Propagation Mechanism Research Articles

Design, preparation, and enhanced mechanical properties of novel plate-like grains toughened (Zr, Ti, W, Ta)C medium-entropy ceramics fabricated by in-situ reaction hot-pressing

Novel plate-like (Ti, Zr)B2 toughened (Zr, Ti, W, Ta)C medium-entropy ceramics were designed based on the atomic scale, nanoscale, and microscale, and prepared via the in-situ reaction hot-pressing adopting (Ti, W, Ta)C and ZrB2 powders. The multiphase ceramics were primarily composed of the (Zr, Ti, W, Ta)C matrix and plate-like (Ti, Zr)B2. When the addition of ZrB2 exceeds 50 mol%, the residual ZrB2 causes the secondary primary phase to change from (Ti, Zr)B2 to (Zr, Ti)B2. The improvement of fracture toughness is primarily influenced by the plate-like (Ti, Zr)B2 on crack propagation mechanisms such as bridging effect and crack deflection. (Ti, W, Ta)C- 50 mol% ZrB2 (raw powder composition) exhibits outstanding mechanical properties, boasting a Vickers hardness of 24.9 GPa, fracture toughness of 5.5 MPa·m1/2, and flexural strength of 552 MPa, which present a novel avenue for achieving a synergistic enhancement in Vickers hardness and fracture toughness within medium-entropy carbide ceramics.

Stabilization of durable tetragonal phase and barrier regions in y-123 ceramic systems with partial substitution mechanism

This study examines the impact of Tb and Zn doping on the Y-123 superconducting system by analyzing crack propagation mechanisms through Vickers microhardness measurements. The measurements are conducted at various application forces ranging from 0.245 N to 2.940 N. The microhardness measurements are used to determine the role of impurity addition on Vickers hardness, modulus of elasticity, brittleness index, fracture toughness, and yield strengths. It is found that impurity ions serving as strong barrier regions improve the surface residual compressive stress sites and interactivity between the adjacent layers. Similarly, the sensitivity to the external forces reduce significantly with the substitution mechanism due to the induced new slip systems and ionic bond formations. Accordingly, all the mechanical performance properties are recorded to increase significantly with the impurity ions. Especially, the replacement of Zn by Cu ions in the Y-123 matrix exhibits higher resistance to failure, mechanical strength, and stabilization of the durable tetragonal phase. Accordingly, Zn/Cu substitution in Y-123 ceramics paves the way for the applications of ceramic compounds in the fields of heavy-industrial technology and industrial power systems. All the ceramic materials also exhibit indentation size effect feature based on the recovery mechanism. Additionally, load-independent microhardness parameters are semi-empirically modeled by Meyer's law, Hays-Kendall, indentation-induced cracking, elastic–plastic deformation, and proportional sample resistance model for the first time. According to the comparisons, the IIC model is identified as the most suitable for interpreting the real microhardness results of newly produced Y-123 ceramic matrices.

The trend of digital finance: unveiling the multidimensional network of cryptocurrency risk propagation

ABSTRACT This study primarily explores the mechanisms of risk propagation among cryptocurrencies, unveiling for the first time the frequency dimension of risk propagation within the cryptocurrency market and identifying the role of oscillation frequency in this process. By employing Variational Mode Decomposition (VMD) and the DY spillover matrix to construct a complex network, the paper analyzes the frequency dimension risk propagation mechanisms of nine major cryptocurrencies from 2017 to 2023. Key findings include the significant risk propagation capabilities of Ethereum (ETH) and Bitcoin (BTC) during periods of high market volatility, while stablecoins such as Tether and USD Coin exhibit minimal risk propagation ability. Additionally, the characteristics of cryptocurrency risk propagation have been enhanced following the COVID-19 pandemic. Overall, the risk propagation of most cryptocurrencies is primarily realized through high-frequency oscillations. The robustness of the conclusions is verified using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results are significant for understanding the dynamic characteristics of the cryptocurrency market, predicting future market risks, and formulating risk management strategies. Furthermore, the methodology and findings of this study provide new perspectives and tools for exploring the risk propagation relationships among cryptocurrencies.

