Simulation of Rotating Asymmetric Sideways Forces during Vertical Displacement Events in CFETR

Tokamak plasmas with elongated cross sections are susceptible to vertical displacement events(VDEs),which can damage the first wall via heat flux or electromagnetic(EM)forces.We present a 3D nonlinear reduced magnetohydrodynamic(MHD)simulation of CFETR plasma during a cold VDE following the thermal quench,focusing on the relationship among the EM force,plasma displacement,and the n=1 mode.The dominant mode,identified as m/n=2/1,becomes destabilized when most of the current is contracted within the q=2 surface.The displacement of the plasma current centroid is less than that of the magnetic axis due to the presence of SOL current in the open field line region.Hence,the symmetric component of the induced vacuum vessel current is significantly mitigated.The direction of the sideways force keeps a constant phase approximately compared to the asymmetric component of the vacuum vessel current and the SOL current,which in turn keep in-phase with the dominant 2/1 mode.Their amplitudes are also closely associated with the growth of the dominant mode.These findings provide insights into potential methods for controlling the phase and amplitude of sideways forces during VDEs in the future.

Shear mechanical properties and frictional sliding responses of rough joint surfaces under dynamic normal displacement conditions

A comprehensive understanding of the dynamic frictional characteristics in rock joints under high normal load and strong confinement is essential for ensuring the safety of deep engineering construction and mitigating geological disasters.This study conducted shear experiments on rough rock joints under displacement-controlled dynamic normal loads,investigating the shear behaviors of joints across varying initial normal loads,normal loading frequencies,and normal loading amplitudes.Experimental results showed that the peak/valley shear force values increased with initial normal loads and normal loading frequencies but showed an initial increase followed by a decrease with normal loading amplitudes.Dynamic normal loading can either increase or decrease shear strength,while this study demonstrates that higher frequencies lead to enhanced friction.Increased initial normal loading and normal loading frequency result in a gradual decrease in joint roughness coefficient(JRC)values of joint surfaces after shearing.Positive correlations existed between frictional energy dissipation and peak shear forces,while post-shear joint surface roughness exhibited a negative correlation with peak shear forces through linear regression analysis.This study contributes to a better understanding of the sliding responses and shear mechanical characteristics of rock joints under dynamic disturbances.

Estimation of Displacement and Extension due to Reverse Drag of Normal Faults: Forward Method

In the case of reverse drag of normal faulting, the displacement and horizontal extension are determined based on the established equations for the three mechanisms: rigid body, vertical shear and inclined shear. There are three sub-cases of basal detachment for the rigid body model: horizontal detachment, antithetic detachment and synthetic detachment. For the rigid body model, the established equations indicate that the total displacement on the synthetic base (Dt2) is the largest, that on the horizontal base (Dt1) is moderate, and that on the antithetic base (Dt3) is the smallest. On the other hand, the value of (Dt1) is larger than the displacement for the vertical shear (Dt4). The value of (Dt1) is larger than or less than the displacement for the inclined shear (Dt5) depending on the original fault dip δ0, bedding angle θ, and the angle of shear direction β. For all original parameters, the value of Dt5 is less than the value of Dt4. Also, by comparing three rotation mechanisms, we find that the inclined shear produces largest extension, the rigid body model with horizontal detachment produces the smallest extension, and the vertical shear model produces moderate extension.

Retinal displacement after surgery for idiopathic macular hole

AIM:To review and summarize the mechanism hypothesis,influencing factors and possible consequences of macular retinal displacement after idiopathic macular hole(IMH)surgery.METHODS:PubMed and Web of Science database was searched for studies published before April 2023 on“Retinal displacement”,“Idiopathic macular holes”,and“Macular displacement”.RESULTS:Recently,more academics have begun to focus on retinal displacement following idiopathic macular holes.They found that internal limiting membrane(ILM)peeling was the main cause of inducing postoperative position shift in the macular region.Moreover,several studies have revealed that the macular hole itself,as well as ILM peeling method,will have an impact on the result.In addition,this phenomenon is related to postoperative changes in macular retinal thickness,cone outer segment tips line recovery,the occurrence of dissociated optic nerve fiber layer(DONFL)and the degree of metamorphopsia.CONCLUSION:As a subclinical phenomenon,the clinical significance of postoperative macular displacement cannot be underestimated as it may affect the recovery of anatomy and function.

