Engineering Example Research Articles

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.

Generation and evaluation of unimaginable three-dimensional structural joints using generative adversarial networks

Compared to the increasingly programmable and automated structural analysis methods, the current joint design suffers from long design time, poor quality and a low degree of automation. Developing an efficient and intelligent method to generate structurally novel and high-performing joints has become an urgent task. This paper combines artificial intelligence, topology optimization and reverse engineering techniques to develop a method named HD-JointGEN, and elaborates the idea and implementation steps of this method through an actual engineering example of a single-layer reticulated shell structure. The joints generated in batch by HD-JointGEN are analyzed and evaluated, revealing that the selected representative joint can reduce self-weight by 29.55%, maximum displacement by 0.99%, and maximum equivalent stress by 1.28% compared to the topology optimization joint. HD-JointGEN not only automatically generates a variety of innovative and previously unimaginable joints but also offers design solutions with superior force performances. It leads the research on intelligent generative design of the connection in complex three-dimensional structures and has broad application prospects.

Optimization of vanillin biosynthesis in Escherichia coli K12 MG1655 through metabolic engineering

Vanillin is an important flavouring agent applied in food, spices, pharmaceutical industries and other fields. Microbial biosynthesis of vanillin is considered a sustainable and economically feasible alternative to traditional chemical synthesis. In this study, Escherichia coli K12 MG1655 was used for the de novo synthesis of VAN by screening highly active carboxylic acid reductases and catechol O-methyltransferases, optimising the protocatechuic acid pathway, and regulating competitive metabolic pathways. Additionally, major alcohol by-products were identified and decreased by deleting three endogenous aldo–keto reductases and three alcohol dehydrogenases. Finally, a highest VAN titer was achieved to 481.2 mg/L in a 5 L fermenter from glucose. This work provides a valuable example of pathway engineering and screens several enzyme variants for the first time in E. coli.

Carbon Cloth Electrodes for Vanadium Redox Flow Batteries

Renewable energy sources such as wind and solar are playing an increasingly important role as the decarbonization of the energy industry moves forward. However, wind and solar-based energy production fluctuates daily and seasonally. For a stable electrical grid, a large-scale energy storage system is required. One promising technology for this is the Vanadium Redox Flow Battery (VRFB), developed in the late 1980s by Maria Skyllas-Kazacos (1), which has been continuously researched and improved over the last decades.VRFBs are already commercially available but must overcome significant lifetime and efficiency challenges. Polarization and pumping losses account for a large part of the efficiency losses due to the high flow-through resistance of the electrolyte in the electrode (2). Therefore, the electrode material must be optimized to achieve high catalytic activity and excellent flow properties to enhance the battery's performance.In a previous study, we identified key factors such as the electrode's structure, wettability, and electrochemical performance. Therefore, we developed a suitable set of characterization techniques (3). This multimodal characterization approach has been applied to evaluate woven and knitted carbon cloths. We designed electrode materials with specific fiber structures and weaving and knitting patterns. The raw material for the fibers is cellulose from hemp. To successfully commercialize VRVBs, the manufacturing process of the material must be low-cost and easy to fabricate on an industrial scale. Here, the electrode materials are fabricated by weaving or knitting, a well-established technique in the textile industry for centuries, and are ideally suited for the large-scale industrial production of electrodes. After weaving or knitting, the cloths are subsequently carbonized.To understand the influence of the fabrication pattern, the flow direction, and the 3D electrode structure on the flow behavior, we used Electrochemical Impedance Spectroscopy (EIS) coupled with the Distribution of Relaxation Times (DRT) analysis (4,5). This method allows simultaneously investigating the electrochemical performance, wettability, and structure in an application-oriented, in-house-designed setup. To develop a deeper understanding of the microscopic structure of the fiber and its influence on the key factors, Scanning Electron Microscopy (SEM) and nano-X-ray computed tomography (nano-CT) were utilized. These results were combined with Dynamic Vapor Sorption measurements (DVS), which are used to investigate the wettability of the material, and Cyclic Voltammetry (CV), which accesses the electrochemical performance. Additionally, we studied the effect of thermal activation on these parameters with EIS and DRT analysis, SEM, nano-CT, DVS, CV, and X-ray photoelectron spectroscopy.The materials presented in this paper exhibited excellent electrode performance, demonstrating good wettability, high catalytic activity, and large electrochemically active surface area. These are promising examples of electrode engineering as a pathway to optimal designs of VRFBs.1. M. Skyllas‐Kazacos, M. Rychcik, R. G. Robins, A. G. Fane and M. A. Green, J. Electrochem. Soc., 133(5), 1057–1058 (1986).2. N. Bevilacqua, L. Eifert, R. Banerjee, K. Köble, T. Faragó, M. Zuber, A. Bazylak and R. Zeis, Journal of Power Sources, 439, 227071 (2019).3. K. Köble, M. Jaugstetter, M. Schilling, M. Braig, T. Diemant, K. Tschulik and R. Zeis, Journal of Power Sources, 569, 233010 (2023).4. M. Schilling, M. Braig, K. Köble and R. Zeis, Electrochimica Acta, 430, 141058 (2022).5. M. Schilling and R. Zeis, submitted. Figure 1