A MODELING OF RF PROPAGATION FOR LOW FREQUENCY IN NATURAL CAVES WITH 3D MODEL SIMULATION

The study of the RF propagation model within natural caves remains challenging. In the past, the propagation model in the tunnel was compared using waveguide theory because of the electrical conductivity of tunnel walls. However, waveguide models have limitations because a cut-off frequency from the tunnel dimension does not allow frequencies lower than the cut-off frequency to propagate. Although the frequency wave lower than the cut-off frequency cannot theoretically propagate, in practice, it can propagate through the cave. Therefore, this paper presents the modelling of RF propagation at low frequencies in natural caves using empirical methods by measuring the waves propagated within cave passages at the Chiang Dao Cave (limestone) and the Patihan Cave (sandstone). The measurement will focus on analyzing and comparing factors, propagation mechanisms, and behavior, which cover the frequencies at 300, 1000, 1650, 2325, and 3000 kHz, to develop the low-frequency attenuation model within the cave proposed in this paper. The 3D cave models obtained from a LiDAR scanner were used to calculate the physical factors of the cave walls, and then the path losses were calculated using our proposed model and compared to the experimental results. In addition, the path losses calculated using 1) CST simulation software and 2) the waveguide model were also compared with the results from the proposed model. The results obtained from the proposed model provide attenuation values similar to those of the experimental results. Finally, the concept of the low-frequency attenuation model can be used for the prediction and analysis of the propagation performance of low-frequency waves in caves, tunnels, or underground mines and lead to the development of cave communication technology and electromagnetic knowledge in the future.

Fatigue crack evaluation of butt weld joints in full-scale aluminum alloy joints: Experimental and numerical study of traction structural stress

In this study, the digital image correlation technique is used to determine the inconsistent mechanical properties and microstructure non-uniformity in different regions of aluminum alloy butt welded joints. A state monitoring method is proposed based on phased-array ultrasonic total focus imaging for crack evolution. A series of surface fatigue crack propagation tests on full-scale aluminum alloy butt welds are investigated, which innovatively revealed the mechanism and evolution law of crack propagation at the surface of the weld. The analytical function for dynamic crack propagation in welded structures is proposed, which provides a new idea and data support for clarifying the state deterioration process of structures. The results show that the traction structural stress can effectively identify the critical fatigue position of the weld and effectively account for the effect of angular misalignment. The phased-array ultrasonic total-focus imaging monitoring analysis method for surface fatigue crack propagation is proposed to improve the completeness of the fatigue crack evolution law.

Mining Delay Propagation Causality within an Airport Network from Historical Data

Airport networks are interconnected through flight routes, with delays at upstream airports leading to delays at downstream airports, thus causing delay propagation. Exploring the mechanisms of delay propagation in airport networks provides scientific insights for managing and controlling delays in aviation systems. Existing methods, such as Granger causality tests and transfer entropy, must be revised to address the nonlinear causal relationships of delays in airport networks. So, this paper proposes a causality mining method for delay propagation in airport networks based on partial correlation-based multivariate conditional independence (PCMCI). This method comprehensively considers all airports and causality mining in two stages. The first stage uses conditional independence tests to obtain the parent node set of the target airport, which includes both true and false causal relationships. The second stage employs instantaneous conditional independence tests to eliminate false causal relationships and obtain test statistics representing the strength of causality. Based on historical delay data from US airports over a year, the experimental results show that multiple factors cause delay propagation in airport networks rather than a single causal relationship. The scope of delay propagation is limited, mainly affecting a few airports closely connected to it. Delays at airports with small flight volumes are more likely to propagate. Few airport pairs in the network mutually propagate delays and, often, delays at airports affected by a particular airport’s delay also exhibit causal relationships with each other. This method provides a new perspective for deepening the understanding of delay propagation mechanisms in airport networks.

Study on the influence of side-blown airflow velocities on plasma and combustion wave generated from fused silica induced by combined pulse laser

This study examines the impact of variations in side-blowing airflow velocity on plasma generation, combustion wave propagation mechanisms, and surface damage in fused silica induced by a combined millisecond-nanosecond pulsed laser. The airflow rate and pulse delay are the main experimental variables. The evolution of plasma motion was recorded using ultrafast time-resolved optical shadowing. The experimental results demonstrate that the expansion velocities of the plasma and combustion wave are influenced differently by the side-blowing airflow at different airflow rates (0.2 Ma, 0.4 Ma, and 0.6 Ma). As the flow rate of the side-blow air stream increases, the initial expansion velocities of the plasma and combustion wave gradually decrease, and the side-blow air stream increasingly suppresses the plasma. It is important to note that the target vapor is always formed and ionized into plasma during the combined pulse laser action. Therefore, the side-blown airflow alone cannot completely clear the plasma. Depending on the delay conditions, the pressure of the side-blowing airflow, the influence of inverse Bremsstrahlung radiation absorption and target surface absorption mechanisms can lead to a phenomenon known as the double combustion waves when using a nanosecond pulse laser. Both simulation and experimental results are consistent, indicating the potential for further exploration of fused silica targets in the laser field.

