One-dimensional Linear Model Research Articles

Impact of north-west Bay of Bengal bathymetry along the off-coast of Digha on the propagation of shallow water waves using finite difference method

Numerical simulation of shallow water equations has been carried out using the Crank-Nicolson scheme of Finite Difference Method (FDM) to study the interaction of waves with different forms of bathymetry especially that of the off-coast of Digha, a tourist spot situated in the north-west coast of Bay of Bengal. MATLAB code has been developed for the simulation. One-dimensional (1-D) linear shallow water model has been considered here. Three different bathymetry profile are chosen for the investigation: (i) flat bathymetry (with or without seamount/basin); (ii) piecewise linear approximation to the real bathymetry of the off-coast of Digha; (iii) real bathymetry of the off-coast of Digha. Real bathymetry data has been obtained from the gridded data set of GEBCO (General Bathymetric Chart of the Oceans) published in 2022. Validation of the present numerical scheme has been performed by considering test problems with results existing in the literature. Comparative study of the effect of the bathymetries, on the propagation of shallow water waves, has been performed and demonstrated with figures and tables. Results of wave-bathymetry interaction obtained here are believed to be new with respect to the present study area and will definitely give some insight into the phenomena in this region.

Research of Green GDP Evaluation System Based on AHP Model

GDP (Gross domestic product) is of extraordinary significance in measuring national economic development, while it tends to have inaccuracy. The concept of Green GDP (hereinafter referred to as GGDP), which introduces environmental factors into the accounting system, is more conducive to sustainable development and the building of a harmonious economy. This paper selects main factors and establish a GGDP evaluation index system using the AHP model, then conducts the robustness test by one-dimensional linear regression model and "children dropping out of school" model. Finally, a sensitivity analysis is carried out to prove the accuracy. The results indicate that GGDP system contributes to improving environmental conditions and promoting sustainable development.

Study on the Relationship Between Professional Commitment and Learning Burnout of Undergraduates Majoring in Public Administration

In order to understand the relationship between professional commitment and learning burnout of undergraduates majoring in public utilities management, the students majoring in public utilities management in C University were coded, and 100 undergraduates with different gender, household registration and classes were randomly selected to distribute scales, and 97 valid scales were collected. Descriptive statistics, one-dimensional linear regression model and multiple linear regression model were used for empirical research. The results show that the emotional commitment factor in professional commitment has a significant impact on the degree of learning burnout. By studying the relationship between professional commitment and learning burnout of undergraduates majoring in public utilities management, this study aims to help students establish a correct professional concept, enhance their professional identity and reduce their learning burnout. And put forward some suggestions for the development of public utilities management specialty to cope with the development crisis of public utilities management specialty.

Numerical simulation analysis of load-bearing capacity at joints of symmetrical cantilever-assembled bridges based on the logistic regression model

Abstract Bridge joint bearing capacity analysis is difficult to fit the requirements of the era of big data. The traditional way of bridge-bearing capacity numerical analysis exists in the wave of digital development of big data information, the Internet of Things, and artificial intelligence technology like a wild development. The traditional way of under the problem: is efficiency, engineering analysis is not accurate, data information paper, work responsibility is not in place, technical equipment, etc. This paper revolves around the numerical simulation analysis of the load-bearing capacity at the joints of symmetrical cantilever-assembled bridges based on the logistic regression model by finding literature to understand the concept and algorithm of the logistic regression model, construct logistic regression model based on dichotomous dependent variable logistic regression algorithm, logistic regression model parameter estimation, logistic regression. The three aspects of the model evaluation index are studied. The numerical simulation analysis of the load-bearing capacity at the symmetrical cantilever joint based on the logistic regression model, the shear strength calculation at the bridge joint, and the bridge longitudinal bar slip deformation are calculated, respectively, and then the actual value of the bridge load bearing capacity is derived. Simulation in the bridge bearing capacity at 0kN, 5kN, 10kN, 15kN, 20kN, 25kN, and 30kN, the model algorithm evaluation index under the three indicators of prediction rate, accuracy, deviation of the verification analysis. The results show that the prediction rate of the logistic regression model algorithm based on the load carrying capacity up to 30kN is 84%, the accuracy rate is 94%, and the deviation is 11%, and the three index values have better performance compared with the one-dimensional linear regression model algorithm and the multiple linear regression model algorithms. This study is beneficial to extend the bridge structural analysis function and make the bridge load capacity assessment more automatic; it can facilitate the comparison of historical data of bridge tests, longitudinal change of indicators at different testing time points, and grasp the current situation and change trend of bridges. Thus it is of great historical significance to the development of the Chinese bridge infrastructure industry.

