Simulation Of Physical Systems Research Articles

More quantum chemistry with fewer qubits

Quantum computation is one of the most promising new paradigms for the simulation of physical systems composed of electrons and atomic nuclei, with applications in chemistry, solid-state physics, materials science, and molecular biology. This requires a truncated representation of the electronic structure Hamiltonian using a finite number of orbitals. While it is, in principle, obvious how to improve on the representation by including more orbitals, this is usually unfeasible in practice (e.g., because of the limited number of qubits available) and severely compromises the accuracy of the obtained results. Here, we propose a quantum algorithm that improves on the representation of the physical problem by virtue of second-order perturbation theory. In particular, our quantum algorithm evaluates the second-order energy correction through a series of time-evolution steps under the unperturbed Hamiltonian. An important application is to go beyond the active-space approximation, allowing to include corrections of virtual orbitals, known as multireference perturbation theory. Here, we exploit the fact that the unperturbed Hamiltonian is diagonal for virtual orbitals and show that the number of qubits is independent of the number of virtual orbitals. This gives rise to more accurate energy estimates without increasing the number of qubits. Moreover, we demonstrate numerically for realistic chemical systems that the total runtime has highly favorable scaling in the number of virtual orbitals compared to previous studies. Numerical calculations confirm the necessity of the multireference perturbation theory energy corrections to reach accurate ground-state energy estimates. Our perturbation theory quantum algorithm can also be applied to symmetry-adapted perturbation theory. As such, we demonstrate that perturbation theory can help to reduce the quantum hardware requirements for quantum chemistry. Published by the American Physical Society 2024

Programmable silicon-photonic quantum simulator based on a linear combination of unitaries

Simulating the dynamic evolution of physical and molecular systems in a quantum computer is of fundamental interest in many applications. The implementation of dynamics simulation requires efficient quantum algorithms. The Lie-Trotter-Suzuki approximation algorithm, also known as the Trotterization, is basic in Hamiltonian dynamics simulation. A multi-product algorithm that is a linear combination of multiple Trotterizations has been proposed to improve the approximation accuracy. However, implementing such multi-product Trotterization in quantum computers remains challenging due to the requirements of highly controllable and precise quantum entangling operations with high success probability. Here, we report a programmable integrated-photonic quantum simulator based on a linear combination of unitaries, which can be tailored for implementing the linearly combined multiple Trotterizations, and on the simulator we benchmark quantum simulation of Hamiltonian dynamics. We modify the multi-product algorithm by integrating it with oblivious amplitude amplification to simultaneously reach high simulation precision and high success probability. The quantum simulator is devised and fabricated on a large-scale silicon-photonic quantum chip, which allows the initialization, manipulation, and measurement of arbitrary four-qubit states and linearly combined unitary gates. As an example, the quantum simulator is reprogrammed to emulate the dynamics of an electron spin and nuclear spin coupled system. This work promises the practical dynamics simulations of real-world physical and molecular systems in future large-scale quantum computers.

Influence of Spatially Varying Boundary Conditions Based on Material Heterogeneity

When conducting numerical analyses, boundary conditions are generally applied homogeneously, neglecting the inherent heterogeneity of the material being represented. Whilst the heterogeneity is often considered within the medium, its influence on the response at the boundary should also be accounted for. In this study, A novel approach to applying heterogeneous boundary conditions in the simulation of physical systems is presented, particularly focusing on moisture transport in unsaturated soils. The proposed method divides the surface into blocks or “macro-elements” and scales the boundary conditions based on the variation of material properties within these blocks. The principle of using overlapping kernel functions allows local effects to be considered, impacting neighbouring regions. To demonstrate the efficacy of the approach, a set of analyses were conducted that considered infiltration into a body of unsaturated soil, with various configurations of material properties and boundary conditions. The numerical simulations indicate that the application of scaled boundary conditions leads to a more natural and realistic response in the system. The applied method is independent on the numerical techniques employed in the simulation process, making it adaptable to existing computational codes, offering flexibility in capturing complex behaviours, and providing insights into how heterogeneity influences the system’s overall response.

