A Model of the Fatigue Life Distribution of Composite Laminates Based on Their Static Strength Distribution

AbstractThe reasons of the static strength dispersion and the fatigue life dispersion of composite laminates are analyzed in this article. It is concluded that the inner original defects, which derived from the manufacturing process of composite laminates, are the common and major reason of causing the random distributions of the static strength and the fatigue life. And there is a correlative relation between the two distributions. With the study of statistical relationship between the fatigue loading and the fatigue life in the uniform confidence level and the same survival rate S-N curves of material, the relationship between the static strength distribution and the fatigue life distribution through a material S-N curve model has been obtained. And then the model which is used to describe the distributions of fatigue life of composites, based on their distributions of static strength, is set up. This model reasonably reflects the effects of the inner original defects on the static strength dispersion and on the fatigue life dispersion of composite laminates. The experimental data of three kinds of composite laminates are employed to verify this model, and the results show that this model can predict the random distributions of fatigue life for composites under any fatigue loads fairly well

Synergistic Damage Mechanic Model for Stiffness Properties of Early Fatigue Damage in Composite Laminates

AbstractIn the initial period of the life in the composite laminates, the principal types of damage are diffused ones, such as matrix crack, diffused fiber breaking and local delamination. On account of these diffused damages, a synergistic damage mechanic model was proposed for the stiffness properties. The model included the microcosmic responses of the physical damage and macroscopic performance of the material's stiffness. In micro-level, mesoscopic RVE(representative volume element) model was established to obtain crack opening displacement and crack sliding displacement, which were used to define the damage tensor. In macro- level, through homogenizing the material strain and the surface displacement of the damage, the relationship of the stiffness matrix of unidirectional laminate or laminates in damage statue and damage tense was set up. Due to restriction of NDT (non- destructive testing) technology development, only the constitutive relations of matrix cracks were constructed. The influences of the transverse matrix cracks on the stiffness properties of the laminates [0/±45]s was analyzed with the present model and showed that it is capable to predict the reduction of the stiffness properties resulted from the fatigue diffused damage in the laminates

A Micro–Macro Damage Mechanics-based Model for Fatigue Damage and Life Prediction of Fiber-reinforced Composite Laminates

A multidirectional damage model was proposed to predict fatigue damage evolution and final failure of composite laminates in this paper. A damage characterization model for composite laminates was established to characterize the influence of three main damage modes on the damaged mechanical behavior of composite laminates at micro–macro level. The damage evolution model was also established based on damage mechanics to predict the evolution of the three damage modes and stiffness degradation of composite laminates by means of damage characterization model. Then, a relationship between residual stiffness and residual strength was introduced, from which the residual strength could be obtained according to the predicted residual stiffness. When the residual strength is calculated to decrease to the maximum applied stress of fatigue loading after several cycles, the composite laminate was assumed to fail, and accordingly the fatigue life could be obtained. In order to verify the model, the predicted stiffness degradation and fatigue life of two cross-ply laminates under fatigue loadings with different stress levels were compared to experimental results. The standard derivation of stiffness degradation and average errors of fatigue between prediction results and experimental results were less than 0.1 and 8.26%, respectively, indicating the effectiveness and reliability of proposed model

MUSER: A Multi-View Similar Case Retrieval Dataset

Similar case retrieval (SCR) is a representative legal AI application that plays a pivotal role in promoting judicial fairness. However, existing SCR datasets only focus on the fact description section when judging the similarity between cases, ignoring other valuable sections (e.g., the court's opinion) that can provide insightful reasoning process behind. Furthermore, the case similarities are typically measured solely by the textual semantics of the fact descriptions, which may fail to capture the full complexity of legal cases from the perspective of legal knowledge. In this work, we present MUSER, a similar case retrieval dataset based on multi-view similarity measurement and comprehensive legal element with sentence-level legal element annotations. Specifically, we select three perspectives (legal fact, dispute focus, and law statutory) and build a comprehensive and structured label schema of legal elements for each of them, to enable accurate and knowledgeable evaluation of case similarities. The constructed dataset originates from Chinese civil cases and contains 100 query cases and 4,024 candidate cases. We implement several text classification algorithms for legal element prediction and various retrieval methods for retrieving similar cases on MUSER. The experimental results indicate that incorporating legal elements can benefit the performance of SCR models, but further efforts are still required to address the remaining challenges posed by MUSER. The source code and dataset are released at https://github.com/THUlawtech/MUSER.Comment: Accepted by CIKM 2023 Resource Trac

An adaptive fault current limiting control for MMC and its application in DC grid

This paper proposes an adaptive fault current limiting control (AFCLC) for modular multilevel converters (MMC). Without introducing extra current limiting devices, this control scheme enables fast fault current suppression during DC faults. The AFCLC will be triggered automatically once DC faults occur. By adaptively reducing the output DC voltages of MMCs, the fault current can be suppressed. Compared with the existing current limiting methods, the proposed AFCLC has a better performance on fault current limiting, since it only depends on the real-time operating condition and no fault detection delay is imposed. Firstly, the principle of the proposed AFCLC together with the mathematical analysis is disclosed. Then, the sensitivity analysis of the impact of key control parameters on the current limiting effect is investigated. Finally, the effectiveness of AFCLC is demonstrated in a four-terminal HVDC grid test model. The simulation results show that the proposed AFCLC can reduce the interrupted current and energy absorption of a DCCB from 10.39 kA and 38.24 MJ to 4.62 kA and 8.32 MJ, respectively. The simulation results also prove that the AFCLC will not affect the accuracy of the DC fault detection algorithms under DC faults

Variable-Permeability Well-Testing Models and Pressure Response in Low-Permeability Reservoirs with non-Darcy Flow

This paper proposes the concept of variable-permeability effect and sets up the one-dimensional and two-dimensional non-Darcy well testing models. The finite difference algorithm is employed to solve the differential equations of the variable-permeability model, and the non-convergence of the numerical solutions is solved by using the geometric mean of permeability. The type curves of pressure and pressure derivative with variable-permeability effect are obtained, and sensitivity analysis is conducted. The results show that the type curves upturn in the middle and late sections, and the curves turn more upward with the severer of the variable-permeability effect. The severer the non-Darcy effect is, the less obviously the curve upturns caused by boundary effect. Furthermore, the boundary effect is increased by increasing the number of impermeable boundaries or decreasing the distance between the well and boundary

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring

Reforestation in southern China: revisiting soil N mineralization and nitrification after 8 years restoration

Nitrogen availability and tree species selection play important roles in reforestation. However, long-term field studies on the effects and mechanisms of tree species composition on N transformation are very limited. Eight years after tree seedlings were planted in a field experiment, we revisited the site and tested how tree species composition affects the dynamics of N mineralization and nitrification. Both tree species composition and season significantly influenced the soil dissolved organic carbon (DOC) and nitrogen (DON). N-fixing Acacia crassicarpa monoculture had the highest DON, and 10-mixed species plantation had the highest DOC. The lowest DOC and DON concentrations were both observed in Eucalyptus urophylla monoculture. The tree species composition also significantly affected net N mineralization rates. The highest rate of net N mineralization was found in A. crassicarpa monoculture, which was over twice than that in Castanopsis hystrix monoculture. The annual net N mineralization rates of 10-mixed and 30-mixed plantations were similar as that of N-fixing monoculture. Since mixed plantations have good performance in increasing soil DOC, DON, N mineralization and plant biodiversity, we recommend that mixed species plantations should be used as a sustainable approach for the restoration of degraded land in southern China