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The primary focus of this study is to analyze the nonlinear vibration patterns and parametric excitation of embedded Euler–Bernoulli nanobeams subjected to thermo-magneto-mechanical loads. The Euler–Bernoulli nanobeam is developed with external parametric excitation. By utilizing nonlocal continuum theory and nonlinear von Karman beam theory, the governing equation of motion is derived. Subsequently, the homotopy perturbation technique is employed to determine the vibration frequencies. Finally, the modulation equation of Euler–Bernoulli nanobeams is derived for simply supported boundary conditions. The impacts of magnetic potential, temperature, damping coefficient, Winkler coefficient, and nonlocal parameters are tested numerically on nonlinear frequency–amplitude and parametric excitation–amplitude responses. Results demonstrate that physical variables significantly influence both nonlinear frequency behavior and parametric excitation.

Static response of simply supported functionally graded (FG) shallow shells using a new higher-order shear and normal deformation theory is focused in this article. The effects of transverse strains and stresses on the bending response of FG shell are considered by the present theory. The current theory considers the impacts of transverse normal and shear deformations that meet the requirements for traction-free boundary conditions. The virtual work principle is applied to the mathematical formulation of the present theory. The simply supported doubly curved shallow shell problems under the static transverse load are analyzed using Navier’s solution technique. To verify the existing theory, the current results are, whenever possible, compared with those that have already been published. Additionally, a few benchmark results are presented in this article that will be helpful to researchers in the future.

In this study, the influence of the torsional rigidity of the connected shafts, couplings and the working body of the machine, as well as the damping capacity of the coupling, on the torsional impact moment generated in the machine transmission is investigated. Unlike existing classical calculation models, the torsional stiffness of the connected shafts, the torsional damping ability of the coupling and the effects of the moment ratio are taken into consideration together. Under these conditions an analytical expression for the shock moment or resonance coefficient is obtained. The main novelties in obtaining of this expression are the ratio of the torsional stiffness of the connected shafts with the torsional stiffness of the coupling and the acceptance of the moment of resistance of the working body of the machine depending on the torsional stiffness. It has been found that the considered factors have a significant effect on the resonance zone. Finally, different and overlapping conditions are determined when determining the value of the resonance coefficient characterizing the torque impact moment, calculated according to the classical and proposed models.

This article proposes a new algorithm for computing dispersion curves of ultrasonic guided waves in multi-layered composites with large number of layers. The algorithm is based on balancing the eigenvalue problem of the Spectral Collocation Method (SCM) formulation. The SCM has proven effective in analyzing single-layer and simple waveguides. However, it struggles with large multi-layer structures due to numerical instability caused by irregular and sparse matrices in the eigenvalue problem. The proposed algorithm for balancing has significantly reduced both the conditioning measure and the matrix norm in the matrix system. The optimization of spectral formulation enables accurate calculation of dispersion curves and characterization of displacement/stress profiles using Matlab software. This precise characterization and mode separation are essential for selecting ultrasonic sensors for damage detection. A comparison was made with Dispersion Calculator (DC) software. The algorithm was first validated using a hybrid multi-layered composite [CFRP-Al-CFRP-Al], which was successful. The validated algorithm was then quantitatively evaluated using a cross-play laminate T800M913 [0/90/0/90] in terms of three parameters: Number of collocation points, wave propagation direction and thickness. The algorithm is then applied to a large number of layers using three configurations: Symmetric layup T80M913[0/90]10s, hybrid layup [CFRP-Al]50 and challenging layup T800M913[0/90]100. The study found that the balancing algorithm, when combined with the SCM, is effective for structures with a large number of layers. Finally, optimizing the SCM will enhance its competitiveness as an effective tool in ultrasonic non-destructive testing for the studied structures of great industrial interest.