Large-scale physical simulation of injection and production of hot dry rock in Gonghe Basin, Qinghai Province, China

Based on the independently developed true triaxial multi-physical field large-scale physical simulation system of in-situ injection and production, we conducted physical simulation of long-term multi-well injection and production in the hot dry rocks of the Gonghe Basin, Qinghai Province, NW China. Through multi-well connectivity experiments, the spatial distribution characteristics of the natural fracture system in the rock samples and the connectivity between fracture and wellbore were clarified. The injection and production wells were selected to conduct the experiments, namely one injection well and two production wells, one injection well and one production well. The variation of several physical parameters in the production well was analyzed, such as flow rate, temperature, heat recovery rate and fluid recovery. The results show that under the combination of thermal shock and injection pressure, the fracture conductivity was enhanced, and the production temperature showed a downward trend. The larger the flow rate, the faster the decrease. When the local closed area of the fracture was gradually activated, new heat transfer areas were generated, resulting in a lower rate of increase or decrease in the mining temperature. The heat recovery rate was mainly controlled by the extraction flow rate and the temperature difference between injection and production fluid. As the conductivity of the leak-off channel increased, the fluid recovery of the production well rapidly decreased. The influence mechanisms of dominant channels and fluid leak-off on thermal recovery performance are different. The former limits the heat exchange area, while the latter affects the flow rate of the produced fluid. Both of them are important factors affecting the long-term and efficient development of hot dry rock.

Research on the operation and quality control of small rock hole shotcrete robot

A small rock shotcrete robot is applied in the deep tunnel dynamic disaster physical simulation system in order to simulate the final operation process of the tunnel project. The robot’s spraying operation and control include many kind of factors. In this paper, the main parameters affecting the spraying operation are analyzed by establishing the spraying model and coating growth model. For matching reasonably the spraying parameters, the matching equation of spraying quality and spraying gun parameter model and spraying feed speed has been derived. Based on the matching equation, the spraying gun structure and robot control system are designed, and the spraying distance and spraying gun movement velocity including horizontal and vertical driving have been controlled better. On the basis of theoretical analysis, a small rock hole shotcrete robot prototype has been developed and the spraying experiments have been finished. The experimental results showed that the model and equation established can guided the spraying quality control. With the increase of the injection elevation angle, the length of the long and short axes of the elliptic coating becomes larger, and the average thickness of the coating center is between 2.23 and 2.34 mm, which meets the design index of the injection robot. The rock hole shotcrete robot meet the design requirements.

Influence of power dissipation value and deformation activation energy on recrystallization in compression deformation behavior of Mg-Li-Zn-Y alloy

The thermal compression deformation behavior of Mg-6Li-11Zn-2.5Y alloy was systematically investigated by using the isothermal hot compression tests using the Gleeble-3500 thermal mechanical physical simulation system with the conditions of the temperature of 513–593 K and the strain rate of 0.01–10 s−1. The constitutive model and thermal processing map were established. The variation pattern of the unstable region under different strains was analyzed and the safe processing region was determined. The influence of different power dissipation values and deformation activation energies on the dynamic recrystallization mechanism was clarified. The results demonstrate that the deformation activation energy is 156.08 kJ/mol. The unstable region gradually expands from the low temperature and low strain region to the high temperature and high strain region with the strain increasing. In the unstable region, adiabatic shear band pear in α-Mg phase, of which the main reason is the poor processing formability in the unstable region. The dominant nucleation mechanism is discontinuous dynamic recrystallization under the conditions of high power dissipation and low deformation activation energy. While, under the conditions of low power dissipation and high deformation activation energy, the nucleation mechanism is mainly driven by the particle stimulated nucleation.

Programmable photonic system for quantum simulation in arbitrary topologies

Synthetic dimensions have generated great interest for studying many types of topological, quantum, and many-body physics, and they offer a flexible platform for simulation of interesting physical systems, especially in high dimensions. In this paper, we describe a programmable photonic device capable of emulating the dynamics of a broad class of Hamiltonians in lattices with arbitrary topologies and dimensions. We derive a correspondence between the physics of the device and the Hamiltonians of interest, and we simulate the physics of the device to observe a wide variety of physical phenomena, including chiral states in a Hall ladder, effective gauge potentials, and oscillations in high-dimensional lattices. Our proposed device opens new possibilities for studying topological and many-body physics in near-term experimental platforms.

