Precise Calculation Research Articles

Dynamic Analysis of a Vehicle–Bridge System Under Excitation of Random Road Irregularities

This paper presents a comprehensive study of the dynamic response analysis of vehicle–bridge coupled systems, with detailed simulation methods for the vehicles, bridges, and wheel–road coupling relationships. The simulation of the entire vehicle–bridge coupling system is carried out using the open-source finite element analysis platform OpenSees. A novel three-dimensional wheel–road coupling element is introduced to model the interactions between the wheel and road nodes. This element facilitates precise computation of the dynamic responses within the vehicle–bridge coupled system, including both vehicle and bridge behaviors, along with the interaction forces between the wheels and the bridge surface. The coupling element consists of a wheel node and all potential road nodes on the bridge surface that the wheel may traverse. This configuration preserves the finite element model of the entire vehicle–bridge coupled system throughout the vehicle’s movement, thereby improving the efficiency of numerical simulations of vehicle–road interactions. The study accounts for the impact of random road irregularities on the dynamic responses of both the vehicle and the bridge. These irregularities are treated as input parameters for the wheel–road coupling element rather than being accounted for through the wheel–road interaction constraint equations, thereby improving the convenience of simulating random road irregularities.

Systematic Uncertainties from Gribov Copies in Lattice Calculation of Parton Distributions in the Coulomb gauge

Abstract Recently, it has been proposed to compute parton distributions from boosted correlators fixed in the Coulomb gauge within the framework of Large-Momentum Effective Theory. This method does not involve Wilson lines and could greatly improve the efficiency and precision of lattice QCD calculations. However, there are concerns about whether the systematic uncertainties from Gribov copies, which correspond to the ambiguity in lattice gauge-fixing, are under control. This work gives an assessment of the Gribov copies’ effect in the Coulomb-gauge-fixed quark correlators. We utilize different strategies for the Coulomb-gauge fixing, selecting two different groups of Gribov copies based on the lattice gauge configurations. We test the difference in the resulted spatial quark correlators in the vacuum and a pion state. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise. This demonstrates that the effect of the Gribov copies can be neglected in the practical lattice calculation of the quark parton distributions.

Advanced Modeling of Hydrogen Turbines Using Generalized Conformable Calculus

This article addresses critical challenges in the transition to clean energy sources by highlighting the importance of advanced mathematical modeling and computational techniques in turbine design and operation. Specifically, we extend and generalize the work of Camporeale to advance the modeling of hydrogen turbine systems. By utilizing conformable calculus, we develop dynamic equations that analyze key aspects of turbine performance, including temperature variations in turbine blades, angular velocities of rotating shafts, and mass--energy balances within the plenum and combustion chamber. Furthermore, we incorporate Kirchhoff's equation in its generalized conformable integral form, enhancing the precision of energy balance calculations and improving the representation of heat transfer processes in the combustion chamber. This methodology introduces novel perspectives in hydrogen turbine research, contributing to the advancement of sustainable and efficient technologies. Our comprehensive approach aims to provide more accurate and efficient predictions of turbine behavior, thereby impacting the design and optimization of hydrogen-based clean energy systems.

Modified Easley formula for elastic critical global shear buckling stress of corrugated steel webs considering real boundary conditions

