamic encoding mechanism, temperature gradient weight matrix, density penalty term, quantity penalty term, and regularization term to optimize sampling point distribution. Dynamic weight coefficients for each objective function term and adaptive crossover/mutation probabilities were introduced to balance global exploration (early iterations) and local exploitation (late iterations) during optimization. Comparative analysis showed that the TDGA achieved a mean squared error (MSE) of 15.52K, far lower than those of traditional Latin Hypercube Sampling (75.07K) and Bootstrap Sampling (64.38K). It allocated 70.11% of sampling points to high-temperature gradient regions while reducing the total number of sampling points to 2765. During the middle stage of the gas turbine start-up process, compared with the traditional Latin Hypercube Sampling and Bootstrap Sampling, the average error of the proposed sampling algorithm is reduced by 17.4% and 13.3%, respectively. The proposed TDGA-based framework effectively balances simulation accuracy and computational efficiency, providing a reliable approach for the transient thermal analysis of gas turbines.