Boundary Conditions Research Articles

An improved incompressible SPH with unified semi-analytical wall boundary conditions for free surface flow

An improved incompressible SPH with unified semi-analytical wall boundary conditions for free surface flow

Dynamics of periodic soliton solutions to the AB system under vanishing boundary condition

Dynamics of periodic soliton solutions to the AB system under vanishing boundary condition

An existence of the solution for generalized system of fractional q-differential inclusions involving p-Laplacian operator and sequential derivatives

In this paper, we investigate the presence of positive solutions for system of fractional q-differential inclusions involving sequential derivatives with respect to the p-Laplacian operator. By using fixed point technique we obtain a new solution for inclusion or boundary value problems with special integral and derivative conditions. At the end, we give an example to show the effect of the solution of this device. The conclusion is expressed to introduce future works.

Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations

Introduction. Computer vision is widely used for semantic segmentation of Earth remote sensing (ERS) data. The method allows monitoring ecosystems, including aquatic ones. Algorithms that maintain the quality of semantic segmentation of ERS images are in demand, specifically, to identify areas with phytoplankton, where water blooms— the cause of suffocation — are possible. The objective of the study is to create an algorithm that processes satellite data as input information for the formation and checking of mathematical models of hydrodynamics, which are used to monitor the state of water bodies. Various algorithms for semantic segmentation are described in the literature. New research focuses on enhancing the reliability of recognition — often using neural networks. This approach is modified in the presented work. To develop the direction, a new set of information from open sources and synthetic data are proposed. They are aimed at improving the generalization ability of the model. For the first time, the contour area of the phytoplankton population is compared to the database — and thus the boundary conditions are formed for the implementation of mathematical models and the construction of boundary-adaptive grids.Materials and Methods. The set of remote sensing images was supplemented with the author's augmentation algorithm in Python. Computer vision segmented areas of phytoplankton populations in the images. The U-Net convolutional neural network (CNN) was trained on the basis of NVIDIA Tesla T4 computing accelerators.Results. To automate the detection of phytoplankton distribution areas, a computer vision algorithm based on the U-Net CNN was developed. The model was evaluated by the calculated values of the main quality metrics related to segmentation tasks. The following metric values were obtained: Precision = 0.89, Recall = 0.88, F1 = 0.87, Dice = 0.87, and IoU = 0.79. Graphical visualization of the results of CNN learning on the training and validation sets showed good quality of model learning. This is evidenced by small changes in the loss function at the end of training. The segmentation performed by the model turned out to be close to manual marking, which indicated the high quality of the proposed solution. The area of the segmented region of the phytoplankton population was calculated by the area of one pixel. The result obtained for the original image was 51202.5 (based on information about the number of pixels related to the bloom of blue-green algae). The corresponding result of the modeling was 51312.Discussion and Conclusion. The study expands theoretical and practical knowledge on the use of convolutional neural networks for semantic segmentation of space imagery data. Given the results of the work, it is possible to assess the potential for automating the process of semantic segmentation of remote sensing data to determine the boundaries of phytoplankton populations using artificial intelligence. The use of the proposed computer vision model to obtain contours of water bloom due to phytoplankton will provide for the creation of databases — the basis for environmental monitoring of water resources and predictive modeling of hydrobiological processes.

Numerical modeling of heat transfer mechanism in remote sensing bulb of thermal expansion valves

Thermal management of automotive systems has garnered a lot of focus in recent times to improve the energy efficiency of vehicles and address the challenge of global warming. In combustible fuel vehicles, the heat pump compressor is driven by the engine power which leads to changes in operating condition of the cycle while the vehicle is in motion. In electric vehicles, a combined thermal management strategy handles both cabin and battery pack cooling/heating depending on the ambient conditions and power requirements by adjusting refrigerant flow rates and flow directions with sophisticated valves. Irrespective of the vehicle type, effective variable refrigerant flow control is prerequisite to ensure optimum thermal comfort with minimum energy. Thermal expansion valves (TXV) are the most widely used method for regulating mass flow rate of refrigerant during the expansion step of the vapor compression cycle based on the principle of trying to maintain a fixed degree of superheat at the evaporator outlet. Conventional models of the thermal expansion valve ignore the time lag between fluctuation of temperature at the evaporator outlet and the corresponding change in pressure of the TXV remote sensing bulb, treating the valve as an instantaneous element during transient operation. This paper presents a novel method for determining the temperature profile and time constant of the remote sensing bulb response using numerical methods, in an attempt to capture the dynamics of thermal expansion valve in active heat pump cycle. Finite difference method was used to solve for the conduction heat transfer between the bulb and the heat exchanger outlet tube in perfect thermal contact applying the necessary boundary conditions. This improved modeling approach will be helpful in better prediction of the dynamic behavior of thermal expansion valves and how it influences the evaporative heat exchanger performance during transient operations to determine control algorithm and strategies.