The real juncture between corrugated steel webs (CSWs) and flanges follows a multi-segmented line, distinct from that of flat steel webs. Classic methods may yield significant deviations in predicting the elastic global shear buckling capacity of CSWs of various scales due to their failure to consider real boundary constraints. Therefore, a universally applicable formula for calculating the elastic critical global shear buckling stress of CSWs, which accounts for real boundary conditions, is proposed. This formula is pertinent to both large-scale engineering CSWs and small-scale testing CSWs. This study commenced with a comprehensive reassessment of the elastic global shear buckling calculation method. Subsequently, the influence of geometric parameter ratios on the elastic critical global buckling stress was examined. The primary parameter was identified and employed to improve the global buckling coefficient. The proposed calculation method was validated using different corrugation configurations, including 1000-type, 1200-type, 1600-type, 1800-type, and 2000-type CSWs, as well as other CSWs used in experimental settings. These results were compared with those obtained from other reference methods. Findings indicate that the accuracy of the classic theoretical method is affected by variations in both boundary conditions and geometric dimensions due to the constraint effect of real boundary conditions. Under the real boundary conditions, the elastic critical global shear buckling stress of CSWs with simply supported boundary conditions is close to that of CSWs with consolidated boundary conditions. The ratio of web height to corrugation depth primarily affects the elastic global shear buckling capacity, which decreases as the ratio increases. The Easley formula can be modified based on the web height to corrugation depth ratio. Comparisons of numerous numerical and theoretical results reveal that the proposed calculation method exhibits commendable computational precision. In comparison to alternative formulas, the proposed method demonstrates enhanced consistency for calculating CSWs with varying geometric dimensions and boundary conditions, thereby demonstrating its favorable applicability. These conclusions provide valuable reference for the shear design of CSWs.

Comparison of intraocular lens power formulas for negative-diopter intraocular lens implantation for high myopia.

To compare the accuracy of intraocular lens (IOL) power calculation formulas for myopic eyes requiring negative diopter powered IOLs. Retrospective case series. K… hospital and Y… Hospital, …, …. Sixty-one eyes that underwent phacoemulsification with implantation of a negative power IOL were investigated. The trueness, precision and accuracy of IOL power calculation were assessed for the Barrett Universal II (BUII), EVO 2.0, Haigis, Hoffer QST, Holladay 1 and SRK/T formulas using the Eyetemis online tool. The analysis was performed using 1) the ULIB IOL constants and 2) after constant optimization. With ULIB constants, the Haigis, Holladay 1 and SRK/T resulted in a hyperopic mean prediction error (PE) >1.00 diopter (D), which was significantly different from zero (adjusted p <0.05). The mean PE of the remaining formulas was closer to zero. The absolute PE was significantly higher with the Holladay 1 and SRK/T (adjusted p <0.05) with respect to the remaining formulas. After constant optimization, the outcomes of traditional formulas improved and no statistically significant differences were found among any of the formulas in terms of trueness, precision and accuracy. The percentage of eyes with an absolute PE within 0.50 D was low (<50%) even after constant optimization. With ULIB constants, the BUII, EVO 2.0 and Hoffer QST were more accurate than traditional formulas in eyes with negative-diopter IOLs. The results of IOL power calculation in these eyes remain poor even after constant optimization.

Sequential bias-corrected weighted least squares solution of mixed additive and multiplicative error models

In the era of big data, the number of observations in adjustment calculations may reach tens or even hundreds of thousands. When dealing with these large dataset problems, many matrix operations are often required. At this time, the dimensions of the matrix will be large, which will generate a great computational burden. At present, no research results have been published on the computational efficiency of bias-corrected weighted least squares (bcWLS) for mixed additive and multiplicative error models (MAMEM). Sequential adjustment (SEA) groups the observations for calculation and can provide the same computational precision while greatly improving computational efficiency. This paper applies the idea of SEA to the calculation of bcWLS and proposes an iterative solution for sequential bcWLS (SEbcWLS). Using three simulation experiments to verify the effectiveness of our method, it was found that when the number of observations is 10000, the effect is better when the number of groups does not exceed 100, achieving the same precision as the original method while having high computational efficiency. The calculation results of line fitting and plane fitting are not affected by the number of grouping groups. For DEM (Digital elevation model) experiments with strong nonlinearity, when the number of grouping groups is too large, the effect is not very good, but the calculation efficiency is also higher than the original method, and the difference in calculation results is not significant.

Precision computation of one-dimensional quadratures

The calculation of quadratures arises in many physical and technical applications. In this paper, the best quadrature formulas are selected and their quantitative comparison is carried out on a number of representative examples.

