Simulation Of Physical Systems Research Articles

Study on the flow characteristics of tight oil reservoirs with linear injection and production for volume-fractured horizontal wells

New technology for developing horizontal well linear injection and production can reduce the spacing and establish an effective replacement system. However, the seepage mechanism needs to be studied systematically. This study considers the outcrop cores in the Ordos Basin using a high-temperature, high-pressure large-scale physical simulation system. A corresponding plate model is used to research the development of volume-fractured horizontal wells with linear injection and production in tight oil reservoirs. The effects of different injection and production spacings and fracture lengths are analyzed and discussed. The results show that the water flooding oil displacement efficiencies of 50 × 50 × 3 and 50 × 40 × 3 cm3 models are different for the same 0.11 MPa/m injection pressure gradient. The latter model has the highest oil displacement efficiency of 32.91%, and its injection spacing is 18 cm. The injection water is first conducted symmetrically and uniformly along the two injection fractures to the single production fracture direction and advanced uniformly. The pressures at the two ends of the injection fracture are V-shaped and symmetrically distributed relative to the production fracture location. A longer fracture length corresponds to a higher oil displacement efficiency in the models with 36, 41, and 46 cm fracture lengths and the oil and water flow linearly. The seepage distance is smaller than the row distance of conventional well network injection and production wells, so the seepage resistance is significantly reduced, effectively increasing the oil-repelling range. The oil-repelling efficiency is at least 8% higher than the depletion development, demonstrating considerable application potential.

Microscopic Production Characteristics of Pore Crude Oil and Influencing Factors during Enhanced Oil Recovery by Air Injection in Shale Oil Reservoirs.

High-pressure air injection (HPAI) is one of the effective methods to improve shale oil recovery after the primary depletion process. However, the seepage mechanisms and microscopic production characteristics between air and crude oil are complicated in porous media during the air flooding process. In this paper, an online nuclear magnetic resonance (NMR) dynamic physical simulation method for enhanced oil recovery (EOR) by air injection in shale oil was established by combining high-temperature and high-pressure physical simulation systems with NMR. The microscopic production characteristics of air flooding were investigated by quantifying fluid saturation, recovery, and residual oil distribution in different sizes of pores, and the air displacement mechanism of shale oil was discussed. On this basis, the effects of air oxygen concentration, permeability, injection pressure, and fracture on recovery were studied, and the migration mode of crude oil in fractures was explored. The results show that the shale oil is mainly found in <0.1 μm (small pores), followed by 0.1-1 μm (medium pores), and 1-10 μm (macropores); thus, it is critical to enhancing oil recovery in pores less than 0.1 and 0.1-1 μm. The low-temperature oxidation (LTO) reaction can occur by injecting air into depleted shale reservoirs, which has a certain effect on oil expansion, viscosity reduction, and thermal mixing phases, thereby greatly improving shale oil recovery. There is a positive relationship between air oxygen concentration and oil recovery; the recoveries of small pores and macropores can increase by 3.53 and 4.28%, respectively, and they contribute 45.87-53.68% of the produced oil. High permeability means good pore-throat connectivity and greater oil recovery, and the production degree of crude oil in three types of pores can be increased by 10.36-24.69%. Appropriate injection pressure is beneficial to increasing the oil-gas contact time and delaying gas breakthrough, but high injection pressure will result in early gas channeling, which causes the crude oil in small pores to be difficult to produce. Notably, the matrix can supply oil to fractures due to the mass exchange between matrix fractures and the increase of the oil drainage area, and the recoveries of medium pores and macropores in fractured cores increased by 9.01 and 18.39%, respectively; fractures can act as bridges for matrix crude oil migration, which means that proper fracturing before gas injection can make the EOR better. This study provides a new idea and a theoretical basis for improving shale oil recovery and clarifies the microscopic production characteristics of shale reservoirs.

A Simplified GPU Implementation of the Hybrid Lattice Boltzmann Model for Three-Dimensional High Rayleigh Number Flows

This paper provides an analysis of the numerical performance of a hybrid computational fluid dynamics (CFD) solver for 3D natural convection. We propose to use the lattice Boltzmann equations with the two-relaxation time approximation for the fluid flow, whereas thermodynamics is described by the macroscopic energy equation with the finite difference solution. An in-house parallel graphics processing unit (GPU) code is written in MATLAB. The execution time of every single step of the algorithm is studied. It is found that the explicit finite difference scheme is not as stable as the implicit one for high Rayleigh numbers. The most time-consuming steps are energy and collide, while stream, boundary conditions, and macroscopic parameters recovery are executed in no time, despite the grid size under consideration. GPU code is more than 30 times faster than a typical low-end central processing unit-based code. The proposed hybrid model can be used for real-time simulation of physical systems under laminar flow behavior and on mid-range segment GPUs.