Effect of high-multiple water injection on rock pore structure and oil displacement efficiency

Experimental methods,including mercury pressure,nuclear magnetic resonance(NMR)and core(wateroil)displacement,are used to examine the effects of high-multiple water injection(i.e.water injection with high injected pore volume)on rock properties,pore structure and oil displacement efficiency of an oilfield in the western South China Sea.The results show an increase in the permeability of rocks along with particle migration,an increase in the pore volume and the average pore throat radius,and enhanced heterogeneity after high-multiple water injection.Compared with normal water injection methods,a high-multiple water injection is more effective in improving the oil displacement efficiency.The degree of recovery increases faster in the early stage due to the expansion of the swept area,and the transition from oil-wet to water-wet.The degree of recovery increases less in the late stage due to various factors,including the enhancement of heterogeneity in the rocks.Considering both the economic aspect and the production limit of water flooding,it is recommended to adopt other technologies to further enhance oil recovery after 300 PV water injection.

Co-seismic surface displacement of the June 21, 2022 M_(W)6 Khōst,Afghanistan earthquake from InSAR observations

A robust estimation of the earthquake location, seismic moment, and fault geometry is essential for objective seismic hazard assessment. Seismic events in a remote location, specifically in the absence of seismic and GNSS networks, can be investigated effectively using the In SAR-based technique. This study adopts the Differential Interferometric SAR(DIn SAR) technique to quantify the co-seismic surface displacement caused by the June 21, 2022, Khōst M_(W)6 earthquake that occurred along the western plate boundary between the Indian and Eurasian plate. The interferograms show that the maximum surface deformation occurred on the northwest and southwest of the fault line. From coherence, the Line of Sight(LOS) displacement, and the co-seismic surface displacement analysis, it has been observed that surface deformation was most pronounced in the southwest region of the fault line, and the surface has moved to the opposite direction along the fault line, which indicates a sinistral slightly oblique strike-slip movement. This In SAR-based observation appears consistent with the seismic waveforms derived from co-seismic surface displacements. Further, it has been argued that the slip deficit accumulated during the period of the last about 48 years along the frontal region of the northward extension of the Suleiman range and associated fault zone is qualitatively estimated at about 1.5 m, which is consistent with the seismic waveforms derived finite slip model.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging(GPI)diagnostic has been developed and operated on EAST since 2012,and the time-delay estimation(TDE)method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data.However,with the TDE method it is difficult to analyze the data with fast transient events,such as edge-localized mode(ELM).Consequently,a method called the spatial displacement estimation(SDE)algorithm is developed to estimate the turbulence velocity with high temporal resolution.Based on the SDE algorithm,we make some improvements,including an adaptive median filter and super-resolution technology.After the development of the algorithm,a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm,and the calculated speed agrees well with preset speed.This SDE algorithm is applied to the EAST GPI data analysis,and the derived propagation velocity of turbulence is consistent with that from the TDE method,but with much higher temporal resolution.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).

Semi-analytical solution for mechanical analysis of tunnels crossing strike-slip fault zone considering nonuniform fault displacement and uncertain fault plane position

The tunnel subjected to strike-slip fault dislocation exhibits severe and catastrophic damage.The existing analysis models frequently assume uniform fault displacement and fixed fault plane position.In contrast,post-earthquake observations indicate that the displacement near the fault zone is typically nonuniform,and the fault plane position is uncertain.In this study,we first established a series of improved governing equations to analyze the mechanical response of tunnels under strike-slip fault dislocation.The proposed methodology incorporated key factors such as nonuniform fault displacement and uncertain fault plane position into the governing equations,thereby significantly enhancing the applicability range and accuracy of the model.In contrast to previous analytical models,the maximum computational error has decreased from 57.1%to 1.1%.Subsequently,we conducted a rigorous validation of the proposed methodology by undertaking a comparative analysis with a 3D finite element numerical model,and the results from both approaches exhibited a high degree of qualitative and quantitative agreement with a maximum error of 9.9%.Finally,the proposed methodology was utilized to perform a parametric analysis to explore the effects of various parameters,such as fault displacement,fault zone width,fault zone strength,the ratio of maximum fault displacement of the hanging wall to the footwall,and fault plane position,on the response of tunnels subjected to strike-slip fault dislocation.The findings indicate a progressive increase in the peak internal forces of the tunnel with the rise in fault displacement and fault zone strength.Conversely,an augmentation in fault zone width is found to contribute to a decrease in the peak internal forces.For example,for a fault zone width of 10 m,the peak values of bending moment,shear force,and axial force are approximately 46.9%,102.4%,and 28.7% higher,respectively,compared to those observed for a fault zone width of 50 m.Furthermore,the position of the peak internal forces is influenced by variations in the ratio of maximum fault displacement of the hanging wall to footwall and the fault plane location,while the peak values of shear force and axial force always align with the fault plane.The maximum peak internal forces are observed when the footwall exclusively bears the entirety of the fault displacement,corresponding to a ratio of 0:1.The peak values of bending moment,shear force,and axial force for the ratio of 0:1 amount to approximately 123.8%,148.6%,and 111.1% of those for the ratio of 0.5:0.5,respectively.

