Adsorption behavior of silica nanofluid on coal and its injection enhancement mechanism

Coal seam water injection has proven effective in mitigating coal and gas outburst disasters in mines. However, its drawbacks, including the poor wettability of coal seams and the susceptibility to filtration loss of injected water, have led to low construction efficiency and uneven control outcomes. In this study, we propose a novel approach to address these issues by utilizing a water-based silica nanofluid to alter surface wettability. The four-stage deposition process of nanoparticles on the coal surface is identified, and the time-varying behaviour of modified coal wettability is revealed under the influence of key parameters, such as particle concentration. These findings provide a foundation for the application of nanofluids in modifying the wettability of reservoirs

Dynamic analysis of offset press gear-cylinder-bearing system applying finite element method

A dynamic model of offset press gear transmission system made up of gears, cylinders and bearings is proposed in this study. The model based on finite element method (FEM) includes some nonlinearity such as time-varying meshing stiffness, backlash, static transmission error and contact nonlinearity, which lead to complex nonlinear coupling. The Darren Bell principle and Lagrangian approach are applied to derive the motion equations of system, then the Newmark method is used to solve the equations for meshing force, acceleration, shoulder iron and rubber contact force. Eigenvalue solution is used to predict the critical speed, moreover, the influence of the radial and axial stiffness on the first-order critical speed is discussed. Considering the importance of acceleration and meshing force, the RMS value of acceleration and dynamic factor are also studied in this paper. The dynamic orbits of system are observed from the phase diagram, power spectrum, Lyapunov exponent and Poincare map. The figures clearly indicate that there are various forms of periodic and chaotic motions in different conditions. The simulation results show that with the increase of rotating speed, dynamic orbits transfer from periodic motion to chaotic motion in the cylinder discrete state

No-Regret Online Reinforcement Learning with Adversarial Losses and Transitions

Existing online learning algorithms for adversarial Markov Decision Processes achieve ${O}(\sqrt{T})$ regret after $T$ rounds of interactions even if the loss functions are chosen arbitrarily by an adversary, with the caveat that the transition function has to be fixed. This is because it has been shown that adversarial transition functions make no-regret learning impossible. Despite such impossibility results, in this work, we develop algorithms that can handle both adversarial losses and adversarial transitions, with regret increasing smoothly in the degree of maliciousness of the adversary. More concretely, we first propose an algorithm that enjoys $\widetilde{{O}}(\sqrt{T} + C^{\textsf{P}})$ regret where $C^{\textsf{P}}$ measures how adversarial the transition functions are and can be at most ${O}(T)$. While this algorithm itself requires knowledge of $C^{\textsf{P}}$, we further develop a black-box reduction approach that removes this requirement. Moreover, we also show that further refinements of the algorithm not only maintains the same regret bound, but also simultaneously adapts to easier environments (where losses are generated in a certain stochastically constrained manner as in Jin et al. [2021]) and achieves $\widetilde{{O}}(U + \sqrt{UC^{\textsf{L}}} + C^{\textsf{P}})$ regret, where $U$ is some standard gap-dependent coefficient and $C^{\textsf{L}}$ is the amount of corruption on losses.Comment: 66 page