Algorithm For Numerical Simulation Research Articles

Prediction of Shale Gas Well Productivity Based on a Cuckoo-Optimized Neural Network

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.

Research on the Wear State Detection and Identification Method of Huller Rollers Based on Point Cloud Data

Throughout the huller shelling process, the rubber rollers progressively deteriorate. The velocity of the rubber rollers decreases as the distance between the rollers rises. These modifications significantly influence the rate at which rice hulling occurs. Hence, the implementation of real-time online detection is crucial for maintaining the operational efficiency of the huller. Currently, the prevailing inspection methods include manual inspection, 2D vision inspection, deep learning methods, and machine vision methods. Nevertheless, these conventional techniques lack the ability to provide detailed information about the faulty components, making it challenging to conduct comprehensive defect identification in three dimensions. To address this issue, point cloud technology has been incorporated into the overall detection of the working condition of the huller. Specifically, the Random Sample Consensus segmentation algorithm and the adaptive boundary extraction algorithm have been developed to identify abnormal wear on the rubber rollers by analyzing the point cloud data on their surface. A solution technique has been developed for the huller to compensate for the speed of the rubber rollers and calculate the mean values of their radii. Additionally, a numerical simulation algorithm is proposed to address the dynamic change in the roller spacing detection. The results show that point cloud data can be utilized to achieve real-time and precise correction of anomalous wear patterns on the surface of rubber rollers.

Experimental, numerical simulation and genetic algorithm as a hybrid method for the design of hydrogen peroxide separation via non-dispersive solvent extraction

This work employed a hybrid approach combining experiments, numerical simulation, and genetic algorithm (GA) to optimize the non-dispersive solvent extraction (NDSX) process of H2O2. A numerical simulation model with high accuracy was developed. The relative error between the simulation results and experimental data was controlled within 2.2 %. The results indicated the velocity of lumen side and temperature are the key factors affecting the extraction efficiency. The non-dominated sorting GA-II algorithm coupled with the numerical simulation was used to optimize the NDSX process. The Pareto optimal design of the NDSX process can be achieved within the water outlet concentration range of 13.44 to 550.2 kg m-3. Intensification factors of NDSX versus extraction towers are evaluated to be 17.9 and 13.2. In summary, this work provides a design framework for NDSX of H2O2, which is expected to simultaneously promote the mass transfer efficiency and safety of the H2O2 separation process.

Design, modeling, and experiment of a multi-degree-of-freedom pneumatic soft bionic actuator

Drawing on the driving mechanism of biological muscles and combining the nonlinear hyperelastic characteristics of silicone rubber, a multi-degree-of-freedom pneumatic soft bionic actuator is designed, which can be used as the executing mechanism for soft robots and robotic arms. Using response surface analysis and numerical simulation algorithms, the optimal combination of structural dimensions parameters is determined with the maximum bending and torsion angles output by the actuator as the optimization objectives. Based on the idea of flexible mechanism Piecewise Constant Curvature (PCC) modeling, the kinematics equation of the actuator is derived. Analyze the equivalent motion structure of the actuator and establish a dynamic model of the actuator based on the Lagrange method. The physical model of the actuator is manufactured using rapid prototyping technology. Finally, experimental testing and analysis are conducted on the physical model to obtain the motion and dynamic characteristics curves and empirical formulas of the actuator, which are compared with theoretical and simulation results to verify that the pneumatic soft bionic actuator is feasible and effective. The above research methods, processes, and results can provide reference and inspiration for the research and implementation of pneumatic and hydraulic soft robots and gripping actuators for soft robotic arms.

