Severe Accident Research Articles

Exothermic characteristics evaluation of the Pt coated α-Al2O3 catalyst in bed for passive airborne radioactive material reduction system under forced flow conditions

After the severe accident at the Fukushima nuclear power plant, there has been heightened interest in the development of severe accident countermeasures. As part of these efforts, a passive filtration system capable of passive operation during power loss is under development.This paper focuses on evaluating the performance of a catalyst-filled bed designed to create a temperature gradient through exothermic reactions between combustible gases generated during accidents and a catalyst. The temperature gradient facilitates the formation of natural convection within the system, enabling passive operation. The catalytic bed is designed in a bed-type configuration filled with exothermic catalysts. To ensure physical and chemical stability under severe accident conditions, heat resistance tests were conducted using inorganic materials as a base to select an optimized exothermic catalyst. A test apparatus was designed for unit evaluation testing of the catalyst-filled bed equipped with the catalytic bed. Exothermic characteristics evaluation tests were conducted by varying the hydrogen volume fraction and face velocity, measuring heat generation in the catalytic bed under each condition. The correlation between these variables and the heat generation performance of the catalytic bed was determined. These findings will be utilized to inform the design parameters of the passive airborne radioactive material reduction system.

Validation and Application of a Code for Three-Dimensional Analysis of Hydrogen–Steam Behavior in a Nuclear Reactor Containment during Severe Accidents

In a pressurized water reactor (PWR) during a loss of coolant accident (LOCA) or a station blackout (SBO) accident, water and steam are released into the containment building. The water vapor mixes with the atmosphere, partially condensing into droplets or condensing on the containment walls. Although a significant amount of water vapor condenses, it coexists with hydrogen generated by the reactor core oxidation. As water vapor condenses, the volume fraction of hydrogen increases, raising the risk of explosion or flame acceleration. As such, water vapor’s behavior directly affects hydrogen distribution. To conservatively evaluate hydrogen safety in a PWR during a severe accident, lumped-parameter codes have been heavily used. As a best-estimate approach for hydrogen safety analysis in a PWR containment, a turbulence-resolved CFD code called contain3D has been developed. This paper presents the validation results of the code and simulation results of hydrogen behavior affected by water vapor condensation and hydrogen removal by passive autocatalytic recombiners (PARs) in the APR1400 containment. The results provide insight into the three-dimensional behaviors of the hydrogen in the containment.

Intelligent risk identification for drilling lost circulation incidents using data-driven machine learning

In drilling operations, lost circulation (LC) poses a significant challenge, potentially leading to severe accidents such as overflow or blowouts if not accurately identified and controlled. This study introduces a method for identifying LC risk severity using data-driven machine learning and statistical algorithms, specifically demonstrated in the Ying-Qiong Basin of the South China Sea. The method begins with variance filtering to eliminate invalid data, followed by the application of the Pearson correlation coefficient to remove redundant data. This process results in the selection of 12 logging parameters out of the initial 38. Combined with indirect judgments of LC severity based on relevant prior knowledge, the data is labeled to construct the original dataset. A feature extraction and optimization method is proposed, which employs sliding time windows to extract 21 features. These features are then selected and optimized using a random forest algorithm to construct the optimal feature set. To enhance the controllability of the Extreme Learning Machine (ELM) model, the sparrow search algorithm is integrated. Additionally, several fitness functions are introduced for both regression and classification models, resulting in the development of the improved ELM (IELM) model. The dataset is split into training and test sets, and the performance of the IELM model is compared to that of the standard ELM and BP neural network models. The results indicate that the IELM model outperforms the other two, achieving an impressive test F1 score of 97.22 % for accurately identifying LC severity. Moreover, the model trained using the optimal feature set demonstrates higher accuracy and faster convergence. The proposed feature extraction and optimization method effectively increases the amount of information contained in the data, thereby enhancing the model's performance.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

Two-stage auxiliary drifting path tracking control for distributed driving three-axle commercial vehicles

