Propagation Prediction Research Articles

Crack propagation and mechanical behavior prediction of graphene reinforced functionally gradient high-entropy cemented carbides

Crack propagation and mechanical behavior prediction of graphene reinforced functionally gradient high-entropy cemented carbides

Modeling the delay propagation dynamics in high-speed railway networks with a traffic-driven SIS epidemic model

The propagation of delays is a prevalent issue in high-speed railway (HSR) networks. However, the underlying properties of delay propagation have not been fully explored, hindering the effective management of this problem. In this paper, considering the impact of traffic flow, we model the delay propagation as a traffic-driven susceptible–infected–susceptible (SIS) epidemic spreading process. We introduce a traffic frequency matrix to estimate the traffic rate and further derive the propagation dynamic equations using a continuous-time Markov chain method. We then derive the epidemic threshold of the model, which is proportional to the recovery rate and inversely proportional to the average path length. Finally, through simulation, we study the impacts of various factors including the infection rate, recovery rate, and the degree and number of initial delay stations. Our work provides some insights for the prediction and control of delay propagation in HSR networks.

AI-Enhanced Prediction of Pavement Crack Propagation: A Study Using Traffic Load, Environmental and Material Data

This study develops an AI-based predictive model for forecasting pavement crack propagation by integrating traffic load, environmental conditions, and material property data. Traditional pavement management systems often struggle to accurately predict crack growth due to the complex interactions between these influencing factors. By leveraging data from various sources, including sensor-based traffic metrics, meteorological data, and material composition tests, this study identifies significant variables contributing to crack initiation and progression. The proposed model utilizes a blend of machine learning algorithms, including Random Forest and neural networks, with a cross-validation approach to ensure robustness. Results indicate that the model achieves high prediction accuracy, with an RMSE of 1.2 mm/year and an R-squared value close to 0.93. The findings support the use of AI-enhanced models as reliable tools for road infrastructure planning and maintenance, promising reductions in maintenance costs and improved pavement durability.

New Prediction Of Cracks Propagation In Repaired Steel Plate With Bonded Composite Patch At Cyclic Loading

Abstract This study presents a numerical prediction of the fatigue life of steel panels repaired by a composite patch. The effect of length cracks, the stress ratio R and properties of the patch is presented. The obtained results show that the bonded composite repair significantly reduces the stress intensity factors at the tip of repaired cracks. The results are in a good agreement with those in the literature. The Monte Carlo method is used to predict the distribution function governing crack propagation in fatigue analysis. In computing the failure probability of the structure, we consider the statistical uncertainty associated with key variables, along with the previously discussed model uncertainty. The results obtained highlight the considerable impact of variations in crack length and stress ratio on the distribution function. Notably, uncertainty in these parameters significantly amplifies the probability of structural failure in plates, thereby diminishing overall structural durability.

Crack Propagation Tests for Load Sequences Developed Using Different Flight Parameters of a Trainer Aircraft

Abstract Military aircraft are subjected to highly variable and unpredictable loads due to diverse mission profiles, armament configurations, and individual piloting styles. This variability complicates the definition of precise load spectra, particularly in cases where data loss occurs due to Flight Data Recorder (FDR) malfunctions or data mishandling. This paper investigates the use of different flight parameters, such as load factor (nz ), barometric height (Hb ), and horizontal velocity (Vp ), to define load sequences for the PZL-130 “Orlik” TC-II military trainer aircraft. These sequences were then used to evaluate crack propagation using Compact Tension (CT) specimens. The results show that the incorporation of additional flight parameters improves the accuracy of crack propagation predictions when compared to direct strain measurements. This study highlights the potential of using available flight data to develop reliable load spectra for fatigue life estimation in military aircraft, even when direct load measurements are not financially feasible.

DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction.

Predicting propagation cascades is crucial for understanding information propagation in social networks. Existing methods always focus on structure or order of infected users in a single cascade sequence, ignoring the global dependencies of cascades and users, which is insufficient to characterize their dynamic interaction preferences. Moreover, existing methods are poor at addressing the problem of model robustness. To address these issues, we propose a predication model named DropMessage Hypergraph Attention Networks, which constructs a hypergraph based on the cascade sequence. Specifically, to dynamically obtain user preferences, we divide the diffusion hypergraph into multiple subgraphs according to the time stamps, develop hypergraph attention networks to explicitly learn complete interactions, and adopt a gated fusion strategy to connect them for user cascade prediction. In addition, a new drop immediately method DropMessage is added to increase the robustness of the model. Experimental results on three real-world datasets indicate that proposed model significantly outperforms the most advanced information propagation prediction model in both MAP@k and Hits@K metrics, and the experiment also proves that the model achieves more significant prediction performance than the existing model under data perturbation.

