Multiple Failure Research Articles

Data-Model Fusion-Driven Method for Fault Quantitative Diagnosis of Heat Exchanger

Heat exchangers play essential roles in the oil and gas production process for convective heat transfer and heat conduction. The health management of heat exchangers stays in the direct monitoring of performance parameters. Aiming at the difficulty of precise fault identification and quantification for heat exchangers in multiple unknown failure modes, a data-model fusion-driven fault quantitative diagnosis method is proposed. Firstly, based on the monitoring data such as temperature, pressure and flow rate, the secondary parameters characterizing the heat exchanger running state are constructed combined with structural physical parameters. Then, by analyzing the correlation among parameter variation, failure modes and deterioration degree, a qualitative inference model of heat exchanger is formed for fault identification, where weights of parameters are introduced based on their sensitivity for different failure modes. After the fault mode is identified, to achieve quantitative analysis of the failure degree, an index-integrated mechanism equation is constructed using monitoring data and secondary parameters, where the index is dynamically modified by online data. Finally, a heat exchanger experiment is carried out to demonstrate the robustness and accuracy of the proposed method.

Preparation of a novel laser cladding Ni-based alloy with promising applications in high-temperature conditions: Genetic design, multi-phase evolution, and synergy mechanism

Developing novel Ni-based alloys is crucial for remanufacturing key friction parts at 800-1200 ℃ in metallurgical industry with multiple failure mechanisms, such as wear, corrosion, and adhesion. This paper adopts genetic algorithm to design high-temperature Ni-based alloy with three genetic structures of wear resistance, anti-corrosion, and self-lubricating. By combining simulation calculation and experimental methods, the evolution of the three genetic structures of the laser cladding alloy and the performance strengthening mechanism were deeply studied. The laser clad Ni17Cr10Co7Al5Ti3W2C1S sample of optimized composition had three genetic structures: Cr7C3/TiC wear resistance phases, γ/γ′ anti-oxidation and anti-corrosion phases, Ti2SC/TiS self-lubricating phases. Compared with Cr28Ni48W5 superalloy, the wear rate of the optimal sample was 2.31×10-5 mm3/(N·m) under 800 ℃ friction condition, and reduced by 97%. The friction coefficient of the sample at high temperature was 0.2, approximately half of the substrate alloy, and the self-lubricating performance was significantly improved. Electrochemical tests indicated that the polarizing potential of the sample was -0.16 VSCE in 3.5wt. % NaCl solution. The mechanism of three genetic structures evolution and synergistic enhanced wear resistance, anti-corrosion, and self-lubricating properties were elaborated. Specifically, the key gene of Ti2SC plays a profound role in improving the high-temperature wear resistance and self-lubricating performance of the novel laser clad Ni-based alloy sample. These findings present a novel avenue of material design and preparation for laser additive manufacturing and remanufacturing of key friction parts with high-performance at 800-1200 ℃ in complex metallurgical working conditions.

Investigation of Multiple LED Array Failure on DCO-FBMC Modulation for Indoor Visible Light Communication

Visible light communication (VLC) is a high-speed and power-efficient optical wireless communication method that has gained momentum for data transmission in indoor environments. Indoor VLC systems often use multiple LED arrays to increase the uniformity of brightness and enhance the received energy and the four LED array model is the most commonly used. To suppress the impact of intersymbol interference (ISI) and achieve higher data rates, multicarrier modulation techniques such as orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC) have been introduced in VLC systems. The FBMC technique is a promising candidate for the 5G communication system and it has recently been applied to VLC systems. FBMC has several advantages: higher spectrum efficiency, narrower out-of-band radiations and asynchronous transmission. This study investigates the performance of an FBMC-based VLC system when one or more lamps in the multiple LED arrays experience failure. The VLC- FBMC model was implemented using a 4-QAM and 16-QAM format with three various LED array configurations while assuming a line of sight (LOS) model. The LED array model was evaluated based on its ability to achieve good bit error rate (BER) performance, communication quality and high bit rate and meet the required signal-to-noise ratio (SNR). The proposed FBMC model simulation's outcome indicates that the system is still able to transmit data reliably, even in the event of a failure of up to 50% of the lamps. The results indicate that the 2- and 4-lamp models yielded good BER performance, achieving a value of 3.6 × 10−5 and 5.4 × 10−5 respectively. Furthermore, the simulation demonstrated that the optical FBMC-based VLC system attained a high bit rate of 73.2 Mbit/s in the 4-QAM format using the 2-lamp model and a bit rate of 29.3 Mbit/s in 16-QAM format using the 4-lamp model, while maintaining acceptable BER performance.

