Application Of Simulation Research Articles

Numerical Simulation and Engineering Application of Temporary Stress Field in Coal Mine Roadway

The imbalance between excavation and mining is significant as it restricts the efficient development of coal resources. Slow tunneling speed is primarily due to the inability to concurrently conduct excavation and permanent support operations, and temporary support is considered a key solution to this problem. However, the mechanism by which temporary support affects the surrounding rock in unsupported are as remains unclear, hindering the assurance of stability in these areas and the determination of a reasonable unsupported span. To address this issue, this work proposed a stress distribution model as temporary support, elucidating the distribution law of support forces within the surrounding rock. By analyzing the stress differences between areas with and without temporary support, the stress field distribution characteristics of temporary support were determined. Subsequently, the evolution of stress and strain in the surrounding rock within unsupported areas was analyzed concerning changes in temporary support length, support force, and unsupported distance. The results indicated that, although temporary support does not directly act on unsupported areas, it still generates a supportive stress field within them. The maximum unsupported distance should not exceed 3 m, and there is a strong linear relationship between the optimal temporary support force and the unsupported span. Furthermore, the length of temporary support should not exceed 17 m from the tunnel face. The successful application of the shield tunneling robot system verifies that temporary support can ensure the stability of the surrounding rock in unsupported areas, confirming the validity of the temporary support stress distribution model. This research can be used to design and optimize cutting parameters and temporary support parameters, arrange equipment, and design and optimize tunnel excavation processes to achieve safe and efficient tunneling.

Research and Simulation Application of Automatic Generation of Judging Curves for IoT Mobile Devices in Train Driving Operations Incorporating Hybrid Genetic Algorithms

A control system for automatically generating a train operation evaluation curve based on a hybrid genetic algorithm has been proposed in order to improve the safety of automatic train operation. The system is based on Internet of Things (IoT) mobile devices and utilizes various sensors such as accelerometers, gyroscopes, barometers, and GPS to collect real-time data on the driver’s acceleration, angular velocity, air pressure, and position, among other parameters. 5G wireless communication technology is used to achieve high-speed data transmission and real-time communication with the cloud. Based on cloud data, a spatial grid area planning model is constructed to automatically generate the train operation evaluation curve. Using a spatial dynamic programming method, the entire network of Electric Multiple Unit (EMU) trains is treated as a whole, and a dynamic model of the EMU train is constructed. The spatial area parameters of the EMU train’s automatic driving operation evaluation curve are combined with the dynamic model analysis method. By identifying and analyzing environmental parameters such as train speed and distance, the EMU train’s automatic driving operation evaluation curve is optimized. A hybrid genetic evolution learning optimization algorithm is used to fit the motion spatial parameters of the EMU train’s automatically generated driving operation evaluation curve, and a spatial behavior analysis simulation is created for the EMU train’s automatically generated control driving operation curve. Through the use of hybrid genetic evolution learning optimization technology, adaptive control and automatic driving operation curve simulation planning for the automatically generated driving operation evaluation curve of the maglev train are achieved, as well as simulation and algorithm optimization design for the automatically generated driving operation evaluation curve of the maglev train. The simulation results show that the method has good adaptability and strong automatic control capability. The experimental results demonstrate the control effect of the proposed method on energy consumption and train stopping error, indicating that the proposed method can effectively improve the parameter adjustment and offset correction ability of the evaluation curve generated by the high-speed train driving operation.

BIMBINGAN TEKNIS (BIMTEK) AWAL TAHUN AKADEMIK 2024/2025: PENYUSUNAN PERANGKAT AJAR KURIKULUM MERDEKA DI UPTD SDN BATUPLAT 2 KUPANG

A technical guidance of creating teaching materials based on Merdeka curriculum framework was conducted in the beginning of academic year 2024/2025 from August 1 – 3, 2024, in UPTD SDN Batuplat 2 Kupang. The community service activity was participated by 29 participants of teachers and education personnels of UPTD SDN Batuplat 2 Kupang. This activity was carried out using training and assisting method. Qualitatively, the final survey showed that all participants obtained meaningful knowledge, understanding, and skill to create teaching materials incorporating differentiated learning. The participants were able to integrate digital learning media in the teaching materials they had created. Besides, quantitatively, the final survey indicated that as many as 26 teachers could produce differentiated learning modules according to classes and levels where they teach, 5 teachers and education personnels could produce HTML and android-based application for AKM simulation using I spring suite. Moreover, 9 teachers and education personnels could produce interactive teaching materials, quizzes, and games using web-based application, such as genially, educaplay, and wordwall.