This paper is devoted to presenting the results of the computer-aided design, the multibody dynamic analysis, and the proportional-derivative control synthesis of an adaptive mechanism serving as a lifting table. More specifically, the first part of the manuscript deals with the methodological approach and the mathematical background employed in the entire research work, whereas the second part of the present paper is focused on the model development and the numerical experiments carried out in this investigation. The analytical derivations presented herein demonstrate that the desired adaptive behavior can be successfully obtained for the virtual prototype of the lift mechanism devised in this investigation. Furthermore, a detailed CAD model of the proposed design was constructed in this investigation using SOLIDWORKS. To this end, particular attention was paid to the actual assembly and disassembly of each mechanical component, in conjunction with the choice of the actuators and sensors that are necessary for the proper functioning of the lifting mechanism. Then, a three-dimensional multibody model was developed starting from the CAD model of the virtual prototype devised in this investigation. The resulting multibody model, following appropriate simplifications, was subsequently imported into the MATLAB virtual environment, thereby allowing for readily performing kinematic and dynamic simulations of the nonlinear behavior of the mechanical system under study by using the SIMSCAPE MULTIBODY computational software. By doing so, the development of an appropriate control strategy, as well as the analysis of its behavior under loading and unloading goods conditions, was carried out and tested in the case of four different scenarios considered as the case study. For this purpose, the applicative scenarios considered are: 1) an impulsive loading scenario; 2) a progressive loading scenario; 3) an impulsive unloading scenario; 4) and a progressive unloading scenario. The performance of the feedforward plus feedback control strategy devised in this study was discussed based on several computer simulations. Numerical simulations demonstrate that the desired adaptive behavior is successfully obtained for the virtual prototype of the lift table designed in this study.

A ply-level based modeling strategy for predicting the delamination behavior of laminated composites under pure and mixed mode loading conditions is implemented within the framework of the Finite Element Method. Each ply and each interface of the laminate is explicitly modeled, with the plies represented by various element types such as conventional shell, continuum shell, and continuum elements, and the interfaces are discretized using cohesive zone elements. The comparison between all models is examined in terms of delamination onset and growth including load-displacement curves, delamination area, computation time, and mode-mixity. The results show that all ply-level based modeling strategies exhibit very good agreement with the analytical results. Moreover, ply-level approach based on shell elements in combination with finite thickness cohesive zone elements offers a numerically efficient simulation tool to predict delamination behavior in laminates.

Flywheels used for energy storage may be assembled by press fitting or bonding of concentric rotor parts. During operation, each part is subjected to a superposition of stress fields associated with centrifugal loading and with contact at the component interfaces. To capture the highest overall energy storage capacity, design properties must be selected to achieve the maximum moment of inertia, subject to constraints on the stress occurring within each part at the maximum operating speed. This paper proposes a mathematical formulation for parametric shape optimization problems which can be used to design a multi-disk flywheel rotor to maximize the energy storage capacity. It is shown that the energy storage function for each part of the flywheel can be expressed as a product of two fundamental shape-factors: one that accounts for the stress distribution and the other accounts for the mass distribution. These factors are expressed analytically for cases involving hyperbolic disks, leading to a finite dimension shape optimization problem for a multi-disk rotor. Case studies are presented based on the derived analytical solutions that show how the theory can be usefully applied in flywheel design optimization problems. The results from the case study show significant improvements in specific energy compared to previous research results. This advancement is particularly relevant to develop high-performance, cost-effective flywheel systems, offering potential for widespread application in energy storage technologies.

Due to lack of a design resistance for cellular beam-columns under the axial-shear-flexure interaction, the design interaction resistances were investigated through a validated finite element analysis. The beam-columns are simply supported and subjected to a mid-span point load and a compressive axial load, to provide the interaction. A modified interaction based on ANSI/AISC 360-16 and the shear design of SCI P355 were integrated to design the interaction resistance. In the parametric study of the interaction in various steel sections, the non-dimensional slenderness and opening parameters were examined. For beam-columns under high shear forces, Vierendeel mechanism and web-post buckling constitute the dominant failure modes depending on the opening parameters. Under high compression loads or flexural moment, flexural failure about the major axis is the dominant failure mode. However, the failure behaviors are generally combinations of the three modes. The design interactional resistance was examined in terms of failure shear forces computed from failure criteria of the three modes. Since design of the web-post buckling in SCI P355 does not consider effects of the interaction, their resistances are significantly higher than the FE results. The resistance is not governed by safety and requires modifications. On considering the co-existing actions in a quadratic interaction criterion, the proposed interaction resistance obviously complies with the FE results for cases of the web-post buckling failure, and agrees well with the FE results for cases of Vierendeel and flexural failure.