UPI-LT: Enhancing Information Propagation Predictions in Social Networks Through User Influence and Temporal Dynamics

The TEST pervasive use of social media has highlighted the importance of developing sophisticated models for early information warning systems within online communities. Despite the advancements that have been made, existing models often fail to adequately consider the pivotal role of network topology and temporal dynamics in information dissemination. This results in suboptimal predictions of content propagation patterns. This study introduces the User Propagation Influence-based Linear Threshold (UPI-LT) model, which represents a novel approach to the simulation of information spread. The UPI-LT model introduces an innovative approach to consider the number of active neighboring nodes, incorporating a time decay factor to account for the evolving influence of information over time. The model’s technical innovations include the incorporation of a homophily ratio, which assesses the similarity between users, and a dynamic adjustment of activation thresholds, which reflect a deeper understanding of social influence mechanisms. Empirical results on real-world datasets validate the UPI-LT model’s enhanced predictive capabilities for information spread.

Accounting for the vehicle influence on tyre noise through acoustic simulation based on equivalent monopole sources

Road traffic noise poses a risk to human health and amongst the different noise sources, tyre-road interaction plays a fundamental role. Due to the regulations introduced to limit noise levels, tyre manufacturers are committed to design acoustically optimized tyres. In this context, a numerical-experimental approach for including vehicle influence on tyre noise has been investigated. As a first step, equivalent monopole sources are identified based on the measurements of the radiated sound field of a rolling tyre in a semi-anechoic chamber. In a second step, the same sound sources are then employed for the prediction of sound propagation when tyres are mounted on a vehicle, which is included in the acoustic simulation. Finally, a validation against indoor experimental measurements is performed, yielding promising results.

An aircraft structural risk assessment method considering fatigue crack propagation based on fatigue damage diagnosis and prognosis

An aircraft structural risk assessment method based on fatigue damage diagnosis and prognosis has been developed, considering fatigue crack propagation. The process is divided into three stages: initial crack diagnosis, crack diagnosis, and prediction, utilizing Monte Carlo simulation. Using 2024 aluminum alloy specimens with central holes, the study indicates that in the initial crack diagnosis stage, an inspection standard with a Single Flight Probability of Failure (SFPOF) less than 10-7 and a threshold method enhances structural fatigue crack diagnosis. In the crack diagnosis and prediction stages, iterative updates using Gaussian Process Regression (GPR) within a Dynamic Bayesian Network (DBN) improve crack propagation prediction and risk assessment accuracy. The diagnostic interval significantly impacts SFPOF, with an optimized interval balancing accuracy and computation time. Simplified and precise K value calculation methods enhance efficiency and accuracy. The method reduces costs and improves risk assessment accuracy, providing new insights for SPHM-based aircraft structural risk assessment.

Propagation prediction of asymmetrically originated fractures by use of displacement discontinuity method

In existing fracture propagation simulation studies, the fracture propagation simulation is based on the assumption that the fracture originates symmetrically with respect to the wellbore. However, the drilling treatments can induce stress reorientation near the wellbore, resulting in asymmetrically originated fractures, which is different from the assumption of existing studies. In such cases, it can be difficult to predict the fracture geometries. In this work, the authors propose a fracture propagation model to simulate the propagation of asymmetrically originated fractures with the displacement discontinuity method. With the aid of the proposed model, the authors investigate the impact of the redistribution of stress field on the propagation of asymmetrically originated fractures and conduct a comprehensive study to investigate the effects of various geological and fluid parameters on the propagation of asymmetrically originated fractures. The results show that the redistribution of the stress field renders the newly generated fracture segments to deviate from the initial originations so that although the initial fracture origination is asymmetric, the asymmetrically originated fractures rapidly propagate along an approximately symmetrical path and propagate parallel to the direction of maximum principal stress as it goes away from the wellbore. The closer the two wings of the fracture are to the symmetric origin, the faster it is for the change of propagation direction to the direction of the maximum principal stress. A large principal stress difference tends to cause asymmetrically originated fractures to quickly deviate from the initial originations and propagate parallel to the direction of maximum principal stress. As the fluid power law index decreases, the fluid viscosity is significantly reduced due to the effect of shear thinning, causing the asymmetrically originated fractures to propagate parallel to the direction of maximum principal stress. The fluid leak-off coefficient only influences the fracture length and width, whereas its influence on the propagation trajectory can be slight.

