Engineering Situations Research Articles

Particle migration due to non-uniform laminar flow

Abstract Using methods of granular mechanics in the quasi-static limit, with inter-particle interactions derived from the lubrication limit, the intensity of velocity fluctuations in the slurry is associated with fluctuations in the local distribution of inter-particle distances. These are shown to consist of a vector intensity and a scalar intensity; the former couples to the first velocity gradient, the latter (which is associated with solidosity fluctuations) couples to the second velocity gradient. Rheologies for both are presented, as is the rheology that links the particle pressure to the intensity of the velocity fluctuations (also known as the ‘granular temperature’) to the dispersive pressure. The rheologies are informed by experimental results. The granular temperature profile, modified from previous work, is responsible for axial particle migration (Bagnold effect). Two broad categories are assessed: symmetrical vertical and non-symmetrical lateral flow. For the latter the roughness of the boundary walls and a non-zero density contrast are important; this case is studied for a system in which flow effects are confined to the immediate vicinity of the boundary. Sensitivity analysis reveals several key variables including the parameters that control a slipping boundary condition and the mean solidosity in the conduit. For lateral flow, a sedimentary deposit with a solidosity profile may develop near the upper or lower boundary. The theory predicts an approximate relation between the fluid-particle density contrast and sediment thickness as a function of the mean flow rate, conduit width, the mean particle diameter and fluid viscosity that has utility in a range of engineering and geological situations where particulate matter is transported in the laminar flow regime.

Improving tensile properties of glass fiber-reinforced epoxy resin composites based on enhanced multiphase structure: The modification of resin systems and glass fibers

Glass fiber-reinforced polymer (GFRP) composites have been widely used as reinforced materials in marine engineering due to their good corrosion resistance and economic benefits. However, the mechanical properties of GFRP are lower than that of composites reinforced by carbon and aramid fibers etc. Methods to improve the mechanical properties of GFRP received ongoing attention. GFRP have various mechanical properties due to the characteristics of its multiple phases. This study developed an optimized processing method based on multiphase structures for GFRP composites, significantly enhancing the tensile properties of GFRP laminates. The modified GFRP can be produced efficiently by this method to meet more utilization situations in marine engineering. The influence of curing agents, silane coupling agents (SCA)-treated glass fibers (GFs), and nanomaterials on GFRP's tensile properties was investigated. The findings reveal a 10.64 % increase in the characteristic load of GF bundles due to the improved bonding between fibers by SCA. Besides, the contact angle between SCA-treated GFs and epoxy resin shows a significant reduction (26.69 %). Variations in ply thickness and resin system distinctly influence the tensile properties and failure modes of GFRP laminates. Laminates with 4-ply GF fabrics facilitate more effective load transfer across the matrix-fiber boundary, yielding optimal tensile properties for GFRP. Epoxy/phenolic amine resin system with more reactive functional groups (-OH) enhances the fiber-resin bonding, further improving the tensile properties of GFRP. The enhancement mechanism of the multiphase structure in GFRP was elucidated by microscale characterization, indicating that the multiphase structures in GFRP, enhanced by SCA, nanomaterials, and functional curing agents, significantly improve its tensile properties.

Numerical investigation of slip effects on heat and mass transfer in a vertical channel with immiscible micropolar and viscous fluids of variable viscosity

AbstractThis study investigates the fluid flow, heat, and mass transfer phenomena within a vertical channel containing two immiscible fluids, with a particular focus on slip effects. These effects include no slip, velocity slip, thermal slip, and multiple slips, each analyzed with appropriate boundary conditions. The study thoroughly examines key characteristics, such as variations in thermal conductivity and viscosity. Using a sixth‐order Runge–Kutta numerical method implemented using Mathematica, the study achieves precise solutions for complex scenarios. The detailed results show how the various slip mechanisms and relevant parameters interact with each other in complex ways. These findings are useful for both theoretical understanding and application in real‐life engineering situations. This study also gives important information about how fluid flow, heat transfer, and mass transfer change under different slip effects. It looks at these effects and shows how they change visually. It also carefully calculates and analyzes engineering parameters like the Nusselt number, shear stress, and Sherwood number using bar charts, showing how they affect and behave. The study resulted in velocity slip having a minimal impact on temperature, whereas thermal slip resulted in higher temperatures. Both velocity and thermal slip conditions simultaneously result in the lowest temperatures.