Biosafety management strategy based on multiple linear regression model

Abstract With the rise of artificial intelligence technology in the context of digital development of big data and information, the biosafety management method under the traditional model can hardly fit the development requirements of the times, and the biosafety management under the traditional model has various problems such as slow manual efficiency, tedious management work, and humanization of work. This paper focuses on studying a biosafety management strategy based on a multiple regression model, then analyzes and compares the two methods of the one-dimensional linear regression model and the multiple linear regression model. The study is conducted under the three indicators of biosafety management weight score. Arithmetic means to score and significance of 100 enterprises (schools, pharmaceutical companies, hospitals, and other types of these four major types) under the multiple linear regression model. The results show schools topped the list with a biosafety management weight score of 0.793. The arithmetic mean scores of the four categories of enterprises were ranked in descending order: schools, hospitals, pharmaceutical plants, and other categories. The size of the enterprise has a significant and positive effect on biosafety management, which means that the larger the size of the enterprise, the more resources the enterprise has under its control, and then the implementation of biosafety management and the improvement of its performance will be guaranteed. Analyzing the efficiency of biosafety management in hospitals based on three different methods over the past 20 years, it was found that the efficiency of biosafety management based on multiple linear regression models fluctuated from 60% to 95% compared to the other two methods. This study promotes university laboratories as important and safe bases for society to cultivate highly qualified and skilled personnel in line with the new era of production, management, and service lines and to contribute to the country’s experimental research work. This study has substantial reference value for biosafety management and promotes bioscience development in China.

One-dimensional analysis method of pulsatile blood flow in arterial network for REBOA operations

Based on the generalized Darcy model, here we develop a linear one-dimensional (1D) composite model to predict the effects of the inserted balloon under REBOA operations on the dynamic characteristics of blood flow in flexible arterial networks. We first consider the effect of the decrease of cardiac output under different degrees of blood loss through employing the fourth-order lumped parameter model of cardiovascular system. Then, the effect of the inserted balloon is included by developing the relation between flow resistance and occlusion ratio with the neural network approach. Finally, the accuracy of the developed 1D composite model for REBOA operations, which can be solved analytically in the frequency domain, is verified by comparing to computational fluid dynamics (CFD) simulations. It is demonstrated that the 1D model is able to reproduce main features of the systemic circulation under balloon occlusion of the aorta during REBOA surgery. The 1D composite model could substantially reduce the computational time, which makes it possible to give the instant prediction of the working parameters during RABOA operations.

Perturbation approach for damage localization in beam-type structures: analytical, experimental and numerical exposition

ABSTRACT Structures are usually designed to undergo some yielding, cracking and damage during any catastrophic events like earthquakes. It is necessary to identify these damage locations to avoid the failure of the structure. In this study, a novel method of damage localization for a linear one-dimensional mathematical model of Euler-Bernoulli beam is developed. The damage is modeled as a normalized box-car function. The first-order perturbation in the form of a small change in the stiffness is introduced to the structure. This study employed an effective boxcar filtration technique in damage localization. The proposed formulation shows a distinct peak at the damage location. Further, a two-point roving technique is employed on the experimental model of an overhanging beam under impact loading to check the effectiveness of the proposed localization procedure under real measurement conditions. For its entirety, the finite element model for different end conditions through numerical simulations is also briefly addressed. It is observed that the results obtained from the experimental investigation and the simulation studies are in agreement with the proposed formulation. The proposed methodology does not consider the effect of noise, which can be addressed as the future scope of the present study.

