Improve Calculation Accuracy Research Articles

Calculating the water production rate in a low-saturation reservoir based on the modified curved capillary theory

Low-saturation reservoirs have complex geological backgrounds, diverse genetic mechanisms, poor physical properties, variable pore structures, and low permeabilities. It also has a wide range of oil–water coexistences and low oil saturations, which make it difficult to accurately calculate their water production rate. To address these issues, this study investigates low-saturation reservoirs in the Nanpu Sag, focusing on petrophysical characteristics and relative permeability variations in samples with different pore structures. A range of petrophysical experiments, including coupled oil-water phase percolation, petroelectrical testing, nuclear magnetic resonance (NMR), and X-ray diffraction (XRD), were conducted to analyze fluid interactions and pore structure impacts. From these experiments, a new pore structure factor was proposed to enhance the existing curved capillary theory. The results showed that this modification improved calculation accuracy for water production rate. The enhanced model was applied to actual logging data and validated against test production results, demonstrating its effectiveness. This study offers both theoretical and practical advancements. It improves the understanding of fluid dynamics in low-saturation reservoirs and provides a reliable tool for fluid identification during exploration. This model can guide more accurate reservoir assessments and support optimized production strategies, improving efficiency in managing these challenging reservoir environments.

The PSO-IFAH optimization algorithm for transient electromagnetic inversion.

As a non-contact method, the transient electromagnetic (TEM) method has the characteristics of high efficiency, small impact of device, no limitation of site range, and high resolution, and is a hot topic in current research. However, the research on the refined data processing method of TEM is lag, which seriously restricts the application in superficial engineering investigation and is a key problem that needs to be solved urgently. The particle swarm optimization (PSO) algorithm and firefly algorithm (FA) were successful swarm intelligence algorithms inspired by nature. However, the accuracy and efficiency of the algorithm restrict its further development. In this paper, the particle moving velocity of FA algorithm is defined according to the concept of particle moving velocity in PSO algorithm, so as to improve the local fast convergence ability of FA algorithm. On this basis, the appropriate velocity of particle movement is improved, so that the improved algorithm can overcome the oscillation problem around the optimal solution and improve the computational efficiency. And finally, an improved PSO-IFA hybrid optimization algorithm (PSO-IFAH) was proposed in the paper. The proposed algorithm can exploit the strong points of both PSO and FA algorithm mechanisms. A typical layered model was established, and the PSO algorithm, FA algorithm, and PSO-IFAH algorithm were applied to inversion calculations. The results show that the PSO-IFAH algorithm improves calculation accuracy by more than 80% and efficiency by over 60% compared to the PSO and FA algorithms, respectively. The PSO-IFAH algorithm also exhibits high inversion accuracy and stability, with superior anti-noise properties compared to the other algorithms. When implemented in ground TEM measurement data processing, the PSO-IFAH algorithm enhances the resolution of anomalies and low-resistance details, aligning well with actual excavation results. This highlights the algorithm's capability to depict underground electrical structures and karst developments accurately, thereby improving the precision of TEM data processing and interpretation.

APPLICATION OF DATA ANALYSIS TOOLS IN PYTHON TO ASSESS THE STANDARD OF LIVING OF THE POPULATION: THE CASE OF KAZAKHSTAN

The article discusses the application of Python data analysis tools to assess the standard of living of the Kazakhstani population based on statistical data for the period 1991–2021. The main focus is using modern methods and technologies, such as linear regression, correlation analysis, and time series processing. The study uses Python libraries (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn) and the PostgreSQL relational database to store and process large amounts of information. The analysis revealed key trends, including a steady increase in per capita income and a decrease in unemployment. The constructed regression models showed that population incomes are closely related to the subsistence minimum and minimum wage. Forecasting for the coming years indicates a continued increase in living standards while maintaining current economic trends. The results confirm that information technology can improve calculation accuracy, optimize data analysis, and present the results in an easy-to-interpret form. This work can be helpful for government agencies, analysts, and researchers involved in assessing socio-economic indicators. This article highlights the importance of information technologies in data analysis and forecasting and demonstrates their potential to solve urgent social policy problems. Keywords: standard of living, data analysis, Python, regression analysis, forecasting, correlation analysis, time series.