An overview of potential excavation compensation method for tunnelling in deep rock engineering

The complicated geological environment of deep rocks poses new challenges to tunnel and mining engineering. Some thorny disasters such as large deformation of soft rock and rockburst are becoming more and more prominent. However, the classic tunnelling methods represented by the mine tunnelling method and the new Austrian tunnelling method are generally unsatisfactory in addressing these issues due to the limited self-stability of surrounding rock mass. Therefore, the excavation compensation method (ECM) with the core of active stress compensation has been proposed and applied in practical engineering construction to solve the above problems. After extensive engineering practice, the theoretical foundation, key technologies, and construction system of ECM have been established and improved. This article provides a comprehensive overview of this novel tunnelling method. In addition, its controlling effects on surrounding rock are demonstrated by two typical engineering examples. It could provide some new ideas and references for the development of future tunnelling technology.

Theta-regularized Kriging: Modeling and algorithms

To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implementation details for the iterative process, including the regularized optimization algorithm and the geometric search cross-validation tuning algorithm. Three distinct penalty methods, Lasso, Ridge, and Elastic-net regularization, were meticulously considered. Meanwhile, the proposed Theta-regularized Kriging models were tested on nine common numerical functions and two practical engineering examples. The results demonstrate that, compared with other penalized Kriging models, the proposed model performs better in terms of accuracy and stability.

Wear prediction study considering TBM hob slip

Aiming at the problem of serious tool wear and untimely tool change affecting the construction efficiency when TBM is digging in microfractured tuff strata, the hob wear prediction study is carried out based on engineering examples. By analysing the rock-breaking mechanism of hob wear, a linear wear rate model and a life prediction model considering hob slip are derived based on the CSM model and the abrasive wear theory; the wear rate and the wear amount at the tool change point are further calculated by the prediction model in comparison with the measured value, and the furthest digging distance of the hob is calculated by the life prediction model. The results show that: the relative error between the measured average wear rate and the calculated linear wear rate considering the hob slip is 5.3%; the life prediction model predicts that the longest digging distance of the positive hob is 857 rings and the shortest distance is 198 rings. The results of the study are of guiding significance for the prediction of tool wear and the selection of the timing of opening and changing the hob in TBM tunneling.

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.

Just passing through: Deploying aquaporins in microbial cell factories.

As microbial membranes are naturally impermeable to even the smallest biomolecules, transporter proteins are physiologically essential for normal cell functioning. This makes transporters a key target area for engineering enhanced cell factories. As part of the wider cellular transportome, aquaporins (AQPs) are responsible for transporting small polar solutes, encompassing many compounds which are of great interest for industrial biotechnology, including cell feedstocks, numerous commercially relevant polyols and even weak organic acids. In this review, examples of cell factory engineering by targeting AQPs are presented. These AQP modifications aid in redirecting carbon fluxes and boosting bioconversions either by enhanced feedstock uptake, improved intermediate retention, increasing product export into the media or superior cell viability against stressors with applications in both bacterial and yeast production platforms. Additionally, the future potential for AQP deployment and targeting is discussed, showcasing hurdles and considerations of this strategy as well as recent advances and future directions in the field. By leveraging the natural diversity of AQPs and breakthroughs in channel protein engineering, these transporters are poised to be promising tools capable of enhancing a wide variety of biotechnological processes.