In engineering, DIC is a widely used measurement technique. Its major advantage is that it provides real-time results (displacements, accelerations, stresses, strains, deformations) of the surface under examination relatively quickly and without contact. However, its application in medicine, biomechanics, and the field of criminalistics is novel. The present research focuses mainly on the frontier areas of forensics and medicine. The research aims to define the test boundary conditions and preparatory activities to measure the surface of the animal and then human skin. Injuries caused by low-energy ballistic bullets, blunt-force trauma, and cuts and punctures caused by knives and/or blades will be investigated. The present research focuses on puncture injuries in animal skin. The main challenge is to create a speckle pattern on the surface that can track deformation well. The research is about developing and validating this. The GOM ARAMIS measurement system was applied for the measurements. This paper demonstrates that a suitable preparation, painting procedure, and measurement setup has been established to measure the above effects, i.e., to identify displacements and deformations of up to tenths of a millimeter with sufficient accuracy. The evaluation of the results will also show that this method could be used in forensic applications, the automotive industry, medical orthopedics, and the textile industry.

The article is focused on the vibrations of a heavy chain with a mass on the end when the hoisting machines are in a nonstationary regime of motion, assuming that motor or brake torque is a linear function of the speed of the motion mechanism. An approach based on the application of approximate analytical solution of the system of differential equations of motion has been presented. The solution was obtained by two iterations based on the Fourier and small parameter methods, the representation of the motion of any point of the chain, and the intensity of the inertial perturbation forces by Eigenfunction series of the free vibration task. Our analytical results have been illustrated with a numerical example on the basis of the technical characteristics of the crane trolley and a visual Fortran computer program has been created.

The elite implementations of the trihybrid nanoliquid are converting solar thermal energy, photovoltaic heat collectors, and power storage. This work explores the irreversibility analysis in an upright microchannel subjected to convective ternary hybrid nanoliquid flow with quadratic thermal radiation. The consequences of the Darcy Forchheimer rule, heat source coefficient, and no slip condition on the thermal performance are studied. In the base liquid engine oil, the three distinct types of nanoparticles having different shapes including spherical (Ti), cylindrical (MgO) and platelet (Cu) configurations were considered in this scrutiny. The combined model is used to approach trihybrid and hybrid nanoliquid thermophysical properties. The governing partial differential equation is transferred into the ordinary differential equation by employing dimensionless terms. In the next step to solve the ordinary differential equations, the RKF45 numerical technique is used. The acquired upshots divulge that the improved temperature parameter strongly supports the flow profile. Thermal profile inflates with increased Darcy number. The entropy production shows dual behaviour for the escalated quadratic radiation parameter. With an enhanced darcy number of 400%, the entropy generation rises by 14% in the ternary hybrid nanoliquid circumstances and rises by 11% in the hybrid nanoliquid situation. In addition, when the combination of spherical, cylindrical, and platelet nanoparticles is considered the rate of thermal transfer is greater compared to spherical and cylindrical nanoparticle combinations.

