Initial Model Parameters Research Articles

Underground Goaf Parameters Estimation by Cross-Iteration with InSAR Measurements

Determining the geographic location and spatial distribution of underground goaf is of great significance for the prevention of mining subsidence hazards and the detection of illegal mining. However, traditional goaf detection techniques mainly focus on geophysical methods that are labor intensive, have low efficiency, and are expensive. Due to the large range and off-site monitoring capability of interferometric synthetic aperture radar (InSAR) techniques, research on goaf location detection based on InSAR measurements has been increasing. This paper proposes a new method for locating underground goaf based on cross-iteration and InSAR measurements. Firstly, the functional relationship between the geometric parameters of the goaf and the line of sight (LOS) deformation retrieved by InSAR techniques is constructed. Then, the three initial model parameters of the probability integration method (PIM) are determined by mining geological conditions. Finally, the cross-iteration method is used to determine the parameters to characterize the spatial location of underground goaf. The experimental results show that the average relative errors of the simulated experiment and the real experiment are 1.5% and 5.1%, respectively, and the inverted goaf parameters are in good agreement with the real values. Moreover, the proposed method only requires the main lithology of the overlying rock in the goaf and does not depend on the accuracy of PIM model parameters. Therefore, this method has engineering application value for the detection of goaf lacking actual measurement data or that caused by illegal mining.

Turbulent relaxation after a quench in the Heisenberg model

We predict the emergence of turbulent scaling in the quench dynamics of the two-dimensional Heisenberg model for a wide range of initial conditions and model parameters. In the isotropic Heisenberg model, we find that the spin-spin correlation function exhibits universal scaling consistent with a turbulent energy cascade. When the spin rotational symmetry is broken by an easy-plane exchange anisotropy, we find a dual cascade of energy and an emergent conserved charge associated to transverse magnetization fluctuations. The scaling exponents are estimated analytically and agree with numerical simulations using phase space methods. We also define the range of initial conditions and model parameters (energy, magnetization, and spin number $S$) that lead to a turbulent cascade. The universal character of the cascade, insensitive to microscopic details or initial conditions, suggests that turbulence in spin systems can be broadly realized in cold atom and solid-state experiments.

Computational inference of vibratory system with incomplete modal information using parallel, interactive and adaptive Markov chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

An Enhancing Particle Swarm Optimization Algorithm (EHVPSO) for damage identification in 3D transmission tower

In this paper, a novel enhanced Particle Swarm Optimization (PSO) algorithm is introduced for solving damage identification problems. For the first time, the algorithm is applied to a complex structure, namely an Electric Power Transmission with 44.05 m height. The process of structural damage assessment is implemented using SAP2000 commercial software combined with MATLAB. Using the Open Application Programming Interface (OAPI) source code, which is available in SAP2000, a strong MATLAB environment program has been developed in this research. This program can allow the user to adjust the initial model's parameters in SAP2000 to create a continuous two ways data exchange between SAP2000 and MATLAB. Thus, the process of detecting the location and level of damage in the structure is performed by applying a new version of PSO, namely Enhancing Particle Swarm Optimization Algorithm (EHVPSO), using stochastic parameters. The key factor in the EHVPSO algorithm is to introduce two novel equations. The first equation is used to control the convergence rate in each movement of particleith, and the second equation is used to control the balance between local optimal value and global optimal value. The results demonstrate that the proposed algorithm can detect damage with very high accuracy and reliability.

By , I.; , A. I.; , I.C.; , H.M. and , C.M.

This work was carried out to evaluate the hydrogeological and geophysical properties of Abakaliki and Afikpo environs. Forty-seven (47) Vertical Electrical Sounding data were acquired with electrode spacing (AB/2) of 500m using Schlumberger electrode configuration. Conventional curve matching techniques were used to interpret the data and obtain initial model parameters. Results of the study showed 3-7 electric layers which indicate multiple aquifer system. QQH, QHK, KHK and QQHK models were the Vertical Sounding curves obtained. The aquiferous layers make up the third and fourth layers. The resistivity of the aquifer varies from 10.74Ωm to 13201Ωm with an average of 1013.58 Ωm. Aquifer depth ranges from 6.2m to 92.5m with an average aquifer depth of 27.07m, while the aquifer thickness varies from 2.3m to 55.87m with an average of 15.61m. Transverse resistance varies from 65.34Ωm2to 7079.14 Ωm2 with an average of 9966.29 Ωm2; and the longitudinal conductance varies from 0.00018Ω-1 to 1.65 Ω-1 with an average of 0.026 Ω-1. From the study, 49% of the study area falls under the poor protective rating while 21% falls under the weak rating with 25% and 5% which makes up the moderate and good ratings respectively. Using the average longitudinal conductance, it is observed that the study area is moderately protected.