A stratified beta-sphere sampling method combined with important sampling and active learning for rare event analysis

Accurate and efficient estimation of small failure probability subjected to high-dimensional and multiple failure domains is still a challenging task in structural reliability engineering. In this paper, we propose a stratified beta-spheres sampling method (SBSS) to tackle this task. Initially, the whole support space of random input variables is divided into a series of subdomains by using multiple specified beta-spheres, which is a hypersphere centered in the origin in standard normal space, then, the corresponding samples truncated by beta-spheres are generated explicitly and efficiently. Based on the truncated samples, the real failure probability can be estimated by the sum of failure probabilities of these subdomains. Next, we discuss and demonstrate the unbiasedness of the estimation of failure probability. The proposed method stands out for inheriting the advantages of Monte Carlo simulation (MCS) for highly nonlinear, high-dimensional problems, and problems with multiple failure domains, while overcoming the disadvantages of MCS for rare event. Furthermore, the SBSS method equipped with importance sampling technique (SBSS-IS) is also proposed to improve the robustness of estimation. Additionally, we combine the proposed SBSS and SBSS-IS methods with GPR model and active learning strategy so as to further substantially reduce the computational cost under the desired requirement of estimated accuracy. Finally, the superiorities of the proposed methods are demonstrated by six examples with different problem settings.

Optimal mud pressure design using nonlinear failure criteria for wellbores in shaley sedimentary reservoir

AbstractWellbore drilling disturbs the equilibrium stress state in the rock mass, resulting in stress redistribution around the opening. Wellbore stability in the altered stress state is vital for engineering applications, as the wellbore instability results in cost overrun. Accurate estimation of rock mechanical properties, in‐situ stresses, and required mud pressure is crucial for safe drilling. If the mud pressure is lower than required, the shear failure of the rock takes place, and conversely, if the mud pressure is higher than the upper limit, tensile failure occurs. A strength criterion that can accurately predict the mud pressure may help significantly reduce non‐productive time and cost in well drilling. The commonly used Mogi‐Coulomb (MGC) failure criterion for estimating critical mud pressure neglects the few fundamental aspects of rock failure characteristics observed in the laboratory, such as nonlinear strength response in major‐minor principal stress space and prediction of multiple failure stress values near the triaxial axial extension boundary. The present study uses the Modified Mohr‐Coulomb true‐triaxial failure criterion (MMC_TT), which predicts the strength of rock better than the MGC in laboratory true‐triaxial tests to overcome the limitations. Moreover, based on the data from previously published five vertical wells in the Krishna‐Godavari basin (K‐G basin), an empirical relationship is proposed to obtain the strength parameters for the MMC_TT criterion for shaley sedimentary rocks as the existing correlations do not cater for the parameters required for MMC_TT criterion. The comparative study of the MMC_TT with MGC, Modified Mohr‐Coulomb triaxial, and Mohr‐Coulomb failure criteria, showed that for most of the K‐G basin wells, the MMC_TT criterion predicted close to the MGC. However, for well‐13, the MMC_TT criterion results are closer to the mud pressure used in actual drilling than the MGC.

Reliability Analysis of High-Pressure Tunnel System Under Multiple Failure Modes Based on Improved Sparrow Search Algorithm–Kriging–Monte Carlo Simulation Method

It is often difficult for a structural safety design method based on deterministic analysis to fully and reasonably reflect the randomness of mechanical parameters, while the traditional reliability analysis method has a large calculation cost and low accuracy. In this paper, based on the seepage–stress coupling numerical model, the random variables affecting the reliability of the collaborative bearing of surrounding rock and lining structures are successfully identified. Then, the improved sparrow search algorithm (ISSA) is used to optimize the hyper-parameters of the Kriging surrogate model, in order to improve the computational efficiency and accuracy of the reliability analysis model. Finally, the ISSA-Kriging-MCS model is used to quantitatively evaluate the reliability of the surrounding rock-reinforced concrete lining structure under multiple failure modes, and the sensitivity of each random variable is discussed in depth. The results show that the high-pressure tunnel structure has high safety and reliability. The reliability indexes of each failure mode decrease with the increase in the coefficient of variation (COV) of random variables. In addition, the same random variable also exhibits varying degrees of influence in different failure modes.

