Multi-factor Coupling Research Articles

Fault isolation considering real mechanical coupling effects with application to liquid oxygen/kerosene staged combustion rocket engines

Liquid rocket engines are crucial for space exploration, necessitating reliable fault diagnosis systems. This paper focuses on fault isolation in liquid oxygen (LOX)/kerosene staged combustion rocket engines. Addressing deficiencies in current methods, we first propose a theorem to evaluate the adaptability between the analytical model and fault factors based on available sensors. Through the analysis of fault mechanisms, this paper defines fault modes using an innovative multi-factor coupling approach based on engine operational principles. Moreover, a fault isolation method with two steps is established, incorporating the optimal selection of sensors to accommodate robust requirements. This method features a clear process, low computational complexity, and comprehensive fault coverage, and it can also be applied to systems with similar complex operational mechanisms.

Propagation Effect Analysis of Existing Cracks in Box Girder Bridges Based on the Criterion of Compound Crack Propagation

Cracking in concrete box girder bridges will have a significant impact on the safety and durability of the structure, and many box girder bridges which are in service have undergone varying degrees of cracking. Currently, the safety design of actual bridge projects place an emphasis on the stress or the load value of a cross section at the limit value specified in the code for safety control. This design method assumes that the member itself is of uniform and continuous material and is internally undamaged. However, the bridge structure is more or less cracked to varying degrees during the period from casting to construction to operation of the concrete members. In this paper, a finite element computational model of a three-span prestressed concrete box girder bridge with existing cracks is established based on the fracture mechanics theory, and the critical parameters of crack extension are introduced to evaluate the extension state of cracks. At the same time, the extended stability of the existing cracks of the box girder bridge is analyzed by considering the temperature effect, vehicle loading, and prestressing loss, and the sensitivity of crack extension under each working condition is investigated. The results show that, with the increase in crack length and depth, the crack expansion is promoted, but the effect is relatively small, and the maximum stress intensity factor is only 6.48 MPa mm1/2. Under the multi-factor coupling effect, the cracks show a composite crack expansion dominated by type I cracks, the longitudinal cracks of the existing base plate are in a stable state, the maximum value of the crack expansion critical parameter of the vertical cracks of the webs reaches 1.087, and there is a tendency to expand locally. The maximum value of the critical parameter for crack extension of the vertical crack in the web plate reaches 1.087, and there is a tendency towards local expansion. The crack extension evaluation criteria proposed in this paper have a certain reference value for crack extension research on similar concrete box girder bridges and provide a scientific basis for the optimized design of similar bridges.

Experimental investigation on the residual strength of concrete under the multi-factor coupling action of chloride penetration, freeze-thaw, and fatigue effect

With the ongoing process of globalization and the rapid expansion of the global economy, the proliferation of coastal constructions and infrastructures has witnessed a notable surge. These structures often encounter a confluence of environmental stressors in practical engineering, including cyclic fatigue loading, chloride salt erosion, and freeze-thaw cycles. These synergistic factors accelerate concrete’s failure, resulting in accelerated deterioration and significantly reduced durability. A comprehensive understanding of the damage characteristics of concrete materials in offshore environments, under the multi-factor coupled action of freeze-thaw phenomena, along with an assessment of their residual strength, is crucial for the development of relevant structural analysis and lifetime prediction models. To address this, twenty-seven dedicated specimens were designed and fabricated for this investigation. They were employed to scrutinize the residual strength of concrete under the combined influences of fatigue, freeze-thaw, and chloride penetration. Nine distinct multi-factor coupling actions were meticulously devised and characterized by different parameters. Following the multi-factor coupling action, tests were conducted to assess the residual flexural and compressive strengths, as well as the chloride ion content near the specimen edges. The findings revealed a substantial negative correlation between the residual flexural and compressive strengths with both the number of freeze-thaw cycles and fatigue loading iterations. In contrast, chloride ion concentration and the stress level of fatigue loading (stress ratio: 0.1–0.3) exhibited a comparatively marginal influence. Despite the relatively short test duration, it was evident that both freeze-thaw cycles and fatigue loading exerted a discernible promotional effect on chloride ion ingress in the concrete of the specimens.