Active contour model based on local intensity fitting and atlas correcting information for medical image segmentation

Intensity inhomogeneity and noises often occur in real medical images, which present a large degree of challenge to image segmentation. At the same time, most of the existing image segmentation algorithms are sensitive to initial conditions and model parameters. This paper presents an accurate and robust active contour model to solve the above problems. Inspired by the idea of the region-scalable fitting (RSF) model, we first define a local atlas fitting term transformed by the segmentation contour of the coherent local intensity clustering (CLIC) model. Then, we define a new energy functional by merging the atlas term into the energy functional of the RSF model. The advantage of this operation is that it makes full use of the existing segmentation features and advantages of the two models and avoids cumbersome adjustment of model parameters and initial contours. The experimental results clearly show that the improved model not only has better segmentation results than the RSF model and other active contour models such as the LINC, REGAC and SMAP models, but also solves the problem of sensitivity to initial contours, parameters adjustment and noise.

Building epidemic models for living populations and computer networks.

Accurate modeling of viral outbreaks in living populations and computer networks is a prominent research field. Many researchers are in search for simple and realistic models to manage preventive resources and implement effective measures against hazardous circumstances. The ongoing Covid-19 pandemic has revealed the fact about deficiencies in health resource planning of some countries having relatively high case count and death toll. A unique epidemic model incorporating stochastic processes and queuing theory is presented, which was evaluated by computer simulation using pre-processed data obtained from an urban clinic providing family health services. Covid-19 data from a local corona-center was used as the initial model parameters (e.g. , infection rate, local population size, number of contacts with infected individuals, and recovery rate). A long-run trend analysis for 1 year was simulated. The results fit well to the current case data of the sample corona center. Effective preventive and reactive resource planning basically depends on accurately designed models, tools, and techniques needed for the prediction of feature threats, risks, and mitigation costs. In order to sufficiently analyze the transmission and recovery dynamics of epidemics it is important to choose concise mathematical models. Hence, a unique stochastic modeling approach tied to queueing theory and computer simulation has been chosen. The methods used here can also serve as a guidance for accurate modeling and classification of stages (or compartments) of epidemics in general.

Synthetic Gravitational Waves from a Rolling Axion Monodromy

In string theory inspired models of axion-like fields, sub-leading non-perturbative effects, if sufficiently large, can introduce steep cliffs and gentle plateaus onto the underlying scalar potential. During inflation, the motion of a spectator axion σ on this potential becomes temporarily fast, leading to localized amplification of one helicity state of gauge fields. In this model, the tensor and scalar correlators sourced by the vector fields exhibit localized peak(s) in momentum space corresponding to the modes that exit the horizon while the roll of σ is fast. Thanks to the gravitational coupling of gauge fields with the visible sector and the localized nature of particle production, this model can generate observable gravitational waves (GWs) at CMB scales while satisfying the current limits on scalar perturbations. The resulting GW signal breaks parity and exhibit sizeable non-Gaussianity that can be probed by future CMB B-mode missions. Depending on the initial conditions and model parameters, the roll of the spectator axion can also generate an observably large GW signature at interferometer scales while respecting the bounds on the scalar fluctuations from primordial black hole limits. In our analysis, we carefully investigate bounds on the model parameters that arise through back-reaction and perturbativity considerations to show that these limits are satisfied by the implementations of the model that generate GW signals at CMB and sub-CMB scales.

A comparison of ray-based traveltime and waveform inversion results for depth velocity model building in complex fold and thrust belts

In onshore fold and thrust belt geology, seismic wave propagation leads to complex waveforms due to large lateral variations in the earth parameters and multiple scattering effects. Moreover, due to acquisition difficulties in mountainous terrain, data coverage can be limited. Although waveform inversion should help in imaging in this context, finding a sufficiently accurate initial model parameter is challenging. Efficient ray-based traveltime inversions could be used when we focus on the early arrivals. However, they rely on a high-frequency assumption that may not be justified in this context. Indeed, significant interferences may occur within the first Fresnel zone. To evaluate the potential challenges, we have inverted 3D passive and 2D active data sets recorded over a fold and thrust belt region in Albania. We compare ray-based high-frequency traveltime and finite-frequency waveform inversion results obtained by inverting local earthquake events and first arrivals from 2D active seismic data. We consider only acoustic propagation. The two results differ significantly. The ray-based traveltime inversion we used does not always give a velocity model that can be used as an initial model for waveform inversion. This could be due to a bias toward the initial model when the data coverage is limited because the inversions have a nonzero null space, or it could be a limitation of the ray-based traveltime inversion that assumes the phases of the picked events are linear in frequency over the considered frequency band.