Abstract 4126304: A Cardiac Targeting Peptide Linked to miRNA106a Targets and Suppresses Genes Known to Cause Heart Failure: Reversing Heart Failure at the Source

Background: About one in five adults at age 40 will develop heart failure (HF) during their lifetime. Risk factors for HF (e.g., hypertension, etc.) alter cardiomyocytes structure and function resulting in HF. At the cellular level, these changes consist of mis/over-expression of genes that regulate cardiac identity (e.g., CamK2d, PKC, etc). The current paradigm for treating HF is pharmacological intervention or surgery; however, with few exceptions, the condition worsens with time. We are proposing a paradigm-shift for treating HF from a drug-centric system to a molecular approach that would reverse hypertrophy and adverse remodeling. Methods: A cardiac targeting peptide (CTP) was generated and linked by disulfide bond to miRNA106a, which was then introduced into a HF mouse model to measure its ability to reverse multiple heart failure parameters. CTP-miRNA106a was also introduced into a human cardiac cell line, followed by multiple analyses including Western Blot, qPCR, FACS, immunofluorescence all used to identify mechanism(s) utilized by CTP-miRNA106a to reverse HF characteristics. Results: Bio-distribution studies showed CTP delivered its miRNA106a cargo specifically to the heart within 30 mins, followed by clearance of CTP from the heart to the kidneys and liver by 3-5hrs post systemic drug injection. MiRNA106a persisted in cardiomyocytes until day 7 (latest tested time-point). CTP-miRNA106a reversed Ang2/Iso induced hypertrophy in 90% of the experimental mice ( Fig1 ). We also identified two potential HF intracellular signaling mechanisms (PLCb1/PKC/IP3 and NfkB) targeted by CTP-miRNA106a that could benefit both types of HF, HFrEF and HFpEF. PLCβ1 is a direct target for miRNA106a while the Nfkb pathway is indirectly targeted by decreased Camk2d (an miRNA106a target) signaling to IκBα. NfkB activity is quelled by miRNA106a. Conclusions: Our approach utilizes the function of miRNA106a to downregulate genes involved in cardiac hypertrophy and inflammation through the PLCb1 and CamKIId/Nfkb pathways. Most importantly, using a CTP-miRNA106a, we show delivery of miRNA106a cargo is specific to cardiomyocytes both in vitro and in vivo and that once delivered, many HF parameters including hypertrophy are reversed.

Abstract 4134975: ROS-Traf6-Yap Promotes the Development of Cardiac Hypertrophy

Introduction: Heart failure (HF) is caused by structural and functional abnormalities of the heart and is associated with high morbidity and mortality. Pathological myocardial hypertrophy accounts for a significant portion heart failure. The Yap protein was found to be responsive to mechanotransduction signals and can limit organ size by inducing cell cycle arrest during development. In the heart, pathological activation of Yap has been shown to drive cardiac hypertrophy, but regenerative activation of Yap can promote myocardial cell-cycle re-entry. However, how Yap is pathologically activated in the beating cardiomyocyte remains elusive. Hypothesis: ROS-Traf6 promotes the development of cardiac hypertrophy by activating Yap in a non-canonical fashion. Methods: Traf6 protein levels in heart failure patients biopsies as well as in various murine failing hearts were examined by immunofluorescence. Molecular interrogation of ROS-Traf6-Yap signaling axis was performed in primary neonatal rat cardiomyocytes (NRCM) and human induced pluripotent stem cell derived cardiomyocytes. Yap protein stability and activation by Traf6 was examined by immunoprecipitation, CHX induction, and point mutation. Myocardial specific Traf6 knockout and Yap mutant animals were used to evaluate Traf6-Yap signaling in vivo . Results: We found that Traf6 expression was significantly elevated in multiple heart failure models. Using the transverse aortic constriction (TAC)-induced cardiac hypertrophy model, pressure overload increased ROS levels which activated Traf6. Traf6 overexpression induced myocardial hypertrophy while knockdown blocked myocardial hypertrophy in Ang II treated NRCMs. Mechanistically, Traf6 binds to and ubiquitinates Yap-K234 – this promotes Yap protein stability and nuclear translocation evidenced by increase in phosphorylated Yap. Myocardial Traf6 deficiency prevents TAC-induced cardiac hypertrophy. Moreover, overexpression of Yap-K237 mutant did not restore TAC-induced cardiac hypertrophy in myocardial Yap deficient mice. Conclusions: Our results demonstrate that non-canonical Yap activation is responsible for pressure overload induced cardiac hypertrophy. Targeting of the newly identified ROS-Traf6-Yap signaling axis may provide a new approach to prevent pathogenic cardiac hypertrophy.

Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

Uncertainty Quantification in the Prediction of Remaining Useful Life Considering Multiple Failure Modes

Abstract Despite the substantive literature on remaining useful life (RUL) prediction, less attention is paid to the influence of epistemic uncertainty and aleatory uncertainty in multiple failure behaviors in the accuracy of RUL. The research question in this study was: can uncertainties be quantified in predicting the RUL of systems with multiple failure modes? The first objective was to quantify the uncertainties in the prediction of RUL, considering known multiple failure modes. This objective used vibration data from accelerated degradation experiments of rolling element bearings. The second objective was to calculate the uncertainties in the prediction of RUL, considering the multiple failure modes as unknown. The experimental data used in this objective were from run-to-failure tests of Li-ion batteries. An analysis was performed on how the uncertainties affect the RUL prediction in systems with known multiple failure modes and systems where the multiple failure modes were unknown. A Bayesian neural network (BNN) was used to quantify epistemic and aleatory uncertainty while predicting RUL. The results of the qualitative uncertainties on RUL in systems with multiple failure modes were presented and discussed. Also, the study yielded an RUL uncertainty quantification model for multiple failure modes. The proposed framework's performance in the RUL prediction was demonstrated. Finally, the epistemic and aleatory uncertainties were quantified in the system's RUL. It was shown that systems that fail due to the same failure mode tend to have similar uncertainty values over time. The results in this paper may lead to the design of more reliable systems that exhibit multiple failure modes.

Vulnerability Comparisons of Various Complex Urban Metro Networks Under Multiple Failure Scenarios

Urban metro networks, characterized by their complex systems of interdependent components, are susceptible to a wide range of operational disturbances and threats. Such disruptions can cascade through the system, leading to service delays, operational inefficiencies, and substantial economic losses. Consequently, assessing and understanding network vulnerabilities have become crucial to ensuring resilient metro operations. While many studies focus on single-failure scenarios, comparative vulnerability analyses of various urban metro networks under multiple or simultaneous failures remain limited. To address this gap, our study introduces a comprehensive analytical framework comprising three key components: quantitative indices operating at both network and node levels, methodological approaches to assess the importance of network components (nodes, edges, and lines), and systematic protocols for evaluating vulnerabilities across multiple failure scenarios (stations, tunnels, lines, and areas). A comparative analysis of the Shenzhen Metro Network (SZMN) and the Zhengzhou Metro Network (ZZMN) validates the proposed methods. The results indicate that the SZMN demonstrates higher connectivity and accessibility than the ZZMN, despite a lower network density. Both networks are disassortative and heterogeneous, with edges connecting multiline transfer stations showing significantly higher edge betweenness centrality compared to those connecting general stations. In the SZMN, 6.63% of node failures and 4.74% of tunnel failures exceed a vulnerability threshold of 0.03, compared to 13.74% and 11.27% in the ZZMN. Failures across different lines and areas yield varying impacts on network performance and vulnerability. This study provides essential theoretical and practical insights, helping metro safety managers identify vulnerable points and strengthen the sustainable development of urban metro systems.

