Time-varying Simulation Research Articles

Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal

Multi-synchrosqueezing transform (MSST) enhances the concentration of the time–frequency representation (TFR) of time-varying signals by introducing multi-instantaneous frequency (IF) estimation operator. However, the existence of estimation bias may lead to uncertainty in the discrete values of IF during computer computations, causing issues with non-reassigned points. In this paper, a maximum probability multi-synchrosqueezing transform (MPMSST) is proposed, which incorporates distribution probability during the discretization process of the IF, taking the frequency corresponding to the maximum distribution probability as the determined value of IF. Through verification with multi-component time-varying simulation signals, MPMSST not only effectively eliminates non-reassigned points but also accurately estimates the theoretical time–frequency curve. By applying MPMSST to analyze spindle motor current signals during deep hole machining, TFRs under different tool wear states are obtained. The singular value decomposition (SVD) method is utilized to extract the principal component of the TFR, and a tool wear state feature index is constructed based on the sum of squares of the first 10 singular values. The correlation coefficient and covariance between the proposed feature index and the tool wear curve are 0.835 and 5.704 respectively, indicating a good consistency of the index with tool wear changes, and a high sensitivity to variations in tool wear state.

Time-dependent model for two-phase flow in ultra-high water-cut reservoirs: Time-varying permeability and relative permeability

For the ultra-high water-cut reservoirs, after long-term water injection exploitation, the physical properties of the reservoir change and the heterogeneity of the reservoir becomes increasingly severe, which further aggravates the spatial difference of the flow field. In this study, the displacement experiments were employed to investigate the variations in core permeability, porosity, and relative permeability after a large amount of water injection. A relative permeability endpoint model was proposed by utilizing the alternating conditional expectation (ACE) transformation to describe the variation in relative permeability based on the experimental data. Based on the time dependent models for permeability and relative permeability, the traditional oil-water two-phase model was improved and discretized using the mimetic finite difference method (MFD). The two cases were launched to confirm the validation of the proposed model. The impact of time-varying physical features on reservoir production performance was studied in a real water flooding reservoir. The experimental results indicate that the overall relative permeability curve shifts to the right as water injection increases. This shift corresponds to a transition towards a more hydrophilic wettability and a decrease in residual oil saturation. The endpoint model demonstrates excellent accuracy and can be applied to time-varying simulations of reservoir physics. The impact of variations in permeability and relative permeability on the reservoir production performance yields two distinct outcomes. The time-varying permeability of the reservoir results in intensified water channeling and poor development effects. On the other hand, the time-varying relative permeability enhances the oil phase seepage capacity, facilitating oil displacement. The comprehensive time-varying behavior is the result of the combined influence of these two parameters, which closely resemble the actual conditions observed in oil field exploitation. The time-varying simulation technique of reservoir physical properties proposed in this paper can continuously and stably characterize the dynamic changes of reservoir physical properties during water drive development. This approach ensures the reliability of the simulation results regarding residual oil distribution.

A 3D-Printed Piezoelectric Microdevice for Human Energy Harvesting for Wearable Biosensors.

The human body is a source of multiple types of energy, such as mechanical, thermal and biochemical, which can be scavenged through appropriate technological means. Mechanical vibrations originating from contraction and expansion of the radial artery represent a reliable source of displacement to be picked up and exploited by a harvester. The continuous monitoring of physiological biomarkers is an essential part of the timely and accurate diagnosis of a disease with subsequent medical treatment, and wearable biosensors are increasingly utilized for biomedical data acquisition of important biomarkers. However, they rely on batteries and their replacement introduces a discontinuity in measured signals, which could be critical for the patients and also causes discomfort. In the present work, the research into a novel 3D-printed wearable energy harvesting platform for scavenging energy from arterial pulsations via a piezoelectric material is described. An elastic thermoplastic polyurethane (TPU) film, which forms an air chamber between the skin and the piezoelectric disc electrode, was introduced to provide better adsorption to the skin, prevent damage to the piezoelectric disc and electrically isolate components in the platform from the human body. Computational fluid dynamics in the framework of COMSOL Multiphysics 6.1 software was employed to perform a series of coupled time-varying simulations of the interaction among a number of associated physical phenomena. The mathematical model of the harvester was investigated computationally, and quantification of the output energy and power parameters was used for comparisons. A prototype wearable platform enclosure was designed and manufactured using fused filament fabrication (FFF). The influence of the piezoelectric disc material and its diameter on the electrical output were studied and various geometrical parameters of the enclosure and the TPU film were optimized based on theoretical and empirical data. Physiological data, such as interdependency between the harvester skin fit and voltage output, were obtained.