Simulating droplet distribution characteristics in sprinkler irrigation using a modified ballistic model under multifactor coupling

External factors affecting the processes of sprinkler irrigation water flow generation, flight, and landing have not been thoroughly considered in existing ballistic models. This result indicates that ballistic models with better prediction effects under specific conditions are not sufficient for extension to multi-factor coupled scenarios in large-scale farmlands. Therefore, wind, evaporation, surface slope, and tilted sprinkler riser factors were comprehensively considered in this study. Differential equations for jet and droplet motion under the influence of wind, differential equations of droplet evaporation, sprinkler riser deflection angle matrix, and surface slope angle matrix were constructed to establish a droplet distribution model for sprinkler irrigation considering multifactor coupling using MATLAB 2018a software. The results showed that, under different working conditions, the data points of the droplet landing diameter, velocity, and angle were distributed near the 1:1 line. The Nash efficiency coefficients (NSE) for the droplet landing diameter, velocity, and angle varied from 0.821 to 0.932, 0.616 to 0.931, and 0.770 to 0.911, respectively. The increase in slope resulted in droplets with diameters larger than 4.63 mm concentrating on the land in the reverse slope direction. When the ambient temperature increases from 10 to 45 °C and the total evaporation rate increases from 0.45 to 4.37 %, the larger droplets have a larger area of contact with the air, and the higher the temperature, the greater the energy loss to the larger droplet diameters. The higher the wind speed, the more droplets in the downwind direction fall to the ground at a smaller landing angle, which can easily increase the risk of soil shear damage. If the sprinkler riser was tilted east, the droplets on both the east and west sides tended to be distributed centrally; the maximum droplet landing velocity occurred on the east side (tilted side), and the maximum droplet landing angle occurred on the west side. This study considers various factors that may affect the motion of sprinkler irrigation water flow, extends the application scenarios of the theoretical model, and improves the applicability of the theoretical model for sprinkler irrigation droplet motion in more complex and practical agricultural environments.

Corrosion behavior and mechanism of 921 A high-strength low-alloy steel in harsh marine atmospheric environments

High-strength low-alloy 921 A steel faces significant corrosion challenges in harsh marine atmospheric conditions, threatening marine and energy industry safety. This study investigates the corrosion behavior of 921 A steel through accelerated tests in a specially developed multi-factor coupled corrosion chamber. Combining the multi-factor coupling cyclic corrosion test method with finite element numerical simulation, the long-term corrosion behavior can be accurately predicted, and the power function relationship between the corrosion rate and time of the test steel is obtained, which is expressed as C=0.2259 t–0.5182. Analysis identified α-FeOOH, Fe3O4, γ-FeOOH, Cl—, and Cr in corrosion layers. First-principles calculations and XPS analysis showed Cr oxidation inhibits Cl— adsorption and diffusion, highlighting the steel’s corrosion resistance. Finite element analysis and outdoor corrosion data confirmed the corrosion rate and time correlation, proving simulation reliability. This research provides crucial insights into 921 A steel’s corrosion resistance in marine environments, benefiting marine engineering.

Multi-factor coupling analysis of electric field distribution in electrochemical machining with electrolyte suction tool

Electrochemical machining (ECM) technology holds significant potential for applications in high accuracy engineering and manufacturing. To confine the electrolyte region during ECM and mitigate the effects of reaction products and heat generation, an electrolyte suction tool has been proposed. However, the complex electric field distribution on the anode surface during the application of pulse voltage, limits the further utilization of ECM with suction tool. A multi-physics simulation model is established to calculate the region of electrolyte confinement by the suction tool, current density distribution within the electrolyte region, and material removal. The model considers the combined effects of polarization and double layer, and all key parameters are calibrated through classical electrochemical testing experiments rather than fitted based on the predicted results. This study elucidates the transient response of current density on the anode surface during the application of pulse voltage. The simulation accuracy is validated by comparing experimental and simulated current waveforms, as well as material removal profiles under different pulse parameters and electrode shapes. This research is of great significance for improving surface precision and controllability of complex structures in ECM with suction tool, promoting its further application in the field of precision machining.

Rail crack identification of in-service turnout through Bayesian inference

In-service railway turnout, suffering from multi-factor coupling of operational and environmental loadings and special wheel–rail interaction, is prone to structural damage. The paper aims to develop a Bayesian damage identification method for crack-alike damage of the turnout rail under uncertainties. It consists of three core parts, including a damage index (DI) constructed by the transformation of time–frequency components of responses for generating damage-sensitive relationships, Bayesian models describing the crack-sensitive relationships hidden in the members of the damage index, and a mechanism synthesizing individual assessment results associated with the different reference models for providing one quantitative solution. The abnormal change in the stable relationships derived from the index can reflect damage occurrence and severity. The Bayesian approach is adopted to model the relationships under uncertainties of in-service railway turnout. The models trained by using monitoring data of the turnout rail before being damaged serve as a reference for healthy state, and deviations of actual observations from model predictions may indicate the existence of damage. The synthesizing process helps to offer a more rational assessment result through the weighted summation of individual quantitative assessment results. Rail monitoring data of a railway turnout are acquired to examine the damage detection performance of the proposed method. By exempting loadings measurement and physical model derivation, this data-driven methodology is potentially capable of supporting the damage identification and quantitative assessment of other structures in railway engineering.