Implementation of low-storage Runge-Kutta time integration schemes in scalable asynchronous partial differential equation solvers

The asynchronous computing method based on finite-difference schemes has shown promise in significantly improving the scalability of time-dependent partial differential equation (PDE) solvers by either relaxing data synchronization or avoiding communication between processing elements (PEs) on massively parallel machines. This method uses high-order accurate asynchrony-tolerant (AT) schemes coupled with appropriate time integration schemes to provide the desired accuracy required by high-fidelity solvers such as the direct numerical simulations for fluid flow. For time integration, Runge-Kutta (RK) schemes, particularly the low-storage implementation, are widely used due to their ability to provide good stability properties and be computationally efficient. However, the implementation of AT schemes with multi-stage RK schemes necessitates an over-decomposition of the spatial domain in a parallel setting, leading to increased message sizes for communication and redundant computations. In this paper, we propose a novel method to couple asynchrony-tolerant and low-storage explicit RK schemes in solving time-dependent PDEs that would result in a significant reduction in communications and relaxed synchronizations. We develop new asynchrony-tolerant schemes for ghost or buffer point updates that are necessary to maintain desired order of accuracy. The accuracy of this method is investigated, both theoretically and numerically, using simple one-dimensional linear model equations. Thereafter, we demonstrate the scalability of the proposed numerical method through three-dimensional simulations of decaying Burgers' turbulence, performed using two different asynchronous algorithms: communication-avoiding and synchronization-avoiding algorithms. The scalability studies up to 27,000 cores were found to yield speed-ups up to 6× compared to a baseline synchronous algorithm. Overall, the proposed approach shows the potential to improve the scalability of exascale PDE solvers significantly.

Grid-Oriented multiphysics model of Power-to-Hydrogen electrolyzers

This paper proposes a multiphysics model of Power-to-Hydrogen electrolyzer that possesses outperforming mathematical properties for fast calculation. The proposed model features a one-dimensional linear diphasic-flow model to describe the bubble effect in low-temperature water electrolysis, which is a crucial physical process affecting cell performance. According to the characteristics of the multiphysics in electrolysis cells, the bubble layer is introduced to only solve the bubble distributions near the electrodes, decreasing the computing scale compared with traditional Calculating Fluid Dynamics (CFD) models. Besides, by the homogeneous flow assumptions and the liquid-phase steady parallel flow, the analytical solution of the momentum conservation is derived to eliminate the nonlinearity of the Naiver-Stokes equation. Once the thickness of the bubble layer is determined, the proposed model can be computed over 10 times faster than the traditional diphasic-flow models, at the expense of the accuracy loss of the i-V characteristics within 0.5%. As a result, the model can be applied to the integrated simulation in the energy system to efficiently acquire both operating characteristics and the multiphysics of the PtH electrolyzer.

Acoustics interaction in a complex piping network with multiple pulsatile sources

The interactions between multiple pulsatile sources and the acoustic eigenmodes are analysed to elucidate the influence of source characteristics and contrasting phase differences on the acoustic response in a liquid-filled complex piping network with multiple branches. For illustration, the interactions between two reciprocating pumps operating in a two-branched network are analysed using a one-dimensional linear model. The interactions between sources and acoustic eigenmodes lead to enhanced or suppressed acoustic responses depending on the spatial separation between the sources and their phase differences. The acoustic field measurements from the sweep of driving frequency of a mono-ethylene-glycol (MEG) pump module with two reciprocating pumps in a three-branched network are analysed. The linear model captures the approximate resonance profile of the field measurement data. The presence of plane acoustic wave attenuation in the practical case is influenced by the losses associated with pipe fittings.

A new uncertain linear regression model based on equation deformation

When the observed data are imprecise, the uncertain regression model is more suitable for the linear regression analysis. Least squares estimation can fully consider the given data and minimize the sum of squares of residual error and can effectively solve the linear regression equation of imprecisely observed data. On the basis of uncertainty theory, this paper presents an equation deformation method for solving unknown parameters in uncertain linear regression equations. We first establish the equation deformation method of one-dimensional linear regression model and then extend it to the case of multiple linear regression model. We also combine the equation deformation method with Cramer’s rule and matrix and propose the Cramer’s rule and matrix elementary transformation method to solve the unknown parameters of the uncertain linear regression equation. Numerical example show that the equation deformation method can effectively solve the unknown parameters of the uncertain linear regression equation.