An Optimal Two-Dimensional Maintenance Policy for Self-Service Systems with Multi-Task Demands and Subject to Competing Sudden and Deterioration-Induced Failures

Self-service systems, such as electric vehicle charging piles (EVCPs), are typically deployed without on-site personnel. While frequent maintenance ensures high service revenue, it also leads to significant maintenance setup costs. Therefore, balancing service revenue and maintenance costs is essential for profit maximization. In this paper, we develop a maintenance policy optimization framework to maximize the profit rate of a fleet of self-service systems. First, we propose a maintenance policy that ensures sufficient functional systems while preventing high corrective maintenance costs. Next, we model the fleet's state transition process and its profit rate by characterizing two unique failure-induced demand-and-system interactions: demand switching and stepwise demand arrival rates, where the demands involve multiple tasks and systems are subject to multiple failure modes with non-constant occurrence rates. We develop a Tabu-search algorithm with random exploration to optimize the maintenance policy. Building on this, we investigate the impacts of key model parameters through a case study of thirteen EVCPs in Hong Kong and draw implications for profit maximization.

Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention

Abstract Background Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB). Methods We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests. Results The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75). Discussion ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.

Control for multiple risk factors and incident heart failure and mortality in patients with diabetes mellitus: insights from the kailuan cohort study

Abstract Purpose This study aimed to evaluate the relationship between control for multiple risk factors and diabetes-related heart failure and all-cause mortality, and to explore the extent to which the excess risk can be reduced. Methods Diabetes participants with pre-existing heart failure documented in discharge register or medical insurance records and incomplete baseline risk factor information were excluded. Each of these patients was then matched randomly to four control subjects. The degree of risk factor control was defined by attainment of target values for blood pressure, body mass index , low-density lipoprotein cholesterol, fasting blood glucose, high-sensitive C-reactive protein and smoking. The primary outcome was newly diagnosed heart failure, and the secondary outcome was all-cause mortality.Fine-Gray and Cox regression were performed to estimate the risk of heart failure and all-cause mortality among diabetes patients in relation to the degree of control for multiple risk factors respectively, and the group with ≤1 risk factor control was set as the reference group. All-cause mortality was used as a competing risk in the Fine-Gray model. Results A total of 17,676 patients with diabetes and 69,493 matched non-diabetic control subjects were included. Of these, 84.2% were men and the mean age was 56.2±10.7 years. Over a median follow-up of 11.2 (13.1-15.0) years, a total of 1,070 cases of heart failure and 4,121 cases of all-cause mortality were identified among patients with diabetes. Significant inverse associations were observed between the number of well-controlled risk factors and the risk of incident adverse outcomes in diabetes patients. For each additional risk factor that was controlled, there was an associated 16% decrease in the risk of heart failure and a 10% decrease in the risk of all-cause mortality. The incidence rate of heart failure among diabetes patients was at its lowest for those with ≥5 risk factors (HR: 0.46, 95% CI: 0.34-0.62), and it constantly increased with a lower number of well-controlled risk factors. As for all-cause mortality, the lowest risk was associated with optimal risk factor control (HR: 0.59, 95% CI: 0.50-0.68). Among diabetes patients with ≥5 risk factors that were well controlled, the adjusted hazard ratio for heart failure and all-cause mortality was 1.25 (95%CI: 0.99-1.56) and 1.17(95%CI: 1.05-1.31) respectively, as compared with controls. The protective impact of comprehensive risk factor control on the risk of heart failure was more pronounced among men than women, as well as among those using antihypertensive medications than non-users. Conclusions Control for multiple risk factors is related to a lower risk of heart failure and all-cause mortality in an accumulative and sex-specific manner. Compared to the general population, patients with diabetes mellitus remains associated with an increased risk for the outcomes in spite of the optimization of risk factor control.

An efficient surrogate model for system reliability with multiple failure modes

AbstractThe failure modes of complex systems are often highly dependent. Aiming at the reliability analysis of engineering systems with multiple failure modes, an efficient surrogate model for system reliability with multiple failure modes is proposed, namely, the Copula Adaptive Kriging surrogate model (CAKSM). First, the initial Kriging model is established based on the initial samples. In order to select more effective added samples to update the Kriging model, a new learning function, namely, Copula Unites with Expected Improvement Function (CUEIF), is established. The function can make a reasonable sample selection by considering the failure correlation. Then, based on the optimal Copula function, the reliability data of each failure mode are coupled and analyzed. In order to adapt to various computing scales, regularization is introduced to improve the prediction speed of the surrogate model, and the probability density function of each performance function is obtained by introducing nonparametric kernel density estimation. Finally, the effectiveness of the CAKSM method and the learning function CUEIF is verified through four examples.

PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region

In practical engineering, system reliability analysis is highly concerned since many structures or products have multiple failure modes. Accordingly, this paper develops an innovative method for system reliability analysis by parallel learning of influential component limit-state functions with filtered sample region (PLIC-FSR-SYS) based on Kriging modeling. Different from the traditional adaptive learning methods that train only one component in each iteration when constructing the surrogate of the composite limit-state function, a new strategy is explored to adaptively identify several important components in one iteration so as to train them simultaneously. In the meanwhile, a filtering formula is explored to determine the fatal region so that the unimportant samples can be removed to further accelerate the training process. Based on the join forces of parallel learning of influential components and avoiding the training at unimportant samples, PLIC-FSR-SYS can achieve a fairly efficient system reliability analysis with multiple failure modes. Finally, four different case studies, including an engineering application to the ultra-voltage on-load tap-changer, are conducted to prove the effectiveness of the proposed method. The results indicate that compared to traditional adaptive learning methods, the proposed method makes a significant efficiency improvement for system reliability analysis with multiple failure modes.

Availability modeling of competing risk system with multiple failure modes incorporating hybrid hazard rate model

Availability modeling of competing risk system with multiple failure modes incorporating hybrid hazard rate model

Tough Monolayer Silver Nanowire-Reinforced Double-Layer Graphene.

Mixed-dimensional nanomaterials composed of one-dimensional (1D) and two-dimensional (2D) nanomaterials, such as graphene-silver nanowire (AgNW) composite sandwiched structures, are promising candidates as building blocks for multifunctional structures and materials. However, their mechanical behavior and failure mechanism have not yet been fully understood. In this work, we have performed integrated experimental, theoretical, and numerical studies to explore the performance and failure modes of graphene-AgNW composite under tensile and impact loading conditions. In situ tensile tests using a nanoindenter, implemented with a push-to-pull device and a laser-induced projectile impact test system, are used to shed light on load-bearing mechanisms in graphene-AgNW composites. Multiple failure modes have been observed in both experimental setups and analyzed with numerical and theoretical models. Results show that in the tensile loading the distribution of AgNW, as characterized by the effective free length, is the key parameter determining the failure mode. As for the impact failure scenarios, compared with failure modes observed in pure graphene cases, the mechanical reinforcing effect of AgNW will transform the failure mode from a scattered tensile fracture along radial directions to a shear failure that is constrained in a relatively local domain. Theoretical analysis using shear lag modeling, Timoshenko plate theory, molecular dynamics modeling, and finite element modeling approaches are adopted to further establish the failure modes.

Complete Kidney Recovery after Prolonged Hemodialysis Dependency in Multiple Myeloma-Associated Kidney Failure: A Case Report

Complete Kidney Recovery after Prolonged Hemodialysis Dependency in Multiple Myeloma-Associated Kidney Failure: A Case Report

Comparison of stability analysis methods for safe design of volcanic rock slope

The performance of the stability chart (SC), limit equilibrium (LE), and finite element (FE) methods for stability analysis of weathered volcanic rock slopes under static and earthquake loads is not fully understood. This research aimed to evaluate the performance of the SC, LE, and FE methods for slope stability analyses by studying a case of a weathered volcanic rock slope at the Leuwikeris Dam diversion tunnel in Indonesia. Site characterisations were carried out to obtain the input parameters for the static and pseudostatic slope stability analyses. The results showed that using various search methods of failure surface and the suggested optimum number of finite elements, multiple non-circular failure surfaces were predicted from the LE and FE methods that were not anticipated in the initial slope design based on the SC method. The location and shape of the failure surfaces from the LE method agreed with those from the FE method. The difference between the Fs values from the static LE analysis and those from the static FE analyses was less than 14%, but the discrepancy increased up to 78% in the pseudostatic analyses. This study highlights the importance of validating the results from one method with the other methods to prevent slope failures.