Coati optimization algorithm based Deep Convolutional Forest method for prediction of atmospheric and oceanic parameters

Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson’s correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.

Modeling Gravity Gradients from Surface Gravity Anomaly Data

Gravity gradients are useful to characterize near mass anomalies since they are much more sensitive to short wavelength anomalies than gravitational accelerations. Estimating gravity gradients from surface gravity data is based on numerical implementations of solutions to geodetic boundary value problem for determination of disturbing potential. One of methods to solve this problem is least-squares collocation which is basically based on data and a defined covariance function. This study deals with estimating gravity gradient tensor components from along track surface gravity anomaly data. The Least-Squares Collocation solution is based on a stationary local covariance function defined for the disturbing potential which allows upward continuation of the observations to a desired altitude. The modeling method is evaluated in using Earth Gravitational Model 2008(EGM2008) and real airborne gravity gradiometry data collected over Southern Texas, Oklahoma region. The results show that modeled gravity gradients estimated in both on the ground and at a certain altitude have basically good agreement with EGM08 gradients. Modeled gradients including horizontal components in the east-west direction exhibit some discrepancies in comparison to the airborne gradiometry data, which may be attributed to some measurement errors in the gradient data.

Investigation of an Exact Solution of a Mixed Boundary Value Problem Using the Residue Method

Investigation of an Exact Solution of a Mixed Boundary Value Problem Using the Residue Method

Self-locking and stiffening deployable tubular structures

Deployable tubular structures, designed for functional expansion, serve a wide range of applications, from flexible pipes to stiff structural elements. These structures, which transform from compact states, are crucial for creating adaptive solutions across engineering and scientific fields. A significant barrier to advancing their performance is balancing expandability with stiffness. Using compliant materials, these structures achieve more flexible transformations than those possible with rigid mechanisms. However, they typically exhibit reduced stiffness when subjected to external pressures (e.g., tube wall loading). Here, we utilize origami-inspired techniques and internal stiffeners to meet conflicting performance requirements. A self-locking mechanism is proposed, which combines the folding behavior observed in curved-crease origami and elastic shell buckling. This mechanism employs simple shell components, including internal diaphragms that undergo pseudofolding in a confined boundary condition to enable a snap-through transition. We reveal that the deployed tube is self-locked through geometrical interference, creating a braced tubular arrangement. This arrangement gives a direction-dependent structural performance, ranging from elastic response to crushing, thereby offering the potential for programmable structures. We demonstrate that our approach can advance existing deployment mechanisms (e.g., coiled and inflatable systems) and create diverse structural designs (e.g., metamaterials, adaptive structures, cantilevers, and lightweight panels).Weanticipate our design to be a starting point to drive technological advancement in real-world deployable tubular structures.

Dynamics of Love-type wave propagation in composite transversely isotropic porous structures

The present study aims to analyze the propagation behavior of Love-type wave in a composite transversely isotropic porous structure. The structure comprises an inhomogeneous sandy porous layer lying between a non-homogeneous magneto-poroelastic layer and a heterogeneous fractured porous half-space. Analytic solutions of the field equations of the respective media involve the application of the variable separable method and Wentzel-Kramers-Brillouin (WKB) asymptotic approach for the conversion of partial differential equations into ordinary differential equations. Through careful imposition of boundary conditions and subsequent elimination of arbitrary constants, we derive a complex dispersion relation governing the propagation of Love-type waves. This dispersion equation yields both the phase velocity curve, corresponding to the real expression, and the damping velocity curve, derived from the imaginary expression. To represent our findings, we conduct extensive calculations and graphical simulations illustrating the influence of various material parameters such as heterogeneity, porosity, volume fraction of fractures, sandiness, magnet-oelastic coupling, angle at which wave crosses the magnetic field, and layer thickness on the dispersive nature of Love-type waves using MATHEMATICA software. Furthermore, we conduct case-specific analyses, revealing instances where the dispersion equation simplifies to the standard Love wave equation, thereby validating our mathematical framework. Our findings underscore the significant influence of the aforementioned material parameters on the phase and damping velocities of Love-type wave. This interdisciplinary investigation into different porous media opens new avenues for future research and has significant implications in various disciplines, ranging from engineering and geophysics to environmental science and beyond.