Comparative fracture surface analysis of adhesively bonded dissimilar lap joints: Nanosilica effect

AbstractThis research presents a comparative analysis of the fracture surfaces of adhesively bonded dissimilar single lap joints (DSLJs) and investigates the effect of nanosilica (NS) additives on their fracture behavior and performance. The study focuses on DSLJs composed of aluminum (Al), stainless steel (SS), copper (Cu), and carbon fiber reinforced polymer (CFRP) adherends bonded with an epoxy adhesive. The fracture surface of DSLJs is further analyzed by introducing two parameters: fractional total fracture area (aT) and the surface sensitivity of each adherend (aSurface). These parameters serve as benchmarks to predict the final load capacity of the adhesive joints. It is found that a decrease in and an increase in the sensitivity of the adherend with a lower Young's modulus lead to higher load capacity. Tensile tests revealed that incorporating NS up to a critical weight percentage of 0.6 wt% improves the proposed surface parameters and significantly contributes to higher levels of load capacity and absorbing greater amounts of energy to failure. Furthermore, numerical simulations offer insights into stress distribution and the mechanism of plastic deformation propagation, providing additional support for the experimental findings.Highlights NS up to 0.6 wt% enhances the mechanical properties of DSLJs. NS–epoxy interactions characterized through Fourier transform infrared and transmission electron microscopy analysis. Fracture surface patterns help to predict the final load capacity of DSLJs. Plastic propagation in epoxy verifies fracture surface pattern in DSLJs.

In situ seeding assay: A novel technique for direct tissue localization of bioactive tau.

Proteins exhibiting prion-like properties are implicated in tauopathies. The prion-like traits of tau influence disease progression and correlate with severity. Techniques to measure tau bioactivity such as RT-QuIC and biosensor cells lack spatial specificity. Therefore, we developed a histological probe aimed at detecting and localizing bioactive tau in situ. We first induced the recruitment of a tagged probe by bioactive Tau in human brain tissue slices using biosensor cell lysates containing a fluorescent probe. We then enhanced sensitivity and flexibility by designing a recombinant probe with a myc tag. The probe design aimed to replicate the recruitment process seen in prion-like mechanisms based on the cryo-EM structure of tau aggregates in Alzheimer disease (AD). Using this novel probe, we observed selective staining of misfolded tau in pre- and post-synaptic structures within neurofibrillary tangles and neurites, whether or not associated with neuritic plaques. The probe specifically targeted AD-associated bioactive tau and did not recognize bioactive tau from other neurodegenerative diseases. Electron microscopy and immunolabeling further confirmed the identification of fibrillar and non-fibrillar tau. Finally, we established a correlation between quantifying bioactive tau using this technique and gold standard biosensor cells. This technique presents a robust approach for detecting bioactive tau in AD tissues and has potential applications for deciphering mechanisms of tau propagation and degradation pathways.

Anisotropies of diffusive ultra-high energy cosmic rays in [formula omitted] gravity theory

Understanding the anisotropy of ultra high-energy cosmic rays (UHECRs) is crucial for unravelling the origins and propagation mechanisms of these enigmatic particles. In this work, we studied the dipolar anisotropy of UHECRs in the diffusive regime by considering three cosmological models: the standard ΛCDM model, f(R) gravity power-law model and the Starobinsky model. This work aims to see the role of the f(R) gravity theory in understanding the anisotropy of UHECRs without condoning the standard cosmology. We found that the amplitude of the dipolar anisotropy is sensitive to these cosmological models, with the f(R) power-law model predicting the largest amplitude, while the ΛCDM model predicting the smallest amplitude at most of the energies in the range considered. The predicted amplitude of the Starobinsky model lies within the range of the ΛCDM one. This work not only provides a way for exploration of UHECRs anisotropy within different cosmological contexts but also may pave the way for new avenues of research at the intersection of high-energy astrophysics.