Numerical simulation and theoretical study on the impact of wind-sand flow of high-speed trains in long tunnel space

When high-speed trains (HST) run in enclosed spaces such as long tunnels, the thermal accumulation of their suspension devices is continuous and cannot be effectively dissipated. In addition, previous experiments or simulations for the heat dissipation of HST in tunnel spaces did not consider the impact of sand. To clarify the impact of HWS-LT on the heat accumulation of HST equipment cabin, this study used the CFD method to numerically simulate the impact of different wind-sand flow concentrations or no-sand wind on the cooling of equipment in the long tunnel space. Firstly, the sand particles in the wind-sand flow gather at the tunnel entrance and enter the equipment cabin with the train as it enters the tunnel. This boundary condition is more in line with actual engineering situations. Secondly, both flows show asymmetric intrusion into the cabin due to the asymmetrical tunnel arrangement, but the sand particles in the wind-sand flow are affected by the vortices and tunnel walls, resulting in more asymmetric flow and some particles being trapped in the grids or filters, leading to outflow ρQ < inflow ρQ. Under the wind-sand flow condition, the temperature of some equipment surfaces shows more significant increases than under the no-sand wind. Finally, contrary to popular perception, the wind-sand flow carrying sand particles can dissipate heat more effectively than no-sand wind, and the higher the volume fraction φ within a certain concentration range, the better the heat dissipation effect. This is because the wind-sand flow has a higher specific heat capacity, which can remove some heat from the contact point between the sand particles and the equipment wall upon contact. The higher sand particle concentration increases the contact frequency and contact area between the sand particles and the equipment wall, and the heat transfer pathway and heat dissipation efficiency are improved.

IDENTIFICATION AND ANALYSIS OF RISKS IN CIVIL ENGINEERING PROJECTS

&amp;lt;p&amp;gt;The main and primary goal of the research in this paper is to analyze the actual current situation in domestic civil engineering, by determining the causes of missing deadlines and exceeding budgets of civil engineering projects. Since the results of this research also indicate the most likely problems that could arise during the construction of a project, the findings and conclusions could be used in risk assessment and drawing up dynamic plans for actual projects, which would improve the situation in domestic civil engineering and increase productivity of this important industry.&amp;amp;nbsp;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;The results obtained in this paper provide a modern approach to the problem of risk in construction, provide a realistic insight into risk factors on civil engineering projects in the Republic of Serbia and the surrounding area, and facilitate the establishment of a better correlation between theory and practice in project management and risk management.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;nbsp;&amp;lt;/p&amp;gt;

Effects of wind-wave-current-earthquake interaction on the wave height and hydrodynamic pressure based on CFD method

Wind-wave-current interaction is a phenomenon which is present in many practical engineering situations and vertical earthquakes may have significant influence on the hydrodynamic pressure of seawater in regions of active seismicity. This study is devoted to probe the wave height and hydrodynamic pressure of seawater caused by the joint wind, wave, current and earthquake action based on computational fluid dynamics (CFD) method, where a homogeneous multiphase model based on the noncompressible Reynolds Averaged Navier-Stokes (RANS) equation and the k-ε turbulence model implemented in ANSYS-Fluent code was used. First, a two-dimensional (2D) numerical flume excited by wind, wave, current and earthquake loadings was established through the secondary development of a user-defined function (UDF). Subsequent, effects of wind-wave interaction and wind-wave-current interaction on the wave height and hydrodynamic pressure of seawater were investigated. Finally, effects of wave-earthquake interaction and wave-current-earthquake interaction on the wave height and hydrodynamic pressure of seawater were investigated. The nonlinearity of seawater under joint wind, wave, current and earthquake action was also studied.