Risk coupling analysis of causal factors in construction fall-from-height accidents

PurposeConstruction fall-from-height accidents are not only caused by a single factor but also by the risk coupling between two or more factors. The purpose of this paper is to quantitatively analyze the risk coupling relationships between multiple factors and identify critical factors in construction fall-from-height accidents.Design/methodology/approachA cause analysis framework was established from the perspective of human, machine, material, management and environmental factors. The definition, the classification and the process of risk coupling were proposed. The data from 824 historical accident reports from 2011 to 2021 were collected on government websites. A risk coupling analysis model was constructed to quantitatively analyze the risk coupling relationships of multiple factors based on the N-K model. The results were classified using K-means clustering analysis.FindingsThe results indicated that the greater the number of causal factors involved in risk coupling, the higher the risk coupling value and the higher the risk of accidents. However, specific risk coupling combinations occurred when the number of their coupling factors was not large. Human, machine and material factors were determined to be the critical factors when risk coupling between them tended to pose a greater risk of accidents.Originality/valueThis study established a cause analysis framework from five aspects and constructed a theoretical model to quantitatively analyze multi-factor coupling. Several suggestions were proposed for construction units to manage accident risks more effectively by controlling the number of factors and paying more attention to critical factors coupling and management and environmental factors.

Combining BioTRIZ and Multi-Factor Coupling for Bionic Mechatronic System Design

To realize the design process of bionic mechatronic systems, involving mapping from engineering to biology and inversion from biology to engineering, a novel design paradigm is introduced that integrates BioTRIZ with multi-factor coupling bionics. In the mapping stage from engineering to biology, BioTRIZ is employed to frame the concrete engineering issue as a general conflicting problem. The biological solution is refined by amalgamating the BioTRIZ solution derived from the contradiction matrix with biological instances. In the inversion stage of biology to engineering, a novel approach is proposed for constructing a bionic multi-factor coupling model, drawing inspiration from the establishment of biological multi-factor coupling model. This allows for a seamless correspondence between biological elements, such as morphology and behavior, and their respective engineering counterparts, including structure and algorithms. This correspondence ultimately achieves the engineering conceptual model that is rooted in biological principles. The practical application of this methodology is exemplified through a multi-biometric fusion bionic active vision system, underscoring its feasibility and efficacy.

Performance-Degradation Analysis of the Planetary Roller Screw Mechanism under Multi-Factor Coupling Effects.

The performance-degradation pattern of the planetary roller screw mechanism (PRSM) is difficult to predict and evaluate due to a variety of factors. Load-carrying capacity, transmission accuracy, and efficiency are the main indicators for evaluating the performance of the PRSM. In this paper, a testing device for the comprehensive performance of the PRSM is designed by taking into account the coupling relationships among temperature rise, vibration, speed, and load. First, the functional design and error calibration of the testing device were conducted. Secondly, the PRSM designed in the supported project was taken as the research object to conduct degradation tests on its load-bearing capacity and transmission accuracy and analyze the changes in transmission efficiency. Third, the thread profile and wear condition were scanned and inspected using a universal tool microscope and an optical microscope. Finally, based on the monitoring module of the testing device, the vibration status during the PRSM testing process was collected in real time, laying a foundation for the subsequent assessment of the changes in the performance state of the PRSM. The test results reveal the law of performance degradation of the PRSM under the coupled effects of temperature, vibration, speed, and load.

Corrosion effects on bond strength in reinforced concrete with fly ash

Reinforcing steel bars provides additional strength and ductility to concrete by forming a strong bond with the concrete matrix. Corrosion of steel bars can significantly reduce the bonding effect between the steel bar and concrete, which can compromise the structural integrity and durability of reinforced concrete structures. An experimental investigation was performed to examine the influence of fly ash (FA) and corrosion on concrete–steel bond behavior. The accelerated corrosion method was employed to obtain the desired level of corrosion. The FA content, corrosion percentage, and presence of stirrups were considered significant factors influencing bond behavior. The bond–slip curves and bond toughness were used to discuss bond behaviors. The findings indicated that mild corrosion (below 2.5%) can enhance concrete–steel bond performance, and the addition of 20% FA leads to higher ultimate bond strength (τu ), significantly mitigating the decrease in bond strength caused by severe rebar corrosion. Also, this study analyzed the influence of stirrups on the bond strength of FA concrete and had a nearly insignificant effect compared to regular concrete. The results and discussions of the present study carry significant importance in the establishment of a bond–slip constitutive model under multifactor coupling conditions. Furthermore, it offers valuable insights into the design of engineering structures.