Bedding effect on progressive crack propagation in layered sandstone grotto roof

Roof failure significantly threatens the safety and stability of grottoes. For the layered roof of sandstone grottoes, the failure process is closely related to bedding effects of crack propagation. Taking the Yuanjue Cave as an example, the progressive failure process of the layered roof was summarized. The mechanisms of crack propagation in the roof were analyzed theoretically, and bedding effects together with influencing factors of crack propagation were explored. Results showed that the instability process of the layered roof in sandstone grottoes can be summarized into four stages: tensile fracture of the roof, vertical expansion of cracks, horizontal deflection of cracks, and instability collapse of blocks. The development and propagation of cracks constituted the intrinsic cause of the instability of layered roofs. A fracture mechanics model for crack propagation in layered roofs was established considering the bedding effect, and the criteria for vertical expansion of cracks in grotto roofs was derived. Moreover, the fracture criterion of crack competition was modified in combination with the bedding effect, revealing that an increase in interlaminar cohesion inhibited the crack deflection mode. The crack deflection mode in grotto roofs was jointly determined by the stress at the crack-tip (σfxx and σfyy), the strength of the rock layers and bedding plane (σ't, μ and c). To control the crack propagation in roofs, the engineering reinforcement of grotto roofs can be achieved by weakening bedding effects and indirectly reducing the roof span. This study is of great significance for revealing the progressive failure process and optimizing the reinforcement scheme of layered roofs in grottoes.

A graph propagation model with rich event structures for joint event relation extraction

The task of event relation extraction (ERE) aims to organize multiple events and their relations as a directed graph. However, existing ERE methods exhibit two limitations: (1) Events in a document are typically expressed with merely a trigger word or phrase neglecting the rich semantic structure of event arguments and roles. (2) Various event relations such as coreference, temporal, causal, and subevent relations are correlated with each other, while little work has explored the benefits of extracting all relations in a joint manner. In this work, we investigate an event-relation graph propagation network and a novel document-level semantically-enriched event representation for joint ERE. First, we address the lack of event argument annotations by extracting cross-sentence implicit arguments based on the AMR graph. Then triggers and argument roles are aggregated with a structure-aware encoder. Second, based on the rich event information, event relation interactions are incorporated with a subgraph propagation and aggregation mechanism. During training, we further develop a novel triadic contrastive loss to capture high-order event pair relationships. We conduct experiments based on the public MAVEN-ERE benchmark and the results show that our model achieves 60.7%, 37.4%, 32.9% F1-scores on temporal, causal and subevent relations, and 86.1% MUC-score on coreferences, outperforming the current joint models by a large margin. Further in-depth analysis shows the effectiveness of our model in capturing event-event dependencies in document context. The proposed model can be used for event graph construction and storyline understanding.

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

Underwater optics have seen a notable surge of interest in recent years, emerging as a critical medium for conveying information crucial to underwater resource exploration, autonomous underwater vehicle navigation, etc. The intricate dynamics of underwater optical transmission, influenced by factors such as the absorption by the water and scattering by multiple particles, present considerable challenges. One of the most critical issues is that the optical information representation methods fail to take into account the impact of the underwater physical environment. We conducted a comprehensive review and analysis of recent advancements in underwater optical transmission laws and models. We summarized and analyzed relevant research on the effects of underwater particles and turbulence on light and analyzed the polarization effects in various environments. Then, the roles of various types of underwater optical propagation models were analyzed. Although optical models in complex environments are still mostly based on Monte Carlo methods, many underwater optical propagation mechanisms have been revealed and can promote the impacts of optical information expression. We delved into the cutting-edge research findings across three key domains: the enhancement of underwater optical image quality, the 3D reconstruction from monocular images, and the underwater wireless optical communication, examining the pivotal role played by light transmission laws and models in these areas. Drawing upon our extensive experience in underwater optics, including underwater optical sensor development and experiments, we identified and underscored future directions in this field. We advocate for the necessity of further advancements in the comprehension of underwater optical laws and physical models, emphasizing the importance of their expanded application in underwater optical information representations. Deeper exploration into these areas is not only warranted but essential for pushing the boundaries of current underwater optical technologies and unlocking new potential for their application in underwater optical sensor developments, underwater exploration, environmental monitoring, and beyond.