How ChatGPT shapes the future labour market situation of software engineers: A Finnish Delphi study

ChatGPT is changing our working lives, conjuring visions of revolutionary shifts ahead. ChatGPT is a chatbot based on generative artificial intelligence, which can, e.g., write computer code. This article explores how it might shape the future labour market situation of software engineers. A Delphi study with 14 experts was conducted in Finland. The first round identified possible futures, and the second round assessed their probabilities. Five scenarios prevailed: the unlikely scenario that the status quo persists; the ambivalent scenario that ChatGPT can replace software engineers to a large extent; the likely scenario that computer departments in startups embrace ChatGPT; the likely scenario that ChatGPT use proliferates among software engineers to increase productivity; and the highly likely scenario that ChatGPT makes computer programming accessible to the masses. Findings contradict previous discussions that technological advancements might take over especially routine tasks. ChatGPT can also take over non-routine tasks. Moreover, findings underline that digitalisation does not only bring about a choice between upskilling and employability loss, but also a democratisation of knowledge and expertise. Software engineers and companies might use the findings as an impetus for upskilling, while universities might feel nudged to incorporate ChatGPT more strongly into their curricula.

Interpretable Siamese dual attention enhancement transfer compound diagnostic model for unbalanced samples

The intelligent transfer diagnosis model is used to address the issue of feature drift caused by the changing working conditions of rotating parts in engineering. However, few models can perform transfer diagnosis on multiple unbalanced samples of rotating parts simultaneously, and even fewer models can visually enhance the domain-invariant features, making them more interpretable. To address these issues, we propose a novel interpretable Siamese dual attention enhancement transfer compound diagnosis model for unbalanced samples. The model can diagnose multiple rotating parts simultaneously and consists of a channel feature attention enhancement (CFAE) network, a fragment feature attention enhancement (FFAE) network, and a Siamese feature fusion (SFF) network. The CFAE network enhances features of different convolutional channels, the FFAE network improves segment features in various frequency domains, and the SFF network extracts domain-invariant features of diverse rotating components under varying working conditions. The model is validated using bearing fault data collected under different loads and planetary gear fault data obtained at varying speeds. Its diagnostic accuracy remains above 96.4%, and the diagnostic variance is controlled within 1.0%. The model has good interpretability for imbalanced sample domain-invariant features, providing an effective tool for interpretable transfer diagnosis in this compound engineering situation.

Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Abstract In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-intensive simulations. Data-driven methods can mitigate this challenge by harnessing historical data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering situations. Additionally, there is an inherent trade-off between training speed and accuracy, with faster methods sometimes compromising precision. In our paper, we demonstrate that a recently released general-purpose transformer-based classification model, TabPFN, is both fast and accurate. Notably, it requires no dataset-specific training to assess new tabular data. TabPFN is a prior-data fitted network, which undergoes a one-time offline training across a broad spectrum of synthetic datasets and performs in-context learning. We evaluated TabPFN’s efficacy across eight engineering design classification problems, contrasting it with seven other algorithms, including a state-of-the-art automated machine learning (AutoML) method. For these classification challenges, TabPFN consistently outperforms in speed and accuracy. It is also the most data-efficient and provides the added advantage of being differentiable and giving uncertainty estimates. Our findings advocate for the potential of pre-trained models that learn from synthetic data and require no domain-specific tuning to make data-driven engineering design accessible to a broader community and open ways to efficient general-purpose models valid across applications. Furthermore, we share a benchmark problem set for evaluating new classification algorithms in engineering design.

Research on project schedule optimization model construction and engineering efficiency improvement method

Abstract Project schedule management is an important guarantee for the smooth implementation of the project, which is closely related to the interests of all parties involved in the project, and how to improve the efficiency and quality of the project is a problem that still needs to be solved. In this paper, with reference to the actual situation in the project engineering, the objective function of schedule, cost, quality, and safety is proposed, and the multi-objective optimization mathematical model of the project engineering is obtained through multi-attribute utility theory modeling. Based on the objective function and constraints, the fitness function is established, the constructed model is solved using a genetic algorithm, and safeguard measures are designed to improve the efficiency of project engineering. The empirical results show that the duration and labor demand in the project schedule optimization scheme proposed by the model in this paper are much lower than those in the planning scheme of traditional project schedule optimization methods. Compared to the traditional method, the total cost of the project works decreased by 8.70%, while the quality of the work improved by 0.71%. It shows that the model can improve the quality of project efficiency and enhance the economic benefits of the entire project. The model proposed in this paper makes project schedule optimization more scientific, reasonable, and practical and has certain theoretical and practical significance. In addition, the establishment of a perfect schedule risk management mechanism can be considered in future research in this field to further improve the project schedule optimization model.