Variational quantum simulation of the critical Ising model with symmetry averaging

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)--style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. We propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

Physical Simulation Test of Underground Coal Gasification Cavity Evolution in the Horizontal Segment of U-Shaped Well

A key point in the underground coal gasification process is the cavity evolution in the horizontal segment. The morphological evolution law of the gasification cavity has not been clarified, which is the bottleneck restricting the analysis of its controllability. In this paper, a physical simulation system for cavity generation was developed, and the cavity evolution in a targeted coal seam with overburden pressure was duplicated in the laboratory. A set of temperature field synchronous monitoring devices was developed to realize temperature sampling within a cavity and the surrounding rock. By analyzing the relationship between the overall temperature distribution pattern and the gasification agent injection condition, the morphological propagation law of the cavity is verified to be water drop-shaped, and influencing factors including the injection flow rate and the gasification agent component ratio are investigated. The axial length and volume of the cavity increase with an increasing injection flow rate. Higher oxygen content results in increased size in all dimensions. The research results provide theoretical support and reference for applying controlled cavity formation in the horizontal segment of U-shaped wells.

The bonded area growth at diffusion bonding determined by the numerical modelling of Gleeble 3800 experiments and in comparison with the model by Hill and Wallach

Diffusion bonding or HIP (Hot Isostatic Pressing) is a common manufacturing process at several ITER components inside the Vacuum Vessel. The key uncertainties during diffusion bonding the unknown surface contaminations, remaining oxides. Diffusion bonding and reference tests were performed on specimens made from 316 L on a Gleeble 3800 Thermal-Mechanical Physical Simulation System. The contact electrical resistance on the bonded surfaces were determined in earlier work, where the decreasing contact electrical resistance predicted the increasing contact area during the welding process. Using the theoretical calculations to the electrical resistance of the surface asperities modelled by Zhang the bonded area growth was determined in time for the cylindrical specimens.A theoretical diffusion bonded model by Hill and Wallach was used to calculate the bonded area grow (a/b) on the examined tests. The results were comparable between the growth rate of (A/B) estimated by the Zhang model and bonded area growth by Hill and Wallach (a/b). The theoretical diffusion bonding models show much faster bonding, where the ideal model and the presumable re-oxidation of the surface gave the explanation to this phenomenon.

Design of a delay dependent wide area damping controller using Cyber–Physical Power System architecture

With the wide application of information and communication technology in power grids, the integration and collaboration of information networks and physical grid have become an essential feature of the smart grid, constituting a typical power information physical system. However, information network has communication time delay problem, which tends to deteriorate the system controller performance or even counter-tune, leading to system instability. To effectively reduce low-frequency oscillation of the wide-area interconnected grid and overcome the negative impact of the communication network’s delay problem, a delay dependent Wide Area Damping Controller(WADC) is designed using cyber–physical power system architecture. First, we establish a power information physical system simulation platform based on Object linking and embedding for Process Control (OPC) technology, implementing the joint operation of the power system simulation tool DIgSILENT and the information system simulation tool MATLAB. The platform can effectively respond to the system’s information system and physical system interaction effects during simulation. Then, to compensate for communication delays in the power information physical system, this paper proposes a new WADC strategy that uses a Model Predictive Controller (MPC) to generate control commands and an adaptive time delay compensator to compensate for the random communication delays generated in the communication. Finally, a case study with various uncertainties and through the interference test of a four-machine system and 39 bus system, the proposed control strategy is proposed.

Modelling and Simulation of Physical Systems with Dynamically Changing Degrees of Freedom

A new approach is introduced to model and simulate equation-based systems where variables can appear and disappear during simulation without re-generation and re-compilation of code when the numbers of equations and states change during events. The method is presented in a generic, mathematical way and can be in principle applied to all types of declarative, equation-based modelling languages, such as Modelica. A concrete implementation is given for the Julia-based experimental modelling language Modia, which is similar to Modelica. However, Modia features far simpler semantics based on hierarchical collections of name/value pairs and has the ability to support domain-specific algorithms, especially for multibody systems with collision handling. The new method is demonstrated with heat-transfer in a rod, separation of stages of a rocket and gripping operations of a robot.