Residual Pyramidal GAN (RP-GAN) for crack detection and prediction of crack growth in engineered cementitious composites

Crack detection has recently gained credence in the field of engineering automation. Engineered cementitious composites (ECC) are a durable, and environmentally friendly construction material and a suitable replacement for conventional concrete. As such, there is a need to automate the process of detecting the unique ductile crack formation and growth in ECC. This research focuses on detecting ECC cracks, accurately mapping the crack morphology, and predicting the crack growth patterns of this advanced material. We design and present a novel generative adversarial algorithm named Residual Pyramidal Generative Adversarial Network (RP-GAN), capable of ECC crack detection. RP-GAN is an enhanced and improved version of the typical Pix2Pix GAN for paired image-to-image translation. It is an extensive network that could be used for macro and micro-segmentation purposes in addition to domain translation tasks. In this research, we first investigate the ability of RP-GAN to detect ECC micro-cracks and map the dimensions of each crack. Secondly, we compared RP-GAN’s crack detection results with those of known state-of-the-art architectures. We then developed an algorithmic pipeline for live crack tracking using an experimental framework. Finally, we evaluate the possibility of detecting ECC crack growth and propagation using sequential images by re-engineering RP-GAN and fusing Convolutional-LSTM into its architecture. The mean intersection over union (IOU) test results of RP-GAN for ECC crack detection equaled 81.28%, and 77.76% for ECC crack propagation prediction.

Efficient BFGS quasi-Newton method for large deformation phase-field modeling of fracture in hyperelastic materials

The prediction of crack propagation in materials is a crucial problem in solid mechanics, with many practical applications ranging from structural integrity assessment to the design of advanced materials. The phase-field method has emerged as a powerful tool for modeling crack propagation in materials, due to its ability to accurately capture the propagation of cracks. However, current phase-field algorithms suffer from the elevated computational cost associated with the so-called staggered solution scheme, which requires extremely small time increments to advance the crack due to its inherent conditional stability. In this paper, we present, for the first time, a quantitative analysis detailing the numerical implementation and comparison of two common solution strategies for the coupled large-deformation solid-mechanics-phase-field problem, namely the quasi-Newton based Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) and the full Newton based alternating minimization (or staggered) (AM/staggered) algorithm. We demonstrate that the BFGS algorithm is a more efficient and advantageous alternative to the traditional AM/staggered approach for solving coupled large-deformation solid-mechanics-phase-field problems. Our results highlight the potential of the quasi-Newton BFGS algorithm to significantly reduce the computational cost of predicting crack propagation in hyperelastic materials while maintaining the accuracy and robustness of the phase-field method.

An effective drift-diffusion model for pandemic propagation and uncertainty prediction

Predicting pandemic evolution involves complex modeling challenges, typically involving detailed discrete mathematics executed on large volumes of epidemiological data. Making them physics based provides added intuition as well as predictive value. Differential equations have the advantage of offering smooth, well-behaved solutions that try to capture overall predictive trends and averages. In this paper, the canonical susceptible-infected-recovered model is simplified, in the process generating quasi-analytical solutions and fitting functions that agree well with the numerics, as well as infection data across multiple countries. The equations provide an elegant way to visualize the evolution of the pandemic spread, by drawing equivalents with the similar dynamics of a particle, whose location over time represents the growing fraction of the population that is infected. This particle slides down a potential whose shape is set by model epidemiological parameters such as reproduction rate. Potential sources of errors and their growth over time are identified, and the uncertainties are mapped into a diffusive jitter that tends to push the particle away from its minimum. The combined physical understanding and analytical expressions offered by such an intuitive drift-diffusion model sets the foundation for their eventual extension to a multi-patch model while offering practical error bounds and could thus be useful in making policy decisions going forward.