Very high cycle fatigue of fiber‐reinforced polymer composites: Uniaxial ultrasonic fatigue

AbstractThis review explores uniaxial ultrasonic fatigue (USF) testing as a common and dependable method for quantifying the extended fatigue life of fiber‐reinforced polymer (FRP) composites. The objective is to explain the complexities governing the fatigue life behavior of FRPs, particularly in the realm of very high cycle fatigue (VHCF) where the number of loading cycles exceeds 107. To this end, this review encompasses the analysis of VHCF behavior, including the derivation and interpretation of stress–life (S–N) data, the evaluation of various fatigue damage mechanisms (i.e., controlling mechanisms of crack initiation and propagation) exhibited in FRP composites, and a thorough investigation of the frequency‐dependent effects on fatigue responses. Furthermore, this review tries to analyze the microscopic intricacies intrinsic to the VHCF failure of FRP composites, encompassing aspects such as fiber‐matrix de‐bonding, matrix cracking, and delamination, unveiling their modes and effects in a detailed manner. This review also underscores the pivotal integration of simulations, machine learning, and modeling techniques, emphasizing their crucial role in explaining both macroscopic and microscopic interactions governing the VHCF of FRPs.

Percolation behavior analysis on n-layer edge-coupled interdependent networks

The robustness of interdependent networks has always been studied for years and has attracted wide attention from researchers in different fields. Noting that not all networks are coupled by nodes, networks coupled by edge are also worth further studying. At present, most studies on the edge-coupled interdependent networks are built on double layers, however, researches on multi-layer edge-coupled interdependent networks have hardly been conducted. In this paper, an n-layer edge-coupled interdependent network model was proposed, and the cascading failure propagation mechanism caused by attacking a small number of edges was studied. Based on the self-consistent probability theory that can significantly simplify the mathematical analysis of such systems, the phase transition behavior was theoretically analyzed. The research results help to deepen the understanding of the robustness of multi-layer interdependent networks and provide some valuable advice for the protection and improvement of complex network systems.

Dual separated attention-based graph neural network

Graph neural networks (GNNs) offer a viable solution to model the inter-dependencies among labeled and unlabeled samples in a semi-supervised manner. However, their performances can degrade dramatically when the number of labels is extremely limited, which is due to the limitations of typical graph convolutional network design in most existing GNNs, including over-smoothing, difficulty in extending the propagation step, and failure to preserve the distinctiveness of each node. To address the issues, we propose a dual separated attention-based graph neural network (DSA-GNN) to deal with label scarcity in semi-supervised node classification. Firstly, DSA-GNN decouples feature propagation from representation transformation to alleviate the problems of over-smoothing and overfitting. Secondly, DSA-GNN separates self-representation learning from neighbor-representation learning by two feature extractors with different learnable parameters. As a result, the commonality between connected nodes and the distinctiveness of each node can be both preserved. Thirdly, DSA-GNN incorporates an attention-based label propagation mechanism to refine the label prediction of each node, by aggregating label prediction among the neighborhood based on adaptive edge coefficients. The extensive experimental results on the real-world datasets demonstrate the superiority of DSA-GNN for semi-supervised node classification, especially when the observed labels are extremely limited.

Numerical predictions of progressive collapse in reinforced concrete beam-column sub-assemblages: A focus on 3D multiscale modeling

The progressive collapse of reinforced concrete (RC) beam-column sub-assemblage under catenary action (CA) using the alternate load path method to evaluate structures' robustness has raised much attention in the past decade, both in experimental tests and finite element (FE) simulations. However, a comprehensive multiscale method within concrete to assess structural robustness against progressive collapse, explicitly surrounding concrete matrix under CA, is generally lacking and has not been thoroughly explored in the relevant literature. This study aims to develop an FE macromodel to reliably simulate the progressive collapse resistance of seismic and non-seismic RC beam-column sub-assemblages. The proposed macroscale FE models are extensively validated by experimental results dominated by the CA and excessive deformation of RC beam-column sub-assemblages. Then, the macroscale FE model is further partitioned at the critical region with high-stress concentration to establish a sub-model and elucidate the underlying mesoscopic mechanism to motivate the CA by performing sub-modeling analysis. A 150 mm × 150 mm × 150 mm mesoscale heterogeneous model is established to be composed of aggregates, interfacial transition zone (ITZ), pores, and mortar by 3D voxel and Voronoi-based methods. The numerical predictions of the three macroscale models demonstrate good agreement of the overall deformation and load resistance trends with experimental results at both structural and sectional levels, but an overestimation of the load resistance peak is observed when concrete is crushed. Compared to the macroscale models, the sub-modeling analysis provides a more in-depth understanding of localized phenomena such as cracks and fractures. The 3D mesoscale model is further investigated with different aggregate volume fractions following the Fuller gradation. It is found that aggregates and ITZ (higher porosity, lower elastic modulus, and lower tensile strength compared to the bulk mortar) are more prone to concrete failure mechanisms without considering the role of reinforcement leading to the CA at the mesoscale, maintaining the concrete properties of the three macroscale models. In terms of the material constitutive model, the concrete damage plasticity model, and cohesive elements for mortar and ITZ, respectively, under uniaxial compression and tension behavior, the importance of interface bonding in CA of the surrounding concrete matrix is underlined. Additionally, the established mesoscale modeling method facilitates comprehension of the responses exhibited by macroscale RC sub-assemblies, ultimately shedding light on fracture initiation and propagation mechanisms.

