Traditional Simulation Methods Research Articles

Optimization method for predicting coal reservoir fractures in the Laochang area of Eastern Yunnan using paleotectonic stress inversion

IntroductionCoal reservoir fractures serve as critical storage spaces and migration pathways for coalbed methane (CBM), significantly influencing CBM enrichment. The characteristics of coal reservoir fracture development can be obtained using traditional simulation methods, but these still have shortcomings. This work presents an optimization approach for the traditional method.MethodsThis study introduces an optimization approach for traditional methods with two novel contributions. This study integrates the simulation of tectonic stress fields with fracture prediction, using surface sandstone fractures as constraints to reconstruct the paleostress field of the coal seam, while also accounting for the influence of coal thickness on fracture development to calculate fracture density.ResultsThe predicted fracture density results were validated against measured values from the Bailongshan mine and Xiongdong coal mine with a relative error of approximately 12%, suggesting a reasonable degree of reliability.DiscussionBased on the results of the fracture simulation predictions, it is believed that the coal seam fracture density in the study area is mostly 10–20 lines/m and that the sweet spot for CBM development is located in the Yuwang block.

Prediction of Temperature Distribution on an Aircraft Hot-Air Anti-Icing Surface by ROM and Neural Networks

To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction

A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.

Research on the Laser Scattering Characteristics of Three-Dimensional Imaging Based on Electro–Optical Crystal Modulation

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro–optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO–Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles. Finally, according to the fitted scattering characteristic model, we inverted the target imaging gray map. We used the laser 3D imaging restoration principle to reconstruct the 3D point cloud of the target to realize the laser 3D imaging of the target.

A novel model for low-permeability reservoir numerical simulation considering dynamic capillary force

ABSTRACT Capillary force is not only affected by water saturation but also affected by fluid flow velocity, which is necessary to modify the prior numerical simulation methods. First, a dynamic capillary force testing device was developed, then, an experimental procedure was adopted to measure the dynamic capillary force curve. Three dynamic capillary force curves were obtained under different displacement velocity conditions. Finally, the dynamic capillary force was introduced into the flow equation of low-permeability reservoir numerical simulation. The oil-phase equation and water-phase equation were discretized in three-dimensional space, and the IMPES method was used to solve the water saturation and pressure difference. The calculation results between the traditional numerical simulation method and the novel method under heterogeneous and inhomogeneous conditions were analyzed. The results reveal that the difference in water cut is mainly reflected in the rapid rise of water cut. In the early stage of rapid increase, the water cut calculated by the new method is slightly higher than traditional method. When the water cut is more than 65%, the water cut calculated by the new method is slightly lower than the traditional method.

Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review

In recent years, the integration of artificial intelligence (AI) into logistics optimization has gained significant attention, particularly concerning sustainability criteria. This article provides an overview of the diverse AI models and algorithms employed in logistics optimization, with a focus on sustainable practices. The discussion covers several techniques, including generative models, machine learning methods, metaheuristic algorithms, and their synergistic combinations with traditional optimization and simulation methods. By employing AI capabilities, logistics stakeholders can enhance decision-making processes, optimize resource utilization, and minimize environmental impacts. Moreover, this paper identifies and analyzes prominent challenges within sustainable logistics, such as reducing carbon emissions, minimizing waste generation, and optimizing transportation routes while considering ecological factors. Furthermore, the paper explores emerging trends in AI-driven logistics optimization, such as the integration of real-time data analytics, blockchain technology, and autonomous systems, which hold immense potential for enhancing efficiency and sustainability. Finally, the paper outlines future research directions, emphasizing the need for further exploration of hybrid AI approaches, robust optimization frameworks, and scalable solutions that accommodate dynamic and uncertain logistics environments.