On the comparison of AGN with GRMHD simulations – II. M87

ABSTRACT Horizon-scale observations of the jetted active galactic nucleus M87 are compared with simulations spanning a broad range of dissipation mechanisms and plasma content in three-dimensional general relativistic flows around spinning black holes. Observations of synchrotron radiation from radio to X-ray frequencies can be compared with simulations by adding prescriptions specifying the relativistic electron-plus-positron distribution function and associated radiative transfer coefficients. A suite of time-varying simulations with various spins, plasma magnetizations and turbulent heating and equipartition-based emission prescriptions (and piecewise combinations thereof) is chosen to represent distinct possibilities for the M87 jet/accretion flow/black hole system. Simulation jet morphology, polarization, and variation are then ‘observed’ and compared with real observations to infer the rules that govern the polarized emissivity. Our models support several possible spin/emission model/plasma composition combinations supplying the jet in M87, whose black hole shadow has been observed down to the photon ring at 230 GHz by the Event Horizon Telescope (EHT). Net linear polarization and circular polarization constraints favour magnetically arrested disc (MAD) models whereas resolved linear polarization favours standard and normal evolution (SANE) in our parameter space. We also show that some MAD cases dominated by intrinsic circular polarization have near-linear V/I dependence on un-paired electron or positron content while SANE polarization exhibits markedly greater positron-dependent Faraday effects – future probes of the SANE/MAD dichotomy and plasma content with the EHT. This is the second work in a series also applying the ‘observing’ simulations methodology to near-horizon regions of supermassive black holes in Sgr A* and 3C 279.

The study and development of UAV digital twin system

To develop the digital twin system of unmanned aircraft vehicle (UAV), a multi-information fusion and time-varying simulation technology based on the testing data is proposed. And a UAV digital twin system is developed. First, the system composition and the working principle of the UAV digital twin system are introduced, respectively. Second, the Ko displacement theory is applied to realize the integration of the acquired data and the finite element simulation model. Then, the model reduced method is introduced, which can reduce the scale of the finite element model and improve the calculation efficiency. Finally, the experimental verification is carried out. The test results and simulation calculation data show that the relative errors between the proposed calculation method and the test results are within 30%, which meets the requirements of engineering application. The test results show that the proposed UAV digital twin technology is feasible, providing a reference case for the development of UAV digital twin technology, which has practical engineering significance and military value.

A numerical method for spatially-distributed transient simulation to replicate nonlinear ‘defect-irrelevant’ behaviors of no-insulation HTS coil

This paper presents a numerical method, based on the partial element equivalent circuit (PEEC) technique, for spatially-distributed and time-varying simulation to analyze nonlinear ‘defect-irrelevant’ behaviors of a no-insulation (NI) high temperature superconductor (HTS) coil. We suggest a resistivity parameterization approach in combination of the PEEC method to replicate electromagnetic dynamics of an NI HTS coil containing multiple ‘defects.’ The proposed method is adopted to investigate ‘defect-irrelevant’ behaviors of an NI single pancake coil having lap joints as a form of artificial defects. To validate our approach, electromagnetic characteristics of the NI test coil are measured in a bath of liquid nitrogen at 77 K and compared with four key simulation results: (a) local voltages; (b) current distribution; (c) magnetic field;and (d) Joule heating distribution. Experimental measurements of local voltages and the magnetic field are compared to the simulation results to validate our numerical method.

Predicting Heavy Oil Production by Hybrid Data-Driven Intelligent Models

It is difficult to determine the main control factors owing to the complex geological conditions of heavy oil reservoirs, including high viscosity, a wide range of variation of crude oil, and the great difference in production between different recovery methods. In this context, main control factors of heavy oil production in different recovery methods are analyzed and obtained based on the Apriori algorithm. The prediction of heavy oil production is faced with problems such as low prediction precision and insufficient data usage. Therefore, a novel intelligent simulation and prediction model of data-driven heavy oil production with time-varying characteristics is established based on differential simulation, machine learning, and intelligent optimization theory, which overcomes the defects of nonlinear, multifactor, and low fitting precision of dynamic data of heavy oil development. The parameters of the heavy oil production time-varying simulation model are identified by the least square support vector machine (LSSVM) to realize the intelligent prediction of the production. Numerical experiments show that the prediction result of the novel intelligent simulation and prediction model is better than the BP neural network model and the GM (1, N) model. This study provides a novel feasible method for data-driven heavy oil production prediction, and it can be helpful in further study of data-driven heavy oil production.