Damage and Failure Modeling of Composite Material Structures Using the Pam-Crash Code

Simulating composite material structures requires complex constitutive models, which normally require fine meshes to obtain an accurate prediction of their behavior. Pam-Crash software has been used for several years in the automotive industry and has been proved to be an efficient tool for simulating metallic structures, returning good correlations in a fast computational time. However, constitutive models for composite materials in Pam-Crash present some difficulties: some materials are not able to be suitably modeled and the predictive results depend on the mesh refinement. This work proposes a solution for predicting the progressive damage of composite materials in Pam-Crash, which scales the energy dissipated by the damage mechanisms and checks the viability of modeling the material behavior, taking into account the recommended size of finite elements in the automotive industry. The proposed solution is applied for the simulation of Open Hole specimens to evaluate the ultimate strength consistency. After this, it is applied for the simulation of Compact Tension specimens to check the consistency of crack propagation behavior. By considering the target size of the finite elements in the material card definition, the predictions demonstrate great improvement in the equivalence in results between different mesh refinements. Finally, the solution is applied to simulate impact tests on large structures. Good correlations with experimental data are obtained in fast computational times, making this methodology a candidate for application in composite-related automotive simulations.

Development of a model system for in vitro simulated application testing of neurovascular stent systems

Abstract In vitro test setups for simulated application testing are necessary for physician training as well as for development and benchmarking of various devices in interventional neuroradiology. Within the current study, we implemented a test setup for in vitro simulated application testing. Trackability in the distal catheter area, which was positioned inside a complex 3D vascular model was analyzed for two neurovascular stent systems (Neuroform EZ 4.0x20 mm and Enterprise VRD 4.5x14 mm) combined with different microcatheters (Excelsior XT-27 Flex, PX Slim Delivery Microcatheter and Prowler Select Plus Infusion Catheter). The described test setup allows for a diffentiation of neurovascular stent systems with regard to the trackability force as a major procedure related indicator for device handling. Therefore, the simulated application system presented within the current work represents an indispensable tool for safely and efficiently developing and evaluating new technologies for interventional neuroradiology prior to clinical implementation.

Application of numerical simulation of composite pile foundation in settlement and reinforcement analysis of buildings

Application of numerical simulation of composite pile foundation in settlement and reinforcement analysis of buildings

Numerical stability analysis of shock-capturing methods for strong shocks II: high-order finite-volume schemes

The shock instability problem commonly arises in flow simulations involving strong shocks, particularly when employing high-order shock-capturing schemes, limiting their application in hypersonic flow simulations. In the current study, the shock instability problem of the fifth-order finite-volume WENO scheme is investigated. To this end, the matrix stability analysis method for the fifth-order scheme is established. Such an analysis method is capable of providing quantitative insights into the stability of shock-capturing and helping elucidate the mechanism causing shock instabilities. Results from the stability analysis and numerical experiments reveal that even dissipative solvers, which are generally believed to be capable of capturing strong shocks stably, also suffer from the shock instability problem when the spatial accuracy is increased to the fifth-order. Further investigation indicates that this is because the quadratic polynomial degenerated from the WENO reconstruction is not applicable for reconstructing variables on the faces near the numerical shock structure. Moreover, the shock instability problem of the fifth-order scheme is demonstrated to be a multidimensional coupled problem. To capture strong shocks stably, it is necessary to have sufficient dissipation on both transverse faces and normal faces in the vicinity of strong shocks. The spatial location of the perturbation leading to instability is also clarified by the proposed matrix analysis and numerical experiments, revealing that the instability arises from the numerical shock structure for the fifth-order scheme. Additionally, the local characteristic decomposition is demonstrated to be helpful to mitigate shock instabilities in high-order cases. Based on these conclusions and the core idea of the MR-WENO scheme, a shock-stable high-order scheme, the MR-WENO-SD scheme, is proposed. Equipped with the hybrid HLLC/HLL solver, this scheme is capable of capturing strong shocks stably while maintaining high-order accuracy. A series of numerical experiments demonstrate its stability and resolution capabilities.