Wavefield reconstruction inversion in the frequency domain of on-ground GPR data

Full waveform inversion (FWI) is one of the most promising techniques for the quantitative characterization of the shallow subsurface of the Earth. However, the conventional FWI with the reduced approach is highly nonlinear, a challenging source of which is the cycle skipping. It would drive FWI to spurious local minima as soon as the initial model parameters are not accurate enough. By introducing a non-negative penalty term and a penalty parameter, wavefield reconstruction inversion (WRI) extends the search space of full waveform inversion, relaxes the requirement to satisfy exactly the wave equation at each iteration, and mitigates some of the problems related to poor start models, which leads to the cycle skipping phenomenon. Moreover, WRI breaks down FWI into two linear sub-problems and reconstruct the wavefield and estimate the model parameter in a loop and there is no need to update and store the wavefield. In this paper, we perform the model reconstruction of on-ground GPR using WRI in the frequency domain by inverting data of a limited number of frequency components, and compare the results of it with that of the conventional FWI. With a limited illumination of the subsurface, WRI with small penalty parameters has a positive performance.

Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model

For reservoir basins, complex underlying surface conditions, short flood confluence times, and concentrated water volumes make inflow flood forecasting difficult and cause forecast accuracies to be low. Conventional flood forecasting models can no longer meet the required forecast accuracy values for flood control operations. To give full play to the role of reservoirs in flood control and to maximize the use of reservoir flood resources, high-precision inflow flood forecasting is urgently needed as a support mechanism. In this study, the Baipenzhu Reservoir in Guangdong Province was selected as the study case, and an inflow flood forecast scheme was designed for the reservoir by a physically based distributed hydrological model, the Liuxihe model. The results show that the Liuxihe model has strong applicability for flood forecasting in the studied reservoir basin and that the simulation results are very accurate. This study also found that the use of different Digital Elevation Model (DEM) data sources has a certain impact on the structure of the Liuxihe model, but the constructed models can both simulate the inflow flood process of the Baipenzhu Reservoir well. At the same time, the Liuxihe model can reflect the spatial variation in rainfall well, and using the Particle swarm optimization (PSO) algorithm to optimize the initial model parameters can greatly reduce the uncertainty of the model forecasts. According to China’s hydrological information forecast standards, the Liuxihe model forecast schemes constructed by the two data sources are rated as Grade A and can be used for real-time flood forecasting in the Baipenzhu Reservoir basin.

A GIT algorithm for simultaneous estimation of seismic source, site response and regional-distance dependent attenuation parameters: application to synthetic and real data

The generalized inversion technique (GIT) of earthquake recordings is a useful tool in retrieving seismic source spectra, attenuation path parameters and site transfer functions (TF). Further understanding of these three fundamental factors which control seismic ground motion constitutes one of the main challenges in seismology. Especially, the site TF estimation is an important objective that can significantly contribute to seismic hazard assessment. In this study, a parametric GIT algorithm (in MATLAB (The MathWorks, Inc., Natick, Massachusetts, United States, 2017)), based on the one proposed by Drouet et al. (Bull Seism Soc Am 98:198–219, 2008a), is developed, by introducing distance- and regional-dependent attenuation parameters for geometrical spreading and anelastic attenuation terms, respectively. This step is aimed at a more detailed investigation of attenuation path, anticipating to improve the knowledge of all three fundamental factors controlling seismic ground motion. The algorithm is based on a Gauss-Newton iterative inversion method, using initial reasonable model parameters. Source term is parametrically investigated for seismic moment and corner frequency, which can be simultaneously controlled with respect to stress drop. A synthetic dataset, approximating a simplified real dataset was inverted by the proposed inversion algorithm examining its computational validity. Four tests were implemented, with or without reference condition, providing encouraging results. The applicability of the algorithm is supported by the inversion of a real dataset which was also examined by Grendas et al. (Bull Earthq Eng 16:5061–5094, 2018) using a uniform attenuation model in GIT. Approximately 9% reduction of the misfit between real and computed data from the inverted model data is achieved, showing the effectiveness of the proposed algorithm.