A flexible compensation device for single-phase-to-ground fault in distribution networks based on double-loop passivity-based control

Aiming at the problem that the existing single-phase-to-ground fault flexible compensation device has low compensation accuracy, long response time, and needs DC side power supply, a flexible compensation device based on double-loop passivity-based control is proposed. Firstly, the topology and working principle of the device are analyzed. And a compensation current distribution method of the bridge arm is proposed. Secondly, a double-loop passivity-based controller is designed. Finally, the proposed method is verified by MATLAB/Simulink software and 10 kV physical simulation system of distribution network. The comparisons between double-loop passivity-based control, passivity-based control, and proportional integration control is discussed. The result shows that the proposed method can accurately and rapidly compensate the fault current.

Simulating dirty bosons on a quantum computer

Quantum computers hold the potential to unlock new discoveries in complex quantum systems by enabling the simulation of physical systems that have heretofore been impossible to implement on classical computers due to intractability. A system of particular interest is that of dirty bosons, whose physics highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one and two dimensions. Specifically, we explore the disorder-induced delocalized-to-localized transition using adiabatic state preparation. In one dimension, the quantum circuits can be compressed to small enough depths for execution on currently available quantum computers. In two dimensions, the compression scheme is no longer applicable, thereby requiring the use of large-scale classical state vector simulations to emulate quantum computer performance. In addition, simulating interacting bosons via emulation of a noisy quantum computer allowed us to study the effect of quantum hardware noise on the physical properties of the simulated system. Our results suggest that scaling laws control how noise modifies observables versus its strength, the circuit depth, and the number of qubits. Moreover, we observe that noise impacts the delocalized and localized phases differently. A better understanding of how noise alters the observed properties of the simulated system is essential for leveraging near-term quantum devices for simulation of dirty bosons, and indeed for condensed matter systems in general.

Modeling and Simulation of Physical Systems Formed by Bond Graphs and Multibond Graphs

Current physical systems are built in more that one coordinate: for example, electrical power systems, aeronautical systems and robotic systems can be modeled in multibond graphs (MBG). However, in these systems, some elements use only one axis or dimension—for example, actuators and controllers—which can be modeled in bond graphs (BG). Therefore, in this paper, modeling of systems in multibond graphs and bond graphs (MBG-BG) is presented. Likewise, the junction structure of systems represented by (MBG-BG) is introduced. From this structure, mathematical modeling in the state space is presented. Likewise, modeling of systems on a platform (MBG-BG) can be seen as symmetric to the mathematical model that represents these systems. Finally, a synchronous generator modeled by (MBG-BG) as a case study is developed, and simulation results using 20-Sim software are shown. Furthermore, an electrical power system connected to the power supply of a DC motor as another case study is explained.

Research on precise route control of unmanned aerial vehicles based on physical simulation systems

In order to understand the precise route control system of unmanned aerial vehicles, the author proposes a research on precise route control of unmanned aerial vehicles based on wireless sensor networks and physical simulation. The author first introduces the definitions of various coordinate systems and related motion parameters of unmanned aerial vehicles. On this basis, the nonlinear dynamic equations and kinematic equations of the unmanned aerial vehicle were obtained through force analysis. Secondly, based on wireless sensor networks, a route control system for unmanned aerial vehicles was designed. There may be deviations between the actual route and the planned route of drones during flight, which not only reduces the quality of operations but also affects operational efficiency. Wireless sensor network is a technology closely integrated with unmanned aerial vehicles, which can be used to control the route of unmanned aerial vehicles. The system consists of four parts: unmanned aerial vehicle platform, sensor nodes, convergence nodes, and control center. The control of the route is completed through a two-dimensional coordinate system following algorithm. Finally, the drone will undergo flight tests before and after the installation of this route control system. The drone before the installation of this system will use traditional route control methods. There are two types of planned routes: straight and curved, with the curve being a circular shape with a radius of 5 km; Record the deviation value between the actual route and the planned route every 150 s during the flight, and compare the control accuracy of the two methods on the route. The experimental results show that the wireless sensor network is more stable in tracking straight and curved routes, and has high accuracy in route control.