The current paper studies the influence of the temperature ratio of ion-to-electron α, dust concentration μ, and κ- deformed parameter on dust ion acoustic solitary waves in an unmagnetized plasma with Kaniadakis distributed electrons. More precisely, the reductive perturbation technique is utilized to extract the Korteweg-de Vries and modified Korteweg-de Vries equations. Both compressive and rarefactive Korteweg-de Vries solitons are found to exist in the ranges 0 < μ ≤ 0.677 and 0.677 < μ < 1, respectively, and only compressive modified Korteweg-de Vries solitons in the range 0 < μ ≤ 0.11. In an unmagnetized plasma with Kaniadakis distributed electrons, the influence of the ion-to-electron temperature ratio on dust ion acoustic solitary waves can have several fascinating applications and consequences in plasma physics and astrophysics.

This study examines the generation of irreversibility and the behavior of stagnation point hybrid nanofluid flow on a Riga plate. The effects of nonlinear thermally convective and solar radiation are incorporated in the modeled equations. The nanoparticles of (Cu) and (Al2O3) are mixed with Glycol (C3H8O2) to hybridized it. The leading equations have been changed to dimension-free form by using the set of appropriate variables and then have been evaluated by Artificial Neural Network (ANN) approach. It is revealed in this work that, the velocity panels are amplified with expansion in Grashof number and electromagnetic factor while declined with escalation in magnet/electrode factor and nanoparticles concentration. Upsurge, in Eckert number for both the scenarios (Ec < 0) and (Ec > 0), the radiation factor and nanoparticles concentration cause augmentation in thermal characteristics. Radiation factor has positive impacts on Bejan number and generation of Entropy. Moreover, Bejan number is retarded while entropy is augmented with growth in Brinkman number. It is also established in this work that the principle of entropy generation for hybrid nanoparticles supports the efficient delivery of drug in cancer treatment.

This theoretical work deals with delamination in U-shaped load-carrying structural members. The latter represent multilayered functionally graded systems of non-linear viscoelastic behaviour. The right-hand vertical part of the structure under consideration is delaminated. The structure is clamped in the lower end of the left-hand vertical part. Besides, the structure is supported by a rotational spring which has non-linear elastic behaviour. The strain energy release rate is derived by using an approach that is based on analysis of equilibrium of the various components of the U-shaped structure. The solution derived is checked-up by applying the method of the integral J. The dependence of the strain energy release rate on different parameters is studied and presented in form of various graphs. Analysis of a U-shaped structural member with two rotational spring supports also is carried-out and the strain energy release rate obtained is compared with that in the U-shaped structure with one rotational spring support.

This paper deals with the selection of optimal layout variants of planetary transmissions designed for operation as Light Electric Vehicle main gearboxes. The required transmission ratio was calculated, and the optimal two-carrier gear train with two connecting and three external shafts selected. A computer program was used for structural synthesis and to determine the required basic parameters of the component gear trains. The ring gear reference diameter was used to rank the layout variants according to size, with the smallest variants also ranked according to the calculated efficiency ratio. Finally, an optimal variant of the planetary gear train for light electric vehicle application is proposed. The procedure proposed in this paper may be expanded to other applications.

In the present study, the Multi-Relaxation Time Lattice Boltzmann method (MRT-LBM) was employed to solve the airflow inside a scaled room model with dimensions of 0.914×0.457×0.305 m. This room model is considered a representative space with a 1:10 scale to an actual room. The selected room is equipped with a ventilation system. For optimizing the inlet and outlet locations of the airflow, 32 different positions in terms of length, width, and height for the inlet and 4 positions for the outlet were considered. The Taguchi method was utilized for optimizing the inlet and outlet locations, reducing the required number of experiments from 128 to 16. To assess the number of suspended particles, 86400 particles with a size of 1µm were injected into the room. Then, the particle behaviors were examined for a total duration of 60 seconds. The obtained results indicate that the location of the air conditioning system significantly influences the concentration of airborne particles responsible for disease transmission. Utilizing the Taguchi optimization method, optimal positions for the inlet and outlet air were determined to minimize the number of suspended particles in the room (the best position) and maximize it (the worst position).