KHz Rate Fs/Ps-CARS Thermometry For Supersonic H_2/Air Combustion Studies

Accurate flow field measurements are essential to probe the phenomena involved in aeronautical engines undergoing high temperature and high turbulences. Indeed, reliable experimental datasets are needed to compare and validate numerical models. Among other laser diagnostics, Coherent Anti-stokes Raman Scattering (CARS) is a well-established non-linear spectroscopic technique used to probe harsh environments, since it provides non-intrusive local point measurements of chemical composition and temperature. We report here hybrid fs/ps Coherent Anti-Stokes Raman Scattering (fs/ps-CARS) thermometry campaign performed on N 2 in a supersonic H2 /air combustion. We used a mobile and compact laser architecture that shapes, synchronize and generates the laser beams necessary for CARS spectroscopy. The instrument was moved to a large-scale facility representative of a scramjet combustor (ONERA LAERTE/LAPCAT-II bench). H2 /air was injected during several burst at 0,12 and 0,15 equivalent ratios. Using kHz single-shot measurements combined with motorized translation stages, spatial and temporal resolution were obtained to probe the flow and the combustion at various positions upstream and downstream the hydrogen injectors. At these positions, horizontal and vertical temperature profiles were retrieved and compared with a numerical unsteady fluid dynamic simulation using delayed detached eddy simulations (DDES) developed at ONERA (CEDRE). The position of the combustion inside the combustion chamber could be identified through several horizontal and vertical profile temperature measurements. The signal-to-noise ratio of the detection was optimized by empirically adjusting the number of averaged laser shots based on the level of flow turbulence. For instance, far downstream the H2 injection, averaging over 10 to 100 pulses was possible as the heated air flow was steady. This was confirmed by the good agreement with numerical simulations although some distortion could still be observed and will be used to adjust the numerical simulation algorithm. However, averaging was no longer possible close to the injectors, where a highly turbulent H2 /air combustion was observed. Nevertheless, taking advantages of the high rate single-shot capacity of the spectroscopic technique, temperature could still be retrieved inside this area. These measurements provided a valuable experimental database for comparing and refining computational fluid dynamics modeling.

Development and application of turbulent heat flux model for lead-bismuth eutectic based on deep learning

Existing turbulent heat flux (THF) closure models in Reynolds-averaged Navier–Stokes simulation of scalar transport are insufficient on accuracy for fluids with a low Prandtl number (Pr), such as lead–bismuth eutectic (LBE), especially under complex operating conditions like in heat exchangers of nuclear reactors. This paper aims to propose a THF model suitable for such conditions. Firstly, we established a high-fidelity database of forced convection flow past two tandem cylinders under a Reynolds number (Re) of 1 × 105 and various low Pr conditions by hybrid large eddy simulation and direct numerical simulation algorithm. Based on the database, a deep learning architecture was then built and trained to predict dimensionless THF. Finally, a posteriori test proved that the proposed THF model helps to improve the prediction performance for temperature fields and also performs well when extrapolated to cases of LBE past the tube bundles, especially under the case with large Peclet numbers.

Research and Implementation of Pneumatic Amphibious Soft Bionic Robot

To meet the requirements of amphibious exploration, ocean exploration, and military reconnaissance tasks, a pneumatic amphibious soft bionic robot was developed by taking advantage of the structural characteristics, motion forms, and propulsion mechanisms of the sea lion fore-flippers, inchworms, Carangidae tails, and dolphin tails. Using silicone rubber as the main material of the robot, combined with the driving mechanism of the pneumatic soft bionic actuator, and based on the theory of mechanism design, a systematic structural design of the pneumatic amphibious soft bionic robot was carried out from the aspects of flippers, tail, head–neck, and trunk. Then, a numerical simulation algorithm was used to analyze the main executing mechanisms and their coordinated motion performance of the soft bionic robot and to verify the rationality and feasibility of the robot structure design and motion forms. With the use of rapid prototyping technology to complete the construction of the robot prototype body, based on the motion amplitude, frequency, and phase of the bionic prototype, the main execution mechanisms of the robot were controlled through a pneumatic system to carry out experimental testing. The results show that the performance of the robot is consistent with the original design and numerical simulation predictions, and it can achieve certain maneuverability, flexibility, and environmental adaptability. The significance of this work is the development of a pneumatic soft bionic robot suitable for amphibious environments, which provides a new idea for the bionic design and application of pneumatic soft robots.

The technology of precise tapping of remaining oil during the high water cut stage of offshore multi-layer reservoirs driven by big data intelligence

Abstract Based on traditional numerical simulation and optimization algorithm, combined with the “hard data” monitored by stratified injection and production real-time monitoring and automatic control technology, a fine reservoir model integrated reservoir knowledge and development law was established, and the remaining oil potential exploitation during high water cut stage was quantified and evaluated by deep forest method to adjust well productivity, greatly improving the remaining oil potential exploitation effect. The innovation has formed a set of accurate remaining oil potential tapping technology based on big data intelligent drive in offshore multi-layer reservoirs with high water cuts. This technology fully adopts the automatic and intelligent characteristics of data-driven and deep forest algorithms, and the historical fitting rate of production dynamic during the high water cut stage is more than 90%, which can accurately guide the design and implementation of the remaining oil potential tapping scheme and provide a reference for similar oil fields.

Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment

Reliability evaluation of a multi-state system (MSS) with dependent components makes much practical sense because the independent identical assumption (i.i.d.) assumption between different components is sometimes impractical in the context of real engineering cases. The task becomes more challenging if imprecision gets involved due to the pervasive uncertainty. The loss of monotony resulting from the introduction of imprecise parameters makes many analytical reliability methods not applied. To address this challenge, in this paper, we develop a survival signature-based reliability framework for an MSS taking into account both dependence and uncertainty. In our framework, the survival function is derived through some unique structural reliability treatments. Vine copula and imprecise probability are integrated and embedded within the framework to address the case that dependence and imprecision simultaneously appear. Implementation-wise, two numerical simulation algorithms are developed to address some complicated cases in which the analytical solution is not available. For demonstration and validation, both the numerical case and application examples are presented. The results show the superiority of the proposed method and its potential in real engineering use.

Numerical simulation of supersonic combustion chemical reactions based on multi-GPUs

To achieve a large-scale and efficient numerical solution to supersonic combustion chemical reaction problems, a numerical simulation algorithm for combustion chemical reactions was established on a graphics processing unit (GPU). Numerical simulation of the combustion chemical reaction of a hydrocarbon fuel/air mixture in a supersonic combustor was conducted to verify the accuracy of GPU parallel calculation results and analyze its parallel performance. Numerical results show that GPU parallel computing can accurately simulate the complex flowfield in the combustor. The distribution law of wall pressure is basically consistent with the experimental values, and the numerical agreement is good, indicating that the numerical simulation algorithm for the multi-GPU supersonic combustion chemical reaction established in this paper is correct and reliable. On the Tesla V100 GPU parallel computing platform, the speedup of GPU parallel computing can reach over 100 times, greatly improving computational efficiency. When the grid size is 18.64 million, the parallel efficiency of single and double precision floating-point operations on four GPUs is 66.2% and 69.1%, respectively, which demonstrates that the multi-GPU parallel algorithm has good scalability.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

To efficiently predict the mechanical parameters of granular soil based on its random micro-structure, this study proposed a novel approach combining numerical simulation and machine learning algorithms. Initially, 3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional (3D) discrete element method (DEM) were conducted to construct a database. In this process, the positions of the particles were randomly altered, and the particle assemblages changed. Interestingly, besides confirming the influence of particle size distribution parameters, the stress-strain curves differed despite an identical gradation size statistic when the particle position varied. Subsequently, the obtained data were partitioned into training, validation, and testing datasets at a 7:2:1 ratio. To convert the DEM model into a multi-dimensional matrix that computers can recognize, the 3D DEM models were first sliced to extract multi-layer two-dimensional (2D) cross-sectional data. Redundant information was then eliminated via gray processing, and the data were stacked to form a new 3D matrix representing the granular soil's fabric. Subsequently, utilizing the Python language and Pytorch framework, a 3D convolutional neural networks (CNNs) model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil's fabric. The mean squared error (MSE) function was utilized to assess the loss value during the training process. When the learning rate (LR) fell within the range of 10-5–10-1, and the batch sizes (BSs) were 4, 8, 16, 32, and 64, the loss value stabilized after 100 training epochs in the training and validation dataset. For BS = 32 and LR = 10-3, the loss reached a minimum. In the testing set, a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error (MAPE) of 4.43% under the optimized condition, demonstrating the accuracy of this approach. Thus, by combining DEM and CNNs, the variation of soil's mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.

Studies on acceleration algorithms for numerical simulations of hypersonic conjugate heat transfer problems

This research proposes acceleration algorithms for the coupled numerical simulation of the long-time hypersonic conjugate heat transfer problem from two perspectives. First, the newly developed disturbance region update method (DRUM) is introduced to enhance the convergence speed of the steady flow field. The numerical investigations reveal that, in the context of hypersonic CHT issues, the disturbance prompted by variations in wall temperature remains confined post the detached shock wave. The implementation of DRUM has been shown to substantially diminish the computational demand per iteration and the total iterations required for convergence. DRUM could reduce the time needed to calculate the flow field by over 60 % for a typical CHT case. Second, a new aerodynamic heat flux estimate method is introduced based on the local wall temperature within a coupled time step to increase the coupled time step. Combining this method with the adaptive coupled time step method, a long-time CHT problem with a total time of 1100s requires only 25 coupled time steps, much less than the 220 steps needed for the existing method. Meanwhile, the average error of the obtained solid temperature field, when compared to the temperature obtained from an accurate simulation with a fixed small coupled time step, is maintained below 0.22 %.