Model for investigating cathode dual-population microfluidic microbial fuel cells

Microfluidic microbial fuel cells (MMFC) are one of the most promising power sources. However, due to the lack of clarity in the internal operating mechanism, the output performance is suboptimal. Thus, a comprehensive two-dimensional cathode dual-population model is developed to gain deeper insights into internal workings. Based on verifying the accuracy of the model, the influence of temperature, ionic strength, and spacing of electrode on the performance and microbial growth of MMFC are explored. The finding reveal a nonlinear trend in the performance of MMFC at temperatures of 293.15 K and 313.15 K. Furthermore, the impact of electrode spacing and ionic strength on the performance of MMFC is examined, thereby emphasizing the applicability in experimental research and numerical simulation. This study provided insights into the operating mechanism of dual-population microbial microfluidic fuel cells.

Introducing a simple convex hull method to calibrate diffusion coefficients in Lagrangian particle models

Calibrating Lagrangian particle models (LPMs) is crucial for simulating oil spill trajectories, enabling emergency responses, and mitigating harmful effects on the environment. However, there is a lack of rigorous observation data on oil spill events, especially in water bodies with no serious spills but perceived increasing risk, hampering LPM application. This study utilized trajectory data from three drifters in Lake Erie, collected in 2016, 2018, and 2019. We then proposed a simple and efficient convex hull method to identify the smallest envelope of the simulated particle cloud, covering all drifter trajectories while simultaneously controlling over-diffusion. This approach was employed to calibrate the General NOAA Operational Modeling Environment oil spill model (an LPM) and determine optimal diffusion coefficients. Based on 362 one-day-long drifter trajectory simulation cases, the recommended diffusion coefficient for particle trajectory simulation in Lake Erie was determined to be 14 m2 s−1. The calibrated model demonstrated the ability to effectively capture the movement direction of drifters and control the discrepancy between simulated and observed trajectories. Meanwhile, it underestimated drifter travel distances due to errors in input currents, the primary force driving drifter movement. These results promote LPM application for particle trajectory simulations.

Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand

ABSTRACT Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations.

Evaluation of production line expansion efficiency using computer simulation

Abstract The article discusses the application of computer simulation in the optimization of production processes, particularly in the context of analyzing scenarios related to the addition of new production lines. The conducted research and simulations have shown that computer simulation is a key tool for precise modeling and analysis of various options, allowing for better understanding and optimization of production activities. The article presents the theoretical foundations of simulation along with practical examples of its application, focusing on assessing the impact of different production line configurations on the overall system’s efficiency. The analysis of benefits includes shortening the production cycle time, increasing flexibility, and improving operational efficiency. The challenges associated with implementing computer simulation, such as the need for specialized knowledge and the necessity for continuous updates of simulation models, are also discussed. Based on the research and analyses conducted, the article demonstrates that computer simulation is an effective tool supporting strategic and operational decision-making in production management, particularly in the context of expanding production infrastructure.

Fractal model for heterogeneous effective diffusion and its application in compositional simulations of CO2 flooding in tight reservoirs

Fractal model for heterogeneous effective diffusion and its application in compositional simulations of CO₂ flooding in tight reservoirs

Band Alignment Semimetal Heterojunction-Based Ultrabroadband Photodetector for Noncontact Gesture Interaction with Low Latency.