Impacts of two types of errors on the predictability of terrestrial carbon cycle

AbstractInitial errors and model parameter errors are two of the main factors that produce uncertainties in numerical simulations and predictions. It is crucial to determine in advance which of these types of errors should be reduced to improve numerical simulations and increase their prediction skill. In this study, a fundamental issue related to studies of the predictability about terrestrial carbon cycle is discussed. The relative importance of initial errors and model parameter errors in causing the prediction uncertainty of net primary production (NPP), which is part of the terrestrial carbon cycle, is assessed. The errors in NPP predictions related to initial errors and parameter errors are evaluated within the Lund–Potsdam–Jena (LPJ) model using the conditional nonlinear optimal perturbation approach for these two types of errors (CNOP‐I and CNOP‐P, respectively). This method explores the upper bounds of the prediction errors caused by initial errors and model parameter errors at a particular time in the future. The contributions of initial errors and model parameter errors to NPP prediction uncertainty are found to depend on climate backgrounds and the prediction timescale. For the moist and semimoist regions of China, initial errors play a more important role than model parameter errors in the prediction uncertainty of NPP for short prediction times. As the prediction time scale increases, model parameter errors lead to large errors in NPP predictions. However, in the arid and semiarid regions of China, the uncertainty in NPP predictions caused by model parameter errors is always larger than that caused by initial errors. The above results can be explained by analyzing variations in photosynthesis and autotrophic respiration. The numerical results also suggest that the skill of predictions of the terrestrial carbon cycle may be improved by reducing the different types of errors for different climatic regions and prediction timescales.

The ensemble deep learning model for novel COVID-19 on CT images

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

Constitutive modeling of γ-irradiated silicone rubber foams under compression and shear loading

Under the service conditions of nuclear engineering applications, silicone rubber foams should endure radiation of γ rays accompanied by compression and shear loading. In this paper, the Hyperfoam model is extended to characterize the mechanical behaviors of silicone rubber foams under γ-irradiation. After exposing silicone rubber foams to a wide radiation dose range from 0 to 500 kGy at room temperature, uniaxial compression and simple shear tests were carried out to establish the relationships between the model parameters of Hyperfoam model and radiation dose, and indentation tests were performed to verify the extended Hyperfoam model. The linear radiation dose-related hardening properties of the material are derived using the testing results, including the linear increases of the initial shear modulus, and the compression stress and the shear stress for a given strain condition, which result in the linear radiation dose-dependent relationships for the initial shear modulus-related model parameters and the radiation dose-independent hardening exponent-related model parameters of Hyperfoam model. The performances of the extended Hyperfoam model are demonstrated by comparing the fitted material responses with uniaxial compression and simple shear data. And the prediction ability is verified by comparing the simulated results of indentation using the finite element method with the test data. The extended model is capable of characterizing the coupling stress state of compression and shear in the investigated irradiation dose range, which is expected to aid in evaluating the safety of nuclear engineering structures.

Influence of γ-irradiation on mechanical behaviors of poly methyl-vinyl silicone rubber foams at different temperatures

In this study, the radiation dose effect of γ rays on the temperature-dependent mechanical behaviors of poly methyl-vinyl silicone rubber foams is investigated by uniaxial compression tests. After exposure to a wide radiation dose range from 0 to 500 kGy at room temperature, silicone rubber foams were tested at 10, 40, 70, and 100 °C. The test results show that temperature increase causes linear hardening for both unirradiated and irradiated samples due to the predominating entropy-related deformation of silicone rubber matrix. Moreover, increase of irradiation dose leads to linear hardening as a result of dominating cross-linking reactions of rubber networks. A stress hardening factor is introduced to characterize the effect of irradiation dose on temperature-related hardening behavior, which is defined as the ratio between the compressive stresses of irradiated and unirradiated samples for a given compression strain. The approximated constant stress hardening factor for each radiation dose condition implies that the irradiation dose does not affect the temperature-related hardening behavior. Based on the test findings, the Ogden Hyperfoam model was extended by correlating the initial shear modulus-related model parameters with radiation dose and temperature. The hardening exponent-related model parameters have been shown to be independent of radiation dose and temperature, and the Poisson's ratio-related model parameters are approximated to zero for the high porosity of the material. The prediction performance of the extended model is demonstrated by comparing the fitted model responses with test data over the studied irradiation doses and temperature range. The results are expected to be helpful for the safety evaluation of nuclear engineering structures.

Forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction.