Spectral prediction of all dielectric nanopore metasurface based on DBO-DNN model

Based on the optical properties of symmetric structures independent of each other in the orthogonal direction, a class of all-dielectric nanohole array metasurfaces symmetrical along the diagonal is designed. By adding nanopores of different shapes to break the symmetry of the periodic unit structure, the double Fano resonance is excited. The spectral characteristics of metasurfaces with the same structure type are studied by finitedifference timedomain (FDTD) method. The deep neural network (DNN) is used to establish the nonlinear mapping relationship between the input structural parameters and the transmission spectrum. The number of hidden layers in the DNN and the number of neurons in each layer are optimized by the dung beetle optimization (DBO) algorithm. Therefore, the number of hidden layers of the model is determined to be 5, and the number of neurons in each layer is 120, 30, 150, 60, 90, respectively. The mean square error (MSE) is used to evaluate the training effect of DNN after optimization search. After 35,000 epochs of training, MSE is reduced to 0.0003926. The influence of different gradient descent optimization algorithms on the prediction results is explored respectively, and it is found that Adamax is the most effective. The results show that the prediction model can predict the spectrum within 1 s. Compared with the traditional simulation method, the simulation time is effectively saved. Meet the requirements of efficient and rapid design of ultra-thin lenses. For the same type of metasurface structure, the transmission spectrum can be accurately predicted without multiple data sets.

A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm

Building load prediction plays an important role in building energy savings and mechanical and electrical system optimization control. The dynamic energy consumption can be accurately calculated using the traditional physical energy simulation method that entails a complex setup and verification process due to the numerous input parameters required. It is also difficult to change the physical model once it has been determined. The data-mining method is fast in calculation and simple to use, but its prediction accuracy is limited by historical data quality. It is difficult to predict the load of new buildings without historical data. To solve these problems, this study proposes a hybrid building load prediction method for office buildings. The proposed method uses EnergyPlus to generate a building load database that includes than 25.14 million data cases, covering 35 types of building geometry and 2870 building examples. Based on the above database, the LightGBM algorithm was selected to extract feature variables that affect the load and build a load prediction model. The training results show that there are 24 key feature variables for office building load prediction. The hourly MAPE of the cooling load prediction model is 6.95 % and RMSE is 4.31 W/m2, and the hourly MAPE of the heating load prediction model is 7.09 % and RMSE is 11.64 W/m2 compared with EnergyPlus model. Two actual office buildings are selected as case studies to validate the model prediction accuracy. Results show that comparing predicted results with measured data, the hourly cooling load of the MAPE is 12.42 %. Coparing predicted results with actual heating load, daily MAPE is 7.97 %.

Minimum Energy Atomic Deposition: A novel, efficient atomistic simulation method for thin film growth

Thin-film growth is an area of research concerned with complex phenomena happening at atomic scales. Therefore, molecular simulation has been an important tool to confront experimental results to theoretical assumptions. However, the traditional thin film growth simulation methods, i.e., Molecular Dynamics (MD) and kinetic Monte-Carlo (kMC) and combinations thereof, suffer from limitations inherent to their design, i.e., limitations in system size and simulation time for MD and predetermined reaction rates and reaction sites for kMC. Consequently, it is practically impossible to simulate the evolution of polycrystalline growth resulting in ∼100nm thick films with realistic stress fields and defect structures, such as grain boundaries, stacking faults, etc. In this work, we propose a versatile and efficient atomistic simulation method (Minimum Energy Atomic Deposition) which works by direct insertion of atoms at points of minimal potential energy through efficient scanning of candidate positions and rapid relaxation of the system. This method allows simulating ≥100nm film thickness while mimicking experimental growth rates and high crystallinity and low-defect concentration and enables in-depth studies of atomic growth mechanisms, the evolution of crystal defects, and residual stress build-up. We demonstrate the efficiency and versatility of the method through the deposition of Al on Si, Al on Al, and Si on Si. The simulation results are systematically compared with experimental observations of thin-film deposition, yielding consistent observations. The method has been implemented in open-source LAMMPS software, making it easily accessible to the research community.