Non-contact gesture recognition and interaction (NGRI) revolutionizes the natural user interface, fundamentally transforming human interactions with daily-use technology. Conventional NGRI systems frequently encounter obstacles such as pronounced latency and environmental disturbances, including humidity or lighting conditions, resulting in compromised system fluidity and robustness. This study highlights the utilization of silicon-based semimetal heterojunction photodetectors for precise gesture recognition and seamless human-machine interaction. Through the application of band alignment theory and sophisticated TCAD simulation, heterojunction barriers are successfully optimized by fine-tuning parameters including Si doping concentration and semimetal thickness. By strategically aligning vertical material growth and implementing vertical heterojunction configuration, a room temperature detector with exceptional sensitivity (specific detectivity (D*): ≈1011 Jones), ultra-broad spectral range (405-10600 nm), and rapid response time (≈ µs) is achieved. Harnessing its distinguished speed and sensitivity in detecting human infrared radiation, in conjunction with an advanced spatial-temporal comparison algorithm and a multi-channel high-frequency sampling processing design, a NGRI system with low latency, high precision, minimal energy consumption, and versatility across diverse scenarios has been developed. The results pave the way for non-contact sensor design and may further enhance the practicality and user experience of non-contact human-machine interaction systems.

Mobile network and computer software test path selection application in environmental data collection simulation

Mobile network and computer software test path selection application in environmental data collection simulation

A novel modelling approach for realistically-wrapped state recycled aggregates and its application in meso-scale tensile behaviour simulation of recycled concrete

Most of the existing Finite Element (FE) simulations on the mechanical behaviour of Recycled Aggregate Concrete (RAC) generally simplified the Recycled Aggregates (RAs) as simple shapes (e.g., circular / elliptical and polygonal), with residual mortar created by proportional scaling or segmentation of these shapes, which is not consistent with the real situation. Hence, this study proposed an irregular shapes overlay method and a placement algorithm to randomly generate the real-shaped RAs and build the RAC’s mesoscale geometric model. Then, RAC’s mesoscale FE model was established based on the cohesive zone model and got calibrated. The effects of various key parameters (i.e., the RAs substitution percentage, the residual mortar content of substituted RAs and the relative strength of residual to new mortar) on the RAC’s tensile behaviour were studied. The proposed framework can effectively generate RAC’s mesoscale geometric models with various RAs substitution percentages and residual mortar contents. Embedding zero-thickness interface elements between all solid elements can perfectly simulate the RAC’s crack development under tensile loading, offering more realistic cracking paths compared to RAC with simple shape RAs. For the case of new mortar strength larger than the residual one, the tensile strengths of RAC decrease as the RAs substitution percentage and residual mortar content, with a reduction of up to 15%. Otherwise, the tensile strengths show no significant decrease.

Exploring the application of molecular simulation technology in food sector: Focusing on food component interactions and food quality

The intricate interactions between component molecules that occur during food processing and storage are challenging to elucidate with traditional experimental techniques at the molecular level. Molecular simulation technology, conversely, offer an expedient methodology for investigating these molecular behaviors and anticipating macroscopic experimental phenomena. This review outlines the fundamental principles of molecular simulation techniques commonly employed in the field of food science and their associated computational procedures. It also examines their applications in the context of food component interactions and the quality control of proteins, carbohydrates, and lipids. This technique can be employed to elucidate the interactions between food nutrients, facilitate the rapid detection of pesticide and veterinary drug residues, identify foodborne pathogens and biotoxins, and conduct molecular mechanism analysis of toxicological effects at the atomic and molecular levels. By dynamically characterizing changes in molecular conformation, electron transfer and bonding processes, molecular simulation has become an important tool for optimizing food processing and storage conditions. Further studies should investigate the relationship between molecular conformational changes and food quality in greater depth and seek to optimize the application of molecular simulation in food science in order to provide critical insights into practical operations.