Two-Stage Analysis Method for the Mechanical Response of Adjacent Existing Tunnels Caused by Foundation Pit Excavation

With the advancement of urban underground space networks, there has been a rise in foundation pit projects near existing tunnels. The construction of these foundation pits adjacent to existing tunnels can result in soil disturbance and stress redistribution, leading to additional deformation and internal force within the tunnels. This paper delves into the two-stage analysis method, outlining the calculation of additional stress in the initial stage considering various engineering factors and the methods for determining tunnel displacement and internal force in the subsequent stage. Through an engineering example and numerical simulations, the theoretical calculations were validated. The maximum displacement generated by the tunnel is −4.85 mm and −5.10 mm, respectively. The maximum error is only 5.9%, which confirms the validity of the theoretical approach. The analysis demonstrates that incorporating the unloading model of the bottom and surrounding side walls of the foundation pit is essential when calculating additional stress in the first stage. Moreover, the presence of engineering dewatering and double-hole tunnels can counterbalance the additional stress, with deviations of only 4.4% and 2.5%, respectively. In the second stage, factoring in the shear action and lateral soil action in the foundation and tunnel model enhances the accuracy of stress mode representation (accuracy increased by 18.8% and 29.3%, respectively). Additionally, accounting for the buried depth effect of the tunnel, soil non-uniformity, and foundation nonlinearity helps prevent excessive foundation reactions.

Optimizing mechanical performance and flow characteristics of alkaline fly ash/rice husk ash composite backfill: Insights from multi-factor analysis and engineering optimization

In order to effectively dispose of agricultural waste rice husk ash and improve the mechanical properties of backfill, the properties of fly ash/rice husk ash composite backfill material were studied in the laboratory. The effects of fly ash, rice husk ash, straw fiber and NaOH content on the properties of composite backfilling materials were studied, and the ratio parameters of composite backfilling materials were optimized based on engineering examples. Our findings reveal that fly ash content significantly impacts slump, with its addition enhancing the flow performance of composite backfilling materials. Conversely, straw fiber, rice husk ash, and NaOH content were found to have non-significant effects on slump. We observed that with the increase of fly ash content from 5 % to 20 %, the compressive strength of the backfill firstly increases and then decreases, and the optimal content range is about 10 %. Moreover, increased rice husk ash and NaOH content effectively enhance the UCS. However, higher fly ash and straw fiber content were found to impede the strengthening effect of NaOH on UCS. Nevertheless, higher NaOH content was observed to augment the strengthening effect of rice husk ash content. Furthermore, when the content of fly ash and rice husk ash is 5∼10 % and 3–12 % respectively, rice husk ash and fly ash effectively reduce the size range of internal void structures, enhancing the microstructure density and mechanical properties of the composite backfilling material. Based on engineering example, we determined the optimal ratio parameters of the composite backfilling material to be: 20 % fly ash content, 9 % rice husk ash content, 0.4 % straw fiber content, and 2.5 % NaOH content. This study is helpful to promote the application of waste rice husk ash and straw fiber in mine filling, and provides theoretical guidance for the parameter design of backfilling materials.

Laser-Printed All-Carbon Responsive Material and Soft Robot.

Responsive materials and actuators are the basis for the development of various leading-edge technologies but have so far mostly been designed based on polymers, incurring key limitations related to sensitivity and environmental tolerance. This work reports a new responsive material, laser-printed carbon film (LPCF), produced via direct laser transformation of a liquid organic precursor and consists of graphitic and amorphous carbons. The high activity of amorphous carbon combined with the dual-gradient structure enables the LPCF to have a actuation speed of 9400°s-1 in response to the stimulus of organic vapor. LPCF exhibits a conductivity of 950Sm-1 and excellent resistance to various extreme environmental conditions, which are unachievable for polymer-based materials. Additionally, an LPCF-based all-carbon soft robot that can mimic the complex continuous backward somersaulting motions without manual intervention is constructed. The locomotion velocity of the robot reaches a value of 1.19BLs-1, which is almost one to two orders of magnitude faster than that of reported soft robots. This work not only offers a new paradigm for highly responsive materials but also provides a great design and engineering example for the next generation of biomimetic robots with life-like performance.

