Shale gas condensate reservoirs, characterized by high organic matter content and ultra-low permeability, exhibit complex fluid transport behavior governed by adsorption–desorption and molecular diffusion. Although CO2 huff-n-puff injection has shown promise for alleviating condensate blockage, enhancing hydrocarbon recovery, and enabling CO2 storage, the coupled adsorption–diffusion mechanisms remain poorly understood. In this study, a compositional numerical model was developed by coupling adsorption–diffusion processes with multiple CO2 trapping mechanisms to simulate the huff-n-puff process. The model was used to evaluate the effects of injection parameters, permeability, and total organic carbon (TOC) content on condensate mitigation, hydrocarbon recovery, and CO2 storage performance. Simulation results indicate that molecular diffusion promotes condensate re-vaporization, resulting in a modest increase in gas production but a slight decrease in oil recovery, with its impact diminishing as permeability decreases. Reservoirs with higher TOC exhibit improved methane recovery and greater CO2 sequestration potential due to competitive adsorption between CO2 and CH4. The overall CO2 storage efficiency ranges from 48% to 66%, with adsorption trapping identified as the dominant mechanism. These results highlight the critical role of adsorption in governing both hydrocarbon recovery and CO2 retention. The developed modeling framework provides new insights into optimizing CO2 huff-n-puff operations in shale gas condensate reservoirs, supporting the dual objectives of enhanced recovery and geological carbon storage.
Due to its superior thermodynamic properties and zero-carbon emission characteristics, hydrogen combustion in gas turbines is being actively promoted as a promising strategy for achieving worldwide decarbonization goals. Nevertheless, significantly intensified flow pulsations have been observed at the combustor exit when hydrogen is introduced, representing a major obstacle to its practical application in the gas turbine industry. To clarify the impacts of flow pulsations induced by hydrogen combustion on turbine endwall flow behavior and film cooling performance, URANS numerical predictions and FFT spectral analysis were performed in this paper based on measured flow pulsation data from realistic hydrogen combustion tests. This study systematically investigated the impacts of pulsation frequency (f = 1000, 3000, and 5000 Hz) and pulsation amplitude (K = 5% and 10%) on the endwall secondary flow patterns and film cooling performance at design conditions. The key findings revealed that imposed flow pulsations are detrimental to the endwall film cooling effectiveness, with a non-monotonic degradation of 3.8%-10.1% as the frequency increases. Under the low pulsation amplitude condition (K = 5%), the maximum and minimum reductions in endwall film cooling effectiveness are observed at f = 5000 Hz and f = 3000 Hz, respectively. An increased pulsation amplitude (K = 10%) introduces additional degradation in endwall film cooling effectiveness, and the reduction magnitudes exhibit distinct frequency sensitivity, with responses ranging from 14.2% to 0.2%. Fluctuation peaks in film cooling effectiveness are found in regions downstream of the film holes, near the vane pressure side and around the vane passage throat, and the pitch-averaged fluctuation magnitudes are less than 10% within the vane passage. Notably, spectral analysis demonstrates that flow pulsations induced by hydrogen combustion propagate through turbine cascades while preserving their dominant frequencies, thereby continuously disturbing the endwall flow field within the cascade. In summary, flow pulsations considerably diminish endwall film cooling performance and increase thermal failure risks, necessitating the design of novel pulsation-resistant film cooling schemes.
Abstract Human–robot collaboration (HRC) refers to the interaction between humans and robots in a shared workspace, combining human adaptability with robotic precision to execute tasks. This study formalizes HRC competencies and identifies the characteristics (training components) of a digital environment for learning HRC in construction through separate focus group discussions with industry and academic participants. A thematic analysis, conducted using inductive coding and validated through interrater reliability, reveals the rationale for prioritizing the top-rated HRC competencies across industry and academia. The top-rated HRC knowledge areas emphasize the importance of integrating HRC to enhance efficiency and safety, as well as adapting it to evolving industry needs. Prioritized skills underscore the importance of aligning knowledge and skills to support effective HRC implementation and enhance human–robot interface and communication proficiencies. The top-rated abilities highlight the significance of foundational, adaptive, and collaborative abilities for safe and effective HRC. The characteristics of a digital environment for learning HRC include robotic technologies, such as drones, three-dimensional (3D) printers, and wearable exoskeletons, which are considered essential for HRC training in construction. Applications such as surveying, bricklaying, and high-precision tasks provide hands-on learning opportunities. Facilitating conditions, including robust network connectivity, reliable power supplies, and modular construction processes, enhance the learning experience. This study contributes to knowledge by formalizing HRC competencies and proposing a digital learning environment that incorporates robotic technologies, practical applications, and facilitating conditions based on the perspectives of industry professionals and academic experts. It also contributes to Activity Theory by advancing its use as a conceptual framework for structuring and interpreting relationships among learners, HRC competencies, stakeholder perspectives, and training components within digital learning environments for HRC in construction.
In this wok, the Cauchy problem on the line of the modified Korteweg-de Vries equation with higher-order dispersion is studied, and for data in Sobolev and analytic Gevrey spaces its optimal well-posedness is established. The proofs are based on the derivation of sharp trilinear estimates in Bourgain spaces suggested by the corresponding linear problem. Furthermore, for analytic initial data improved lower bounds for the radius of spatial analyticity of the solution are derived.
Assessment frameworks do not capture the complexity of the social-ecological dynamics of small-scale fisheries (SSF), which support millions of livelihoods yet face persistent sustainability challenges. Reciprocal feedbacks between fish populations and fishers that are central to sustainability remain insufficiently integrated into assessment approaches because conventional fisheries management emphasizes population dynamics, whereas social-ecological systems research focuses on social drivers and faces operational challenges. We propose an integrative diagnostic approach that explicitly links fish population dynamics with theories of fisher behavior and governance. We illustrate its application using co-managed arapaima fisheries in the Amazon Basin, where sustainability emerges from multi-scalar social and ecological interactions. By capturing these feedbacks, the approach bridges ecological and social dimensions to identify key drivers of sustainability. It provides a replicable, interdisciplinary framework for diagnosing SSF sustainability and identifying leverage points to support adaptive governance across diverse contexts.