Analyzing ship collision accidents in China: A framework based on the N-K model and Bayesian networks

Maritime accidents are frequent in China, with collisions constituting a significant portion, leading to substantial casualties, financial losses, and environmental harm. Analyzing collision risk is crucial for safe shipping operations, as such accidents arise when multiple risk factors interact beyond certain thresholds. Thus, understanding the coupled relationships among these factors is essential for accident prevention. This study presents a quantitative coupling risk assessment approach for collisions using the N–K model and Bayesian networks (BNs). Through an analysis of 304 accident reports from the China Maritime Safety Administration (CMSA), four primary risk factors (human, ship, environment, and management) are identified. The N–K model quantifies the coupling degree between these factors, while a BN model further elucidates and quantifies the collision risk based on the calculations of N–K model. Validation of the BN model is followed by sensitivity analysis to assess the impact of risk factors on accident occurrence. Subsequently, the method is applied to a case study on collision risk assessment in Chinese waters. Findings indicate that multifactor coupling has a higher occurrence probability than two-factor coupling, with human-ship-environment-management coupling presenting the highest likelihood. Human and management factors emerge as pivotal in collision incidents, while the impact of ship and environmental factors on coupling becomes more pronounced as their probabilities vary. Leveraging the capability of BNs to handle uncertainties and logical relationships, combined with the data-driven N–K model, mitigates subjectivity. The results propose the formulation of risk management strategies and measures for practitioners to enhance maritime safety.

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

Design parameter optimization method for a prestressed steel structure driven by multi-factor coupling

Time Delay Estimation for Acoustic Temperature Measurement of Loose Coal Based on Quadratic Correlation PHAT-β Algorithm

The acoustic temperature measurement method has a broad application prospect due to its advantages of high precision, non-contact, etc. It is expected to become a new method for hidden fire source detection in mines. The acoustic time of flight (TOF) can directly affect the accuracy of acoustic temperature measurement. We proposed a quadratic correlation-based phase transform weighting (PHAT-β) algorithm for estimating the time delay of the acoustic temperature measurement of a loose coal. Validation was performed using an independently built experimental system for acoustic temperature measurement of loose coals under multi-factor coupling. The results show that the PHAT-β algorithm estimated acoustic TOF values closest to the reference line as the sound travelling distance increased. The results of coal temperature inversion experiments show that the absolute error of the PHAT-β algorithm never exceeds 1 °C, with a maximum value of 0.862 °C. Using the ROTH weighted error maximum, when the particle of the coal samples is 3.0–5.0 cm, the absolute error maximum is 4.896 °C, which is a difference of 3.693 °C from the error minimum of 1.203 °C in this particle size interval. The accuracy of six algorithms was ranked as PHAT-β > GCC > PHAT > SCOT > HB > ROTH, further validating the accuracy and reliability of the PHAT-β algorithm.

Multi-factor coupling optimization of heat recovery effectiveness in a transcritical CO2 heat pump

In the background of energy shortage and climate change, transcritical CO2 heat pump(HTP) technology has attracted lots of attention because of its energy-saving and environmentally friendly advantages. In this research, an experimentally verified simulation model of transcritical CO2 HTP is established to investigate multi-factor coupling optimization of heat recovery effectiveness(ηIHX). First, the coupling optimization mechanism of ηIHX and discharge pressure(pdis) is analyzed. Moreover, this research explores the influence of ηIHX on heating capacity, power consumption, discharge temperature(tdis), and internal heat exchanger(IHX) cost, and further proposes a comprehensive heat recovery index to optimize the above factors. Based on this index the optimal heat recovery effectiveness(ηIHX,opt) for each operating condition is obtained. Also, a failure boundary for the coupling optimization of the ηIHX is also indicated. In addition, the optimal discharge pressure(pdis,opt) prediction correlation for different ηIHXs is proposed, which can be used for heat recovery effectiveness collaborative optimization control. Finally, a general method for evaluating IHX is provided. Taking Xi'an as an example, the optimal heat recovery area(AIHX,opt) of this HTP system is 0.42m2, with which the optimized HTP system operates safely at extreme operating conditions, resulting in an annual lucre of 1,211 CNY.