A Novel Artificial Intelligence Prediction Process of Concrete Dam Deformation Based on a Stacking Model Fusion Method

Deformation effectively represents the structural integrity of concrete dams and acts as a clear indicator of their operational performance. Predicting deformation is critical for monitoring the safety of hydraulic structures. To this end, this paper proposes an artificial intelligence-based process for predicting concrete dam deformation. Initially, using the principles of feature engineering, the preprocessing of deformation safety monitoring data is conducted. Subsequently, employing a stacking model fusion method, a novel prediction process embedded with multiple artificial intelligence algorithms is developed. Moreover, three new performance indicators—a superiority evaluation indicator, an accuracy evaluation indicator, and a generalization evaluation indicator—are introduced to provide a comprehensive assessment of the model’s effectiveness. Finally, an engineering example demonstrates that the ensemble artificial intelligence method proposed herein outperforms traditional statistical models and single machine learning models in both fitting and predictive accuracy, thereby providing a scientific and effective foundation for concrete dam deformation prediction and safety monitoring.

Reliability Estimation of Weibull Distribution with Zero-Failure Data

This paper focuses on the reliability estimation of Weibull distributions with zero-failure data. Since no product failure occurred in the tests, the conventional maximum likelihood method was not applicable. Recent methods ignore the estimation of location parameter and tend to be conservative. This study developed a new estimation technique to address these problems. A prior model between the life dispersion boundary and shape parameter is constructed by considering the historical failure data of similar high-reliability products. The failure probabilities for all samples were inferred using the E-Bayesian method, after which the reliability was estimated. Afterward, a confidence interval for reliability was obtained based on the bootstrap confidence interval and modified maximum likelihood method. A Monte Carlo simulation study demonstrated that the proposed method is satisfactory in terms of point estimates, confidence intervals, coverage probabilities, and computational efficiency. Finally, a real engineering example is introduced to illustrate the application of this method.

Output-only response migration method of single-layer reticulated shells based on generative adversarial network

As an important form of important public building roofs, single-layer latticed shells (SLRSs) are equipped with health monitoring systems (SHMs) to collect extensive data to study their complex response characteristics. For SLRSs without structural SHMs, limited response data can only be obtained through on-site measurements, which limits the further application of data-driven machine learning methods on SLRSs. Based on the measured actual response, a traditional generative network can generate fake responses similar to the actual responses to expand the existing response dataset. However, this method is not subject to enough constraints, leading to a small portion of the generated fake responses do not match the actual situation. This paper proposes a response migration method to expand the response dataset of SLRS. Firstly, the output-only response migration method for SLRS is proposed, utilizing frequency domain responses from two different response domains to expand the responses of the actual SLRS through response migration. Then, more constraints are applied to the response migration process to reduce the occurrence of abnormal fake responses through cycle consistency generation adversarial network (GAN). The adaptive response migration generation adversarial network (ARMGAN) is proposed based on cycle consistency GAN, which can automatically select networks with better migration performance. Finally, two numerical spherical SLRS examples and an engineering example are used to verify the proposed method. The responses migrated through ARMGAN have characteristics similar to the actual responses, and ARMGAN can significantly reduce abnormal fake responses.

A global sensitivity analysis method based on IBWO-SVR-SS

PurposeAiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.Design/methodology/approachIn this paper, a support vector regression (SVR) surrogate model is constructed to solve the Sobol index. The optimal combination of SVR hyperparameters is obtained by using the improved beluga whale optimization (IBWO). Meanwhile, in order to solve the problem that Sobol sequences will form correlation regions in high-dimensional space leading to the uneven distribution of sampling points, a scrambled strategy is introduced in the Sobol sensitivity analysis using IBWO-SVR. Thus, the IBWO-SVR-SS sensitivity analysis model is established.FindingsThe results of two test functions show that the method further improves the accuracy of the sensitivity analysis. Finally, the first-order Sobol index and second-order Sobol index are solved by the IBWO-SVR-SS method using the metro bogie frame as an engineering example. Through the analysis results, the key design parameters of the frame and the design parameter combinations with more obvious coupling relationships are identified, providing a strong reference for the subsequent analysis and structural optimization.Originality/valueSobol sensitivity analysis using the surrogate model method can effectively improve the efficiency of the solution. In addition, IBWO is used for the optimization of the SVR hyperparameters to improve the accuracy and efficiency of the optimization, and finally, the correction of the Sobol sequence through the introduction of the disruption strategy also further improves the accuracy of the sensitivity analysis of Sobol.