Performance Prediction and Heating Parameter Optimization of Organic-Rich Shale In Situ Conversion Based on Numerical Simulation and Artificial Intelligence Algorithms.

In situ conversion technology is a green and effective way to realize the development of organic-rich shale. Supercritical CO2 can be used as a good heating medium for shale in situ conversion. Numerical simulation is an important means to explore the shale in situ conversion process, but it requires a lot of time and computational cost for in situ conversion simulation under different working conditions. Therefore, a computational framework for rapid prediction of shale in situ conversion development performance and heating parameter optimization is proposed by coupling artificial neural network (ANN) and particle swarm optimization (PSO). The results indicated that kerogen pyrolysis and hydrocarbon product release mainly occurred within 2 years of shale in situ conversion. The production curves of pyrolysis hydrocarbon obviously slowed after in situ conversion for 2 years. The database was constructed by a large number of in situ conversion simulations, and Pearson correlation analysis and the random forest method were adopted to obtain seven main controlling factors affecting reservoir temperature and hydrocarbon production. The determination coefficient of the obtained ANN-based prediction models is higher than 97%, and the mean square error (MSE) is lower than 0.3%. The basic reservoir case can choose to inject 350-450 °C supercritical CO2 (Sc-CO2) fluid with a rate of 600 m3/day to obtain a more promising development effect. The heating parameter optimization for three typical reservoir cases using PSO was performed, and reasonable injection temperature and injection rate were obtained. It realized accurate development prediction and rapid heating parameter optimization, which helps the effective application of shale in situ conversion development design.

Implicit lattice Boltzmann flux solver: A novel mesoscopic numerical algorithm for geothermal reservoir simulation

In this work, a novel implicit lattice Boltzmann flux solver (LBFS) for simulating geothermal reservoirs is developed. LBFS is a mesoscopic approach that uses the reconstructed lattice Boltzmann model to calculate fluxes on the cell interface, which has a more solid physics foundation. Compared with macroscopic methods that directly use mathematical approximations, the dynamic changes of pressure and temperature in a reservoir can be simulated more accurately with LBFS. Therefore, in the present work, LBFS is introduced into geothermal reservoir simulation for the first time, and a fully implicit temporal discretization was realized by using the proposed dummy scalar triplet method to assemble the sparse matrix. This means that the adopted time step can far exceed the limitation of the Courant-Friedrichs-Lewy condition. Several test cases are implemented to validate the present algorithm, including the Theis problem, one-dimensional fluid flow and heat transfer, and two-dimensional radial fluid flow and heat transfer in aquifers. Finally, a production-reinjection doublet system was simulated by using an unstructured mesh. All of the obtained results agree well with the corresponding analytical solutions.

Study on the stability and its simulation algorithm of a nonlinear impulsive ABC-fractional coupled system with a Laplacian operator via F-contractive mapping

In this paper, we study the solvability and generalized Ulam–Hyers (UH) stability of a nonlinear Atangana–Baleanu–Caputo (ABC) fractional coupled system with a Laplacian operator and impulses. First, this system becomes a nonimpulsive system by applying an appropriate transformation. Secondly, the existence and uniqueness of the solution are obtained by an F-contractive operator and a fixed-point theorem on metric space. Simultaneously, the generalized UH-stability is established based on nonlinear analysis methods. Thirdly, a novel numerical simulation algorithm is provided. Finally, an example is used to illustrate the correctness and availability of the main results. Our study is a beneficial exploration of the dynamic properties of viscoelastic turbulence problems.

A remark on a strong minimum condition of a fractal variational principle

The fractal variational principle gives a good physical understanding of a discontinuous problem in an energy way, and it is a good tool to revealing the physical phenomenon which cannot be done by the traditional variational theory. A minimum variational principle is very important in ensuring the convergence of artificial intelligence algorithms for numerical simulation and image processing. The strong minimum condition of a fractal variational principle in a fractal space is discussed, and two examples are given to illustrate its simplicity and feasibility.