Evaluation of finite difference based asynchronous partial differential equations solver for reacting flows

Next-generation exascale machines with extreme levels of parallelism will provide massive computing resources for large scale numerical simulations of complex physical systems at unprecedented parameter ranges. However, novel numerical methods, scalable algorithms and re-design of current state-of-the art numerical solvers are required for scaling to these machines with minimal overheads. One such approach for partial differential equations based solvers involves computation of spatial derivatives with possibly delayed or asynchronous data using high-order asynchrony-tolerant (AT) schemes to facilitate mitigation of communication and synchronization bottlenecks without affecting the numerical accuracy. In the present study, an effective methodology of implementing temporal discretization using a multi-stage Runge-Kutta method with AT schemes is presented. Together these schemes are used to perform asynchronous simulations of canonical reacting flow problems, demonstrated in one-dimension including auto-ignition of a premixture, premixed flame propagation and non-premixed autoignition. Simulation results show that the AT schemes incur very small numerical errors in all key quantities of interest including stiff intermediate species despite delayed data at processing element (PE) boundaries. For simulations of supersonic flows, the degraded numerical accuracy of well-known shock-resolving WENO (weighted essentially non-oscillatory) schemes when used with relaxed synchronization is also discussed. To overcome this loss of accuracy, high-order AT-WENO schemes are derived and tested on linear and non-linear equations. Finally the novel AT-WENO schemes are demonstrated in the propagation of a detonation wave with delays at PE boundaries.

Characterization of multimode linear optical networks

Multi-mode optical interferometers represent the most viable platforms for the successful implementation of several quantum information schemes that take advantage of optical processing. Examples range from quantum communication, sensing and computation, including optical neural networks, optical reservoir computing or simulation of complex physical systems. The realization of such routines requires high levels of control and tunability of the parameters that define the operations carried out by the device. This requirement becomes particularly crucial in light of recent technological improvements in integrated photonic technologies, which enable the implementation of progressively larger circuits embedding a considerable amount of tunable parameters. In this work, we formulate efficient procedures for the characterization of optical circuits in the presence of imperfections that typically occur in physical experiments, such as unbalanced losses and phase instabilities in the input and output collection stages. The algorithm aims at reconstructing the transfer matrix that represents the optical interferometer without making any strong assumptions about its internal structure and encoding. We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit, and we demonstrate the effectiveness and robustness of our method. Our findings can find application in a wide range of optical setups, based both on bulk and integrated configurations.

TBPLaS: A tight-binding package for large-scale simulation

TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance-critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalization and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of the time-dependent Schrödinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, optical, plasmon and transport properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculations of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily due to the extensible and modular nature of the code. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications. Program summaryProgram Title:TBPLaSCPC Library link to program files:https://doi.org/10.17632/5cjmzj9jwm.1Code Ocean capsule:https://codeocean.com/capsule/8296734Licensing provisions: BSD 3-clauseProgramming language: Python, Cython, FortranNature of problem: The TB method is a powerful tool for investigating the electronic structures and many other properties in condensed matter physics. However, the costs of memory and CPU time using standard diagonalization-based TB techniques scale as O(N2) and O(N3) with the model size N, severely limiting their applications in large complex systems. The development of numerical methods to solve TB problems without any diagonalization, as well as the implementation of these techniques in software packages, are urgently needed to promote the capability of TB methods to large systems with billions of atoms.Solution method: We proposed the TBPM and implemented it in the TBPLaS package. In TBPM, the electronic properties of a TB model are calculated numerically by solving the time-dependent Schrödinger equation without any diagonalization, which ensures a linear scaling of both memory and CPU costs with the system size. Models with billions of atoms (orbitals) can be easily handled, with many properties available from the evolution of time-dependent correlation functions. The TBPLaS package features an intuitive object-oriented Python API and Cython/Fortran extensions for performance-critical parts, ensuring both flexibility and efficiency. Moreover, TBPLaS implements hybrid MPI+OpenMP parallelism, which can exploit the modern hardware of high-performance computers.

Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

The computerized simulations of physical and socio-economic systems have proliferated in the past decade, at the same time, the capability to develop high-fidelity system predictive models is of growing importance for a multitude of reliability and system safety applications. Traditionally, methodologies for predictive modeling generally fall into two different categories, namely physics-based approaches and machine learning-based approaches. There is a growing consensus that the modeling of complex engineering systems requires novel hybrid methodologies that effectively integrate physics-based modeling with machine learning approaches, referred to as physics-informed machine learning (PIML). Developing advanced PIML techniques is recognized as an important emerging area of research, which could be particularly beneficial in addressing reliability and system safety challenges. With this motivation, this paper provides a review of the state-of-the-art of physics-informed machine learning methods in reliability and system safety applications. The paper highlights different efforts towards aggregating physical information and data-driven models as grouped according to their similarity and application area within each group. The goal is to provide a collection of research articles presenting recent developments of this emergent topic, and shed light on the challenges and future directions which we, as a research community, should focus on for harnessing the full potential of advanced PIML techniques for reliability and safety applications.

Numerical and physical simulations of array laterolog in deviated anisotropic formation

Due to the tremendous amount of high-resolution measurement information, array laterolog is widely used in evaluations of deviated anisotropic reservoirs. However, the precision of a complementary numerical simulation should be improved as high as the core of fine-scale reservoir evaluation. Therefore, the 3D finite element method (3D-FEM) is presented to simulate the array laterolog responses. Notably, a downscaled physical simulation system is introduced to validate and calibrate the precision of the 3D-FEM. First, the size of the downscaled system is determined by COMSOL . Then, the surrounding and investigated beds are represented by a sodium chloride solution and planks soaked in solution, respectively. Finally, a half-space measurement scheme is presented to improve the experimental efficiency. Moreover, the corresponding sensitivity function and separation factor are established to analyze the effects of the formation anisotropy and dipping angle on the array laterolog responses. The numerical and experimental results indicate that the half-space method is practical, and the mean relative error between the numerical and experimental results is less than 5%, which indicates that the numerical simulation is accurate. With the proposed approach, the reversal angle of array laterolog response curves in anisotropic formations can be observed, and this range is determined to be 50°–62°.

Development and Performance Verification of Frequency Control Algorithm and Hardware Controller Using Real-Time Cyber Physical System Simulator

Frequency stability is a critical factor in maintaining the quality of the power grid system. A battery energy storage system (BESS) with quick response and flexibility has recently been used as a primary frequency control (PFC) resource, and many studies on its control algorithms have been conducted. The cyber physic system (CPS) simulator, which can perform virtual physical modelling and verification of many hardware systems connected to the network, is an optimal solution for the performance verification of control algorithms and hardware systems. This study introduces a large-scale real-time dynamic simulator that includes the national power system. This simulator comprises a power grid model, an energy management system (EMS) model, a BESS system model, and a communication model. It performs the control algorithm performance evaluation and the hardware controller’s response performance evaluation. The performance of the control algorithm was evaluated by tracking the power system’s characteristic trajectory in the transient state based on the physical response delay time between the output instruction of the frequency regulation controller (FRC), a hardware controller, and the output response of the BESS. Based on this, we examined the response performance evaluation results by linking them to the optimally designed actual FRC. As a result, we present an analysis of the BESS’s characteristic trajectories in the transient state, such as frequency, power system inertia, and power grid constant, and provide FRC response performance evaluation results at a level of 163 ms, by connecting the BESS installed at the actual site with the CPS simulator.

Physical simulation experiment method of water breakthrough mechanism and plugging effect of horizontal well in tight reservoir

Horizontal wells have become a key technology for the economical and effective development of tight reservoirs. In production, water cut increases rapidly, and even waterflooding is prominent. The mechanism of water breakthrough and the analysis of the water plugging effect are still tricky problems worldwide. Based on the reservoir characteristics of the typical tight oil area in Changqing oilfield, a physical simulation method of water breakthrough and plugging effect of segmented fractured horizontal wells was established based on the parallel connection of double cores and large-scale physical simulation system, and the mechanism of water breakthrough and water plugging effect of horizontal wells were studied. The results show that natural fractures or dynamic fractures are the main factors affecting the characteristics of water breakthrough. The end and waist of staged fractured horizontal wells are the main parts of water breakthrough. Water breakthrough points gradually increase in production, showing multi-direction. The overall water cut decreases after the water-prone area is plugged. Specifically, in this study, water plugging was implemented when the water cut of the horizontal well rose to 91%, and the water cut of a breakthrough part was reduced to about 60% after plugging, which played a good role in controlling the overall water cut. Oil displacement efficiency increased from 32.5% before plugging to 36% after plugging, rising by 4.5 percent. In addition, the duration of oil production increase in horizontal well after water plugging is not long, mainly because of the formation of water injection dominant channel between the injection well and horizontal well fracture before plugging. At this time, the resistance primarily exists in the water-affected area, and the Jamin effect caused by imbibition is the main factor of resistance; then, the swept volume is difficult to expand.