Monitoring of peanut leaves chlorophyll content based on drone-based multispectral image feature extraction

Peanuts are an important cash crop in the national economy, and their chlorophyll content can reflect the fertilizer status of peanuts. Therefore, it is urgent to obtain a rapid and accurate method for monitoring chlorophyll content. In this study, two types of peanuts, Yanghua 1 and Yueyou 45, were planted according to different plant densities, and eight vegetation indices were calculated using Parrot Bluegrass multispectral drone photography, so as to establish different monitoring model, and to compare the accuracy of each model according to the coefficient of determination, root mean square error, and absolute mean error. The chlorophyll content of Yanghua 1 peanut leaves was generally higher than that of Yueyou 45, and the plant density had a greater influence on Yanghua 1 peanut. The one-dimensional linear regression models of NDVI(Normalized Difference Vegetation Index) and GNDVI(Green Normalized Difference Vegetation Index) had a much higher degree of fit and precision than the other indices; and the multiple linear regression model had a more accurate predictive ability than the one-dimensional linear regression model. After testing, BP neural network(Back Propagation Neural Network)is the most suitable model for monitoring the chlorophyll content of peanut with better fit and accuracy than the random forest model. The multispectral drone could rely on the highly accurate predictive model to quickly obtain information on the chlorophyll content in the field and infer the most suitable crop type and planting density for local planting.

Crossdimensional universality classes in static and periodically driven Kitaev models

The Kitaev model on the honeycomb lattice is a paradigmatic system known to host a wealth of nontrivial topological phases and Majorana edge modes. In the static case, the Majorana edge modes are nondispersive. When the system is periodically driven in time, such edge modes can disperse and become chiral. We obtain the full phase diagram of the driven model as a function of the coupling and the driving period. We characterize the quantum criticality of the different topological phase transitions in both the static and driven model via the notions of Majorana-Wannier state correlation functions and momentum-dependent fidelity susceptibilities. We show that the system hosts crossdimensional universality classes: although the static Kitaev model is defined on a two-dimensional (2D) honeycomb lattice, its criticality falls into the universality class of one-dimensional (1D) linear Dirac models. For the periodically driven Kitaev model, in addition to the universality class of prototype 2D linear Dirac models, an additional 1D nodal loop type of criticality exists owing to emergent time-reversal and mirror symmetries, indicating the possibility of engineering multiple universality classes by periodic driving. The manipulation of time-reversal symmetry allows the periodic driving to control the chirality of the Majorana edge states.

Community Resilience Governance on Public Health Crisis in China

The COVID-19 pandemic has immensely affected economic and social order in not only China but the entire world, seriously threatening peoples’ lives and property. In China’s fight against COVID-19, the community is at the front line of joint prevention and control of the disease, yet it faces the problem of insufficient resilience. We explored the manifestations and formation mechanism of the problem of insufficient resilience in community public health crisis governance, based on the complex adaptive system theory, which emphasizes interaction among subjects and between subjects and the environment to improve the adaptability to the environment. Questionnaires and in-depth interviews were conducted in 28 counties (districts) of 14 cities of 7 provinces in China; 2345 questionnaires and 71 interview data were collected, and we conducted descriptive statistical analysis on questionnaire data. It is found that some communities faced insufficient resilience problems such as “simply isolating households and communities”, “blindly setting limits”, “layer-by-layer law”, and “rejecting and repelling all individuals from or even related to Hubei”. These problems are due to the fact that the community have a non-interactive relationship, which is a one-dimensional linear governance model to some extent. The legal content of the building of a “comprehensive disaster-reduction demonstration community” implemented by the Chinese government is compelled to stay at the level of system design to some extent, with its existence playing an ornamental role but lacking a substantial one. In this regard, this study suggests that a resilient governance model of community pluralistic cooperation be established based on the theoretical framework of complex adaptive system. This model is designed to increase the resilience of community public health crisis governance. The authoritative role of central and local policies is expected to be truly developed and played in dealing with the grassroots community public health crisis.