I4.0/5.0 MR training: Investigating MR tools to enhance learning experiences

As the manufacturing industry continues its shift towards highly complex Industry 4.0 production environments, there is an expected exponential increase and change in the demanded skills and qualifications among employees. However, traditional teaching methods may pose challenges when it comes to applying learned skills in real-life engineering situations, given the complexity of these environments. Recent advancements in technologies enabling virtual co-existence have opened up new opportunities for personalised and immersive services in pedagogy. While Mixed Reality (MR) and, more significantly, Metaverse infrastructure are still in their early stages, researchers and educators have the opportunity to lead the exploration of new avenues for reskilling educators and enhancing student learning experiences. This paper presents research conducted at the University of Malta, focusing on exploring the potential transformative pedagogical effects of MR in specialised Industry 4.0/5.0 engineering training. The paper proposes a framework for developing a Virtual Learning Factory (VLF) using MR technology, grounded in established and effective learning methodologies. The envisioned VLF aims to create an immersive experiential learning environment where engineering students can better adapt to the evolving industrial landscape, preparing them to excel in the dynamic era of advanced manufacturing. Additionally, the research delves into the potential impacts of MR-based training on enhancing training precision and efficiency.

Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior

In order to study shaft wall damage resulting from ore drawing in ore passes, a theoretical model for predicting the shaft wall damage to high-depth inclined ore passes is constructed based on field surveys of 25 ore passes in a deep mine in Yunnan, China. The mathematical expression of the total shaft wall damage volume is derived using the contact mechanics theory. Considering the structural characteristics of ore passes, and taking No. 1, 2, 3, and 9 ore passes as examples, combined with numerical simulation and an engineering case, the rationality of the proposed theoretical model is verified with respect to the initial collision position and the damage conditions of the shaft wall. The influence of, and sensitivity to, the ore block size P and the structural parameters of high-depth inclined ore passes on the total shaft wall damage volume Qtol are quantitatively analyzed. The results show that the calculation results of the theoretical model and numerical simulation are in good agreement with the actual engineering situations. Moreover, the ore-pass dip angle θ and the inclined angle of the chute α have a significant impact on the damage to the shaft wall, while the effects of the ore-pass depth H and the shaft diameter D are comparatively minor. With an increase in θ or α, Qtol generally first increases and then decreases. Qtol increases exponentially with P and increases steadily with D. H affects Qtol by influencing the collision frequency between the ore and the shaft wall. Therefore, in the mining design of deep mines, θ and α should be minimized as much as possible or adjusted to approach 90°, thereby reducing damage to the shaft wall. Secondly, ore block size should be strictly controlled to prevent collapses in the shaft wall caused by large ore blocks. This work provides technical support for the long-term safe operation of high-depth inclined ore passes.

Software Engineering and Ethics: Navigating the Ethical Landscape in the Digital Age

In the unexpectedly evolving landscape of software engineering, moral considerations have emerged as an important cornerstone, guiding the moral compass of professionals in the digital age. This summary encapsulates a comprehensive assessment exploring the difficult dating between software engineering and ethics. It navigates through the moral challenges faced by software engineers, inclusive of privateer’s issues, safety vulnerabilities, algorithmic biases, and intellectual property problems. Delving into established moral choice-making frameworks, inclusive of utilitarianism and deontology, this overview analyzes their application in actual global software engineering situations. Through insightful case studies, this paper sheds mild on historic and contemporary moral dilemmas, providing treasured classes for the software program engineering community. The assessment additionally emphasizes the significance of promoting ethical practices within businesses, advocating for transparency, responsibility, and a tradition of obligation. Looking in advance, the paper discusses the future of software engineering and ethics, exploring the moral dimensions of emerging technology like artificial intelligence and the Internet of Things. By supplying a roadmap for ethical software program development, this evaluation underscores the vital for software engineers to navigate the ethical complexities, fostering a moral virtual ecosystem for present-day and future generations.