In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends model's predictions with noisy observations to correct initial state and/or model parameters. We apply this approach on a projection based reduced order model (ROM) of the one-dimensional viscous Burgers equation with a square wave defining a moving shock, and the two-dimensional vorticity transport equation formulating a decay of Kraichnan turbulence. We investigate the capability of the approach to approximate an optimal value for eddy viscosity with different measurement configurations. Specifically, we show that our approach can sufficiently assimilate information either through full-field or sparse noisy measurements to estimate eddy viscosity closure to cure standard Galerkin reduced order model (GROM) predictions. Therefore, our approach provides a modular framework to correct forecasting error from a sparse observational network on a latent space. We highlight that the proposed GROM-FSM framework is promising for emerging digital twin applications, where real-time sensor measurements can be used to update and optimize surrogate model's parameters.

Оценка влияния параметров неопределенности на прогнозирование показателей разработки

At the stage of developing a geological and hydrodynamic reservoir model, uncertainties in input data may lead to errors in simulation results and subsequent inaccurate economic evaluations of oil or gas field potentials. In order to improve predictive reliability, a study was completed to assess how input data of a hydrodynamic model influence forecasts of main parameters of a production using the example of the Tournaisian site of the Soldatovskoye field. The study presents an approximate algorithm reducing uncertainties and improving the forecast reliability of the production parameters obtained using a geological and hydrodynamic reservoir model. The algorithm includes a substantiated selection of the initial sensitivity parameters, an evaluation of the impact of the initial parameters on the hydrodynamic reservoir model using the sensitivity analysis, as well as a selection of an optimal range of variations of the uncertainty parameters as a result of the multivariant hydrodynamic simulation adaptation, calculation and analysis of the multivariant hydrodynamic reservoir model forecast. The study aims to clarify the design process parameters of the development, assess the risks of non-confirmation of the hydrodynamic simulation forecasting, and make recommendations and proposals to study those uncertainty parameters, which influence most on certain predicted production parameters of an asset. As a result, a block diagram of the approach is presented in order to generalize and replicate it on potential and important oil and gas fields. The described approach of the model adaptation and calculations of the predicted options in conditions of uncertainty of the initial model parameters make it possible to obtain a more accurate and less arbitrary hydrodynamic reservoir model, which reduces probability of an incorrect evaluation of potentials of a young field or a field at an early production stage.

Simulation of the round insert face milling process of AISI 316LN stainless steel with machining-based plastic behavior modeling

A proper plastic behavioral model is required to simulate metal cutting. Here, we model the plastic behavior of AISI 316LN stainless steel during face milling. We used a numerical approach to derive a plasticity model appropriate for machining; a two-dimensional cutting force prediction and a genetic algorithm were conducted for that. The force prediction was performed considering a geometrical relationship between the work material and cutting tool. We used the Johnson-Cook (JC) constitutive material model, and initial model parameters were obtained via tension testing at low strain rates (0.001–1 s−1). The genetic algorithm optimized the model parameters; the predictive accuracy with respect to cutting force was high in the model with optimized parameters. We used the optimized JC model for finite element analysis and simulated face milling with a round insert. We measured the cutting forces to validate our modeling approach; the simulated and measured principal forces were in good agreement (error rate ≤ 3.9% under all machining conditions). Our model improved the accuracy of plastic behavior prediction by 93.0% versus the original model. The high accuracy was retained even when the machining environment changed.

High-dimensional uncertainty quantification for Mars atmospheric entry using adaptive generalized polynomial chaos

The probabilistic uncertainties in Mars atmospheric entry degrade the entry guidance performance. The propagation law of high-dimensional uncertainty during Mars atmospheric entry is still an open problem that should be investigated. The current work aims to examine the uncertainty propagation during Mars atmospheric entry due to uncertain initial state and model parameters, with introducing the generalized polynomial chaos method into Mars atmospheric entry dynamics simulations. For more efficient and accurate, generalized polynomial chaos is modified through spectral decomposition and random space decomposition. First, stochastic dynamics are modeled and transformed into equivalent deterministic dynamics in a higher-dimensional space and are updated adaptively when the statistic characteristic of the system state changes greatly. Second, the random space is decomposed when the relative error in variance becomes larger than the predefined threshold. In each random sub-domain, the updated generalized polynomial chaos is employed. Finally, the adaptive generalized polynomial chaos is used to quantify the uncertainty propagation in Mars atmospheric entry dynamics. Comparison studies are also performed with traditional generalized polynomial chaos and Monte-Carlo simulations. The influence levels and the evolution profiles of the initial and parametric uncertainties are revealed through numerical simulations.