Maxwell model is one of the most outstanding and widely used models for the description of viscoelastic materials. In this study, we use an efficient meshfree technique based on the Moving Kriging (MK) interpolation for the numerical solution of Magnetohydrodynamic (MHD) flow of fractional Maxwell fluid. In this scheme to discretize this equation in time and space variables, we use the finite difference method and MK interpolation shape functions, respectively. Also, we calculate the local weak form for every node instead of the computation of the global weak form for the global domain. So, we reduce such problems to a system of algebraic equations. To indicate the efficiency of the present scheme, four examples are discussed in various types of domains and with uniform and nonuniform nodal distribution in 2D cases. Also, to show the validity of the method in this example, a comparison with a valid method has been made. Moreover, in the last example, the accuracy of our scheme in the 3D case is illustrated for the fractional telegraph equation.

In the era of increasing demand for sources of renewable and cleaner energy, electric vehicles offer possible solutions in order to maintain and improve the mobility of transport systems. In parallel, the application of machine learning for digital twin technology greatly contributes to the development and optimization of vehicles and systems, saving time and resources, as well as material resources. In terms of electric vehicle components, electric batteries represent the most expensive elements where machine learning can help to optimize characteristics during exploitation and to predict maintenance time and their lifetime. This article related to the possibilities of future research, which, by intensifying the digitalization and machine learning for digital twin technology, will affect the improvement of the application and disposal of components, but the complete system of electric vehicles, during the entire life cycle, including the recycling.

Questions of increased reliability of responsible connections at a stage of their assembly are considered in the paper. This step provides the necessary operational characteristics of outputs. The special place among such responsible connections is occupied with the group threaded connections. Their assembly technology provides reception in fixing details normalized relatively identical axial efforts in regular intervals loading a sealing layer. The presented work results of experimental research on the group threaded connection assemblage process are given. Alternate calculations of efforts change in tightened pairs of threaded connections and their graphic interpretation are presented. An algorithmic calculation model of overload factors for fixing details has been developed. Recommendations about the use of the received settlement dependences, taking into account comparative theoretical and experimental data on assembly of group threaded connection, are offered.

In this article, a mathematical model is established to determine the working parameters of a hydraulic recoil mechanism, considering the characteristic parameters of the oil. The parameters considered in this study are oil compression and oil temperature. Parameters such as recoil braking force, recoil velocity, recoil length, and recoil time are calculated in the cases of incompressible oil, compressible oil, and different working temperatures of oil. Numerical calculations are applied to 85 mm anti-tank artillery. Calculation results show that oil compression mainly affects the early stages of the recoil movement of the recoil part. When considering the oil’s compressibility, the maximum recoil brake force and maximum hydraulic brake force values are 73406 N, and 61052 N, respectively – reduced by 4.3% and 5.2% compared to the case of uncompressed oil. In addition, the working temperature of the oil also significantly affects the working parameters of the hydraulic recoil mechanism. The calculation results are a reliable theoretical basis for choosing oils suitable for the harsh working conditions of the hydraulic recoil mechanism during maintenance.

The paper provides a systematic analysis of the origin, spread, and causes of shaft failure in the four-axle electric locomotive series 441. Based on this analysis, recommendations for preventive diagnostic maintenance are provided to timely identify the condition and avoid shaft failure, which is a vital component of the locomotive. Failure to maintain it in a timely manner can lead to catastrophic human, ecological, and material consequences. The paper also presents and explains the fracture surfaces of the locomotive’s hollow drive shaft, following a railway incident that caused the derailment of the rail vehicle, resulting in significant material damage. The presented fracture analysis is based on research into the mechanisms of failure, which by their appearance and action cause the formation of an initial crack. The dominant factor in the formation of the initial crack is material fatigue, and the potential presence of inclusions or local initial notches in the material structure formed during the shaft manufacturing process. Research has shown that the measured residual internal stresses after the shaft failure reached up to 470 MPa, significantly exceeding the allowable 300 MPa.