Dynamic risk assessment of chemical process systems using the System-Theoretic accident model and process approach (STAMP) in combination with cascading failure propagation model (CFPM)

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

The effect of borate on acellular bioactivity of novel mesoporous borosilicate bioactive glasses for tissue engineering

Four novel mesoporous borate-based 13–93 bioactive glass has been synthesized by a modified Stöber method. Thermogravimetric analysis, Inductively coupled plasma atomic emission spectroscopy, Fourier Transform Infrared Spectroscopy, X-Ray diffraction, Brunauer–Emmet–Teller and Barrett–Joyner–Halenda, nuclear magnetic resonance and Scanning Electron Microscope attached with Energy-dispersive X-ray spectroscopy analyses were carried out to investigate the physicochemical properties, in-vitro acellular bioactivity, and the degradation rate of the bioactive glasses in simulated body fluid across a range of times up to 21 days. The characterization data indicates that the bioactive glasses are amorphous and possess mesoporous properties. Likewise, an increase in boron content reduced the surface area and pore volume of the bioactive glass series. The observed decline is attributed to borate units' smaller atomic radius than silicate. The ability to deposit a new apatite layer in the bioactive glasses depends on the borate concentration. As such, an increase in boron content resulted in a decrease in apatite formation. Additionally, the studies showed that bioactive glasses made of borate degraded more quickly than their silicate counterpart. Overall, borate-based 13–93 bioactive glasses were effectively synthesized, and the in-vitro results showed potential usage for these bioactive glasses in tissue engineering situations where it is desirable to control bone growth or delay the onset of bioactivity.

Anisotropic behaviors of calcareous sand dependent on loading direction and initial shear stress

Calcareous sand has been a suitable unbound granular filler in island reef engineering. Given its unique microstructure and the complex loading situation in practical engineering, anisotropy is a crucial factor that cannot be ignored. In light of this, we conducted a set of hollow cylindrical shear tests to explore how the loading direction and initial shear affect the anisotropic behaviors of calcareous sand, with Silica sand as the control material. A remarkable finding is that calcareous sand is more anisotropic than silica sand due to its irregular particles, as evidenced by a greater variation in internal friction angle(φf) and stress ratio at failure(Mf) with influencing factors. To accurately represent these variations caused by anisotropy, a parameter for quantifying anisotropy was found. The dependence of the two influencing factors on Mf was formulated based on the parameter. The formulated Mf was then applied to the UH model, and the predicted and experimental results of the shear response were compared to evaluate the model performance.

Groutability classification of granular soils with cement grouts

This research aims to develop a methodology for applying the geostatistical method to generate a groutability classification for granular soils. To ensure the precision of the suggested technique, a total of 103 data samples were used. Predicting the groutability of granular soils has always been difficult because of many soil characteristics. As a result, a new two-dimensional graph, the groutability classification of granular soil (GCS) chart, was developed. GCS establishment was based on data analysis of the grain size of soil and cement-based grouts (N1 and N2), relative density (Dr) and fines content of the soil (FC), water/cement ratio of grout mixture (w/c), and grouting pressure (P), all of which have a direct impact on the groutability of soil media. The geostatistical method was used to develop and compile the GCS graph based on the aforementioned parameters with the use of coefficient S, which is a coefficient of the scoring set of parameters including P, w/c, Dr, and FC. The validation process was carried out hierarchically, with an additional set of 30 data. The proposed method has a prediction accuracy of roughly 96.7%, demonstrating a helpful tool. The proposed approach can be easily implemented in practical engineering situations because it has a comparable syntax to commonly used formulae. It should be noted that the proposed formula was only tested using the data samples collected, and the applicability of the produced procedure to other situations requires more examination.