Implementation of BIM Methodology in Calculating Column and Beam Structures for the Main Lecture Building of P4T Empat Lawang Regency

Reliable volume estimation and BOQ are indispensable for streamlined construction planning. Errors and omissions in manual calculations and communication result in cost overruns, delays, and ultimately lead to conflicts and disputes among the parties involved in the project. This process is time-consuming and prone to human error. One way to improve efficiency is to implement Building Information Modeling (BIM) in volume estimation and BOQ calculations to enhance work effectiveness. Autodesk Revit is one software that can assist in 3D modeling and accurate volume calculations. This research discusses how BIM can aid in the process of calculating the volume of structural columns and beams for the Main Lecture Hall in the Integrated Agricultural Research and Development Center (P4T). Compared to conventional methods, the research results indicate that the implementation of BIM in this project has provided significant benefits in terms of efficiency, improved calculation accuracy, material usage, and cost savings. With the optimization of concrete usage in structural columns and beams by approximately 26.92% and reinforcement volume by approximately 13.55%, BIM has proven to provide significant added value to the project.

Metasurface-Based Image Classification Using Diffractive Deep Neural Network.

The computer-assisted inverse design of photonic computing, especially by leveraging artificial intelligence algorithms, offers great convenience to accelerate the speed of development and improve calculation accuracy. However, traditional thickness-based modulation methods are hindered by large volume and difficult fabrication process, making it hard to meet the data-driven requirements of flexible light modulation. Here, we propose a diffractive deep neural network (D2NN) framework based on a three-layer all-dielectric phased transmitarray as hidden layers, which can perform the classification of handwritten digits. By tailoring the radius of a silicon nanodisk of a meta-atom, the metasurface can realize the phase profile calculated by D2NN and maintain a relative high transmittance of 0.9 at a wavelength of 600 nm. The designed image classifier consists of three layers of phase-only metasurfaces, each of which contains 1024 units, mimicking a fully connected neural network through the diffraction of light fields. The classification task of handwriting digits from the '0' to '5' dataset is verified, with an accuracy of over 90% on the blind test dataset, as well as demonstrated by the full-wave simulation. Furthermore, the performance of the more complex animal image classification task is also validated by increasing the number of neurons to enhance the connectivity of the neural network. This study may provide a possible solution for practical applications such as biomedical detection, image processing, and machine vision based on all-optical computing.

High-Performance Method and Architecture for Attention Computation in DNN Inference.

In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consumption or low accuracy, and deploying them efficiently to DNN accelerators is a challenge. This paper proposes an online-programmable Attention hardware architecture based on compute-in-memory (CIM) marco, which reduces the complexity of Attention in hardware and improves integration density, energy efficiency, and calculation accuracy. First, the Attention computation process is decomposed into multiple cascaded combinatorial matrix operations to reduce the complexity of its implementation on the hardware side; second, in order to reduce the influence of the non-ideal characteristics of the hardware, an online-programmable CIM architecture is designed to improve calculation accuracy by dynamically adjusting the weights; and lastly, it is verified that the proposed Attention hardware architecture can be applied for the inference of deep neural networks through Spice simulation. Based on the 100nm CMOS process, compared with the traditional Attention hardware architectures, the integrated density and energy efficiency are increased by at least 91.38 times, and latency and computing efficiency are improved by about 12.5 times.

An Improved Method for Calculating Wave Velocity Fields in Fractured Rock Based on Wave Propagation Probability

Ultrasonic velocity field imaging offers a robust tool for characterizing and analyzing damage and its evolution within fractured rock masses. The combined application of ultrasonic first arrival waves and coda waves can significantly enhance the accuracy and range of velocity field imaging. This manuscript introduces an improved imaging method that integrates the propagation probability distribution of the first arrival and coda waves to calculate the velocity field. The proposed method was applied to the velocity field imaging of a medium with multiple scatterers and varying degrees of fracturing. The overall error and calculation unit error of the proposed method were analyzed, and its improvement in calculation accuracy and applicable scope was verified. Additionally, this method was employed to image the velocity field during the damage process of fractured rock masses. The imaging results were compared against digital speckle patterns to confirm the method’s suitability. Finally, we discussed the impact of measurement errors and sensor missing on the accuracy of the computational outcomes presented in this method. These two situations will affect the calculation results, and the influence of reducing the number of sensors is smaller than that of measuring time shifts with error.

Dynamic response strength of integrated foundation for multiple wind turbines

The focus of this paper is on the conceptual design of offshore multi wind turbine integrated floating foundation (MWF) and the strength analysis method of floating structures considering motion response. Firstly, the maximum environmental load and ultimate motion attitude of MWF were used as boundary conditions to solve the problem of imprecise constraint loads in traditional analysis methods. Then, a stress solving equation based on motion state and inertia release was established to solve the equivalent accuracy problem of floating body motion state. Finally, through theoretical analysis, numerical simulation, and experimental testing, it was found that the stress solution method considered the motion state of the floating body in this paper to be more accurate and reliable, with an error of less than 10% compared to the stress results of experimental testing. Improved calculation accuracy by 30%. These studies provide more accurate analytical methods and theoretical references for predicting the strength of floating structures in actual oceans.

