ABSTRACT Wall‐condensation heat‐transfer prediction is essential for condenser design, yet model reliability depends on transport mechanisms, operating conditions, and validation quality. This structured critical review evaluates empirical correlations, computational fluid dynamics, and data‐driven and physics‐informed models across internal channels, external steam–gas surfaces, and plate heat exchangers. Published evidence is assessed using source‐specific prediction targets, error metrics, sample sizes, and validation procedures, avoiding unsupported cross‐study accuracy rankings. A convolutional neural network achieved a reported mean absolute relative deviation of 5.82% on 1213 test points, although source‐level independence remains unverified. For steam–non‐condensable‐gas condensation, physics‐constrained XGBoost reported 11.22% mean absolute percentage error versus 21.63% for a comparator on 199 external test points. An illustrative cylindrical thermal‐resistance calculation demonstrates how heat‐transfer‐coefficient variations influence condenser area while highlighting geometry and pressure‐drop constraints. The proposed mechanism‐ and validation‐aware framework supports evidence‐based model selection, identifies transferability limitations, and guides preliminary thermal design without assuming universal predictive superiority.
ABSTRACT The correlation between rock compressive strength and fracture roughness represents a significant area of research within the disciplines of rock mechanics and geological engineering. In this study, laboratory flow experiments were performed on sandstone (with different compressive strengths) single‐fractures created by the Brazilian splitting to investigate the hydro‐mechanical behaviors. The effects of geometric characteristics of fractures with different compressive strengths were examined. For the tested specimens, the results indicate that the fracture surface roughness coefficients ( JRC , R p , and Z 2 ) and mean aperture of fracture with high compressive strength are relatively smaller, indicating that the fracture surface is smoother. For a given constant loading level, a larger contact ratio and a more uniform contact distribution are observed, leading to fewer connected flow channels. The nonlinear flow behaviors of the water can be satisfactorily described by Forchheimer's law. The transmissivity of fracture with high compressive strength is lower than those with low compressive strength, which is attributed to the combined effects of smaller initial aperture, smoother surfaces, larger contact‐area growth under loading, and the resulting shrinkage of effective flow channels. Under the tested conditions and at equivalent pressure gradients, fractures in high‐compressive‐strength sandstone exhibit smaller Reynolds numbers. They also exhibit higher critical Reynolds number and critical pressure gradient. Although based on one specimen per strength level, the obtained results provide an indicative reference for understanding the nonlinear seepage mechanism in single‐fracture sandstone.
ABSTRACT Small hydropower can provide a comparatively persistent renewable‐electricity profile for water electrolysis, but electrolyzer sizing requires a compromise between renewable‐energy capture and equipment utilization. This study evaluates a planned green‐hydrogen system at Miryang Dam, Republic of Korea, using 26,280 h of measured generation data from two 650 kW units during 2021–2023. A 1 MW PEM electrolyzer was simulated by hourly load following, and capacities from 300 to 1200 kW were screened using an equal‐weight ideal‐point criterion and marginal hydrogen‐production gain. The planned 1 MW configuration captured 98.11% of available hydropower energy, achieved a 59.93% capacity factor, and produced 93.75 t‐H 2 /year. The criterion identified 600 kW as the technically balanced capacity, with 84.71% capture, 86.25% capacity factor, and 80.95 t‐H 2 /year. Incremental hydrogen gain declined from 7.03 t/year for the 500–600 kW increase to 3.82 t/year for 600–700 kW. At a PPA price of KRW 160/kWh, audited operating and full LCOH were KRW 20,137/kg‐H 2 and 27,209/kg‐H 2 . The results show that sizing based solely on peak hydropower output can overestimate the electrolyzer capacity required for efficient system utilization.
ABSTRACT As the world navigates intertwined environmental and economic crises, the challenge of sustainable energy provision has grown more pressing than ever. These interconnected imperatives emphasize the need for sustainable energy solutions that protect the environment. Therefore, the renewable energy transition is strategically important for achieving environmental, economic, and sustainable development goals. Because fossil fuels exacerbate the ecological crisis, renewable energy sources are well‐suited to address these problems. However, the transition to renewable energy involves high costs and demands the active implementation of public policies. Public debt plays an important role in this area and has reached historically high levels globally. Therefore, the ultimate effects of public debt will be decisive for energy transformation, despite limited studies on those effects. This study examined the relationship between public debt and renewable energy transition in 12 selected developed countries, using annual data from 1994 to 2021 and panel time‐series analysis techniques. The results show that high public debt can hinder the renewable energy transition, while moderate public debt can be advantageous in some countries. While energy‐based environmental taxes support the transition to renewable energy, public R&D expenditures for renewable energy are still below their potential to produce beneficial effects. Additionally, evidence indicates that sustainable development efforts are a key determinant of and driving force behind the renewable energy transition.
ABSTRACT Mechanics‐conditioned reconstruction of nonlinear stress–strain responses in high‐performance cementitious materials is essential for interpreting experimentally characterized material behavior and supporting the safe design of underground, geotechnical, and structural systems. Owing to the heterogeneous nature and complex damage evolution of these materials, conventional constitutive models often fail to capture their full mechanical behavior under varying loading conditions. This study introduces a Genetic Algorithm Optimized Long Short‐Term Memory (GA‐LSTM) framework that combines evolutionary optimization and deep learning to reconstruct complete stress–strain curves of high‐performance cementitious materials conditioned on known or experimentally derived mechanical descriptors, including elastic modulus, peak stress, peak strain, and toughness. Experimental data from uniaxial compression tests were systematically preprocessed using cleaning, normalization, interpolation, and Savitzky–Golay filtering to preserve physical integrity. The genetic algorithm optimized key hyperparameters, including network depth, hidden units, dropout rate, and sequence length under a multi‐objective, physics‐informed strategy. The proposed model achieved superior curve‐reconstruction accuracy (, RMSE MPa) with minimal deviation in peak stress and post‐peak toughness. Comparative statistical analyses (ANOVA, Tukey's HSD, and Pearson correlation) verified the model's reliability and robustness. The developed GA‐LSTM framework offers a scalable and interpretable tool for mechanics‐conditioned constitutive reconstruction, mechanical‐response characterization, and geomechanical simulation within experimentally characterized high‐performance cementitious material domains.