О ВЛИЯНИИ ОЧИСТНЫХ РАБОТ НА ХАРАКТЕР ДЕФОРМИРОВАНИЯ ДЕМОНТАЖНЫХ ВЫРАБОТОК

The paper presents the results of numerical modeling of the geomechanical behavior of the system “lava — operational excavation — enclosing potash rock mass” during mining operations. Numerical modeling includes a forecast of the mechanical behavior of the system at all stages of the system existence: from the natural stress-strain state (SSS) of the rock mass to excavation and safety measures installation, as well as the longwall mining operations. The results of numerical experiments are verified using the data from field studies of convergence of the roof and floor of the operational excavation and the side walls of this excavation. As a result of the studies, it is established that longwall mining has a significant impact on the operational excavation at a distance of about 40 m or less. In addition, the paper describes an algorithm for numerical simulation of the geomechanical behavior of the considered geotechnical system.

The fractional non-polynomial spline method: Precision and modeling improvements

This research introduces the fractional non-polynomial spline method as a novel scheme for solving the time-fractional Korteweg-de Vries (KdV) equation. The study focuses on numerical analysis and algorithm development for simulation purposes. The proposed method involves the construction of a fractional non-polynomial spline to estimate the equation's solution, offering improved precision and modeling capabilities compared to existing approaches. To assess the stability of the proposed approach, the von Neumann method is employed, demonstrating its unconditional stability within a specific parameter range. To validate the effectiveness of our numerical analysis and simulation algorithm, contour, 2D, and 3D graphs are utilized to compare the solution obtained through our method with an analytical solution. Through rigorous comparative analysis with previous works, the superiority of our approach in terms of accuracy and efficiency is demonstrated. Norm error calculations, specifically the (L2and L∞) error norms, provide a quantitative assessment of the accuracy and reliability of our proposed scheme.

Erosion Life Prediction of Metal Mesh Screen Pipes in Oil Wells Based on Numerical Simulation of Discrete Particle Flow.

High-speed fluid-carried sand particles pose a significant challenge to oil well screen pipes, leading to sand control failure and adversely affecting the well's production and recovery rates. This research targets the commonly used metal mesh screen pipe in oil well sand production and establishes a full-sized three-dimensional structure model. A fluid-solid coupling of discrete particle flow numerical simulation algorithm is incorporated to formulate a novel method for predicting the screen pipe's erosion life. This method provides a solution to the complex task of predicting the pipe's lifespan under oil sand fluid erosion. The analysis elucidates the impact of sand particles on the velocity distribution and erosion rate of the metal mesh at varying velocities, facilitating the prediction of the mesh screen pipe's erosion life. Key findings reveal that the velocity and erosion rate vary across the different layers. The first layer screen experiences velocity and erosion rates that are 3.6-3.76 times and 2.45-2.50 times, respectively, higher than those at the inlet. The second layer screen undergoes velocity and erosion rates 2.32-2.43 times and 1.04-1.06 times, respectively, higher than those at the inlet. Erosion failure primarily occurs in the first layer screen due to velocity expansion instigated by the screen tube's structure. The discrete flow numerical simulation method proves valuable in predicting the mesh screen's erosion life at different flow rates, with a maximum error of 6.38% when compared with field monitoring life values. This method introduces a new technical approach to predicting the life span of metal mesh screens in oil wells. The study concludes by suggesting measures to extend the screen pipe's lifespan, thereby enhancing oil recovery.

Energetic and exergetic analysis of a transcritical CO2 air-source heat pump water heating system in the cold region

To analyze the energetic and exergetic performance of the transcritical CO2 air-source heat pump water heating system in the cold region, the numerical model and simulation algorithm are set up and verified with the experiments. Under the low ambient temperature (−15 °C to 0 °C) and water inlet temperature (9 °C), the performance evaluation of the system is investigated using the developed numerical model at different compressor frequency (75, 80 and 85 Hz). The results show that Qgc, COP and ηex decrease by 14.47%, 12.78% and 20.83% respectively as the ambient temperature decreases from 0 °C to −15 °C. The CO2 outlet state of the gas cooler and corresponding slope of isotherms have significant effects on COP and ηex. The COPopt and ηex,opt exist in the system but at different compressor discharge pressures. With the compressor frequency increasing from 75 Hz to 85 Hz, the increase in Qgc is at the cost of a decrease in COP, and COPopt decreases by 5.31%. The effect of the compressor frequency on ηex is different as the compressor discharge pressure elevates. Thus, a suitable operating condition, considering different needs, should be selected in the system operation.