A technique for enhancing tight oil recovery by multi-field reconstruction and combined displacement and imbibition

A seepage-geomechanical coupled embedded fracture flow model has been established for multi-field coupled simulation in tight oil reservoirs,revealing the patterns of change in pressure field,seepage field,and stress field after long-term water injection in tight oil reservoirs.Based on this,a technique for enhanced oil recovery(EOR)combining multi-field reconstruction and combination of displacement and imbibition in tight oil reservoirs has been proposed.The study shows that after long-term water flooding for tight oil development,the pressure diffusion range is limited,making it difficult to establish an effective displacement system.The variation in geostress exhibits diversity,with the change in horizontal minimum principal stress being greater than that in horizontal maximum principal stress,and the variation around the injection wells being more significant than that around the production wells.The deflection of geostress direction around injection wells is also large.The technology for EOR through multi-field reconstruction and combination of displacement and imbibition employs water injection wells converted to production and large-scale fracturing techniques to restructure the artificial fracture network system.Through a full lifecycle energy replenishment method of pre-fracturing energy supplementation,energy increase during fracturing,well soaking for energy storage,and combination of displacement and imbibition,it effectively addresses the issue of easy channeling of the injection medium and difficult energy replenishment after large-scale fracturing.By intensifying the imbibition effect through the coordination of multiple wells,it reconstructs the combined system of displacement and imbibition under a complex fracture network,transitioning from avoiding fractures to utilizing them,thereby improving microscopic sweep and oil displacement efficiencies.Field application in Block Yuan 284 of the Huaqing Oilfield in the Ordos Basin has demonstrated that this technology increases the recovery factor by 12 percentage points,enabling large scale and efficient development of tight oil.

Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Displacement characteristics of CO_(2)flooding in extra-high water-cut reservoirs

Carbon dioxide(CO_(2))flooding is a widely applied recovery method during the tertiary recovery of oil and gas.A high water saturation condition in reservoirs could induce a‘water shielding’phenomenon after the injection of CO_(2).This would prevent contact between the injected gas and the residual oil,restricting the development of the miscible zone.A micro-visual experiment of dead-end models,used to observe the effect of a film of water on the miscibility process,indicates that CO_(2)can penetrate the water film and come into contact with the residual oil,although the mixing is significantly delayed.However,the dissolution loss of CO_(2)at high water-cut conditions is not negligible.The oil-water partition coefficient,defined as the ratio of CO_(2)solubility in an oil-brine/two-phase system,keeps constant for specific reservoir conditions and changes little with an injection gas.The NMR device shows that when CO_(2)flooding follows water flooding,the residual oil decreasesdnot only in medium and large pores but also in small and micro pores.At levels of higher water saturation,CO_(2)displacement is characterized initially by a low oil production rate and high water-cut.After the CO_(2)breakthrough,the water-cut decreases sharply and the oil production rate increases gradually.The response time of CO_(2)flooding at high watercut reservoirs is typically delayed and prolonged.These results were confirmed in a pilot test for CO_(2)flooding at the P1-1 well group of the Pucheng Oilfield.Observations from this pilot study also suggest that a larger injection gas pore volume available for CO_(2)injection is required to offset the dissolution loss in high water saturation conditions.

Evaluation of Convolutional-Long Short-Term Memory Model in Predicting Surface Displacement of Underground Mines

Surface stability is essential in underground mines health management systems. Unexpected Surface displacement in underground mines could lead to loss of lives, injuries, and economic losses. To reduce or neutralise the adverse effects of surface displacement, it is vital to monitor and accurately predict them and unravel their mechanisms. In recent years, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have proven effective in predicting complex problems. However, CNN neglects the dynamic dependency of the input in the temporal dimension, which affects surface displacement features. The Convolutional-LSTM model can dynamically learn the temporal dependency among input features via the feedback connections in the LSTM to improve accurate captures of surface displacement features. This study focused on evaluating the C-LSTM model in predicting surface displacement of underground mines and assessed the predictive capabilities and generalisation strength of using hybridised ANN models. Geodetic and geotechnical data gathered from a Gold Mine in Ghana was used. The three models were tested on experimental data collected at Monitoring Scan Point 3. It was observed from the prediction output that all the methods could provide applicable and practical results. However, using indicators like root mean square error (RMSE) and correlation coefficient (R) in assessing the output of the prediction, the C-LSTM had the best prediction output. This study contributes to the advancement of accurate and efficient prediction of surface displacement of underground mines, ultimately enhancing and assisting safety operations.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

Phase-Based Optical Flow Method with Optimized Parameter Settings for Enhancing Displacement Measurement Adaptability

To enhance the applicability and measurement accuracy of phase-based optical flow method using complex steerable pyramids in structural displacement measurement engineering applications, an improved method of optimizing parameter settings is proposed. The optimized parameters include the best measurement points of the Region of Interest (ROI) and the levels of pyramid filters. Additionally, to address the issue of updating reference frames in practical applications due to the difficulty in estimating the maximum effective measurement value, a mechanism for dynamically updating reference frames is introduced. Experimental results demonstrate that compared to representative image gradient-based displacement measurement methods, the proposed method exhibits higher measurement accuracy in engineering applications. This provides reliable data support for structural damage identification research based on vibration signals and is expected to broaden the engineering application prospects for structural health monitoring.