On a Class of p(z)-Biharmonic Kirchhoff Type Problems with Indefinite Weight and No-Flow Boundary Condition

On a Class of p(z)-Biharmonic Kirchhoff Type Problems with Indefinite Weight and No-Flow Boundary Condition

Strong Solutions of Brusselator System

The study involves a mathematical analysis of the Brusselator system on a convex bounded three-dimensional open domain, considering Neumann boundary conditions. We establish the global existence and uniqueness of the strong solution for this system. Achieving high regularity for the strong solution requires stringent conditions on the initial data. The study demonstrates the continuous dependence of the solution on the initial conditions.

A new perspective on gravitational collapse: a comprehensive analysis using Θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Theta $$\\end{document} parametrization

In this article, we have studied some new facets of gravitational collapse, beyond conventional notions of black holes, and naked singularities. The present work is focused on the unresolved problems of theoretical physics concerning the final fate of the gravitational collapse of any massive star. By generalizing the foundational work of Oppenheimer and Snyder (Phys Rev 56:455, 1939), we have considered the homogeneous gravitational collapsing system filled with perfect fluid distributions (devoid of dust fluid) within the framework of GR theory. The article investigates a homogeneous gravitational collapsing system using a parametrization scheme for the expansion-scalar (Θ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(\\Theta )$$\\end{document} and determining the exact solution of field equations. Further, by employing the known Darmois–Israel boundary condition, we have discussed the solution in more detail, and the model’s physical and geometrical quantities are obtained in terms of stellar mass (M), making it crucial for astrophysical applications. To assess the astrophysical viability of our model, we consider a massive star-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document}Canis Majoris and throughout this study we have presented a detailed numerical and graphical analysis of our model. The analysis of singularities in the models is conducted through the examination of the apparent horizon. Our findings demonstrate that the star continues to collapse indefinitely in co-moving time, ultimately leading to a space-time singularity-this represents a case of continuous gravitational collapse. We propose that this scenario describes an “Eternal Collapsing Object,” which may be treated as a “mathematical black hole without any finite equilibrium state.” Our presented models undergo rigorous physical tests to ensure their regularity, causality, and stability, and to satisfy the necessary energy conditions.

The impact of group membership on punishment versus partner rejection.

People often display ingroup bias in punishment, punishing outgroup members more harshly than ingroup members. However, the impact of group membership may be less pronounced when people are choosing whether to stop interacting with someone (i.e., partner rejection). In two studies (N = 1667), we investigate the impact of group membership on both response types. Participants were assigned to groups based on a "minimal" groups paradigm (Study 1) or their self-reported political positions (Study 2) and played an incentivized economic game with other players. In this game, participants (Responders) responded to other players (Deciders). In the Punishment condition, participants could decrease the Decider's bonus pay. In the Partner Rejection condition, participants could reject future interactions with the Decider. Participants played once with an ingroup member and once with an outgroup member. To control for the effects of intent and outcome, scenarios also differed based on the Decider's Intent (selfish versus fair) and the Outcome (equal versus unequal distribution of resources). Participants punished outgroup members more than ingroup members, however group membership did not influence decisions to reject partners. These results highlight partner rejection as a boundary condition for the impact of group on responses to transgressions.

The existence of a unique solution and stability results with numerical solutions for the fractional hybrid integro-differential equations with Dirichlet boundary conditions

In this paper, we investigate the fractional hybrid integro-differential equations with Dirichlet boundary conditions. We first prove the existence of a unique solution for the equation using a fixed point technique. Our main goal is to obtain the best approximation using optimal controllers. After studying the stability, we present the reproducing kernel Hilbert space numerical method to obtain approximate solutions to the equation. We finally conclude with numerical results.