Phase-synchronization properties of laminar cylinder wake for periodic external forcings

Abstract

A Sensor Fusion Approach to the Estimation of Instantaneous Velocity Using Single Wearable Sensor During Sprint.

Power-Force-Velocity profile obtained during a sprint test is crucial for designing personalized training and evaluating injury risks. Estimation of instantaneous velocity is requisite for developing these profiles and the predominant method for this estimation assumes it to have a first order exponential behavior. While this method remains appropriate for maximal sprints, the sprint velocity profile may not always show a first-order exponential behavior. Alternately, velocity profile has been estimated using inertial sensors, with a speed radar, or a smartphone application. Existing methods either relied on the exponential behavior or timing gates for drift removal, or estimated only the mean velocity. Thus, there is a need for a more flexible and appropriate approach, allowing for instantaneous velocity estimation during sprint tests. The proposed method aims to solve this problem using a sensor fusion approach, by combining the signals from wearable Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) sensors. We collected data from nine elite sprinters, equipped with a wearable GNSS-IMU sensor, who ran two trials each of 60 and 30/40 m sprints. We developed an algorithm using a gradient descent-based orientation filter, which simplified our model to a linear one-dimensional model, thus allowing us to use a simple Kalman filter (KF) for velocity estimation. We used two cascaded KFs, to segment the sprint data precisely, and to estimate the velocity and the sprint duration, respectively. We validated the estimated velocity and duration with speed radar and photocell data as reference. The median RMS error for the estimated velocity ranged from 6 to 8%, while that for the estimated sprint duration lied between 0.1 and −6.0%. The Bland–Altman plot showed close agreement between the estimated and the reference values of maximum velocity. Examination of fitting errors indicated a second order exponential behavior for the sprint velocity profile, unlike the first order behavior previously suggested in literature. The proposed sensor-fusion algorithm is valid to compute an accurate velocity profile with respect to the radar; it can compensate for and improve upon the accuracy of the individual IMU and GNSS velocities. This method thus enables the use of wearable sensors in the analysis of sprint test.

Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model

Comprehensive study of the microscopic material removal mechanism remains an open challenge facing the robotic belt grinding of complex geometries. In the present paper, a new material removal rate (MRR) model is developed underlying the motion trajectory of single spherical abrasive grain by taking into consideration the elastic deformation of the heterogeneous contact wheel. In this model, the contact pressure distribution at the contact wheel–workpiece interface is determined by converting the three-dimensional contact problem into a one-dimensional linear spring model with respect to the large deformation of the contact wheel. In addition, the combined modulus and Poisson's ratio of the heterogeneous contact wheel are further considered based on the geometric relation of the Young's modulus and Poisson's ratio. Compared with the existing MRR model, the proposed MRR model can significantly reduce both the root mean square error (RMSE) and mean absolute percentage error (MAPE) values from 2.401 to 1.725, and 18.426% to 14.942%, respectively. Particularly, an optimization strategy from the perspective of process parameters is implemented to balance the grinding quality and efficiency.

Global eigenmodes of thin liquid sheets by means of Volume-of-Fluid simulations

The unsteady dynamics of planar liquid sheet flows, interacting with unconfined gaseous environments located on both sides of the liquid phase, is numerically investigated by means of the Volume-of-Fluid (VOF) technique for supercritical regimes. The global behavior of the non-parallel flow is analyzed by perturbing the initial steady configuration by means of a Gaussian bump in the transverse velocity component of relatively small amplitude, thereby exciting sinuous modes. To gain more physical insights into the fluid system, a theoretical linear one-dimensional model is also developed. A physical interpretation of this model relates the sheet dynamics to transverse vibrations of tensional string forced by terms containing the lateral velocity and subjected to a total damping coefficient, which can assume negative values. The VOF simulation satisfactorily confirms that the velocity impulse perturbation splits into two wave fronts traveling downstream with the theoretical wave velocities. A good agreement is found in comparing the crossing times over the entire domain length of such waves with the almost constant spacing between the frequencies of the eigenvalue spectrum. Surface tension plays a stabilizing role, and for relatively high values of density ratio rρ of gaseous-to-liquid phases, the sheet becomes unstable. It is argued that the distribution of transverse velocity component of the gaseous phase represents the forcing term, which leads the system toward the instability when, for relatively high rρ, the total damping becomes negative. An analogy seems to exist between the global unstable behavior exhibited by the liquid sheet as rρ increases and the shear-induced global instability found by Tammisola et al. [Surface tension-induced global instability of planar jets and wakes,” J. Fluid Mech. 713, 632–658 (2012)] in the presence of surface tension. However, for the gravitational sheet, the surface tension is stabilizing.