Parameter analysis and prediction of crack propagation for thermoplastic composite pipes under lateral pressure

ABSTRACT The research investigates the process of crack propagation in thermoplastic composite pipes (TCPs) with initial racks subjected to lateral pressure, through a combination of numerical simulations and experimental studies. In the numerical analysis, a TCP is subjected to lateral pressure, considering parameters such as initial crack length, initial crack angle, and lateral displacement. This analysis employs the extended finite element method (XFEM). The results reveal that crack propagation predominantly occurs perpendicular to the fibre winding angle. Moreover, it is observed that longer initial cracks result in lower crack initiation loads, while the final extent of crack propagation increases with the initial crack length. The experimental study considers three distinct initial crack angles and verifies the conclusions drawn from the numerical simulations. Furthermore, a crack propagation prediction model is developed that combines particle swarm optimization (PSO) with backpropagation (BP) neural networks. The predictive results demonstrate a high level of accuracy.

Research on Fatigue Crack Propagation Prediction for Marine Structures Based on Automated Machine Learning

Research on Fatigue Crack Propagation Prediction for Marine Structures Based on Automated Machine Learning

Exploring the conflict risk characteristics of air weaving sections in Metroplex terminal areas with flight trajectory data and adaptive graph spatial-temporal transformer

Metroplex terminal areas is involved with numerous air weaving sections, where various types of arrival and departure traffic flows intersect or converge, leading to high-risk aircraft conflicts and inefficient Metroplex operation. The primary objective of this study is to explore the conflict risk characteristics of operational interactions between airports in Metroplex terminal areas with large-scale trajectory data, including risk evaluation, risk propagation and risk prediction. One complete month of ADS-B trajectory data is collected from a representative multi-airport system in Central and South China to illustrate the procedure. Specifically, flight trajectories are firstly clustered within Metroplex terminal areas, and a trajectory-tube model is then built to characterize the airport flows within the same cluster. Then, a clustering-based collision probability calculation method is proposed to evaluate the risk of air weaving sections. The spatial patterns of risks in air weaving sections reveal that operational interactions among Metroplex airports are quite different under various runway configurations. Furthermore, an adaptive graph spatial-temporal transformer (ASTT) network is developed to predict the future risk of each air weaving section in Metroplex terminal areas. The results indicate that the developed ASTT network can collaboratively encode the spatiotemporal characteristics in the air weaving section risk data, which achieves more accurate and robust prediction performance than other compared models during multiple look-ahead time steps. Moreover, the adaptive spatial graph technique designed in the ASTT can also reveal the hidden risk propagation effects among air weaving sections. The results of this study could provide insightful suggestions to air traffic authorities for developing effective coordination and conflict mitigation strategies, such as redesigning flight procedures, reallocating routes, and optimizing aircraft sequences to enhance the efficiency and safety of Metroplex operations.

Diagnosing NMC Battery Aging Modes Using Digital Twin

Traditional lithium-ion battery (LiB) modeling does not provide sufficient information to accurately verify battery performance under real-time dynamic operating conditions, particularly when considering various aging modes and mechanisms. This paper proposes a lithium-ion battery digital twin that can capture real-time data and integrate the strong coupling between SEI layer growth, anode crack propagation, drying up of Electrolytes, and lithium plating. It can be utilized to predict the degradation behavior, voltage-current profiles in dynamic aging conditions, and analyze the lithium inventory loss (LLI) of Nickel-Manganese-Cobalt-Oxide (NMC) lithium-ion batteries.Commercially available 18650 cylindrical NMC Samsung battery cells were investigated. The cathode and anode materials are LiNi0.5Co0.2Mn0.3O2 and synthetic graphite, respectively. Four dynamic aging test cases using 1C, 1.3C, MC (multistep charging), and 2C, were designed and performed at 25 °C. The reference performance test (RPT) was repeated every 100 full equivalent cycles (FECs) until the cells reached 20 % capacity fade. Figure 1 shows the real-time voltage and current profile captured and predicted by the digital twin.The model takes into account LLI induced by SEI growth and lithium plating, aligning with the LLI calibrated from the differential voltage (DV) endpoint shift (Figure 1h). Based on the model results, SEI growth is found to be the primary contributor to capacity loss before 500 FECs. SEI-induced degradation is minimal under the MC protocol, with the capacity loss varying by less than 5.22% across four test cases (Figure 1e). After 500 FECs, anode cracking becomes a non-linear accelerated aging factor. The crack propagation stabilizes in the initial 200 FECs, followed by unstable accelerated growth leading to capacity loss. Subsequently, a rapid acceleration in capacity loss occurs after 700 FECs (Figure 1f). The growth of anode cracks under the MC aging protocol has a much slower effect on capacity loss compared to the 2C and 1.3C before 500 FECs, as strongly confirmed by the post-mortem analysis. In addition, the lithium plating phenomenon triggers two orders of magnitude less capacity loss than SEI growth and cracking at 25 °C. The MC protocol demonstrates an effective reduction in lithium plating capacity loss compared to the other three traditional charging protocols (Figure 1g). This digital twin can represent a firm physical foundation for future physics-informed machine learning development to predict LiBs degradation behavior.Figure 1. Real-time voltage and current captured and predicted by our designed digital twin under (a) 1C-WLTC, (b)1.3C-WLTC, (c) MC-WLTC, and (d) 2C-WLTC aging profiles. Additionally, (f) illustrates capacity loss density for the four protocols attributed to (e) SEI film growth, (f) anode crack propagation, (g) Li plating, and (h) LLI prediction by the digital twin and DV measurement. Figure 1