When maneuvering corners at high speeds, commercial vehicles experience significant sideslip angles and tire force saturation, which can lead to severe traffic accidents. Incorporating intelligent driving technology to develop a controllable scheme that surpasses stability constraints and maintains the vehicle in a drift state is crucial for enhancing driving safety. Therefore, based on the model characteristics of distributed drive three-axle(DDTA) commercial vehicles, a two-stage auxiliary drift controller is proposed. In the auxiliary drift stage, time-varying model predictive control (MPC) is employed to track the desired states and achieve steady-state drift path tracking under extreme working conditions. A two-stage controller switching strategy is implemented based on road information. In the yaw stability control stage, an advanced auxiliary system facilitates cooperative control to smoothly restore tire attachment and vehicle yaw. Simulation results demonstrate that the control strategy ensures consistent path tracking performance even when adhesion of the middle and rear axle saturates and peak vehicle sideslip angle reaches 32.09°. After completing the drifting, vehicle yaw successfully returns to a stable state. Subsequently, miniaturized vehicle tests qualitatively analyze relevant conclusions by elucidating transient instability evolution in vehicles subjected to steering and distributed drive. The controllable stability boundary of the vehicle is thus expanded, thereby enhancing the engineering feasibility of drift technology.

Interfacial structure and properties of anodized 6061 Al alloy joints by ultrasonic-assisted hot dipping and soldering using a Sn–9Zn–2Al filler metal

Al alloy joints soldered with Sn-based filler metal are susceptible to corrosion and early failure when exposed to moisture, resulting in severe accidents and substantial economic losses. This study proposes a method to prevent galvanic corrosion by separating Al alloys and Sn-based filler metals with an insulating barrier layer of anodized aluminum oxide (AAO) composed of Al2O3. The successful joining of anodized aluminum alloy (AAA) with Sn–9Zn–2Al was achieved through ultrasonic-assisted hot dipping and soldering in air at 240 °C. Microstructural examination revealed strong bonding between the solder alloy and AAO, with the solder infiltrating the AAO nanotubes and a transition layer consisting of two sublayers at the interface: amorphous ZnO mixed with nanocrystals (ZnO and Al2O3) and amorphous Al2O3. The shear strength of the joints ranged from 31.8 to 32.8 MPa when subjected to ultrasonic hot dipping for 0.5–2.0 s, with no significant difference observed. Fracture initiation and propagation occur within the transition layer at the interface. The shear strength of joints subjected to dipping for more than 3.0 s decreased slightly to 27.9 MPa due to localized peeling off of AAO from the Al substrate. The formation of the interface involves the segregation of Al and Zn on the liquid filler metal surface, driven by differences in atomic activity. Due to the jet force from cavitation, an Al-rich liquid infiltrates the nanotube, resulting in the formation of Zn–O at the interface and Al–O both at the interface and within the nanotube. Throughout this process, the wetting model shifts from a Cassie state to a near-Wenzel state. Immersion tests of two types of joints, Al/SnZnAl/Al and AAA/SnZnAl/AAA, confirmed that galvanic corrosion of Sn/Al was completely inhibited by the latter. Consequently, AAA joints demonstrate excellent corrosion resistance, validating this approach as a promising solution to the Al/Sn galvanic corrosion issue.

Early micro-short circuit fault diagnosis of lithium battery pack based on Pearson correlation coefficient and KPCA

Though lithium batteries have been extensively applied in electric vehicles as the power sources due to the excellent advantages in recent years, the safety risk of them is always existed inarguably. For the effect of early micro-short circuit faults on the electrical characteristics of the batteries is minor, it is hard to be detected and diagnosed timely, as a result it may evolve into direct short circuit and cause severe safety accidents, such as thermal runaway, explosion and so on. This paper proposes a method for diagnosing micro-short circuit fault in battery pack based on Pearson correlation coefficients (PCCs) and kernel principal component analysis (KPCA). Firstly, the voltage values of each battery in various operation states are measured and denoised using a moving filter method, then PCCs between each battery's voltage of the same series branch are calculated to form a dataset. Secondly, the dataset of PCCs is taken to train KPCA algorithm to obtain the control threshold by calculating the squared prediction error (SPE) statistic. After the training process finished, the PCCs of real-time voltage values of the batteries are calculated, then the related SPE statistic is calculated to determine whether the threshold is exceeded or not, meanwhile the contribution graph will be drawn if the threshold is exceeded. Finally, the faulty cell can be cross localized according to the feature of contribution rate in the contribution graph. Experiments are carried out under the condition of Dynamic Stress Test (DST), and comparisons are performed with principal component analysis (PCA) algorithm by using the different input features, such as raw voltage data, Kendall and Spearman correlation coefficient, respectively. The experimental results show that, the proposed method can diagnose the fault as soon as it occurred, and the feature of contribution rate is more obvious than other methods, which is beneficial to the fault localization. Moreover, for the fault judgment only depends on the correlation of the battery's voltages rather than the absolute values of them, which always vary obviously in different operation states, the proposed method can be applied in various engineering scenarios expediently.