Spatio-temporal neural networks for monitoring and prediction of CO[formula omitted] plume migration from measurable field data

Carbon capture, utilization, and storage (CCUS) technologies are crucial for mitigating greenhouse gas emissions. The success of CCUS projects hinges on accurate prediction and monitoring of the CO2 plume migration during and after injection. To address the computational burden of traditional numerical simulation methods, previous studies have successfully used neural networks as proxy models to expedite the prediction of the CO2 plume migration. However, these models rely on uncertain inputs, such as the distribution of heterogeneous permeability and porosity maps, which can lead to erroneous predictions and pose a significant hurdle for their adoption in real-world applications. To address this issue, this study introduces a framework for reconstruction and short-term prediction of CO2 plume migration based solely on field measurements, which can directly provide information on CO2 plumes, thus eliminating the dependency on uncertain geological information as model input. The framework trains a spatio-temporal neural network model using simulated data under various geologic scenarios to capture the plume evolution dynamics without constraining it to specific geological scenarios. Once trained, the model integrates global and local field measurements from multiple sources to reconstruct the CO2 plume and predict its spatio-temporal evolution. The effectiveness of the proposed framework is tested using two case studies: one using a synthetic dataset and another with data generated from a model of a real field in the Southern San Joaquin Basin. The results show that the proposed framework can accurately reconstruct and perform short-term predictions of the CO2 plume migration by integrating various forms of field measurements.

Prediction Method for Hydraulic Fracturing Effect of Producing Well Based on Machine Learning Methods

Abstract Accurate prediction of producing fracturing oil enhancement is a prerequisite for decision-making on the implementation of production enhancement measures in producing wells. The traditional reservoir numerical simulation method requires accurate reservoir geological parameters and fracturing construction parameters, which makes the calculation process complicated and the on-site application effect unsatisfactory. Therefore, a method for predicting fracturing effect of producing well based on machine learning is proposed. A combination of correlation coefficient analysis and gray correlation analysis was used to determine the main controlling factors affecting fracturing effectiveness; Considering the nonlinear mapping relationship between the fracturing effect and the main control factors, a prediction model was established using a support vector machine. The model is applied with an actual block of an oil field as an example, and the evaluation of potential wells is completed with the analysis of economic benefits of fracturing measures. The results show that the model prediction results meet the accuracy requirements for engineering application, and the screened fracturing potential wells have achieved better oil enhancement results after the implementation of measures. The prediction model of oil enhancement effect established by this method is based on the actual production data of producing wells, with reliable prediction results and simple model solution, which has the value of popularization and application in the same type of blocks.

Applying Gaussian Process Regression for Machine Learning-Assisted Reactor Simulations

Abstract This study explores the integration of machine learning, specifically Gaussian Process Regression (GPR), into traditional reactor core simulations. Building upon previous work on Boiling Water Reactors (BWR), GPR is implemented to predict and correct errors in lower-fidelity simulation outcomes. The findings demonstrate significant improvements in prediction accuracy when GPR is coupled with the diffusion-based core simulator, exhibiting remarkable reductions in both keff and nodal power errors. The comparison reveals that the GPR-enhanced core simulation model significantly outperforms both the standalone simulation and a combination of simulation with Multivariate Linear Regression. It also competes effectively with the performance of a Deep Neural Network-enhanced model. Importantly, this methodology enhances simulation accuracy while maintaining low computational costs. The research emphasizes the vast potential of machine learning, particularly GPR, in progressing nuclear reactor simulations, highlighting the immense value of combining traditional simulation methods with advanced statistical learning techniques.

A novel directional simulation method for estimating failure possibility

Failure possibility plays a crucial role in assessing the safety level of structures under fuzzy uncertainty. However, the traditional fuzzy simulation method suffers from computational inefficiency as it requires a large number of samples for accurate estimation. To address this issue, a directional simulation method is proposed to improve the efficiency of estimating failure possibility. The directional simulation method reformulates the failure possibility estimation into two key steps: the generation of direction samples and the estimation of conditional failure possibility under each direction sample in the polar coordinate system of the standard fuzzy space. To ensure direction uniformity, these direction samples are generated by adopting a good lattice point set based on stratified sampling on the unit hypercube. The conditional failure possibility under each direction sample is estimated by solving the minimum root of a nonlinear equation. The proposed method effectively reduces the dimensionality of the fuzzy input and greatly improves the computational efficiency. To further enhance efficiency, an adaptive Kriging model is embedded into the directional simulation method to reduce the number of performance function evaluations. Four examples are performed to illustrate the accuracy and efficiency of the proposed method. The results highlight the superiority of the directional simulation method over the traditional fuzzy simulation method, offering substantial improvements in computational efficiency while maintaining high estimation accuracy.