An improved radial basis reproducing kernel particle method for geometrically nonlinear problem analysis of SMAs

In this paper, the radial basis function (RBF) without shaped parameter is utilized in the radial basis reproducing kernel particle method (RRKPM), and an improved radial basis reproducing kernel particle method (IRRKPM) is proposed. Compared with traditional RKPM, the IRRKPM effectively reduces the impact of different kernel functions on calculation precision, and is further employed to examine geometrically nonlinear problems associated with shape memory alloys (SMAs). The displacement boundary condition is enforced via the penalty function method, while the Galerkin integration method in its weak form, along with the total Lagrangian (TL) approach, is utilized to derive the geometrically nonlinear equations for SMAs within the IRRKPM framework. The equilibrium equations are then solved using the Newton Raphson (N-R) iterative method. The impact of the different penalty factor and the radius control parameter of influence domain on errors is analyzed, the computational precision of the IRRKPM is compared with the RRKPM, and the computational stability is evaluated. Finally, the suitability of the IRRKPM for the analysis of geometrically nonlinearity problems in SMAs are confirmed through specific numerical examples.

Exploring comparative heterogeneity management for precise water saturation assessment in carbonate formations: Dean-Stark measurements and beyond

Accurate determination of water saturation is a critical aspect of reservoir characterization and development potential of a hydrocarbon field. The inherent heterogeneity in carbonate reservoirs significantly influences the Archie equation coefficients and, consequently, water saturation calculations. In this study, 160 direct core sample water saturation measurements using the Dean-Stark method have been used. The primary goal was to identify the most suitable reservoir heterogeneity management approach for water saturation calculation. The research focuses on the Dalan and Kangan formations (equivalent to Khuff Formation), known as some of the most hydrocarbon-rich (gas) reservoirs globally, located in the western part of the Persian Gulf. To manage reservoir heterogeneity, we employed various techniques, including Winland R35, flow zone indicator, Lucia, pore types, and electrofacies, utilizing porosity and permeability core data, thin section studies, and well-logging data to classify the studied intervals into more homogeneous categories. Subsequently, we measured Archie parameters and, for the first time, water saturations derived from Dean-Stark measurements were compared with Archie saturations from various heterogeneity management methods. Additionally, the influence of geological and petrophysical parameters on the accuracy of water saturation calculations in different rock types is discussed. Our results highlighted the significance of geometrical attributes of pore types, pore throat radius, and permeability as key factors affecting the precision of water saturation calculations. Moreover, our investigation revealed that Lucia's rock typing methodology exerted a substantial influence on the control of permeability and the distribution of water saturation within the reservoir. Our analysis revealed that the electrofacies method provided the most precise estimates for water saturation, with a mean difference of 0.07 (v/v) units compared to the Dean-Stark method. Subsequently, the accuracy of predicted water saturation in rock types determined by the Lucia method, with a mean error of 0.09 (v/v), ranked second among the rock typing methods. Winland R35 and flow zone indicator methods exhibited the highest uncertainty in water saturation estimation, with mean differences of 0.23 and 0.21 (v/v), respectively, compared to the Dean-Stark method.

Investigating the Impact of Random Field Element Size on Soil Slope Reliability Analysis

The determination of the optimal random field element (RFE) size is crucial in soil slope reliability analysis as it governs the trade-off between precision in failure probability calculations and computational efficiency. Given the substantial computational burden associated with smaller RFE sizes, studies on their impact on slope failure probability are scarce. This research examines the influence of RFE size on failure probability and safety factor, employing the Karhunen–Loève expansion to generate random fields and integrating the simplified Bishop method with particle swarm optimization (PSO) to assess slope stability. Through Monte Carlo Simulation (MCS), this study investigates the effects of the ratio of slope height to RFE size (H/De) on slope reliability metrics across two illustrative cases. Results reveal a notable influence of H/De on the distribution of safety factors (Fs) and failure probability (PF), with overestimation observed at smaller H/De ratios. When H/De exceeds 10 for Example 1 and 15 for Example 2, the Fs distribution patterns in both scenarios stabilize significantly, displaying minimal variability. The PF of Example 1 and Example 2 decreases with the increase of H/De and remains basically unchanged when H/De exceeds 10 and 15, respectively. Consequently, a recommended H/De ratio of 20 is proposed based on the analyzed cases, facilitating accurate calculations while mitigating computational overhead.

