Modeling of two-phase flow in heterogeneous wet porous media

The characterization of two-phase flow has been commonly based on homogeneous wet capillary models, which are limited to heterogeneous wet porous media. In this work, capillary pressure and relative permeability models for three heterogeneous wet systems are derived, which enable the analysis of the effect of oil-wet ratio on the two-phase flow mechanism. The capillary pressures, relative permeabilities and water cut curves of three systems are simulated at the primary drainage stage. The results show that water-wet and oil-wet systems exhibit drainage and imbibition characteristics, respectively, while heterogeneous wet systems show both of these characteristics, and a large oil- wet ratio is favourable to oil imbibition. Mixed-wet large and mixed-wet small systems have water-wet and oil-wet characteristics, respectively, at the end and the beginning of oil displacement. At the drainage stage, the oil-wet ratio can significantly decrease oil conductivity, while water conductivity is enhanced. The conductivity difference between oil and water firstly decreases and then increases with rising water saturation, and the difference diminishes with the increase in oil-wet ratio. The oil-wet ratio can reduce water displacement efficiency, and its effects on the water cut curves vary between the three systems due to wettability distribution and pore-size mutation. The mixed-wet small system has the strongest oil imbibition ability caused by the largest capillary pressure in oil-wet pores and the smallest drainage pressure in water-wet pores, and high water conductivity causes the greatest water cut. The trend of variations in the mixed-wet large system is opposite to that in the mixed-wet small system, and the fractional-wet system is located between the other two systems.Cited as: Xiao, Y., He, Y., Zheng, J., Zhao, J. Modeling of two-phase flow in heterogeneous wet porous media. Capillarity, 2022, 5(3): 41-50. https://doi.org/10.46690/capi.2022.03.0

Does the policy of rural land rights confirmation promote the transfer of farmland in China?

Land tenure security and land transfer markets are once again a topmost priority in the policy development agenda because of their expected outcomes in terms of equity and efficiency in the rural sector of China. The policy of rural land rights confirmation has been implemented since 2010 to enhance land tenure security and the transferability of farmland. However, only a few studies have been conducted on the effect of rural land rights confirmation on farmland transfer. Therefore, we use household-level survey data from 48 villages across Tianjin City and Shandong Province to explore whether rural land rights confirmation promotes the transfer of farmlands. Our empirical results show that rural land rights confirmation has significant and positive effects on the likelihood and amount of transfer-out land at the 5% significance level, but the effect on transfer-in farmland is insignificant. The results of the study have several policy implications. For instance, the agricultural comparative advantage should be improved through various agricultural subsidy policies. Moreover, the intermediary service network for farmland transfer should be established, and strengthening the non-farm employment skills and improving the non-agricultural employment market are necessary for the rural labour force

Learning from Sparse Offline Datasets via Conservative Density Estimation

Offline reinforcement learning (RL) offers a promising direction for learning policies from pre-collected datasets without requiring further interactions with the environment. However, existing methods struggle to handle out-of-distribution (OOD) extrapolation errors, especially in sparse reward or scarce data settings. In this paper, we propose a novel training algorithm called Conservative Density Estimation (CDE), which addresses this challenge by explicitly imposing constraints on the state-action occupancy stationary distribution. CDE overcomes the limitations of existing approaches, such as the stationary distribution correction method, by addressing the support mismatch issue in marginal importance sampling. Our method achieves state-of-the-art performance on the D4RL benchmark. Notably, CDE consistently outperforms baselines in challenging tasks with sparse rewards or insufficient data, demonstrating the advantages of our approach in addressing the extrapolation error problem in offline RL.Comment: ICLR 202

Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study

In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models. This framework extends beyond traditional graph neural (integer-order) ordinary differential equation (ODE) models by implementing the time-fractional Caputo derivative. Utilizing fractional calculus allows our model to consider long-term memory during the feature updating process, diverging from the memoryless Markovian updates seen in traditional graph neural ODE models. The superiority of graph neural FDE models over graph neural ODE models has been established in environments free from attacks or perturbations. While traditional graph neural ODE models have been verified to possess a degree of stability and resilience in the presence of adversarial attacks in existing literature, the robustness of graph neural FDE models, especially under adversarial conditions, remains largely unexplored. This paper undertakes a detailed assessment of the robustness of graph neural FDE models. We establish a theoretical foundation outlining the robustness characteristics of graph neural FDE models, highlighting that they maintain more stringent output perturbation bounds in the face of input and graph topology disturbances, compared to their integer-order counterparts. Our empirical evaluations further confirm the enhanced robustness of graph neural FDE models, highlighting their potential in adversarially robust applications.Comment: in Proc. AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb. 202

Soliton Molecules and Multisoliton States in Ultrafast Fibre Lasers: Intrinsic Complexes in Dissipative Systems

Benefiting from ultrafast temporal resolution, broadband spectral bandwidth, as well as high peak power, passively mode-locked fibre lasers have attracted growing interest and exhibited great potential from fundamental sciences to industrial and military applications. As a nonlinear system containing complex interactions from gain, loss, nonlinearity, dispersion, etc., ultrafast fibre lasers deliver not only conventional single soliton but also soliton bunching with different types. In analogy to molecules consisting of several atoms in chemistry, soliton molecules (in other words, bound solitons) in fibre lasers are of vital importance for in-depth understanding of the nonlinear interaction mechanism and further exploration for high-capacity fibre-optic communications. In this Review, we summarize the state-of-the-art advances on soliton molecules in ultrafast fibre lasers. A variety of soliton molecules with different numbers of soliton, phase-differences and pulse separations were experimentally observed owing to the flexibility of parameters such as mode-locking techniques and dispersion control. Numerical simulations clearly unravel how different nonlinear interactions contribute to formation of soliton molecules. Analysis of the stability and the underlying physical mechanisms of bound solitons bring important insights to this field. For a complete view of nonlinear optical phenomena in fibre lasers, other dissipative states such as vibrating soliton pairs, soliton rains, rogue waves and coexisting dissipative solitons are also discussed. With development of advanced real-time detection techniques, the internal motion of different pulsing states is anticipated to be characterized, rendering fibre lasers a versatile platform for nonlinear complex dynamics and various practical applications

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the $\epsilon$-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints.Comment: 15 pages, 7 figure

Research on the Design Methods for Green Renovation of Existing Buildings in Lingnan Region

China’s urbanization has entered a new stage with the promotion of “Carbon Peaking and Carbon Neutrality Goals” and “Urban Renewal Strategy”. Problems such as poor comfort, high energy consumption and unreasonable functions of existing buildings have attracted extensive attention from society. The climate-adapted human environment created by traditional buildings in the Lingnan region offers insights for the green transformation of buildings in this area. This paper summarizes the wisdom from the climate-adaptive construction of traditional buildings in Lingnan region, and proposes a green transformation design scheme that meets the requirements of energy efficiency and comfort, which provides a reference for the green renovation design of existing buildings

Measurement of Stimulated Raman Side-Scattering Predominance and Energetic Importance in the Compression Stage of the Double-Cone Ignition Approach to Inertial Confinement Fusion

Due to its particular geometry, stimulated Raman side-scattering (SRSS) drives scattered light emission at non-conventional directions, leading to scarce and complex experimental observations. Experimental campaigns at the SG-II UP facility have measured the scattered light driven by SRSS over a wide range of angles, showing an emission at large polar angles, sensitive to the plasma profile and laser polarization. Furthermore, direct comparison with back-scattering measurement has evidenced SRSS as the dominant Raman scattering process in the compression stage, leading to the scattering loss of about 5\% of the total laser energy. The predominance of SRSS was confirmed by 2D particle-in-cell simulations, and its angular spread has been corroborated by ray-tracing simulations. The main implication is that a complete characterization of the SRS instability and an accurate measurement of the energy losses require the collection of the scattered light in a broad range of directions. Otherwise, spatially limited measurement could lead to an underestimation of the energetic importance of stimulated Raman scattering