A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides

This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.

Effect of Articular Cavity Injection Combined with Bite Splint Treatment on Anterior Disc Displacement Without Reduction

Objective:To observe the clinical effect of articular cavity injection combined with bite splint therapy for the treatment of anterior disc displacement without reduction(ADDWoR).Methods:The research subjects for this study were 30 patients with ADDWoR treated in the temporomandibular joint specialist outpatient clinic from November 2018 to November 2019,with a disease duration of 1 to 6 months.The treatment group was treated with an articular cavity injection of sodium hyaluronate+bite splint.The control group was treated with a simple articular cavity injection of sodium hyaluronate.The two groups were followed up once every 2 weeks to evaluate the treatment effect and observe the clinical efficacy of the two groups.Statistical analysis was carried out using SPSS 24.0.t-test and general linear regression analysis were carried out to compare the data of both groups,andχ^(2)-test and binary logistic regression analysis were performed for pain index comparison.Results:There was no significant difference in terms of the efficacy of the treatment received by both groups.The mouth opening and joint pain of patients in both groups were significantly improved after treatment(P<0.001).Conclusion:Articular cavity injection of sodium hyaluronate and occlusal splint therapy are both effective and safe methods for treating ADDWoR.

Landslide displacement prediction based on the ICEEMDAN,ApEn and the CNN-LSTM models

Landslide deformation is affected by its geological conditions and many environmental factors.So it has the characteristics of dynamic,nonlinear and unstable,which makes the prediction of landslide displacement difficult.In view of the above problems,this paper proposes a dynamic prediction model of landslide displacement based on the improvement of complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN),approximate entropy(ApEn)and convolution long short-term memory(CNN-LSTM)neural network.Firstly,ICEEMDAN and Ap En are used to decompose the cumulative displacements into trend,periodic and random displacements.Then,the least square quintic polynomial function is used to fit the displacement of trend term,and the CNN-LSTM is used to predict the displacement of periodic term and random term.Finally,the displacement prediction results of trend term,periodic term and random term are superimposed to obtain the cumulative displacement prediction value.The proposed model has been verified in Bazimen landslide in the Three Gorges Reservoir area of China.The experimental results show that the model proposed in this paper can more effectively predict the displacement changes of landslides.As compared with long short-term memory(LSTM)neural network,gated recurrent unit(GRU)network model and back propagation(BP)neural network,CNN-LSTM neural network had higher prediction accuracy in predicting the periodic displacement,with the mean absolute percentage error(MAPE)reduced by 3.621%,6.893% and 15.886% respectively,and the root mean square error(RMSE)reduced by 3.834 mm,3.945 mm and 7.422mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide a new insight for practical landslide prevention and control engineering.

Anomaly detection on displacement rates and deformation pattern features using tree-based algorithm in Japan and Indonesia

Research on strain anomalies and large earthquakes based on temporal and spatial crustal activities has been rapidly growing due to data availability, especially in Japan and Indonesia. However, many research works used local-scale case studies that focused on a specific earthquake characteristic using knowledgedriven techniques, such as crustal deformation analysis. In this study, a data-driven-based analysis is used to detect anomalies using displacement rates and deformation pattern features extracted from daily global navigation satellite system(GNSS) data using a machine learning algorithm. The GNSS data with188 and 1181 continuously operating reference stations from Indonesia and Japan, respectively, are used to identify the anomaly of recent major earthquakes in the last two decades. Feature displacement rates and deformation patterns are processed in several window times with 2560 experiment scenarios to produce the best detection using tree-based algorithms. Tree-based algorithms with a single estimator(decision tree), ensemble bagging(bagging, random forest and Extra Trees), and ensemble boosting(AdaBoost, gradient boosting, LGBM, and XGB) are applied in the study. The experiment test using realtime scenario GNSSdailydatareveals high F1-scores and accuracy for anomaly detection using slope windowing 365 and 730 days of 91-day displacement rates and then 7-day deformation pattern features in tree-based algorithms. The results show the potential for medium-term anomaly detection using GNSS data without the need for multiple vulnerability assessments.