Active-Learning Reliability Analysis of Automotive Structures Based on Multi-Software Interaction in the MATLAB Environment

The reliability design of automotive structures is characterized by numerous variables and implicit responses. The traditional design of experiments for metamodel construction often requires manual adjustment of model parameters and extensive finite element analysis, resulting in inefficiency. To address these issues, active learning-based reliability methods are effective solutions. This study proposes an active-learning reliability analysis method based on multi-software interaction. Firstly, through secondary development of different software and MATLAB (version 2023a)’s batch processing function, a multi-software interactive reliability analysis method is developed, achieving automation in structural parametric design, finite element analysis and post-processing. This provides a more efficient and convenient platform for the implementation of active learning. Secondly, the polynomial chaos–kriging (PCK) active-learning method is introduced, combining the advantages of polynomial chaos expansion (PCE) and kriging. The PCK method captures the global behavior of the computational model using regression-based PCE and local variations using interpolation-based kriging. This metamodel is constructed with fewer training samples, effectively replacing the real multi-dimensional implicit response relations, thereby improving the efficiency of modeling and reliability analysis. Finally, the specific implementation scheme is detailed. The accuracy and efficiency of the proposed method are verified by a reliability engineering example of body-in-white bending and torsional stiffness.

Finite Element Analysis and Optimization of PDCPD New Material Wall Formwork

Thermosetting polydicyclopentadiene (PDCPD) is a new material with high performance, which is expected to replace the traditional steel in the application of cast-in-place formwork components in house construction. In this paper, the finite element model is established by Abaqus to simulate and analyze the floor formwork with the largest size and span in the engineering example, so as to verify the feasibility of this component. The results show that the strength of the floor formwork meets the requirements under the vertical pressure of concrete, but its deformation does not meet the requirements. Therefore, the design optimization is carried out on the basis of the original design, and the main longitudinal ribs are reinforced with steel plates along the span direction. The verification results show that the optimization scheme is suitable.

Analysis on characteristics of ground vibration induced by underground high-speed trains based on in-situ test

With the increasingly emerging traffic intersection form of high-speed railways passing underneath integrated transport terminals, the ground vibration induced by trains running on underground high-speed railways has attracted extensive attention. In combination with the engineering example of a high-speed railway in construction passing underneath an airport, the wheel-drop impact test in the high-speed railway tunnel was designed to obtain the vibration characteristics of surrounding soil. The vehicle-track-tunnel-ground semi-analytical three-dimensional model was established to determine the initial dynamic load condition of the vehicle loads. The amplitude-frequency characteristic and transmission law of the ground vibration induced by trains running on underground high-speed railways were predicted and analyzed. The influences of different operation conditions, track parameters, and tunnel burial depths were compared. The research results show that the ground vibration obtained through the wheel-drop test is greatly attenuated within 10 m from the track centerline, with an attenuation rate up to 70%. The vertical vibration amplitude is obviously larger than that of the other two directions. The lateral ground vibration amplitude is the smallest with the slowest vibration attenuation speed. When the high-speed train passes through at the speed of 350 km/h, the vertical and lateral vibration levels of the ground surface within 80 m range are 20∼80 dB and 43∼72 dB, respectively. The maximum attenuation coefficient of Z-vibration level reaches 0.276 dB/m. For each increase of 50 km/h of the train speed, the Z-vibration level of the ground increases by about 2 dB. The smaller fastener stiffness leads to the greater ground vibration within 20∼50 Hz. The ground vibration level is positively correlated with the fastener stiffness above 79 Hz. When the burial depth of the tunnel is greater than 14 m, the burial depth of the tunnel has little effect on the change of the Z-vibration level of the ground.

A new method for predicting consolidation settlements of soft ground based on monitoring results

Excessive settlement or differential settlement of subgrade will lead to the deterioration of line operational conditions, the reduction of passenger comfort, and even endanger the safety of traffic. Therefore, it is of great significance to study the settlement prediction of subgrade. In order to predict the settlement of foundation under the next level of loading earlier during the embankment construction process, a new method of predicting settlement of soft soil subgrade is proposed. Firstly, based on monitoring results of soft soil foundation, the consolidation parameters of soil layer are back-calculated according to the three-point method. Then, combined with the theory of the consolidation degree of graded loading, the formula that can predict settlement under different loading conditions are derived. Eventually, the practical application of the method is verified by the prediction and comparative analysis of measured settlements based on engineering examples. The result of research shows that the method can predict the foundation settlement after loading during construction of engineering fill. This method has obvious advantages over the traditional curve fitting method and can guide the actual engineering construction.