A multi-frequency interpolation method for bi-material topology optimization of vibro-acoustic problems

In practical vibro-acoustic problems, external excitation normally contains a certain frequency band structure, and system optimization needs to be performed through frequency band analysis. To reduce the high computation burden in frequency band optimization, we propose a multi-frequency interpolation method for bi-material topology optimization of the vibro-acoustic interaction system. A highly accurate solution of the vibro-acoustic interaction problem is achieved by combining the advantages of the structural finite element method and the acoustic boundary element method. A structural material interpolation model is established using the solid isotropic material with penalization method, and the topological sensitivity formulation is derived based on the adjoint variable method. On the basis of the smoothness property of the radiation impedance matrix, the matrix interpolation method is adopted to obtain the objective function at the frequency points in the frequency band of interest, thus improving the computational efficiency of frequency band topology optimization. The zero points of the Chebyshev polynomial are selected as the interpolation nodes to improve calculation accuracy. The effectiveness of the proposed multi-frequency interpolation method is verified by several numerical tests, and the radiated sound power is reduced considerably after the developed bi-material topology optimization.

P-wave resonances in the exponential cosine screened Coulomb potential* *This work was supported by the National Natural Science Foundation of China (Grant No. 12174147) and the Chinese Scholarship Council (Grant Nos. 202108210152 and 202006175016).

We perform benchmark calculations of the p-wave resonances in the exponentially cosine screened Coulomb potential using the uniform complex-scaling generalized pseudo-spectral method. The present results show significant improvement in calculation accuracy compared to previous predictions and correct the misidentification of resonance electron configuration in previous works. It is found that the resonance states approximately follow an n 2-scaling law which is similar to the bound counterparts. The birth of a new resonance would distort the trajectory of an adjacent higher-lying resonance.

An implicit solution method for digital construction of temperature fields in concrete structures based on point temperature measurements

PurposeAt present, using the finite element method is difficult to efficiently and accurately construct the temperature field of mass concrete based on temperature measurement points. Thus, there is a need to propose a method for improvement.Design/methodology/approachThis study developed an implicit finite element method that digitally constructs the temperature field of mass concrete based on temperature measurement data. That is, in the proposed method of this paper, the temperature of the measuring point is also one of the boundary conditions, which real-time corrects the calculation error.FindingsIn this method, during the digital construction of the temperature field, the computed temperature approaches the actual measured value at the point of measurement with increasing iteration steps. Using this method and sufficient temperature measurement data, the errors in calculation conditions (such as the boundary conditions, the initial casting temperature and material parameters) can be automatically corrected during the iterative computation process.Originality/valueThis new method can improve calculation accuracy and allows the digitally constructed temperature field to converge to its true value with sufficient measurement data.

Energy-conserving finite difference scheme based on velocity interpolation applicable to unsteady flows using collocated grids

The collocation method uses the Rhie–Chow scheme to find the cell interface velocity by pressure-weighted interpolation. The accuracy of this interpolation method in unsteady flows has not been fully clarified. This study constructs a finite difference scheme for incompressible fluids using a collocated grid in a general curvilinear coordinate system. The velocity at the cell interface is determined by weighted interpolation based on the pressure difference to prevent pressure oscillations. The Poisson equation for the pressure correction value is solved with the cross-derivative term omitted to improve calculation efficiency. In addition, simultaneous relaxation of velocity and pressure is applied to improve convergence. Even without the cross-derivative term, calculations can be stably performed, and convergent solutions are obtained. In unsteady inviscid flow, the conservation of kinetic energy is excellent even in a non-orthogonal grid, and the calculation result has second-order accuracy to time. In viscous analysis at a high Reynolds number, the error decreases compared with that of the Rhie–Chow interpolation method. The present numerical scheme improves calculation accuracy in unsteady flows. The possibility of applying this computational method to high Reynolds number flows is demonstrated through several analyses.