Bayesian mesh optimization for graph neural networks to enhance engineering performance prediction

Abstract In engineering design, surrogate models are widely employed to reduce the computational costs of simulations by approximating design variables and geometric parameters from computer-aided design (CAD) models. However, traditional surrogate models often lose critical information when simplified to lower dimensions, and face challenges in handling the complexity of 3D shapes, especially in industrial datasets. To address these limitations, we propose a Bayesian graph neural network (GNN) framework that directly learns geometric features from CAD mesh representations for accurate engineering performance prediction. Our framework leverages Bayesian optimization (BO) to dynamically determine the optimal mesh element size, significantly improving model accuracy while balancing computational efficiency. This approach addresses the challenge of mesh quality by optimizing mesh resolution, ensuring that critical geometric features are preserved in 3D deep-learning-based surrogate models. Unlike existing models that rely on static or predefined mesh resolutions, our method adapts mesh size based on the task, making it highly flexible for a wide range of engineering applications. Experimental results demonstrate that mesh quality directly impacts prediction accuracy, and the proposed BO-EI GNN model outperforms state-of-the-art models such as 3D CNN, SubdivNet, GCN, and GNN in predicting mass, rim stiffness, and disk stiffness. Our method also significantly reduces the computational costs compared to traditional optimization techniques. The proposed framework shows promising potential for application in finite element analysis (FEA) and other mesh-based simulations, enhancing the utility of surrogate models across various engineering fields.

M630L MUTATION DISRUPTS THE STRUCTURE CONFORMATION OF BRUTON TYROSINE KINASE (BTK) DOMAIN IN PATIENT WITH X-LINKED AGAMMAGLOBULINEMIA: INSIGHTS FROM IN SILICO

X-Linked Agammaglobulinemia (XLA) is a rare inherited disease, attributed to mutations found in the Bruton Tyrosine Kinase (BTK) gene. This research outlines the application of Molecular Modelling and Simulation to predict the effects of a novel non-synonymous Single Nucleotide Polymorphism (nsSNP) reported in the BTK kinase domain protein of a male XLA patient. The mutation at position c.1888A>T causes a substitution of p.M630L within the kinase domain of the BTK protein. Functional assessment using in silico prediction tools (SIFT, Polyphen-2, PROVEAN, MutationAssessor, PANTHER, FATHMM, MutPred and MutationTaster) predicted the mutation to be deleterious, potentially disrupting both the structure and function of the protein. Nevertheless, the mutation is not located at the active site of the kinase domain. Consequently, the molecular dynamics (MD) simulations were performed to investigate the impact of amino acid substitution to the three-dimensional (3D) conformational structure of the BTK protein, contributing to the disease phenotype observed in the reported patient. MD analysis, clustering analysis and Principle Component Analysis (PCA) revealed that the 3D conformational structure of the kinase domain of mutant protein to be more compact and rigid compared to the wildtype. Given that the alterations in protein structure can influence their functional characteristics, the p.M630L mutation within the BTK protein kinase domain might disrupt the protein function, potentially impeding the maturation of B cells and contributing to the onset of XLA disease.

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors

The supercritical carbon dioxide (s-CO2) Brayton cycle direct cooled reactor system has the characteristics of high cycle efficiency, compact equipment, and simple layout, which has attracted the attention of researchers recently. The use of s-CO2 Brayton cycle and the elimination of intermediate heat exchangers make the transient characteristics of this reactor different from light water reactors which have rich operating experience. Transient simulation and safety analysis with system code is an essential stage in reactor design and safety verification. Therefore, a system code, named NUSAS (NUclear Safety Analysis and Simulation), was developed in this study for the transient simulation of s-CO2 Brayton cycle direct cooled reactor system. This code is developed based on the advanced two-fluid two-pressure seven-equation model and has the capability of modelling the turbomachinery in s-CO2 Brayton cycle direct cooled reactor system. The transient characteristics during core power step and precooler cooling water mass flow rate step were analyzed. The load tracking and operation control strategy of the reactor was designed, and transient simulations of the wide range (100% to 50%) load tracking process were conducted. According to the simulation results of NUSAS, the transient characteristics of s-CO2 Brayton cycle direct cooled reactor were demonstrated when operating conditions change.

Offset curves: An application in road simulation.

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes. This research introduces a route planning method that aggregates and reuses various data points to establish a robust framework for developing new routing plans. The primary goal is to analyze the collected data and create a framework for estimating a geometrically smooth centerline of a road map route. This method involves generating a series of parallel curves, known as offset curves, to facilitate the creation of new routes and alternative lanes. The following examples illustrate some empirical results obtained from this approach.