Multi-parameter coupled optimization of Al6061 coating porosity based on the response surface method

The objective of this study is to study the multi-particle deposition process of cold spray through numerical simulation methods and to use multi-factor coupling to optimize the porosity of Al6061 coating to more accurately characterize the real cold spray deposition process. This study aimed to predict and optimize the porosity of Al6061 coatings using numerical simulation methods. The tasks to be solved are: to nest the multi-particle model established by the Python script in the CEL deposition model to simulate the cold spray deposition process. A multi-parameter function with particle temperature, substrate temperature, and particle velocity as independent variables and Al6061 coating porosity as dependent variables was established. The response surface analysis method was used to predict the optimal spraying parameters and coating porosity of Al6061 coating. The methods used are as follows: optimize the porosity of the coating through multi-factor coupling through response surface analysis; use a multi-particle model established through a Python script to be nested in the CEL deposition model to simulate the deposition process of cold-sprayed Al6061 multi-particles. To characterize the coating porosity more accurately, the average value of multiple groups of samples was taken as the final coating porosity value. Conclusions. the porosity value of Al6061 coating obtained by the prediction model is 1.969%; Under the influence of multi-factor coupling, particle velocity has the greatest impact on the porosity of the Al6061 coating, and the substrate temperature has the least impact. Optimum spraying parameters: particle temperature 649.692K, substrate temperature 536.437K, and particle velocity 672.385m/s. Under the optimal spraying parameters, the porosity value of the Al6061 coating is 1.91875%; The error between the predicted value and the actual value obtained by numerical simulation is only 2.55%.

Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

GM(1,N) method for the prediction of critical failure pressure of type III tank in fire scenarios

The determination of the pressure-bearing performance of hydrogen storage tanks (HST) is integral to enhancing their operational safety and risk management capabilities. In this study, an inaugural development of low-cost critical failure pressure (BPc) prediction model for small samples was proposed, which was based on a combination of the fuzzy grey relational analysis (FGRA) and GM(1,N). Specifically, a total of 15 of BPc-related factors were proposed for analysis and projections based on the data from bonfire tests. Results from the FGRA indicated burst pressure was most closely linked to the initial filling pressure (Rij1 = 0.939) of tanks under fire scenario. The normal working pressure (Rij2 = 0.924) and the elasticity modulus (Rij3 = 0.871) also demonstrating significant impacts. Furthermore, three GM(1,N) prediction models which could estimate BPc with multi-factor coupling were developed. Increasing the number of input feature factors could enhance the predictive capability of GM model. The GM(1,16) had optimal prediction performance among the models, achieving a prediction accuracy of 99.8 %. As well, the mean absolute error (MAE) was 0.799 MPa while the mean absolute percentage error (MAPE) was 1.56 %. This paper offered a novel option for establishing the HST critical failure criterion safely and efficiently.

Dynamic performance analysis of ball bearings with multi-factor coupling excitation

Skidding behavior and unstable cage whirling of bearings are key factors limiting the service performance of a spindle-bearing system. This study proposes an improved dynamic model of ball bearings focusing on structure deformations and poor lubrication conditions. The ring deformations induced by the radial preload for the spindle-bearing-pedestal mechanical system are first discussed, and the asperity contact and starved lubrication conditions on the traction effect are further considered. Then, the cage flexibility induced by the centrifugal effect is determined by the lumped mass method, and cage clearance deformations are analyzed. On this basis, the dynamic equations are derived by investigating the motions and stress states among the bearing components, and the calculations are compared with experimental data to verify their accuracy and reliability. Finally, the regulatory mechanism of bearing skidding and cage motion stability from the perspective of bearing preload and structural parameters is revealed. The results show that although surface roughness and starved lubrication weaken the lubrication conditions of the ball-race, the traction effect is enhanced to reduce bearing skidding. Compared with the curvature radius of inner race, the groove diameter decreases the cage guiding effect, and ensures cage motion stability by suppressing the collision behavior of the ball-pocket. The system preload is more effective for improving the bearing service performance, which can optimize the collision force of the ball-pocket and increase the cage guiding effect.

Impact of multi-factor coupling on the corrosion behavior of 254SMo stainless steel in an aggressive oilfield environment containing CO2 and H2S

The passivity and corrosion behavior of 254SMo stainless steel in aggressive oilfield environments containing both carbon dioxide (CO2) and hydrogen sulfide (H2S) were investigated using weight-loss and surface analyses. The results indicated that the corrosion rate increased with the increase in temperature, CO2/H2S partial pressures, tensile stress and flow rate. Compressive stress led to a reduction in sulfide compound contents and promoted the formation of protective oxide films, while tensile stress had an opposite effect. Furthermore, a synergistic effect of stress and flow was observed.