Numerical Calculation of 1P Aerodynamic Loads on Aviation Propellers

To accurately predict the 1P aerodynamic loads of aviation propellers, this paper established a mathematical model of aviation propeller 1P aerodynamic loads based on the coupling method of blade element theory and momentum theory. Correction methods such as the Prandtl tip correction method and the propeller root correction method were implemented to further improve calculation accuracy. A 1P aerodynamic load calculation procedure was developed based on the mathematical model by using the Matlab software. 1P aerodynamic loads of a three-blade propeller were predicted for three different angles including 3 °, 9 °, and 12°. The numerical calculation results show that the calculated aerodynamic characteristic parameters of individual propeller blades obtained based on the propeller 1P aerodynamic load mathematical model deviate less than 6% from the CFD simulation results, and regular periodic pulsations are observed. The numerical calculations in this paper show that the propeller 1P aerodynamic load calculation procedure developed based on this model can accurately predict the propeller 1P aerodynamic load, which can provide some reference for the study of aviation propeller aerodynamic characteristics.

Dynamic Model Inference of Gene Regulatory Network based on Hybrid Parallel Genetic Algorithm and Threshold Qualification Method

Gene regulation is the process by which various substances in cells regulate the behaviour of gene expression, thereby controlling almost all cellular activities. Therefore, studying gene regulation not only helps to uncover the internal laws governing life processes but also plays a crucial role in predicting, diagnosing, treating, and designing drugs for genetic diseases. By utilizing multi-source biological information such as gene expression profiles, transcription factor information, and protein interaction data, a network model can be developed to depict the regulatory relationships between genes, facilitating further research. To address the limitations of traditional gene regulatory network construction methods, a novel dynamic model has been created by combining hybrid genetics and threshold restriction. This model comprises two parts: solution space reduction and parameter fitting. During solution space reduction, singular value decomposition is employed to define a mathematically feasible gene regulatory network, reducing unnecessary calculations. Subsequently, the control genes of each gene are constrained within a certain range using threshold limitation, enhancing computational efficiency while adhering to bioinformatics principles. In the parameter fitting phase, parallel genetic algorithms are utilized to expediently optimize the entire solution space. The mountain climbing method is then applied to solve problems meticulously within a limited scope, improving calculation accuracy. In this study, this approach was applied to establish genetic regulatory systems for complex skin melanoma and type 2 diabetes. Through comparison with actual networks, the validity of the method was confirmed. Compared to traditional genetic and particle swarm optimization methods, the effectiveness of the proposed method was verified. This paper models the intricate mechanism of gene regulation and elucidates the regulatory process involving genes, proteins, and small biological molecules in greater detail than other models, aligning more closely with intracellular dynamics laws.

Flow Characteristics Analysis of a 1 GW Hydraulic Turbine at Rated Condition and Overload Operation Condition

Flow stability is extremely important for hydraulic turbines, especially for 1 GW hydraulic turbines, and has a strong impact on mesh stability. However, turbines often operate under non-design conditions, and current research on this aspect is still lacking. So a model of the fluid domains of a high-quality installed 1 GW Francis turbine was established to investigate the flow characteristics of the turbine and fluid domains. CFD simulations of a 1 GW Francis turbine under rated load and overload operation conditions were performed. According to simulation results, when the turbine is under the overload operation condition, the internal flow stability of the 1 GW hydraulic turbine can be obviously different from that of the rated load. In the overload condition, the flow field is more turbulent and a large number of vortices are generated in the draft tube, resulting in significant changes in pressure, flow rate, and output. In order to improve calculation accuracy, a pure clearance model containing only clearances and pressure balance pipes was established. The results of the full flow channel and pure clearance were compared. It was found that under the rated operating condition and the overload condition, compared with the pure clearance model, the axial force of the runner calculated by the full flow channel model is approximately 2–7% biased, the radial force is biased by approximately 7–8%, and the leakage flow is smaller.

Numerical method used for the simulation of fluid–solid transitions based on the VOF and SAM models and prediction of snow distributions on large-span roofs

During a snowfall, the surfaces of the snow beds on roofs change dynamically with time and wind conditions. The generation of body-fitted computational fluid dynamics grids for simulating dynamic snow surfaces using the moving-grid method remains challenging. Therefore, a numerical method was developed to simulate the fluid–solid transition based on the volume of the fluid model and solidification and melting models. The snowfall process was divided into n stable steps, and a stable state calculation method was adopted to calculate the air and snow phases. The dynamic snow boundary was simulated at each stage by converting the fluid or solid characteristics of the domain in which the grids resided. Based on the results of the (n−1)th stage, the fluid domain grids buried by the snow were filled with liquid and frozen into the solid domain. Instead, the fluid in the frozen grids, where snow was about to be eroded, was evacuated and converted into fluid-domain grids. Thus, a complex and changeable snow boundary could be adapted without re-meshing. A modified wall friction velocity equation was derived to correct the wall friction velocity at the snow boundary owing to the serrated boundary of the solidified grids, thereby improving calculation accuracy. The numerical simulation results were verified via field observations of snow distribution on the stepped flat and gable roof models. The snow variation on a full-scale, three-span, suspended cable roof under snow-fall conditions was predicted using the proposed method.