Quantifying Solar Radiation Over Northern India and Validating with Cropwat for Agricultural and Renewable Energy Applications

Precise computation of global solar energy is crucial for many fields of investigation such as renewable energy, meteorology, and agriculture especially crop water requirement and irrigation requirement. This paper presents the quantification of solar radiation using weather data during the years from 2020-21. The results obtained were validated with the solar radiation estimated with the help of CROPWAT during the year. Statistical and visual approaches were adopted to validate the quantified results. Analysis revealed the high degree of similarity between simulated and CROPWAT determined daily solar radiation during the year 2020-21 over the study area. Very high values of Willmot index of agreement (1.0), Nash-Sutcliffe efficiency (0.999), and Pearson correlation coefficient (0.998), and low values of mean absolute error (0.138), mean bias error (-0.024), and root mean square error (0.182) between them was observed during the year 2020-21 in the area. The minimum solar radiation reaching over the region were observed to be around 5.71 MJ/m2/day during winter season (January) with a normal value of 13.50 MJ/m2/day, while the maximum value of 24.5 MJ/m2/day was observed during the summer season (June) with a normal value of 24.13 MJ/m2/day over the region. The use of these data for further study is recommended as it is worthful quantification over the region.

A Hybrid Nonlinear Whale Optimization Algorithm with Sine Cosine for Global Optimization.

The whale optimization algorithm (WOA) is constructed on a whale's bubble-net scavenging pattern and emulates encompassing prey, bubble-net devouring prey, and stochastic capturing for prey to establish the global optimal values. Nevertheless, the WOA has multiple deficiencies, such as restricted precision, sluggish convergence acceleration, insufficient population variety, easy premature convergence, and restricted operational efficiency. The sine cosine algorithm (SCA) constructed on the oscillation attributes of the cosine and sine coefficients in mathematics is a stochastic optimization methodology. The SCA upgrades population variety, amplifies the search region, and accelerates international investigation and regional extraction. Therefore, a hybrid nonlinear WOA with SCA (SCWOA) is emphasized to estimate benchmark functions and engineering designs, and the ultimate intention is to investigate reasonable solutions. Compared with other algorithms, such as BA, CapSA, MFO, MVO, SAO, MDWA, and WOA, SCWOA exemplifies a superior convergence effectiveness and greater computation profitability. The experimental results emphasize that the SCWOA not only integrates investigation and extraction to avoid premature convergence and realize the most appropriate solution but also exhibits superiority and practicability to locate greater computation precision and faster convergence speed.

Reliability analysis for running safety of vehicle on slab track via an improved second-order fourth-moment approach

The reliability assessment for running safety of vehicle on slab track under temperature action and seismic excitation is challenging due to the extensive computational efforts. This study proposes an improved second-order fourth-moment (ISOFM) approach for the reliability analysis for running safety of vehicle on slab track under temperature action and seismic excitation. The dimensionless limit state function (LSF) for running safety of vehicle on slab track is formulated considering four failure modes. The proposed ISOFM approach is verified by the Monte Carlo simulation (MCS) method. The proposed ISOFM approach greatly reduces the number of computations of nonlinear LSF. The merit of the proposed ISOFM approach lies in significantly enhancing the computation efficiency and precision of reliability considering nonlinear LSF with multi-failure modes. Additionally, the influences of temperature action and seismic excitation on the reliability for running safety of vehicle on slab track are discussed. The reliability for vehicle running safety decreases more significantly with the increment in overall temperature compared to the positive temperature gradient (PTG). The reliability for running safety of vehicle on slab track decreases nonlinearly with the increase of the earthquake intensity.

