Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws

Gas Flow Model of Adhesion Sand Casing Well First Interface Micro Clearance and Application

In order to accurately describe the flow characteristics of the gas channeling in the adhesion sand casing well first interface, this article assumes the first interface annulus composed by adhesion sand casing and cement mantle is a microscopic rough gas flow channel. Using the lattice Boltzmann method, the flow model of gas channeling in first interface rough microscopic channel is established, the fundamental relationship of gas flow rate, gas flow pressure distribution and gas annulus pressure differential are calculated and analyzed. And the flow characteristics of gas under different adhesion sand density is simulated. The results show that the theoretical calculation and the numerical simulation results are in good agreement, the new calculation model reveals the essential rule of cementing gas channeling flow in adhesion sand casing well, which provides a new method for the gas channeling problem in process of subsequent oil well cementing.Key words: Adhesion sand casing well; Rough surface; Cementing first interface; Lattice Boltzmann; Gas channelin

Design of Real-Time Hardware-in-the-Loop TV Guidance System Simulation Platform

This paper presents a novel design of a real-time hardware-in-the-loop (HIL) missile TV guidance system simulation platform, which consists of a development computer, a target computer, a turntable, a control cabin, and a joystick. The guidance system simulation model is created on the development computer by Simulink® and then downloaded to the target computer. Afterwards, Simulink Real-Time™ runs the model in real-time. Meanwhile, the target computer uploads the real-time simulation data back to the development computer. The hardware in the simulation loop is TV camera, encoders, control cabin, servomotors, and target simulator. In terms of hardware and software, the system has been simplified compared with the existing works. The volume of the turntable integrating the target simulator and the seeker simulator is about 0.036 cubic meters compared to the original 8 cubic meters, so it has a compact structure. The platform can perform the closed-loop control, so the simulation has high precision. Taking the TV guidance simulation as an example, in the case of target maneuvering, the final miss distance of the TV guidance missile is 0.11812 m, while the miss distance of the original system is 13 m. The trajectories obtained from the HIL and mathematical simulations substantially coincide. So the simulation results show that the proposed HIL simulation platform is effective

Research and Field Application of Casing Return Well Treatment Technology in Fuyu Oil Field

At present, due to years of development and well pattern adjustment, there are some problems in most oil fields such as close well spacing, dense well pattern, too many oil-water wells, and changeable well conditions. In the process of overhaul, casings in some well are seriously broken, and spit mudstone is serious inside and outside the casing, so the traditional technologies such as overhaul and drawing casing, sweeping plug cannot achieve effective management purposes, and the problem of casing breaking in oil-water wells is becoming increasingly serious, which has become an unfavorable factors restricting the stable productions and efficient development of oil field. Through careful analysis of casing leap mechanism, the casing leap treatment technology has been formed in the process of continuous exploration and summary, which provides some experience and guidance for the future casing leap treatment

Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach

For cyber-physical systems in the 6G era, semantic communications connecting distributed devices for dynamic control and remote state estimation are required to guarantee application-level performance, not merely focus on communication-centric performance. Semantics here is a measure of the usefulness of information transmissions. Semantic-aware transmission scheduling of a large system often involves a large decision-making space, and the optimal policy cannot be obtained by existing algorithms effectively. In this paper, we first investigate the fundamental properties of the optimal semantic-aware scheduling policy and then develop advanced deep reinforcement learning (DRL) algorithms by leveraging the theoretical guidelines. Our numerical results show that the proposed algorithms can substantially reduce training time and enhance training performance compared to benchmark algorithms.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

The Model for Calculating Pore Evaluation of Fractal Rock Body Under Hydraulic Fracturing

In general, the pore medium of rock has fractal characters. In order to calculate the change regulations of porosity and evaluation character of the rock under hydraulic fracturing accurately, in this paper, a new damage variable was defined to describe the change of porosity. The model for calculating pore evaluation of the fractal fracturing rock body was established according to the principle of conservation of energy, considering the strain energy, the cracks propagation energy and the gravitational potential energy of fracturing fluid in the process of fracturing. The change regulations of fracturing parameters were calculated combining with the reservoir pore characters of Xinmin region. The change characters of the porosity were simulated by ANSYS, and the rationality and advancement of the new model was determined by deliverability analysis.Key words: Fractal rock body; Pore characters; Deliverability calculation; Hydraulic fracturin

The Study of Rock Body Damage Constitutive Model on Refracturing

In order to characterize the mechanical behavior of rock body damage evaluation and forming multiple fractures, in this paper in multiple fracturing , we have established rock body damage evaluation constitutive model, and given the point that the rock can bear secondary damage in multiple fracturing. Established the secondary damage evaluation model, and obtained the method for calculating the parameter of the crack in multiple fracturing. We have verified the model by a oil well in Jilin oilfield, the result has well anastomosis with the actual engineering.Key words: Multiple fracturing; Damage evaluation; Secondary damag

Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, by leveraging the theoretical results of structural properties of optimal scheduling policies, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of a multi-sensor remote estimation system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalty to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical results show that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25%, when compared to benchmark DRL algorithms.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Optimal terminal guidance for exoatmospheric interception

AbstractIn this study, two optimal terminal guidance (OTG) laws, one of which takes into account the final velocity vector constraint, are developed for exoatmospheric interception using optimal control theory. In exoatmospheric interception, because the proposed guidance laws give full consideration to the effect of gravity, they consume much less fuel than the traditional guidance laws while requiring a light computational load. In the development of the guidance laws, a unified optimal guidance problem is put forward, where the final velocity vector constraint can be considered or neglected by properly adjusting a parameter in the cost function. To make this problem analytically solvable, a linear model is used to approximate the gravity difference, the difference of the gravitational accelerations of the target and interceptor. Additionally, an example is provided to show that some achievements of this study can be used to significantly improve the fuel efficiency of the pulsed guidance employed by the interceptor whose divert thrust level is fixed

Structure-Enhanced DRL for Optimal Transmission Scheduling

Remote state estimation of large-scale distributed dynamic processes plays an important role in Industry 4.0 applications. In this paper, we focus on the transmission scheduling problem of a remote estimation system. First, we derive some structural properties of the optimal sensor scheduling policy over fading channels. Then, building on these theoretical guidelines, we develop a structure-enhanced deep reinforcement learning (DRL) framework for optimal scheduling of the system to achieve the minimum overall estimation mean-square error (MSE). In particular, we propose a structure-enhanced action selection method, which tends to select actions that obey the policy structure. This explores the action space more effectively and enhances the learning efficiency of DRL agents. Furthermore, we introduce a structure-enhanced loss function to add penalties to actions that do not follow the policy structure. The new loss function guides the DRL to converge to the optimal policy structure quickly. Our numerical experiments illustrate that the proposed structure-enhanced DRL algorithms can save the training time by 50% and reduce the remote estimation MSE by 10% to 25% when compared to benchmark DRL algorithms. In addition, we show that the derived structural properties exist in a wide range of dynamic scheduling problems that go beyond remote state estimation.Comment: Paper submitted to IEEE. Copyright may be transferred without notice, after which this version may no longer be accessible. arXiv admin note: substantial text overlap with arXiv:2211.1082