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

As COVID-19 spread worldwide, many major grain-producing countries have adopted measures to restrict their grain exports; food security has aroused great concern from various parties. How to improve grain production has become one of the most important issues facing all countries. However, crop diseases are a difficult problem for many farmers so it is important to master the severity of crop diseases timely and accurately to help staff take further intervention measures to minimize plants being further infected. In this paper, a restructured residual dense network was proposed for tomato leaf disease identification; this hybrid deep learning model combines the advantages of deep residual networks and dense networks, which can reduce the number of training process parameters to improve calculation accuracy as well as enhance the flow of information and gradients. The original RDN model was first used in image super resolution, so we need to restructure the network architecture for classification tasks through adjusted input image features and hyper parameters. Experimental results show that this model can achieve a top-1 average identification accuracy of 95% on the Tomato test dataset in AI Challenger 2018 datasets, which verifies its satisfactory performance. The restructured residual dense network model can obtain significant improvements over most of the state-of-the-art models in crop leaf identification, as well as requiring less computation to achieve high performance

Modal Analysis of a High-Speed Train Gearbox Housing Using Smoothed Finite Element Method

The gearbox is a crucial component of the power transmission system in high-speed trains, and realistically calculating its natural frequency is vital in avoiding system resonance. In recent decades, numerical techniques have become essential in designing engineering structures, and the finite element method (FEM) has gained widespread recognition and acceptance for its robustness and effectiveness. However, the FEM technique using linear 4-node tetrahedral elements (T4) performs poorly, leading to low accuracy and unreliable results. Consequently, complex hexahedral or second-order elements are often required for better performance. In this paper, we employ the smoothed finite element method (S-FEM), which combines the gradient smoothing technique with the standard FEM to improve calculation accuracy while still using linear tetrahedral elements. We apply S-FEM to accurately obtain the natural frequencies and mode shapes of a high-speed train gearbox using automatically generated linear tetrahedral elements. A test of tolerance to mesh distortion is also carried out on the gearbox. The results demonstrate that S-FEM can significantly enhance the computational accuracy of low-order 4-node tetrahedral elements without altering the mesh topology. It is expected that S-FEM can be an alternative to FEM in the modal analysis of complex structures in the railroad industry.

Boundary dependent physics-informed neural network for solving neutron transport equation

The boundary dependent physics-informed neural network (BDPINN) is proposed for solving the neutron transport equation (NTE). Developed based on physics-informed neural network (PINN), BDPINN eliminates errors caused by boundary conditions, effectively avoids the curse of dimensionality and reduces ray effects caused by solid angle discretization. Original constructions of trial functions are proposed for common boundary conditions in NTE to improve calculation accuracy. Three techniques are introduced to improve BDPINN: third-order tensor transformation, rearranging the training set, and result reconstruction in high order. These techniques reduce computational cost and improve accuracy. In addition, two existing techniques, area decomposition and multi-group iteration are applied to solve multi-group and complex area problems. The accuracy of BDPINN is verified through benchmark comparisons with a nodal method and the mechanism for reducing the ray effect is analyzed. This work provides novel perspectives and techniques for solving NTE.

The moving finite element method with streamline-upwind Petrov–Galerkin for magnetohydrodynamic flows problems at high Hartmann numbers

It is widely acknowledged that both the streamline-upwind Petrov–Galerkin (SUPG) method and moving mesh method can enhance the stability of finite element method in solving convection-dominated convection–diffusion problems. However, in the case of strong convection-dominated conditions, they still exhibit numerical oscillations and the solutions will not even converge. In order to address similar phenomena observed in solving magnetohydrodynamic (MHD) flow problems at high Hartmann numbers with strong convective dominance, the MHD equation was first decoupled into two convection–diffusion equations. Subsequently, a new approach called the moving mesh streamline-upwind Petrov–Galerkin method (MM-SUPG) was proposed by coupling the moving mesh technique with SUPG method. The MM-SUPG method contains two independent part: the finite element method with SUPG for solving the decoupled MHD equation and a mesh-redistribution algorithm. To verify its effectiveness for solving the MHD equation at high Hartmann number, a classical MHD fluid flow problem was numerically tested using both the MM-SUPG method and the moving finite element method (MFEM). Compared to the MFEM, the proposed method not only yields stable numerical solutions under strong convective dominance while reducing unnecessary false oscillations, but also improves calculation accuracy to some extent using the same mesh.