Enhancing seismic feature orientations: A novel approach using directional derivatives and Hilbert transform of gradient structure tensor

Seismic dip calculation serves as a widely employed technique in the realms of seismic interpretation and reservoir characterization, strategically employed to highlight faults and attributes within the seismic volume. Among the various methodologies utilized for estimating structural dip and azimuth, the Gradient Structure Tensor (GST) stands out. This approach involves leveraging the dominant eigenvector of the positive definite GST matrix to ascertain the inline and crossline dip of seismic data.In the initial phase of our innovative proposal, we employed the spectral balancing technique to enhance the fidelity of seismic data. Subsequently, leveraging this groundwork, we introduced an Analytical Directional Gradient Structure Tensor technique, a distinctive adaptation of GST. This novel approach involves the calculation of directive derivatives in both perpendicular and parallel directions to seismic features. By incorporating directive derivatives, our method excels in capturing subtle stratigraphic nuances, particularly in the dipping direction of interest. To validate the accuracy and effectiveness of our approach, we present compelling evidence through the examination of synthetic and real-field seismic volume outcomes. This underscores the robustness and reliability of our proposed method in enhancing the precision of seismic dip calculations and providing valuable insights into subsurface geological features.

Coupling algorithm of cavity expansion theory and finite element for penetrating reinforced concrete

Aiming at the dynamic problem of projectile penetrating reinforced concrete, this paper creatively proposes a coupling algorithm combining cavity expansion theory and the finite element method. This approach effectively resolves the problem of low efficiency in finite element simulations while ensuring computational accuracy. In this study, based on the cavity expansion theory, we have redeveloped the display dynamics software. Radial stress in the concrete is applied to the warhead surface in the normal direction, and the interaction between the projectile and the steel bar is simulated using the finite element contact algorithm. A coupling algorithm has been developed that can stably and quickly simulate the penetration of reinforced concrete by a projectile. The study indicates that the implementation of the coupling algorithm primarily includes four steps: model establishment and meshing, stress load program design, load application, and initial condition setting. Through verification and analysis, the coupling algorithm presented in this paper is demonstrated to effectively compute the dynamic response of a projectile during penetration. It exhibits superior computational stability and speed compared to the finite element method, and shows minimal sensitivity to grid size. When applied to numerical models with small meshes, it achieves both high precision and rapid computation. The coupling algorithm program design proposed in this paper can be implemented in any display dynamics software, offering a robust approach for engineers and researchers to predict projectile penetration effects. Furthermore, it provides a rapid assessment method for the anti-penetration performance of reinforced concrete structures.

A Review on Privacy Protection Techniques in Smart Grid Applications

: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.

Ncorpi[formula omitted]N : A [formula omitted] software for N-body integration in collisional and fragmenting systems

NcorpiON is a general purpose N-body software initially developed for the time-efficient integration of collisional and fragmenting systems of planetesimals or moonlets orbiting a central mass. It features a fragmentation model, based on crater scaling and ejecta models, able to realistically simulate a violent impact.The user of NcorpiON can choose between four different built-in modules to compute self-gravity and detect collisions. One of these makes use of a mesh-based algorithm to treat mutual interactions in O(N) time. Another module, much more efficient than the standard Barnes–Hut tree code, is a O(N) tree-based algorithm called FalcON. It relies on fast multipole expansion for gravity computation and we adapted it to collision detection as well. Computational time is reduced by building the tree structure using a three-dimensional Hilbert curve. For the same precision in mutual gravity computation, NcorpiON is found to be up to 25 times faster than the famous software REBOUND.NcorpiON is written entirely in the C language and only needs a C compiler to run. A python add-on, that requires only basic python libraries, produces animations of the simulations from the output files. NcorpiON can communicate with REBOUND’s webGL viewer via MPI for 3D visualization. The name NcorpiON, reminding of a scorpion, comes from the French N-corps, meaning N-body, and from the mathematical notation O(N), due to the running time of the software being almost linear in the total number N of bodies. NcorpiON detects collisions and computes mutual gravity faster than REBOUND, and unlike other N-body integrators, it can resolve a collision by fragmentation. The fast multipole expansions are implemented up to order eight to allow for a high precision in mutual gravity computation.

