Nonlinear Moving Boundary Model of Low-Permeability Reservoir

At present, there are two main methods for solving oil and gas seepage equations: analytical and numerical methods. In most cases, it is difficult to find the analytical solution, and the numerical solution process is complex with limited accuracy. Based on the mass conservation equation and the steady-state sequential substitution method, the moving boundary nonlinear equations of radial flow under different outer boundary conditions are derived. The quasi-Newton method is used to solve the nonlinear equations. The solutions of the nonlinear equations with an infinite outer boundary, constant pressure outer boundary and closed outer boundary are compared with the analytical solutions. The calculation results show that it is reliable to solve the oil-gas seepage equation with the moving boundary nonlinear equation. To deal with the difficulty in solving analytical solutions for low-permeability reservoirs and numerical solutions of moving boundaries, a quasi-linear model and a nonlinear moving boundary model were proposed based on the characteristics of low-permeability reservoirs. The production decline curve chart of the quasi-linear model and the recovery factor calculation chart were drawn, and the sweep radius calculation formula was also established. The research results can provide a theoretical reference for the policy-making of development technology in low-permeability reservoirs.

Optimal Scheduling Strategy for Real-time Charging of Electric Vehicles Based on Deep Learning

In recent years, the automotive industry has informed the development situation. Under the problems related to automobile exhaust emissions and serious air pollution and energy shortages, new electric vehicles have sufficient advantages due to their low emissions, high energy efficiency, and low noise. It has been recognized by the people and governments of all countries. The research purpose of this paper is to meet the electricity demand of users, adopt the boundary model of charging and discharging energy and fully adopt deep learning to optimize the real-time charging optimization scheduling strategy of electric vehicles. In order to meet the electricity demand of users, the charge-discharge energy boundary model is used to characterize the charge-discharge behavior of electric vehicles. After the day-ahead training and parameter saving of the proposed model, according to the real-time state of system operation at each moment of the day, the charge-discharge scheduling strategy at that moment is generated. It is verified that the proposed charging scheduling method based on deep reinforcement learning can effectively reduce the power fluctuations in the microgrid and reduce the daily charge and discharge costs on the premise of meeting the charging needs of users; during the development of electric vehicles, different electronic components, especially the power consumption of electric motors, must be faced. A deep learning algorithm based on an improved recurrent neural network (MRNN) is proposed. The system is modeled according to different data and parameters inside the vehicle, and the network is modeled by the MRNN deep learning algorithm. Carry on training, predict the power demand and provide the best power, so as to expand the mileage, better optimize the power distribution of the motor, and compare the improved models. Experimental research results show that the efficiency of the related scheduling strategy model is increased by about 37.2% compared with the traditional model. The proposed method is fast in calculation and does not require iterative calculation, which fully meets the needs of real-time scheduling.

Travelling wave and asymptotic analysis of a multiphase moving boundary model for engineered tissue growth

We derive a multiphase, moving boundary model to represent the development of tissue in vitro in a porous tissue engineering scaffold. We consider a cell, extra-cellular liquid and a rigid scaffold phase, and adopt Darcy's law to relate the velocity of the cell and liquid phases to their respective pressures. Cell-cell and cell-scaffold interactions which can drive cellular motion are accounted for by utilising relevant constitutive assumptions for the pressure in the cell phase. We reduce the model to a nonlinear reaction-diffusion equation for the cell phase, coupled to a moving boundary condition for the tissue edge, the diffusivity being dependent on the cell and scaffold volume fractions, cell and liquid viscosities, and parameters that relate to cellular motion. Numerical simulations reveal that the reduced model admits three regimes for the evolution of the tissue edge at large-time: linear, logarithmic and stationary. Employing travelling wave and asymptotic analysis, we characterise these regimes in terms of parameters related to cellular production and motion. The results of our investigation allow us to suggest optimal values for the governing parameters, so as to stimulate tissue growth in an engineering scaffold.

New capabilities in CREATE™-AV Helios Version 11

CREATE™-AV Helios is a high-fidelity coupled CFD/CSD infrastructure developed by the U.S. Dept. of Defense for aeromechanics predictions of rotorcraft. This paper discusses new capabilities added to Helios version 11.0. A new fast-running reduced order aerodynamics option called ROAM has been added to enable faster-turnaround analysis. ROAM is Cartesian-based, employing an actuator line model for the rotor and an immersed boundary model for the fuselage. No near-body grid generation is required and simulations are significantly faster through a combination of larger timesteps and reduced cost per step. ROAM calculations of the JVX tiltrotor configuration give a comparably accurate download prediction to traditional body-fitted calculations with Helios, at 50X less computational cost. The unsteady wake in ROAM is not as well resolved, but wake interactions may be a less critical issue for many design considerations. The second capability discussed is the addition of six-degree-of-freedom capability to model store separation. Helios calculations of a generic wing/store/pylon case with the new 6-DOF capability are found to match identically to calculations with CREATE™-AV Kestrel, a code which has been extensively validated for store separation calculations over the past decade.

Time-dependent quantum correlations in two-dimensional expanding spacetime

In expanding universes, the entanglement entropy must be time-dependent because the background geometry changes with time. For understanding time evolution of quantum correlations, we take into account two distinct holographic models, the dS boundary model and the braneworld model. In this work, we focus on two-dimensional expanding universes for analytic calculation and comparison. Although two holographic models realize expanding universes in totally different ways, we show that they result in the qualitatively same time-dependence for eternal inflation. We further investigate the time-dependent correlations in the radiation-dominated era of the braneworld model. Intriguingly, the holographic result reveals that a thermal system in the expanding universe is dethermalized after a critical time characterized by the subsystem size.