Stepped spillways are spillways provided by steps that are used to dissipate energy and reduce the scour and erosion that occur at the end of the hydraulic structures. Since the spillway is one of the parts of the dam used to get rid of excess water during floods, spillways can be constructed in various forms and sizes depending on the geology of the dam site and the hydraulic conditions of the flow. The stepped type was chosen because it dissipates energy and reduces the size of the basin at the end of the structure. The aim of the research is to review most of the studies that dealt with the subject of stepped spillways as energy dissipaters. This study collected much research on stepped spillways, some of which used physical models and others numerical models. These studies were performed to determine energy dissipation, flow properties, or type of flow in the stepped spillways and to study the effect of the shape of the steps on energy dissipation. The results showed that some studies indicated that when the flow over the stepped spillways is in the form of jet or free flow, it increases the efficiency of energy dissipation and reduces the length of the hydraulic jump. It is worth mentioning that some studies used software such as ANSYS, while others used artificial intelligence (AI) techniques like genetic programming and fuzzy logic.

Abstract We illustrate an application of graph neural networks (GNNs) to predict the pressure, temperature and velocity fields induced by a sudden explosion. The aim of the work is to enable accurate simulation of explosion events in large and geometrically complex domains. Such simulations are currently out of the reach of existing CFD solvers, which represents an opportunity to apply machine learning. The training dataset is obtained from the results of URANS analyses in OpenFOAM. We simulate the transient flow following impulsive events in air in atmospheric conditions. The time history of the fields of pressure, temperature and velocity obtained from a set of such simulations is then recorded to serve as a training database. In the training simulations we model a cubic volume of air enclosed within rigid walls, which also encompass rigid obstacles of random shape, position and orientation. A subset of the cubic volume is initialized to have a higher pressure than the rest of the domain. The ensuing shock initiates the propagation of pressure waves and their reflection and diffraction at the obstacles and walls. A recently proposed GNN framework is extended and adapted to this problem. During the training, the model learns the evolution of thermodynamic quantities in time and space, as well as the effect of the boundary conditions. After training, the model can quickly compute such evolution for unseen geometries and arbitrary initial and boundary conditions, exhibiting good generalization capabilities for domains up to 125 times larger than those used in the training simulations.

This research introduces the Stochastic Extended Isogeometric Analysis (XIGA) method to investigate fracture behavior of isotropic and orthotropic materials under mechanical, thermal, and thermomechanical loads. Employing knot spans from Isogeometric Analysis (IGA) for domain discretization, the study utilizes identical basis functions for geometry construction and solution discretization. Utilizing Extended Finite Element Method (XFEM) enrichment functions, accurate crack face displacement discontinuity and tip singularity within the stress field are characterized. Additionally, employing a second-order perturbation technique within XIGA framework, the research derives mean and coefficient of variance values for mixed-mode Stress Intensity Factors (SIF). Stochastic variations in material elastic properties, crack length, and crack angle are considered in this computation. Credibility and robustness of the study are confirmed through comparative analyses against available literatures and Monte Carlo Simulations (MCS). The observed exceptional agreement validates the precision and reliability of the proposed stochastic XIGA method for fracture analysis in orthotropic material systems under thermomechanical loading conditions.

This paper investigated the influence of process pressure variations on the accuracy and performance of ultrasonic flow meters. Process measurement technology provides a tool for optimizing production processes and dosing operations. Accurate measurement is key and primary to profitability in the business of supply and purchase of liquids like petroleum, gas and chemical products. Three 6” size ultrasonic flow meters were mounted on a skid and used to carry out the experiment parallel in connections each other to take flows from a common header, measure and discharge their individual flows into a common discharge header. The three meters were designate 1, 2 and 3 respectively. Meters 1 and 2 being service meters while Meter 3 is the calibrated master meter. The experiment was carried ten times to increase reliability of results. Experimental data were collected and analyzed using computational formulae technique. Results showed that; Meter 1 had an optimum process pressure of 12.38 and 9.43 bar with respect to flow rate and meter factor respectively as performance indicator. While Meter 2 had an optimum process pressure of 12.4 and 12.41 bar with respect to flow rate and meter factor respectively as performance indicator. Findings indicated significant relationship between process pressure, flow rate and meter factor using ultrasonic flow meter. The outcome of this study will be a useful guide to users of ultrasonic flow meters to maintain optimum process pressures of each meter during fluid supply.

Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30%, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Global Navigation Satellite Systems (GNSS) has become an essential component in modern times for positioning, navigation, and timing. A fast-growing economic region in Malaysia required a GNSS-based augmentation positioning and navigation service. To improve navigation solution, several augmentation techniques exist, such as a differential Global Positioning System (DGPS) assisted by reference station, or augmentation service provided by commercial communication satellite. The DGPS correction is applied through a real-time communication medium and can be received at the user side by several communication methods including internet-based, and this method is favourable for land and near-coast areas. This case study aims to investigate the reliability of internet-based DGPS and SBAS along the Peninsular Malaysia. A test was conducted and data were collected over 15 hours at a rate of 1 Hz from the available GNSS satellite using a geodetic-grade receiver mounted on a moving vehicle. The obtained results showed that DGPS and SBAS perform better than the navigation solution with an accuracy of 1.536 m and 0.955 m respectively, compared to the navigation solution with an accuracy of 3.159 m. The limitations of both augmentation techniques were also analysed and discussed in this study.

Accurate determination of sodium chloride (NaCl) density in water is vital for assessing environmental impact, preventing soil salinization in agriculture, ensuring quality and consistency in industrial processes, facilitating medical treatments, and maintaining taste and preservation standards in the food and beverage industry. This paper introduces a novel microwave sensor design specifically tailored to accurately assess NaCl density in aqueous solutions. Starting with a standard solution of 10 g of salt dissolved in 100 ml of water, resulting in a molarity of approximately 1.71 M, five distinct samples are meticulously prepared. These samples cover a range of NaCl concentrations, with different ratios of salt solution and drinking water, including pure water, 10 ml of salt solution with 90 ml of water, 20 ml of salt solution with 80 ml of water, 30 ml of salt solution with 70 ml of water, and 40 ml of salt solution with 60 ml of water. Each sample undergoes analysis using the developed microwave sensor to determine its transmission coefficient. The magnitude of the transmission coefficient is closely tied to the density of the salt solution based on molarity. Through a detailed regression analysis, a strong quantitative relationship between the transmission coefficient and salt solution density is revealed. This correlation can be accurately represented by a third-order polynomial equation. This research is significant as it advances microwave sensor technology, allowing for accurate and efficient measurement of NaCl density in water.

Requirements Agile approaches have transformed engineering. This paper shows how RE in Agile software development has evolved from documentation-heavy to collaborative, adaptable, and customer-focused. Agile was born in the mid-1990s when the industry realized it needed to respond to changing client needs and market volatility. This evolution includes iterative development, client interaction, and emphasizing communication above documentation, as discussed in the paper. By comparing conventional and Agile RE approaches, we demonstrate the benefits of adapting to change, working with customers, and delivering functional software faster. This analysis provides a persuasive description of Agile RE implementation methodologies and resources through a detailed literature review and real-world experiences. User stories and backlog refinement are notable techniques. The research finishes by exploring how these techniques affect team dynamics, project success, and customer satisfaction. RE's Agile difficulties and opportunities are also examined. The findings illuminate RE methods' successful adaptation to Agile projects' dynamic character. Software development is more responsive and effective due to this adaptation.