Dynamic Recrystallization Behavior of Q370qE Bridge Steel

Bridge steel has been widely used in recent years for its excellent performance. Understanding the high-temperature Dynamic Recrystallization (DRX) behavior of high-performance bridge steel plays an important role in guiding the thermomechanical processing process. In the present study, the hot deformation behavior of Q370qE bridge steel was investigated by hot compression tests conducted on a Gleeble 3800-GTC thermal-mechanical physical simulation system at temperatures ranging from 900 ℃ to 1100 ℃ and strain rates ranging from 0.01 s−1 to 10 s−1. The obtained results were used to plot the true stress-strain and work-hardening rate curves of the experimental steel, with the latter curves used to determine the critical strains for the initiation of DRX. The Zener-Hollomon equation was subsequently applied to establish the correspondence between temperature and strain rate during the high-temperature plastic deformation of bridge steel. In terms of the DRX volume fraction solution, a new method for establishing DRX volume fraction was proposed based on two theoretical models. The good weathering and corrosion resistance of bridge steel lead to difficulties in microstructure etching. To solve this, the MTEX technology was used to further develop EBSD data to characterize the original microstructure of Q370qE bridge steel. This paper lays the theoretical foundation for studying the DRX behavior of Q370qE bridge steel.

Modeling offshore wind farm disturbances and maintenance service responses within the scope of resilience

Offshore wind farms are becoming ever more important means of energy production. Accordingly, they are started to be considered critical infrastructures with heightened attention on their protection and resilience. This paper studies how the maintenance service can sustain or recover offshore wind farm operations under different stressors. We conduct this study by modeling the failures in an offshore wind farm and how maintenance service is able to correct them. Our model enhances the traditional cause-consequence trees by including dynamical aspects, and the modeling of the maintenance process. Special attention in the maintenance model is given to limited personnel and material resources, as well as limited access to wind turbines. This limit is a result of occasional harsh weather conditions that make conducting repairs unsafe. The model is applied for two representative disturbance scenarios: a cyber-attack leading to high failure rates and a high-impact incident causing numerous simultaneous failures. The second scenario integrates results from a physical power system simulation that are used for depicting the incident. The results show that our model can present scenarios where different stressors challenge the operations. These can be used for testing and defining requirements for future countermeasures to improve the resilience.

Dynamic gel-forming properties of cardanol modified phenolic resin gel system in long snake-like simulation core with heterogeneity matching actual reservoir conditions

ABSTRACT Gel system consisting of polymer and phenolic resin cross-linker commonly used for water shutoff is not environmentally friendly enough, and the homogeneity of the simulation core has important influence on the experimental results for evaluating the gel properties in the laboratory. In this paper, a modified cross-linker was obtained by substituting cardanol for phenol in the conventional cross-linker. Environmentally-friendly gel was prepared with polymer and cardanol modified cross-linker. A long snakelike core physical simulation system with multiple nonvolatile fluid sampling ports and core sampling ports was developed by connecting curved sand-packed pipes with straight pipes, and it was used to evaluate the dynamic gel-forming properties of the modified gel. The results showed that the strength and stability of the modified gel were not inferior to those of conventional gel. By intelligently controlling the applied force, the long snakelike core had a heterogeneity that matched the actual formation conditions. When injecting gel-forming solution using modified cross-linker into the snakelike core with porosity of 37.6% and permeability of 16.98 μm2, as the increase of the distance between the sampling port and the injection end, the viscosity of the system formed by placing the sampled gel-forming solution at 45°C for 24 hours decreased but remained consistently above 1000 mPa·s. The gel-forming solution could still form gel dynamically after migrating to 10.2 meters, thereby achieving effective plugging. The proposed long core in this work is helpful to study whether the gel systems can dynamically regulate the deep part of the reservoir, providing guidance for improving the macroscopic application properties of cardanol modified gel and promoting sustainable development for oilfields.