Numerical simulation of two-dimensional fins using the meshless local Petrov – Galerkin method

PurposeThe study aims to highlight the behaviour of one-dimensional and two-dimensional fin models under the natural room conditions, considering the different values of dimensionless Biot number (Bi). The effect of convection and radiation on the heat transfer process has also been demonstrated using the meshless local Petrov–Galerkin (MLPG) approach.Design/methodology/approachIt is true that MLPG method is time-consuming and expensive in terms of man-hours, as it is in the developing stage, but with the advent of computationally fast new-generation computers, there is a big possibility of the development of MLPG software, which will not only reduce the computational time and cost but also enhance the accuracy and precision in the results. Bi values of 0.01 and 0.10 have been taken for the experimental investigation of one-dimensional and two-dimensional rectangular fin models. The numerical simulation results obtained by the analytical method, benchmark numerical method and the MLPG method for both the models have been compared with that of the experimental investigation results for validation and found to be in good agreement. Performance of the fin has also been demonstrated.FindingsThe experimental and numerical investigations have been conducted for one-dimensional and two-dimensional linear and nonlinear fin models of rectangular shape. MLPG is used as a potential numerical method. Effect of radiation is also, implemented successfully. Results are found to be in good agreement with analytical solution, when one-dimensional steady problem is solved; however, two-dimensional results obtained by the MLPG method are compared with that of the finite element method and found that the proposed method is as accurate as the established method. It is also found that for higher Bi, the one-dimensional model is not appropriate, as it does not demonstrate the appreciated error; hence, a two-dimensional model is required to predict the performance of a fin. Radiative fin illustrates more heat transfer than the pure convective fin. The performance parameters show that as the Bi increases, the performance of fin decreases because of high thermal resistance.Research limitations/implicationsThough, best of the efforts have been put to showcase the behaviour of one-dimensional and two-dimensional fins under nonlinear conditions, at different Bi values, yet lot more is to be demonstrated. Nonlinearity, in the present paper, is exhibited by using the thermal and material properties as the function of temperature, but can be further demonstrated with their dependency on the area. Additionally, this paper can be made more elaborative by extending the research for transient problems, with different fin profiles. Natural convection model is adopted in the present study but it can also be studied by using forced convection model.Practical implicationsFins are the most commonly used medium to enhance heat transfer from a hot primary surface. Heat transfer in its natural condition is nonlinear and hence been demonstrated. The outcome is practically viable, as it is applicable at large to the broad areas like automobile, aerospace and electronic and electrical devices.Originality/valueAs per the literature survey, lot of work has been done on fins using different numerical methods; but to the best of authors’ knowledge, this study is first in the area of nonlinear heat transfer of fins using dimensionless Bi by the truly meshfree MLPG method.

Comparison and experimental verification of simplified one-dimensional linear elastic models of multilayer sandwich beams

Three analytical one-dimensional linear elastic models of composite laminated beams are considered – composite Bernoulli-Euler beam (BE), composite Timoshenko beam (T) and multilayer sandwich beam model (MS). They are compared with results obtained via finite element method for a two-dimensional model in plane stress state. Overall system stiffness is verified with experimental data obtained for two statical configurations – three-point bending and four-point bending. The first configuration concerned 8 types of three-panel cross-laminated timber (CLT) beams accounting for various materials and thickness of timber panels as well as various materials and thickness of adhesive layer, while the second one concerned 5 types of two-panel aluminium laminated beams accounting for different thickness of adhesive layer. Simplified multilayer sandwich model is found to be in good accordance with FEA results and with experimental data, while simple BE and T models are shown to provide erroneous estimates.