Digital twin-based non-destructive testing method for ultimate load-carrying capacity prediction

Load-carrying experiments are essential for validating the mechanical performance of structural designs. Unpredictable early structure collapses during experiments would lead to severe safety accidents, and thus the real-time prediction of the ultimate load-carrying capacity (ULC) becomes highly necessary. However, due to multi-source experiment deviations, ULC prediction methods based on finite element (FE) analysis struggle to meet the high-accuracy and real-time requirements. To address this issue, this paper proposes a digital twin-based non-destructive testing (DT-NDT) method for ULC prediction. The method employs a deep neural network (DNN) to construct an experiment digital twin (DT) model in the offline phase. In the online phase, the experiment DT model utilizes the current measured strains to predict the ULC at future time steps in real-time, thus achieving non-destructive testing (NDT). First, a series of virtual experiments considering multi-source experiment deviations are carried out by FE analysis to obtain the virtual experiment dataset. The dataset includes strains and collapse loads corresponding to different experiment deviations and is augmented using the data augmentation approach based on structural symmetry. Subsequently, DNN is trained by the virtual experiment dataset to establish the mapping of strain values to collapse load. Finally, the strains are measured in real-time using strain gauges and the strain values are inputted into the DNN, i.e., the experiment DT model, to predict the collapse load. In this paper, the principle and effectiveness of the proposed method are illustrated through an analytical example and two experiment examples. In the experiment examples, the average relative errors of the DT-NDT method are 0.9 % for an open-hole plate and -1.45 % for a grid-stiffened cylindrical shell. The results illustrate the high prediction accuracy and effectiveness of the DT-NDT method, demonstrating its immense potential in NDT.

Analyses of Jet Buoyant Flow in a Multicompartment Containment Using an Open-Source Solver

Hydrogen mixing and distribution in a nuclear power plant containment during a postulated severe accident is a phenomenon with high safety relevance for current and advanced light water reactors. The Organisation for Economic Co-operation and Development (OECD)/Nuclear Energy Agency (NEA) SESAR Thermal-Hydraulics (SETH) project was carried out to generate experimental data on basic containment phenomena with the required instrumentation to assess computational fluid dynamics (CFD) and lumped parameter codes. In this paper, we analyze the PANDA SETH Test 16 of the OECD/NEA SETH project using containmentFOAM, which is a tailored open-source CFD code package for containment analysis developed at Forschungszentrum Jülich GmbH based on OpenFOAM.® SETH Test 16 addressed steam-air mixture transport driven by jets and plumes in the multicompartment PANDA facility (two vessels and an interconnecting pipe) at the Paul Scherrer Institut in Switzerland. Steam as a wall plume was injected at 0.040 kg/s and 140°C in vessel 1, which was initially filled with dry air at 108°C. The steam-air mixture was vented out from the top of vessel 2. The system pressure was constant at 1.3 bar for the whole test duration (4000 s). To simulate SETH Test 16, the k-ω shear stress transport turbulence model was adopted with the generalized gradient diffusive hypothesis buoyancy turbulence model. The simulation represented the experimental phenomenology reasonably well. The trends for the calculated temperature distribution were similar to those of the experiments and only yielded a slight discrepancy of about 2.7%. In addition, the distribution of the steam molar fraction was well predicted above the injection tube in vessel 1. However, below the injection tube in vessel 1, some discrepancies between the simulations and the experimental results were observed. These may have been caused by the higher turbulent mass diffusivity in the physical model and the challenge of the mesh resolution in the middle region of vessel 1.