Comparison of Machine Learning Models to Predict Lake Area in an Arid Area

Machine learning (ML)-based models are popular for complex physical system simulation and prediction. Lake is the important indicator in arid and semi-arid areas, and to achieve the proper management of the water resources in a lake basin, it is crucial to estimate and predict the lake dynamics, based on hydro-meteorological variations and anthropogenic disturbances. This task is particularly challenging in arid and semi-arid regions, where water scarcity poses a significant threat to human life. In this study, a typical arid area of China was selected as the study area, and the performances of eight widely used ML models (i.e., Bayesian Ridge (BR), K-Nearest Neighbor (KNN), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Random Forest (RF), Adaptive Boosting (AB), Bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XGB)) were evaluated in predicting lake area. Monthly lake area was determined by meteorological (precipitation, air temperature, Standardised Precipitation Evapotranspiration Index (SPEI)) and anthropogenic factors (ETc, NDVI, LUCC). Lake area determined by Landsat satellite image classification for 2000–2020 was analysed side-by-side with the Standardised Precipitation Evapotranspiration Index (SPEI) on 9 and 12-month time scales. With the evaluation of six input variables and eight ML algorithms, it was found that the RF models performed best when using the SPEI-9 index, with R2 = 0.88, RMSE = 1.37, LCCC = 0.95, and PRD = 1331.4 for the test samples. Furthermore, the performance of the ML model constructed with the 9-month time scale SPEI (SPEI-9) as an input variable (MLSPEI-9) depended on seasonal variations, with the average relative errors of up to 0.62 in spring and a minimum of 0.12 in summer. Overall, this study provides valuable insights into the effectiveness of different ML models for predicting lake area by demonstrating that the right inputs can lead to a remarkable increase in performance of up to 13.89%. These findings have important implications for future research on lake area prediction in arid zones and demonstrate the power of ML models in advancing scientific understanding of complex natural systems.

PhySR: Physics-informed deep super-resolution for spatiotemporal data

High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in leveraging deep learning to augment scientific data based on the coarse-grained simulations, which is of cheap computational expense and retains satisfactory solution accuracy. However, the major existing work focuses on data-driven approaches which rely on rich training datasets and lack sufficient physical constraints. To this end, we propose a novel and efficient spatiotemporal super-resolution framework via physics-informed learning (i.e., PhySR), inspired by the independence between temporal and spatial derivatives in partial differential equations (PDEs). The general principle is to leverage the temporal interpolation for flow estimation, and then introduce convolutional-recurrent neural networks for learning temporal refinement. Furthermore, we employ the stacked residual blocks with wide activation and sub-pixel layers with pixelshuffle for spatial reconstruction, where feature extraction is conducted in a low-resolution latent space. Moreover, we consider hard imposition of boundary conditions in the network to improve reconstruction accuracy. Results demonstrate the superior effectiveness and efficiency of the proposed method, which outperforms existing baseline algorithms, based on extensive numerical experiments.

CYBERSECURITY ANALYSIS OF INDUSTRIAL CONTROL SYSTEMS

The current frontiers in the description and simulation of advanced physical and biological Industrial control systems (ICS) used to control various critical industrial and social systems. ICS integrates modern computing, communication, and Internet technologies. The integration of these technologies makes ICS open to the outside world, which makes it vulnerable to various cyber-attacks. ICS’s cybersecurity is becoming one of the most important issues due to the significant damage caused by cyberattacks to organizations and society. This article analyzes the cybersecurity issues of ICS. In particular, an analysis of the main components and architectures of the ICS, security aspects of the ICS, vulnerabilities, and threats to the cybersecurity of the ICS, as well as measures and means to ensure the cybersecurity of the ICS, is carried out. The analysis will help to give some insight into the cybersecurity issues of ICS and identify various research objectives necessary to ensure the cybersecurity of ICS.

Finite Element Analysis of Dynamic Recrystallization Model and Microstructural Evolution for GCr15 Bearing Steel Warm-Hot Deformation Process.