Review on unrelated parallel machine scheduling problem with additional resources

This study deals with an unrelated parallel machine scheduling problem with additional resources (UPMR). That is one of the important sub-problems in the scheduling. UPMR consists of scheduling a set of jobs on unrelated machines. In addition to that, a number of one or more additional resources are needed. UPMR is very important and its importance comes from the wealth of applications; they are applicable to engineering and scientific situations and manufacturing systems such as industrial robots, nurses, machine operators, bus drivers, tools, assembly plant machines, fixtures, pallets, electricity, mechanics, dies, automated guided vehicles, fuel, and more. The importance also comes from the concern about the limitation of resources that are dedicated for the production process. Therefore, researchers and decision makers are still working on UPMR problem to get an optimum schedule for all instances which have not been obtained to this day. The optimum schedule is able to increase the profits and decrease the costs whilst satisfying the customers’ needs. This research aims to review and discuss studies related to unrelated parallel machines and additional resources. Overall, the review demonstrates the criticality of resolving the UPMR problem. Metaheuristic techniques exhibit significant effectiveness in generating results and surpassing other algorithms. Nevertheless, continued improvement is essential to satisfy the evolving requirements of UPMR, which are subject to operational changes based on customer demand.

Review on unrelated parallel machine scheduling problem with additional resources

This study deals with an unrelated parallel machine scheduling problem with additional resources (UPMR). That is one of the important sub-problems in the scheduling. UPMR consists of scheduling a set of jobs on unrelated machines. In addition to that, a number of one or more additional resources are needed. UPMR is very important and its importance comes from the wealth of applications; they are applicable to engineering and scientific situations and manufacturing systems such as industrial robots, nurses, machine operators, bus drivers, tools, assembly plant machines, fixtures, pallets, electricity, mechanics, dies, automated guided vehicles, fuel, and more. The importance also comes from the concern about the limitation of resources that are dedicated for the production process. Therefore, researchers and decision makers are still working on UPMR problem to get an optimum schedule for all instances which have not been obtained to this day. The optimum schedule is able to increase the profits and decrease the costs whilst satisfying the customers’ needs. This research aims to review and discuss studies related to unrelated parallel machines and additional resources. Overall, the review demonstrates the criticality of resolving the UPMR problem. Metaheuristic techniques exhibit significant effectiveness in generating results and surpassing other algorithms. Nevertheless, continued improvement is essential to satisfy the evolving requirements of UPMR, which are subject to operational changes based on customer demand.

Elastic full-wave field simulation in 3D tunnel space with the variable staggered-grid finite-difference method in cylindrical coordinates

Tunnel advance detection technology is an important method for determining the structure of complex geological bodies in front of the tunnel face. Among the tunnel advance detection technologies, the seismic method is one of the most accurate methods with long detection distances. In seismic tunnel advance detection, the cylindrical configuration aggravates the complexity of the wave field in the 3D tunnel space and significantly influences the accuracy of the detection results. Thus, it is crucial to simulate an accurate seismic full-wave field of the tunnel space and to understand the propagation and wave-field characteristics of individual seismic waves for seismic tunnel advance detection. Usually, in 3D Cartesian coordinates, the tunnel wall is approximated with a staircase boundary, but it is not sufficiently accurate in shape and generates numerical dispersion in the simulation, especially in the presence of surface waves. Therefore, we developed a variable staggered-grid finite-difference method in cylindrical coordinates to simulate the elastic full-wave field in a 3D tunnel space. Properly setting free-surface boundary conditions solves the propagation of surface waves on the tunnel wall and face. The interference of the instability and discontinuity of the pole axis in the seismic wave field simulation was eliminated using our method. According to practical engineering situations, models containing three types of geological bodies in front of the tunnel face were designed. Their seismic wave propagation and wave field characteristics were analyzed. Compared with the simulation results in Cartesian coordinates, the results in cylindrical coordinates show that numerical dispersion is negligible and conclude that a higher signal-to-noise ratio and more accurate seismic wave field can be simulated with cylindrical coordinates in the 3D tunnel space. Our simulation method provides theoretical and practical guidance for analyzing and interpreting seismic wave fields in tunnel advance detection.