Distinct element modeling of hydraulic fracture propagation with discrete fracture network at Gonghe enhanced geothermal system site, northwest China

Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System (EGS) design. We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data, where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation. A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation, with the explicit representation of natural fractures via the discrete fracture network (DFN) approach. The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed. The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well, driven by smaller injection rates and viscosities that promote massive activation of natural fractures, ultimately dominating the direction of hydraulic fracturing. Furthermore, the increase of in situ differential stress may promote shear damage of natural fracture surfaces, with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state. Finally, we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters. This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.

Dynamic prediction of fracture propagation in horizontal well hydraulic fracturing: A data-driven approach for geo-energy exploitation

Accurate and efficient prediction of hydraulic fracturing fracture propagation is of utmost importance for unconventional reservoir development. Traditional numerical simulation methods require specialized domain knowledge and technical expertise, and machine learning approach can be an alternative predictive tool for fracture propagation. Additionally, existing neural networks cannot be directly applied to hydraulic fracturing scenarios influenced by multiple coupled factors. Thus, this study presents an innovative approach, “AE-ATT-ConvLSTM”, that integrates an additional Convolutional Layer, Autoencoder, and an Attention Mechanism Feature Fusion Layer into the architecture of a Convolutional Long Short-Term Memory (ConvLSTM) sequential image prediction network. This approach aims to predict fracture propagations in different fracturing stages of horizontal wells. The proposed model is trained on a sample dataset consisting of 5000 instances of hydraulic fracturing in horizontal wells. The dataset includes crucial data such as fracture propagation images, wellbore perforation images, natural fracture images, pumping schedules, and reservoir properties. The model achieves remarkable results, with an MSE less than 15 × 10−5, a maximum SSIM of 0.93, and an average FMAE less than 48. The research findings demonstrate that this method significantly enhances prediction efficiency and provides new insights for predicting fracture morphology and optimizing fracturing design.

A prediction of crack propagation on aircraft wing via AK-TCN

The crack propagation of wing rivet holes has a significant impact on flight safety. However, accurate crack prediction is difficult. On the one hand, the calculated values based on traditional mechanics theory are conservative and have a large safety margin. On the other hand, the predicted values of deep learning do not match the mechanical theory, and the significant role of peak load in crack propagation has not been fully reflected. Therefore, this paper proposes a novel AK-TCN model for the crack prediction, considering the theoretical information constraints. Firstly, the “theoretical information assistance” technology is adopted. The calculated values (strength, life) based on mechanical theory and the measured values (stress) based on sensors are jointly used as inputs, enabling the AK-TCN model to fully extract valuable information. Secondly, the “multi-dimension feature fusion” technology is adopted. The theoretical information and measured information are mixed, and the importance of different information is adaptively adjusted, enabling the AK-TCN model to balance the foundational nature of theoretical information and the time-varying nature of measured information. Then, the “adaptive multi-scale convolution” technique is adopted. The temporal features in different periods are perceived by multi-scale convolution, and weights of them are assigned by attention mechanisms, so that the AK-TCN model can focus on certain key features that have a significant impact on crack propagation. Finally, the accuracy, effectiveness, and robustness of the proposed AK-TCN are verified by comparative experiments, ablation experiments, and noise experiments.