Warm deformation is a plastic-forming process that differs from traditional cold and hot forming techniques. At the macro level, it can effectively reduce the problem of high deformation resistance in cold deformation and improve the surface decarburization issues during the hot deformation process. Microscopically, it has significant advantages in controlling product structure, refining grain size, and enhancing product mechanical properties. The Gleeble-1500D thermal-mechanical physical simulation system was used to conduct isothermal compression tests on GCr15 bearing steel. The tests were conducted at temperatures of 600-1050 °C and strain rates of 0.01-5 s-1. Based on the experimental data, the critical strain model and dynamic recrystallization model for the warm-hot forming of GCr15 bearing steel were established in this paper. The model accuracy is evaluated using statistical indicators such as the correlation coefficient (R). The dynamic recrystallization model exhibits high predictive accuracy, as indicated by an R-value of 0.986. The established dynamic recrystallization model for GCr15 bearing steel was integrated into the Forge® 3.2 numerical simulation software through secondary program development to simulate the compression process of GCr15 warm-hot forming. The dynamic recrystallization fraction was analyzed in various deformation regions. The grain size of the severe deformation zone, small deformation zone, and difficult deformation zone was compared based on simulated compression specimens under the conditions of 1050 °C and 0.1 s-1 with the corresponding grain size obtained with measurement based on metallographic photos; the relative error between the two is 5.75%. This verifies the accuracy of the established dynamic recrystallization and critical strain models for warm-hot deformation of GCr15 bearing steel. These models provide a theoretical basis for the finite element method analysis and microstructure control of the warm-hot forming process in bearing races.

Investigation of variability in apparent values of materials properties in thermo-mechanical uniaxial tensile tests on sheet metals

Thermo-mechanical uniaxial tensile testing is commonly carried out to characterise the mechanical properties of materials under conditions which mimic advanced industrial forming processes, such as hot stamping of steels and aluminium alloys, and to generate microstructures for metallographic investigation. However, in this type of testing, heat loss to the specimen grips can lead to nonuniform temperature distributions along the gauge length, resulting in challenges in determining absolute values of materials properties at the nominal temperature of interest. The present study investigates the effect of these nonuniform temperature distributions on the variability in the thermo-mechanical properties as measured in the tests, and in the microstructures of the tested specimens. For this purpose, uniaxial tensile tests on the boron steel 22MnB5 and aluminium alloy AA6082 were performed under hot stamping conditions using a Gleeble 3800 thermal-mechanical physical simulation system, in which the specimens were heated using resistance heating and the strain fields were measured using digital image correlation (DIC). The nonuniformity of the temperature distributions along the gauge length was quantified. Both the strains and the strain rates along the gauge length were then computed and the effects of factors such as pre-forming gauge length, post-forming gauge length and specimen design on the spatial distribution of strains and strain rates were investigated. The effects of these factors on the values of thermo-mechanical properties determined from the tests, such as the ductility and the ultimate tensile strength (UTS), were also analysed and quantified. This study reveals the variability in the apparent values of materials properties as determined by thermo-mechanical testing resulting from nonuniform temperature distributions, and provides experimental data for the development of new standards for thermo-mechanical tests in future.

Interconnected digital twins and the future of digital manufacturing: Insights from a Delphi study

AbstractDigital twins (DTs) are virtual representations of real‐world entities like production assets, processes, or products. They are updated at a defined fidelity and frequency along the entire life cycle from development and engineering over the production or implementation of a product or process until its usage stage. Interconnected digital twins (IDTs) are DTs shared and connected across organizations with the objective to create holistic simulation and decision models of an entire physical system. In this paper, we investigate how IDTs shape future digital manufacturing scenarios and impact innovation management. We present the results of a real‐time Delphi study, analyzing quantitative and qualitative estimates on a set of 24 projections, forecasting the future of digital manufacturing with a projection horizon towards 2030. Using this data and 22 additional use cases of IDTs in manufacturing companies, we present a baseline scenario where our Delphi panel reached a consensus, representing a likely future of digital manufacturing in 2030. By analyzing projections where our expert panels' evaluations vary widely, we identify key design decisions that may impact innovation management along the dimensions of variation, choice, and control in digital manufacturing. We explain how IDTs will impact external knowledge inflows, the emergence and governance of industrial data spaces, and the potential of data‐driven and AI‐enabled applications for prediction and regulation to drive better decision‐making and continuous innovation.