Causal involvement of dorsomedial prefrontal cortex in learning the predictability of observable actions

Social learning is well established across species. While recent neuroimaging studies show that dorsomedial prefrontal cortex (DMPFC/preSMA) activation correlates with observational learning signals, the precise computations that are implemented by DMPFC/preSMA have remained unclear. To identify whether DMPFC/preSMA supports learning from observed outcomes or observed actions, or possibly encodes even a higher order factor (such as the reliability of the demonstrator), we downregulate DMPFC/preSMA excitability with continuous theta burst stimulation (cTBS) and assess different forms of observational learning. Relative to a vertex-cTBS control condition, DMPFC/preSMA downregulation decreases performance during action-based learning but has no effect on outcome-based learning. Computational modeling reveals that DMPFC/preSMA cTBS disrupts learning the predictability, a proxy of reliability, of the demonstrator and modulates the rate of learning from observed actions. Thus, our results suggest that the DMPFC is causally involved in observational action learning, mainly by adjusting the speed of learning about the predictability of the demonstrator.

Absolute quantitation of human serum cystatin C: candidate reference method by 15N-labeled recombinant protein isotope dilution UPLC-MS/MS.

Serum cystatin C (CysC) is a reliable and ideal endogenous marker for accurately assessing early changes in glomerular filtration rate (GFR), surpassing the limitations of creatinine-based estimated GFR. To improve the precision of GFR calculation, the development of strategies for accurately measuring serum CysC is crucial. In this study, the full-length CysC pure product and fully recombinant 15N-labeled CysC internal standard were subjected to protein cleavage. Subsequently, an LC-MS/MS method was developed for the absolute quantification ofserum CysC. The traceability of the method was assigned calibrator using the amino acid reference measurement procedure (RMP). It involved calibrating the instrument using an amino acid reference material with known amino acid concentrations for calibration and comparison purposes. The total imprecision of the method was determined to be≤8.2 %, and a lower functional limit of quantification (LLoQ) was achieved. The recoveries ranged from 97.36 to 103.26 %. The relative bias between this candidate RMP for measurement of ERM-DA471-IFCC and the target value was 1.74 %. The linearity response was observed within the concentration range of 0.21-10.13 mg/L, with a high R2 value of 0.999. The results obtained using our method was consistent with those obtained using other certified RMPs. With the establishment of this highly selective and accurate serum CysC measurement method, it is now possible to assess the correlation between immunoassay results of serum CysC and the intended target when discrepancies are suspected in the clinical setting.

Intelligent cross-entropy optimizer: A novel machine learning-based meta-heuristic for global optimization

Machine Learning (ML) features are extensively applied in various domains, notably in the context of Metaheuristic (MH) optimization methods. While MHs are known for their exploitation and exploration capabilities in navigating large and complex search spaces, they are not without their inherent weaknesses. These weaknesses include slow convergence rates and a struggle to strike an optimal balance between exploration and exploitation, as well as the challenge of effective knowledge extraction from complex data. To address these shortcomings, an AI-based global optimization technique is introduced, known as the Intelligent Cross-Entropy Optimizer (ICEO). This method draws inspiration from the concept of Cross Entropy (CE), a strategy that uses Kullback–Leibler or cross-entropy divergence as a measure of closeness between two sampling distributions, and it uses the potential of Machine Learning (ML) to facilitate the extraction of knowledge from the search data to learn and guide dynamically within complex search spaces. ICEO employs the Self-Organizing Map (SOM), to train and map the intricate, high-dimensional relationships within the search space onto a reduced lattice structure. This combination empowers ICEO to effectively address the weaknesses of traditional MH algorithms. To validate the effectiveness of ICEO, a rigorous evaluation involving well-established benchmark functions, including the CEC 2017 test suite, as well as real-world engineering problems have been conducted. A comprehensive statistical analysis, employing the Wilcoxon test, ranks ICEO against other prominent optimization approaches. The results demonstrate the superiority of ICEO in achieving the optimal balance between computational efficiency, precision, and reliability. In particular, it excels in enhancing convergence rates and exploration-exploitation balance.