Executing realistic earthquake simulations in unreal engine with material calibration

Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine, specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.

Integrated simulation method and experimental validation for the vacuum induction melting process

In Vacuum Induction Melting (VIM), reduction of defects such as porosity and cracking is critical to improve electrode integrity. Traditional simulation method ignores the temperature drop of liquid metal as it passes through the tundish, leading to inaccuracies in predicting the solidification process in the mold. Additionally, the crack of ingot may occur during demolding process, which is always overlooked. This paper presents an integrated simulation method that includes the flow model of liquid metal in the tundish, as well as the solidification model and the stress model of ingot in the mold. Based on these models, the criterion for demolding time is established based on First and Fourth Strength Theories. The entire VIM process from pouring to demolding can be analyzed comprehensively through this simulation method. The method is validated by 500 kg Inconel 718 VIM trial including the temperature measurement of outer mold wall, the height measurement of liquid metal in the tundish and longitudinal sectioned experiment of ingot. The suitable criteria for porosity of 500 kg Inconel 718 ingot, including shrinkage porosity and shrinkage cavity, have been found to be the Niyama criterion threshold of 15 K0.5s0.5cm−1 and Classical porosity model. Finally, the impact of each process parameter on porosity and demolding time is studied through this method for 500 kg Inconel 718 ingot. This method is not only applied to 500 kg ingot but also to larger ingot, thereby providing a basis for the VIM process optimization.

Hybrid artificial neural networks-based approaches predicting the heat transfer and flow characteristics of manifold microchannels

Aiming at meeting the requirement of high heat flux electronics cooling, the geometric complexity of manifold microchannels as an energy-saving technique has been increasing rapidly. It is well worth exploring optimal prediction models for the heat transfer and flow characteristics of manifold microchannels to reduce computing resources and time costs in the optimization process. In this paper, hybrid prediction approaches based on artificial neural networks are proposed for manifold microchannels. Four optimization algorithms, namely, genetic algorithm, particle swarm optimization algorithm, artificial hummingbird algorithm, and zebra optimization algorithm were adopted to enhance the prediction performance. Initially, all prediction models were trained and tested by a numerical database including six input parameters, and the thermal resistance and pumping power were taken as the target outputs. Compared to the original method, hybrid prediction models show higher accuracy and reliability on the numerical data with regression coefficients beyond 0.987. The prediction model optimized by the artificial hummingbird algorithm shows the best performance with the mean average error for thermal resistance and pumping power decreased by 88.7% and 81.4% respectively. Additionally, the runtime of all prediction models is compared and the model optimized by the zebra optimization algorithm takes the longest time, but is still two orders of magnitude shorter than that of the traditional simulation method. Finally, the generalizability of all prediction models is further validated by a database amassed from six experimental studies on manifold microchannels. The results showed that three hybrid models can provide accurate outputs with regression coefficients beyond 0.84 which differs from the poor prediction of the original method. The model optimized by the zebra optimization algorithm shows superior prediction performance for six individual studies, two fluids, three fin shapes, and wide flow rate conditions. The trained hybrid prediction models in this study perform higher accuracy and can be cost-effective and reliable prediction tools for the rapid optimization process of various manifold microchannel applications.

High -resolution Time Interval Measurement Research based on FPGA

In modern scientific research and engineering practice, high -precision measurement of time interval between two or more physical events is often required. After years of development, there are currently many ways to measure a variety of high -precision time interval. If you implement different technologies, it can be divided into two categories: simulation and numbers. The simulation method is mainly to convert T to voltage by charging capacitance and then convert it into a digital amount through the modulus. However, the traditional simulation methods include the shortcomings of temperature sensitivity and long conversion time. Therefore, digital methods are generally used to achieve measurement. Common digital measurement methods include direct counting method, tap latency method, and cursor method. However, common measurement methods cannot meet actual needs. Therefore, this article relies on the FPGA platform to achieve a time interval measurement method with resolution to Ana second -level.