Computational Design of Sensitized Combustion Chemistry Experiments

In combustion chemistry experiments, reaction rates are often extracted from complex experiments using detailed models. To aid in this process, experiments are performed such that measurable quantities, such as species concentrations, flame speed, and ignition delay, are sensitive to reaction rates of interest. In this work, a systematic method for determining such sensitized experimental conditions is demonstrated. An open-source python script was created using the Cantera module to simulate thousands of 0D and hundreds of 1D combustion chemistry experiments in parallel across a broad, user-defined range of mixture conditions. The results of the simulation are post-processed to normalize and compare sensitivity values among reactions and across initial conditions for time-varying and steady-state simulations, in order to determine the “most useful” experimental conditions. This software can be utilized by researchers as a fast, user-friendly screening tool to determine the thermodynamic and mixture parameters for an experimental campaign. We demonstrate this software through two case studies comparing results of the 0D script against a shock tube experiment and results of the 1D script against a spherical flame experiment. In the shock tube case study we present mixture conditions compared to those used in the literature to study H + O2 (+M)→HO2(+M). In the flame case study, we present mixture conditions compared to those in the literature to study formyl radical (HCO) decomposition and oxidation reactions. The systematically determined experimental conditions identified in the present work are similar to the conditions chosen in the literature.

Radio Frequency Link and User Selection Algorithm for 5G Mobile Communication System

Based on the millimetre-wave time-domain channel detector, this paper conducts channel measurement and modelling for different scenarios and antenna types in typical frequency bands and thus carries out a study on the propagation characteristics of millimetre-wave channels in typical frequency bands and scenarios for 5G. The time-varying characteristics and modelling methods of millimetre-wave clusters are studied, and the time-varying channel simulation strategy and linear regression fitting method are used to establish the time-varying generation and extinction model of clusters by combining the measured data of 26 GHz large-scale antenna channels. The algorithm is based on the correlation dictionary and achieves the efficient and optimal selection of large-scale antenna arrays in the millimetre-wave band. A low-complexity multiuser hybrid precoding design scheme is proposed based on the wideband millimetre-wave large-scale Multiple-Input Multiple-Output (MIMO) system. The scheme aims to maximize the system spectral efficiency and introduces the net spectral efficiency as a bridge to decouple the analogy precoding design from the digital precoding design, thus significantly reducing the computational complexity. In the analogy precoding stage, a Hungarian algorithm-based beam assignment method is proposed to avoid beam conflicts and maximize the net spectral efficiency of the system; in the digital precoding stage, the equivalent channel matrix of each subcarrier is diagonalized to eliminate multiuser interference. Finally, the effectiveness of the scheme is verified by simulation. In this paper, we focus on the radio frequency (RF) link and user selection algorithm of 5G mobile communication system. Through the study of these two aspects, the RF link of our communication system has high stability and reliability, and through the study of the algorithm used for selection, our results also have a strong practical value.

Time-varying Numerical Simulation Of Reservoir Physical Properties Based On Surface Flux

Most of the domestic oilfields are developed by water flooding. The long-term erosion of injected water will transform the reservoir and change the physical properties of the reservoir. These changes continue to progress slowly, but the cumulative change range cannot be ignored. The reservoir physical property changes will affect the oil-water movement ability, and have a great impact on the oilfield development influence and the distribution of remaining oil in the later period. However, the mainstream commercial numerical simulation software at present fails to fully consider the time-varying phenomenon of reservoir physical property. According to the literature review, the numerical simulation methods considering the time-varying reservoir physical properties have some defects in continuity representation, directional representation and stability of calculation results. To solve this problem, it is necessary to establish a numerical simulation method based on the time-varying reservoir parameters. In this paper, a useful attempt is made to solve this problem, and an Eclipse-based time-varying simulation program is developed. Through the verification of various examples, all functions meet the design requirements and can realize time-varying simulation under different conditions.