Development of a MELCOR Model for LVR-15 Severe Accidents Assessment

LVR-15 is a light-water-tank-type research reactor placed in a stainless-steel vessel under a shielding cover located in the Research Centre Rez (CVR) near Prague. It is operated at a steady-state power of up to 10 MWt under atmospheric pressure and is cooled by forced circulation. In 2011, the fuel was replaced, going from high-enriched uranium (HEU) to low-enriched uranium (LEU). After 2017, the State Office for Nuclear Safety (SUJB) asked CVR to evaluate the LVR-15 under Design Extended Conditions B (DEC-B). For this reason, a new model was developed in the MELCOR code, which allows for modelling the progression of a severe accident (SA) in light-water nuclear power plants and estimating the behaviour of the reactor under SA conditions. The model was built by collecting information about the LVR-15. Since the research reactor can have different core configurations according to the location of the core components, the core configuration with the most fuel (hottest campaign K221) was selected. Then, to create the radial nodalisation, the details of the core components were obtained and grouped in five radial rings and 27 axial levels. The simulation was run with the boundary conditions collected from campaign K221, and the results were compared with the reference values of the campaign with a negligible percentage of error. For the coolant inlet and outlet temperature, the reference values were 318.18 K and 323.5 K, respectively, while for the simulation, the steady state reached 319 K for the inlet temperature and 324 K for the outlet temperature. Additionally, the cladding temperature of the hottest assembly was compared with the reference value (353.72 K) and the steady-state simulation results (362 K). In future work, different transients leading to severe accidents will be simulated. When simulating the LVR-15 reactor with MELCOR, specific attention is required for the aluminium-cladded fuel assemblies, as the model requires some assumptions to cope with the phenomenological limitations.

Method for determining the realistic size of a crack in a containment in the case of a severe accident

Method for determining the realistic size of a crack in a containment in the case of a severe accident

Application of moving particle semi-implicit method on simulating melt spreading within OECD/ROSAU project

In the context of severe accidents, considerable research efforts throughout the world are currently directed towards ex-vessel corium behavior. The NEA Reduction of Severe Accident Uncertainties (ROSAU) project aims to reduce knowledge gaps and uncertainties associated with two areas: the spreading of core melt in the containment cavity as well as ex-vessel core melt and debris coolability. One pre-test and five large underwater melt spread tests (MST) with molten prototypic material in a newly designed facility are conducted at the Argonne National Laboratory (ANL) in the United States, under the co-ordination with the US Nuclear Regulatory Commission. Part of KTH contributions is to provide numerical results with the developed Moving Particle Semi-implicit (MPS) method code, with specific focus on the temperature distribution and leading-edge progression. The MST-0 and MST-2, conducted in a dry and wet spreading channel, are simulated in the present study. The predicted temperature by MPS code indicates a noticeable decrease at the melt leading edge and a slow decrease in bulk melt in both simulations. Additionally, it is found that the MPS code underestimates the melt average thickness in both simulations due to the absence of a debris porosity model. Overall, the simulation results suggest that the MPS code predicts the melt leading-edge progression and immobilization for all the tests.

Molybdenum Disilicide (MoSi2) and Ferrophosphorus Particles (FemPn) Improve the Anti-Seizure Properties of Copper-Tin-Bismuth Composites under Conformal and Nonconformal Contacts

Seizure is a severe accident that can abruptly stop or even damage running equipment. Improving the anti-seizure properties of friction pair materials is also crucial as lubricating and wear resistance. While the tribological performances of molybdenum disilicide (MoSi2) and ferrophosphorus (FemPn) particle-doped copper-tin-bismuth (Cu-Sn-Bi) composites have been investigated in previous studies, the specific effects of MoSi2 and FemPn particles on the anti-seizure property of Cu-Sn-Bi materials remain unclear. This study explores the seizure resistance of Cu-Sn-Bi composites containing MoSi2 and/or FemPn under conformal and nonconformal contact conditions. The materials were prepared using powder sintering technology, and the metallographic structures of four types of self-lubricating copper composites were observed. Seizure resistance under starved lubrication conditions was investigated, and the worn surfaces under different contact types were discussed and analyzed. The results indicate that the addition of MoSi2 and/or FemPn particles effectively refines the grain size, and these particles exhibit different distribution trends after powder sintering. The MoSi2 and FemPn particles improved the maximum seizure load of copper-based composite samples by 66.67% and 84.62%, respectively. The seizure forms under different contact types were also discussed.