Mobileception-ResNet for transient stability prediction of novel power systems

Power system transient stability prediction (TSP) is particularly important as power systems change and evolve, including the rapid growth of renewable energy, the proliferation of electric vehicles, and the construction of smart grids. Traditional time-domain simulation methods are time-consuming and cannot achieve online prediction. Direct methods are poorly adapted and cannot be applied to complex power systems. Existing machine learning algorithms only classify the transient stability without providing the degree of transient stability of the system. Therefore, a fast and accurate power system TSP method is needed to assist operators in implementing timely measures to improve the stability of the power system running. This study proposes a Mobileception-ResNet network, Mobileception-ResNet is formed by Inception-ResNet-v2, MobileNet-v2, and a fully connected layer. In this study, Mobileception-ResNet and nine comparison models are experimented on two node systems, i.e., the IEEE 10–39 and 69–300 systems. In the IEEE 10–39 system, the root mean square error, mean absolute error, and mean absolute percentage error of Mobileception-ResNet are 44.13 %, 36.74 %, and 39.96 % lower, and the coefficient of determination is 0.04 % higher, respectively, when compared to the comparative model with the best evaluation indicator; in the IEEE 69–300 system, the corresponding values are 2.6 %, 12.83 %, 12.55 %, and 0.01 %, respectively.

Improving Cardiopulmonary Resuscitation skills for layperson in cases of heart attack: a scoping review

The proficiency of trained individuals in effectively administering Cardiopulmonary Resuscitation (CPR) is paramount in mitigating the impact of out-of-hospital cardiac arrest incidents. Inadequate CPR skills among laypersons can result in missed opportunities to save lives. Therefore, it is imperative to prioritize unified efforts to enhance CPR competencies within the general populace. This scoping review aims to consolidate literature discussing the enhancement of laypeople's CPR skills using various methodologies.This scoping review employed the PRISMA methodology and encompassed an extensive search across four critical databases (Science Direct, Proquest, Pubmed, and Google Scholar) for literature published between 2013 and 2023. The search utilized the keywords "CPR Teaching," "Skill," and "Layperson." Out of 487 collected articles, 20 were deemed relevant. The findings of all relevant articles consistently indicated an improvement in laypeople's CPR skills following training.Incorporating concise CPR instructional videos and smartphone applications has shown potential for enhancing CPR knowledge and improving emergency responsiveness. Nonetheless, integrating these modern technological approaches with traditional simulation methods has demonstrated greater efficacy in CPR training, resulting in enhanced compression quality and depth.In conclusion, Integrating traditional and technology-based learning methodologies improves the capability of individuals without specialized medical training to administer CPR effectively.

Mesoscale simulation method for preplaced aggregate concrete based on physics engine

Preplaced aggregate concrete (PAC) has up to 60 % aggregate volume content, making mesoscale modelling difficult. A novel mesoscale simulation method based on physics engine was proposed in this study to accurately simulate the coarse aggregate skeleton of PAC. Unlike in conventional mesoscale simulations, point-to-point contacts between aggregates were formed due to virtual gravity in the physics engine. To verify this novel method, mechanical property and shrinkage behavior tests of PAC were conducted under different aggregate gradings and with different expansive agents. For the mechanical property test, the simulation results were compared with the test results in terms of the aggregate stacking state, failure mode, stress–strain curve, and mechanical property parameters. Repeatability and aggregate shape analyses were performed using finite element (FE) model. A similar comparison between the simulation and test results was also performed for the shrinkage behavior. A shrinkage coefficient reflecting the relationship between grout and PAC shrinkage was proposed. This novel mesoscale simulation method based on physics engine is particularly suitable for PAC. However, it is not limited to PAC and can be applied to the simulation of conventional concrete, thus solving the problem of traditional simulation methods that struggle to achieve a high aggregate fraction.