Control allocation for a class of morphing aircraft with integer constraints based on Lévy flight

Aiming at tracking control of a class of innovative control effector (ICE) aircraft with distributed arrays of actuators, this paper proposes a control allocation scheme based on the Levy flight. Different from the conventional aircraft control allocation problem, the particular characteristic of actuators makes the actuator control command totally subject to integer constraints. In order to tackle this problem, first, the control allocation problem is described as an integer programming problem with two desired objectives. Then considering the requirement of real-time, a metaheuristic algorithm based on the Levy flight is introduced to tackling this problem. In order to improve the searching efficiency, several targeted and heuristic strategies including variable step length and inherited population initialization according to feedback and so on are designed. Moreover, to prevent the incertitude of the metaheuristic algorithm and ensure the flight stability, a guaranteed control strategy is designed. Finally, a time-varying simulation model is introduced to verifying the effectiveness of the proposed scheme. The contrastive simulation results indicate that the proposed scheme achieves superior tracking performance with appropriate actuator dynamics and computational time, and the improvements for efficiency are active and the parameter settings are reasonable.

Modeling the surface water and groundwater budgets of the US using MODFLOW-OWHM

Assessments of groundwater and surface water budgets at a large scale, such as the contiguous United States, often separately analyze the complex dynamics linking the surface and subsurface categories of water resources. These dynamics include recharge and groundwater contributions to streamflow. The time-varying simulation of these complex hydrologic dynamics, across large spatial and temporal scales, remains a scientific challenge due to the complexity of the processes and data availability. In this study, groundwater fluxes and surface hydrologic processes are simulated across the contiguous US for 1950-2010. The simulation estimates the monthly water budget components, such as groundwater recharge, surface runoff, and evapotranspiration; streamflow in major rivers is routed while accounting for groundwater exchange. Human impacts are included through groundwater pumping, and climate variability is included, including variability in precipitation, temperature and potential evapotranspiration. The simulated groundwater level and river discharge have strong correlation with USGS observation wells and streamflow gages, with R2 values of 0.992 and 0.946, respectively. The simulated evapotranspiration is compared with three other published estimation methods, showing that it is able to capture the magnitude and seasonality of evapotranspiration over the Mississippi River basin. As such, the model is able to reasonably simulate the surface and groundwater budgets over the US, allowing for questions of the relative importance of climate and human impacts to be explored in the future.

Event-based exploration and comparison on time-varying ensembles

We propose an event-based analysis system for comparison of several ensemble time-varying simulations. In this pipeline, users can customize the selection of events (i.e., the keyframes of the simulations) for each simulation on the timeline view. The associated rendered thumbnails are tiled in the rendered thumbnail view. The ticks on the timeline and the rendered thumbnails are connected by a link. Users are allowed to do 3D exploration on the render thumbnails and the high-resolution view on which the details can be displayed. Switching between different variables is supported to assist users in exploring the rendering of different ensemble variables or even combinations of variables. We apply our system into the deep water impact ensemble dataset. The system is proved to have the ability to help users better explore the simulations.

Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications

In this work, a frame work for time-varying channel modeling and simulation is proposed by using neural network (NN) to overcome the shortcomings in geometry based stochastic model (GBSM) and simulation approach. Two NN models are developed for modeling of path loss together with shadow fading (SF) and joint small scale channel parameters. The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell. The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment. Moreover, the sparse feature of clusters, delay and angular spread, channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.

Efficient Low-Resource Compression of HIFU Data

Large-scale numerical simulations of high-intensity focused ultrasound (HIFU), important for model-based treatment planning, generate large amounts of data. Typically, it is necessary to save hundreds of gigabytes during simulation. We propose a novel algorithm for time-varying simulation data compression specialised for HIFU. Our approach is particularly focused on on-the-fly parallel data compression during simulations. The algorithm is able to compress 3D pressure time series of linear and non-linear simulations with very acceptable compression ratios and errors (over 80% of the space can be saved with an acceptable error). The proposed compression enables significant reduction of resources, such as storage space, network bandwidth, CPU time, and so forth, enabling better treatment planning using fast volume data visualisations. The paper describes the proposed method, its experimental evaluation, and comparisons to the state of the arts.