Design of lattice metamaterials under the equal strength concept and research on the mechanical properties of their sandwich structures

This study proposes a novel lattice metamaterial with equal strength struts based on the concept of equal strength of beams, which has been designated the strut-enhanced body-centered cubic lattice structure (EBCC). The structure is capable of effectively reducing the stress concentration of the lattice structure at the nodes and increasing the structural energy absorption. The effective elastic properties of EBCC unit cells are investigated in detail under periodic boundary conditions (PBC), and the results show that the deformation response of the unit cell under compressive loading is dominated by bending. A novel sandwich structure, designated S-EBCC, was developed utilizing EBCC as the core material. The effects of geometrical parameters and relative density on the deformation mechanism and mechanical properties of the sandwich structure were analyzed through experiments, numerical simulations and theoretical predictions. The results show that under compressive loading, the stress concentration at the nodes of the EBCC structure is effectively improved and the peak stress is reduced while the energy absorption is increased compared with the traditional S-BCC structure. Under three-point bending load, the larger the strut angle of the S-EBCC structure, the higher the load capacity. Comparison of experimental and theoretical results shows that the failure modes of the S-EBCC structure under both compressive and three-point bending loads are plastic yield.

Exploring the effective implementation of population-based SHM in existing buildings. Part I: Structural condition assessment

Despite major critical progress over the past few decades, condition-based decision-making concerning the immediate occupancy or efficient operation of an existing building continues to face unresolved issues. The key issue is to determine the structural condition and its residual capacity using data-driven-based methods without knowledge of its life cycle since design (ageing, extreme events, etc.). Environmental and operational variations, boundary conditions and a lack of damage-state building data lead to further uncertainties in the actual assessment of the structural capacity and affect the effectiveness of implementing structural health monitoring (SHM) solution. Recently, population-based SHM has been developed to address some of these issues to enable transfer from data collected from a group of nominally identical buildings, and to model missing data for the target building. This study presents the Build’Health™ framework for operational data-driven SHM, considering populations of buildings evaluated under operational conditions. Fifty-five period-height empirical models obtained from methods based on ambient vibrations recorded in nominally identical reinforced concrete buildings were first identified in the literature to define the building population models, along with design features (such as boundary conditions, geometry or material design). The shift in structural condition of the target building is inferred from the nominally identical building population, after adding empirical variability in the resonance period due to temperature, assumed herein to be the main environmental cause of structural variation. This work presents the framework implemented and its application to five target buildings under different monitoring and structural conditions. This manuscript is the first of a two-part article on population-based SHM relative to structural condition assessment (part I) and damage-feature classification (part II) presenting the Build’Health™ concept.

A Universal Model for Ultrasonic Energy Transmission in Various Media

This study presents a comprehensive model for ultrasonic energy transfer (UET) using a 33-mode piezoelectric transducer to advance wireless sensor powering in challenging environments. One of the advantages of UET is that it is not stoppable by electromagnetic shielding and can penetrate metal. Existing models focus on feasibility and numerical analysis but lack an effective link between input and output power in different media applications. The proposed model fills this gap by incorporating key factors of link loss, including resonant frequency, impedance matching, acoustic coupling, and boundary conditions, to predict energy transfer efficiency more accurately. The model is validated through numerical simulations and experimental tests in air, metal, and underwater environments. An error analysis has shown that the maximum error between theoretical and experimental responses is 3.11% (air), 27.37% (water), and 1.76% (aluminum). This research provides valuable insights into UET dynamics and offers practical guidelines for developing efficient wireless powering solutions for sensors in difficult-to-access or electromagnetically shielded conditions.

Venture creation through social connections: Role of social networking ties in developing entrepreneurial passion and intentions among university students

Drawing upon S-O-R theory, this study explores the direct and indirect influence of social networking ties (SNTs) on entrepreneurial intention (EI), via the mediation of entrepreneurial passion (EP). It also investigates the moderation effect of the need for achievement (nAch) on the direct relationship between SNTs and EP and further investigates the conditional moderation of nAch on the indirect path from SNTs to EI. Using a sample of 307 Pakistani university students, we employed the PLS-SEM approach to test the model. The results of the measurement model affirmed that the data meet the assumptions of convergent and divergent validities. The findings from the structural model showed that SNTs influence EI both directly and indirectly. Additionally, nAch is also found to positively moderate the link between SNTs and EP. Further, the results of conditional indirect effects infer that the indirect effect of SNTs becomes the most significant for the students who perceive a high level of nAch and the least significant for the students who perceive a low level of nAch. This study adds to the literature by employing the S-O-R approach to understand the link between SNTs and EI via EP and incorporating nAch as a boundary condition.

Non-homogeneous initial boundary value problems and small data scattering of the Hirota equations posed on the half line

Non-homogeneous initial boundary value problems and small data scattering of the Hirota equations posed on the half line