Oxidation of molten zirconium-containing droplet in water

During a severe accident in light water reactors, the molten reactor core (corium) falls into a water pool in the form of a jet. Complex interactions may occur between the melt and coolant known as molten fuel–coolant interactions (FCI), including energetic coolant evaporation and metallic melt (e.g., Zr and Fe) oxidation. This may further lead to steam and hydrogen explosions, which are both substantial safety risks for nuclear power plants.The heat of reaction and hydrogen production during oxidation can influence the progress and severity of the accidents. For example, the reaction heat may prolong the liquid state of corium, potentially leading to high-intensity explosions, whereas the generated hydrogen can create a combustible atmosphere, increasing the risk of hydrogen explosion. Therefore, this study evaluates the hydrogen production and oxidation degree of molten metallic droplets falling into a water pool to improve the FCI models for the risk evaluation of severe accident safety. The MISTEE-OX facility at KTH, which has been primarily built to study steam explosions is modified to investigate oxidation during FCI and provide experimental data on the oxidation behaviour of metallic droplets (Zr/Fe) quenched in a subcooled water pool. The dynamics of the falling droplets and generated bubbles are recorded using a high-speed camera, and the total volume of the bubbles is measured using a graduated cylinder. This study presents preliminary experimental results of the oxidation between Zr/Fe droplets and water, as well as recent improvements in measurement methods and facility upgrades. Our research findings are useful to enhance the knowledge of the oxidation process in FCI phenomena and validate the related mechanistic models in FCI codes.

Simulation of the key phenomena in nuclear reactor two-phase flow and severe accident with particle method

Simulation of the key phenomena in nuclear reactor two-phase flow and severe accident with particle method

Research on Measures to Improve the Severe Accident Punishment Act

Research on Measures to Improve the Severe Accident Punishment Act

Capture of the Morphology Transfer Process in a High-Momentum Centrally Aerated Bubble Column with a Hybrid Multifield Two-Fluid Model

The role of pool scrubbing in attenuating radioactivity release after severe accidents has been explored extensively. It is known that scrubbing efficiency is largely determined by the hydrodynamic phenomenology in pools, which is still insufficiently understood. Aerosol gas forms large globules at the nozzle exit, which subsequently break up into a swarm of stable bubbles, where the change of bubble size can reach more than two orders depending on the injection rate. Furthermore, with the increase of flow rates, the injection regime changes from globule to jet characterized by a continuous gas structure. The flow field in the pool can be divided into two zones, injection and rise (swarm), according to the gas-liquid interface morphology. In different zones, scrubbing is governed by different mechanisms such as inertial impact, diffusion, and gravity, whereby bubble rise velocity is one major influential parameter. So far, numerical analysis of pool scrubbing is routinely based on system codes, which rely on empirical correlations for the determination of hydrodynamic parameters as well as scrubbing zones. More recently, owing to the increasing availability of computational resources, knowledge is improved through three-dimensional computational fluid dynamics simulations. Nevertheless, morphology and regime change still present a challenge for both two-fluid and interface-tracking models. Because of the limitation of closures, the conventional two-fluid model (TFM) is generally effective for bubble size smaller than cell size while interface-tracking (capturing) methods demand dozens of cells per bubble size. Combining the two kinds of approaches into one simulation is not straightforward. The present work aims to present a hybrid multifield TFM with extensions for capturing the transfer between different morphologies and for considering the effect of mesh resolution in momentum exchange. The developments are validated by simulating the complex hydrodynamic process in a pool-scrubbing column, which operates in the globule regime with an injection Weber number between 103 and 105. By comparison with experimental data, the results are shown to be promising, which provides a versatile framework for investigation of particle scrubbing in the future. The most attractive feature of the model is that compared to one-fluid interface-tracking models, it reliably predicts the bubble rise velocity. Furthermore, it is able to capture the lateral gas distribution without the need of applying a population balance model since most large bubbles are resolved.