Crack Initiation and Propagation Mechanism of Flawed Rock Mass Based on Improved Maximum Distortion Energy Theory and PFC Numerical Simulation

Crack Initiation and Propagation Mechanism of Flawed Rock Mass Based on Improved Maximum Distortion Energy Theory and PFC Numerical Simulation

Quantitative analysis of debonding gaps in concrete-filled steel tubes on the Qinghai-Tibet Plateau under severely harsh conditions

In recent years, the maximum span of concrete-filled steel tube (CFST) arch bridges has substantially increased to nearly 700 m, rendering it a preferred bridge type for traffic nodes located in mountainous regions and deep canyons owing to its robust earthquake resistance and adaptability to varying terrain. Construction defects, particularly debonding gaps, significantly diminish the structural bearing capacity of these bridges. This study aims to realize the accurate quantitative calculation of debonding gaps in CFSTs through ultrasonic non-destructive testing. The first wave's acoustic time and overall ultrasonic pulse velocity (UPV) were employed as the state variables of ultrasonic pulse propagation in CFSTs with debonding gaps. Ultrasonic non-destructive tests were conducted on CFST specimens produced in the Qinghai-Tibet Plateau and Guangxi. An approximate calculation path of ultrasonic propagation in CFSTs was proposed, and the differences in the UPVs of CFSTs produced at different air pressures were analyzed. A calculation model of the UPVs of CFSTs was formulated using different air pressure influence coefficients. Additionally, a numerical simulation of the ultrasonic energy transfer in CFSTs with diverse debonding gaps was performed, elucidating the propagation mechanism of ultrasonic energy across the three-phase steel-concrete-air interface in CFSTs. The propagation law of ultrasonic waves in CFSTs was verified through dynamic ultrasonic energy snapshots and the state variable calculation results. Based on the experimental and numerical findings, a quantitative analysis method and correlation model for CFST debonding gaps were proposed, based on their UPVs at different air pressures. The quantitative calculation model was modified by analyzing the values measured at an actual CFST bridge, enabling the accurate quantitative analysis and calculation of the degree and scope of debonding gaps in CFSTs.

Vibration fatigue assessment and crack propagation mechanism of directionally solidified superalloy with film cooling holes

Film cooling represents a critical protective measure for turbine blades, yet the presence of film cooling holes (FCHs) under vibrational loads can significantly impact structural strength and integrity. This study conducts random fatigue tests on DZ125L directionally solidified superalloy specimens with FCHs. It investigates how various FCH types and vibration signal intensities influence the vibration fatigue behavior of DZ125L alloy. Characterization of vibration fatigue fracture and surface cracks of FCH specimens utilizes ultra depth of field microscopy and scanning electron microscopy. Additionally, the crack propagation mechanism for FCH specimens under random processes is proposed based on finite element stress distribution and fracture morphology. Results reveal the generation of two types of cracks, namely hole cracks and edge cracks, under vibration load in FCH specimens. The crack propagation process produces water wave-like fatigue striations. Notably, a low stress zone exists between the two dangerous holes in the multi-hole specimen, mitigating the expansion trend of hole cracks between the FCHs compared to cracks expanding towards the edges. Furthermore, two novel models, New1 and New2, are introduced to enhance the applicability of the frequency domain method for predicting the fatigue life of FCH specimens under random processes. Accuracy and error analyses of the models suggest that New2, incorporating the FCH stress concentration coefficient KT and intensity function f(ξ), exhibits superior accuracy and stability.