Adaptive fuzzy finite‐time fault‐tolerant control design for non‐linear systems under sensor faults

In this article, an adaptive fuzzy finite-time fault-tolerant control (FTC) scheme for uncertain non-linear systems under sensor faults is proposed. Compared with the existing methods, the considered system contains unknown time-varying fault parameters, uncertain non-linear functions, and can guarantee the performance of the system in finite time. The coupling between fault parameters and actual states is solved by the fault parameters separation method. The fuzzy logic system (FLS) is used to approximate the unknown functions, and combining the backstepping technology an adaptive fault-tolerant controller is designed. The finite-time stability of the closed-loop system is proved by the Lyapunov theory. At last, the numerical simulation and the real physical system simulation verified the effectiveness of the proposed scheme.

A Tensor Network based Decision Diagram for Representation of Quantum Circuits

Tensor networks have been successfully applied in simulation of quantum physical systems for decades. Recently, they have also been employed in classical simulation of quantum computing, in particular, random quantum circuits. This article proposes a decision diagram style data structure, called Tensor Decision Diagram (TDD), for more principled and convenient applications of tensor networks. This new data structure provides a compact and canonical representation for quantum circuits. By exploiting circuit partition, the TDD of a quantum circuit can be computed efficiently. Furthermore, we show that the operations of tensor networks essential in their applications (e.g., addition and contraction) can also be implemented efficiently in TDDs. A proof-of-concept implementation of TDDs is presented and its efficiency is evaluated on a set of benchmark quantum circuits. It is expected that TDDs will play an important role in various design automation tasks related to quantum circuits, including but not limited to equivalence checking, error detection, synthesis, simulation, and verification.

Dominant motion identification of multi-particle system using deep learning from video

Identifying underlying governing equations and relevant information from high-dimensional observable data has always been a challenge in physical sciences. With the recent advances in sensing technology and available datasets, various machine learning techniques have made it possible to distill underlying mathematical models from sufficiently clean and usable datasets. However, most of these techniques rely on prior knowledge of the system and noise free data obtained by simulation of physical system or by direct measurements of the signals. Hence, the inference obtained by using these techniques is often unreliable to be used in the real world where observed data are noisy and require feature engineering to extract relevant features. In this work, we provide a deep-learning framework that extracts relevant information from real-world videos of highly stochastic systems, with no prior knowledge and distills the underlying governing equation representing the system. We demonstrate this approach on videos of confined multi-agent/particle systems of ants, termites, fishes as well as a simulated confined multi-particle system with elastic collision interactions. Furthermore, we explore how these seemingly diverse systems have predictable underlying behavior. In this study, we have used motion tracking to extract spatial trajectories of individual agents/particles in a system, and by using LSTM VAE we projected these features on to a low-dimensional latent space from which the underlying differential equation representing the data was extracted using SINDy framework.

Practical Prescribed-Time Sampled-Data Control of Linear Systems With Applications to the Air-Bearing Testbed

This article studies the practical prescribed time sampled-data control problem for input-constrained linear time invariant continuous time systems. By using the solution to a parametric Lyapunov equation, a bounded linear time-varying (LTV) sampled-data controller is designed to solve such a problem. In addition, stability analysis and performance analysis of the closed-loop sampled-data control system are presented by using the methodology of scalarization. Finally, the developed LTV sampled-data state controller is utilized to the whole physical simulation system of air-bearing testbed and experimental results verify the effectiveness of the proposed approach.

Modeling and Simulation of Robotic Grasping in Simulink Through Simscape Multibody.

Grasping and dexterous manipulation remain fundamental challenges in robotics, above all when performed with multifingered robotic hands. Having simulation tools to design and test grasp and manipulation control strategies is paramount to get functional robotic manipulation systems. In this paper, we present a framework for modeling and simulating grasps in the Simulink environment, by connecting SynGrasp, a well established MATLAB toolbox for grasp simulation and analysis, and Simscape Multibody, a Simulink Library allowing the simulation of physical systems. The proposed approach can be used to simulate the grasp dynamics in Simscape, and then analyse the obtained grasps in SynGrasp. The devised functions and blocks can be easily customized to simulate different hands and objects.