A method for deriving a non-singular state-space formulation based on Rubin's model for Pogo analysis

A method for deriving a non-singular state-space formulation based on Rubin's model for Pogo analysis is presented in this study. Because of the non-singularity, the state-space model can be directly used in frequency-domain analyses and time-domain simulations. To describe the assembly method concisely, the dynamic equations of the nine types of independent elements are described in a standard manner. The nine types of elements are divided into two classes according to characteristics of the dynamic equations. The mapping relationship between the local and global numbers of elements and nodes is obtained by numbering all of the elements and nodes. By integrating the element stiffness matrixes to obtain the total stiffness matrix used in the finite element method, the coefficient matrixes of the improved Rubin's model are assembled from the coefficient matrixes of all of the elements according to the mapping relationship. Based on the non-singular model, the time-varying simulation with the nonlinear property of the accumulator can be done conveniently by revising the state-space model. The successful application of this method to a Pogo analysis of a certain type of CZ rocket used in China verifies the correctness and efficiency of the method of this study.

Extraction, classification and visualization of 3-dimensional clouds simulated by cloud-resolving atmospheric model

Cloud-resolving atmospheric general circulation models using large-scale supercomputers reproduce realistic behavior of 3-dimensional atmospheric field on a global scale. To understand the simulation result for scientists, conventional visualization methods based on 2-dimensional cloud classification are not enough for understanding individual clouds and their physical characteristics. In this study, we propose a new 3-dimensional extraction and classification method of simulated clouds based on their 3-dimensional shape and physical properties. Our proposed method extracts individual clouds by cloud water and cloud ice, and classifies them into six types by their altitude and upward flow. We applied the method to time-varying atmospheric simulation data, and attempted to visualize atmospheric phenomena on the tropics such as developing cumulonimbus and tropical cyclone. Two case studies clearly visualize the behavior of individual cloud type and clarify that some cloud types have a relationship with rainfall during active weather phenomena. The proposed method has the potential to analyze such phenomena that develop in the vertical direction as well as the horizontal direction.

Visual Trends Analysis in Time-Varying Ensembles

Visualization and analysis techniques play a key role in the discovery of relevant features in ensemble data. Trends, in the form of persisting commonalities or differences in time-varying ensemble datasets, constitute one of the most expressive feature types in ensemble analysis. We develop a flow-graph representation as the core of a system designed for the visual analysis of trends in time-varying ensembles. In our interactive analysis framework, this graph is linked to a representation of ensemble parameter-space and the ensemble itself. This facilitates a detailed examination of trends and their correlations to properties of input-space. We demonstrate the utility of the proposed trends analysis framework in several benchmark data sets, highlighting its capability to support goal-driven design of time-varying simulations.

Development of vector following mesh generator for analysis of two-dimensional tokamak plasma transport

A field-based new adaptive mesh generator, VEGA (VEctor-following Grid generator for Adaptive mesh), is developed for 2-D core–edge coupled tokamak plasma transport simulations. VEGA can generate time-varying and spatially non-uniform grids by using a stretching function. It provides two operation modes for generating non-uniform radial distributions. One is so-called ion mode where the grid is automatically generated by considering the ion temperature gradient which plays an important role in the ion and the momentum transport mechanism of a tokamak plasma. The other is so-called high-gradient mode where the grid is produced by considering the locality of plasma profiles which appears particularly in transport barriers. VEGA is benchmarked with a conventional code for a reference double null (DN) KSTAR divertor configuration. Three factors are newly introduced in this work to evaluate the quality of a grid. It is found that VEGA is particularly suitable for delicate integrated simulations of the plasma edge and the scrape off layer (SOL) due to its high cell orthogonality and low radial flux deviation. Quality of non-uniform grids generated by the two operation modes of VEGA, the ion mode and the high-gradient mode is examined. A more refined grid is found near the edge region characterized with steeper gradients whereas coarser one in the core region. Such fine grids at the edge region can result in highly reduced radial flux deviation, which is indeed important for analysis of edge–SOL physics with time-varying simulations.

Cubic Spline Interpolation Method for the Envelope Tracking of Middle and Low Frequency Voltage Flicker

Flicker envelope tracking is the key of analyzing voltage flicker signal. A novel method, i.e., cubic spline interpolation method is applied in the detection of the voltage flicker envelope. The typical flicker signals, such as one or multi-frequencies and frequency time-varying flicker signal are selected and analyzed by this method. Simulation results indicate that this method is effective in the detection of the envelope of voltage flicker signal. Results of one or multi-frequencies flicker signal simulation show that, when flicker frequency is less than 15Hz , the error of this method is quite tiny. The detection precision of this method is higher, especially in the scope of low frequencies of flicker and this method makes up for the disadvantage of square demodulation. Furthermore, results of frequency time-varying flicker signal simulation show that, the occurrence and recover time of voltage flicker signal can be accurately detected by cubic spline interpolation method.