Workplace accidents, economic determinants and underreporting: an empirical analysis in Italy

PurposeThis paper proposes an analysis of occupational accidents in Italy at the regional level. For this purpose, our panel is composed of 20 regions over the 2010–2019 time span.Design/methodology/approachWe apply different econometric estimation techniques (pooled OLS model, panel fixed and random effects models and semiparametric fixed model) using INAIL and ISTAT data. Our models investigate workplace accidents at the regional level by accounting for socioeconomic, labour market and productive system variables and controlling for possible underreporting bias.FindingsOverall results reveal the existence of a relevant under-notification phenomenon of accidents at work with respect to moderate accidents, that is higher especially for the southern regions of Italy. However, when considering as outcome variable an alternative set of more severe workplace accidents our model specification remains highly jointly statistically significant. Among our main findings, the analysis shows that worker skills (blue collar) strongly affect the regional pattern of workplace accidents, i.e. an increase of 1% of low paid employees generates about an increase of 1.8 severe workplace accidents per thousand workers. Moreover, we provide evidence that the size of the firm is inversely related to the occupational accident rates. Finally, our results highlight a nonlinear relationship between GDP and occupational accidents for the Italian regional context, confirmed by the high statistical significance of the quadratic term in all the estimated linear models and by the semi-parametric analysis.Originality/valueA first element of originality of our study consists of investigating the macro determinants of occupation accidents at a regional Italian level. Second, the empirical literature (Boone and Van Ours, 2006) highlights the possible bias of underreporting behaviours on nonfatal accidents in contrast to fatal accidents that are always reported. From this perspective, we have identified a few analyses (namely, Boone et al., 2011) considering different accident sets characterised by different severity degrees. Thus, this paper contributes to the literature considering five alternative subsets of accidents stratified by degree of severity (i.e. moderate, severe, moderate plus severe, severe plus fatal and total accident rates) to test for possible underreporting bias affecting our econometric model.

Nuclear safety Enhanced: A Deep dive into current and future RAVEN applications

As the horizon of nuclear energy expands with the advent of small modular reactors, Generation IV reactors, and fusion reactors, there is a growing perspective that the licensing process could benefit from a more comprehensive approach. Moving beyond traditional deterministic and probabilistic risk assessment analyses might pave the way for a novel safety analysis paradigm propelled by the increasing computational power at our disposal.This paper explores different methodologies that can improve the outcomes of nuclear safety analysis. These range from uncertainty quantification techniques, aimed at enhancing the precision of safety margins, to deploying dynamic event trees by driving system code simulations, capturing the potential evolutions of severe accidents. These methodologies offer a better understanding of the management and consequences of nuclear accident scenarios, significantly improving the accuracy and efficiency of safety predictions compared to traditional methods.Specific case studies illustrate the practical application of these advanced techniques, demonstrating substantial improvements in predicting and managing the dynamics of severe accidents. These findings underscore the effectiveness of these methodologies in enhancing risk assessment capabilities and informing decision-making processes for nuclear safety management. The paper also emphasizes the importance of adaptability and continuous evolution, a call for action to address emerging nuclear safety concerns and highlights the utility of advanced tools like RAVEN.

Ultrasonic dynamic plane wave imaging for high-speed railway inspection

The detection of internal defects within railway tracks stands as a critical aspect of ensuring transport security, given its potential to precipitate severe accidents. Phased array ultrasonic testing methods have gained extensive usage in rail inspection owing to their remarkable precision and expansive coverage. Traditional ultrasonic inspection techniques typically require either a fixed relative position between the transducer and the specimens or a gentle movement of the transducer to beamform. However, the unavoidable vibration stemming from the vehicle–rail coupled interactions often results in time-variant media, significantly impacting the beamforming. This paper presents a refined wavenumber-domain plane wave imaging technique tailored to enable real-time dynamic imaging, specifically accommodating the vertical vibrational movement of the transducer. Real-time calculation of the transmission focal law is achieved by numerical fitting. The wavenumber-domain technique is utilized to realize real-time accurately reconstructed images. The efficacy of the proposed method is assessed through simulations and experiments performed on a convex cylindrical specimen and a rail head sample, respectively. It can be seen that this approach can achieve a frame rate of 83.3 fps with a pipeline architecture in this study, demonstrating the capability to reconstruct images in vibrating environments, such as during the high-speed inspection of railways.