In the dynamic field of Customer Retention Prediction (CRP), strategic marketing and promotion efforts targeting specific customers are crucial. Understanding customer behavior and identifying churn indicators are vital for devising effective retention strategies. However, identifying customers likely to terminate services presents a challenge, leading to data imbalance issues. Existing CRP studies using Machine Learning (ML) techniques and data imbalance methods face problems such as overfitting and computational complexity. Similarly, recent CRP studies employing Deep Learning (DL) approaches rely on data sampling techniques, which can result in overfitting and a lack of cost sensitivity. Additionally, DL approaches struggle with slow convergence and get stuck in local minima. This paper introduces an effective hybrid of Deep Learning (DL) classifiers focusing on cost-metric integration to address data imbalance issues and period-shift Cosine Annealing Learning Rate (ps-CALR) to accelerate model training, ultimately enhancing performance. Three Telecom datasets, namely IBM, Iranian, and Orange, were used to assess the model performance. Empirical findings show that the hybrid DL classifiers significantly improved CRP over conventional ML. This paper contributes methodological advancements and practical insights for effective customer retention in the telecom industry.

The increase in global temperature has led to a significant surge in energy consumption within the air conditioning industry, resulting in environmental deterioration. Electrochromic (EC) windows have emerged as a promising solution to address these challenges. Vanadium pentoxide (V2O5) stands out among all metal oxide materials due to its remarkable EC properties, including substantial Li+ ion insertion capacity and multicolor capabilities. Despite the potential of V2O5, there remains a lack of comprehensive research on the structural and optical properties of V2O5 films with varying thicknesses. Therefore, this study aims to investigate the structural and optical properties of V2O5 thin films with thicknesses ranging from 46 to 344 nm. By employing the sol-gel spin coating method, V2O5 thin films were fabricated and analyzed using X-ray diffraction (XRD) spectroscopy and ultraviolet-visible (UV-Vis) spectrophotometry. The fabricated V2O5 thin films with thicknesses of 46-274 nm demonstrated an average film transparency of 83 %. XRD analysis further revealed that the V2O5 thin films reached their peak crystallinity at a thickness of 344 nm. Moreover, CV analysis revealed that the V2O5 device, with a thickness of 274 nm, exhibited a cathodic peak current of -1.63 mA, indicating its excellent ability to facilitate Li+ ion diffusion. Additionally, CA measurements displayed a high optical modulation of 37.78 %. Ultimately, this research contributes to the development of energy-efficient solutions for sustainable environmental practices.

Research on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3 %.

Due to the presence of heavy metals in wastewater treatment and manufacturing water purification processes in industries, investigations into potential methods have been widely conducted. In this paper, we report the incorporation of cation-exchange for removing heavy metals from aqueous solutions. We further investigated the properties cation-exchange using potential step method on PPy/PVS film at titanium electrode. Satisfactory results have been obtained with the small scale of experimental. It was found that the PPy/PVS film is able to remove heavy metal ions such as copper, nickel, and cobalt at very low concentrations, less than or equal to 10 mg/L, reducing them to approximately 1 mg/L. The Leica Q500MC Image Processing and Analysis system displayed the uneven deposition of heavy metal ions on the surface of PPy/PVS. Furthermore, Electron Spectroscopy for Chemical Analysis (ESCA) demonstrated the deposition of heavy metals on the PPy/PVS film.

The purpose of this study was to compare ankle stability and dynamic single-leg balance between jumping athletes and non-athletes, and to examine the correlation between ankle stability and dynamic single-leg balance. Thirty-eight jumping athletes and thirty-seven non-athletes participated in this study. The Cumberland Ankle Instability Tool (CAIT) was used to assess ankle stability. The Y-Balance Test (YBT) was used to examine single-leg balance in the anterior (AN), posteromedial (PM), and posterolateral (PL) directions. The results show that 42.11% of jumping athletes and 21.62% of non-athletes exhibited chronic ankle instability (CAI) in their examined leg. In addition, jumping athletes exhibited significantly worse ankle stability than non-athletes (p = 0.038). The two groups showed no significant difference in the YBT scores in all directions (p = 0.113 AN, 0.567 PM, 0.542 PL). Very low correlations were found between the CAIT and the YBT scores in all directions (r < 0.107). In conclusion, single-leg jumping athletes experienced a higher prevalence of CAI and significantly worse ankle stability than non-athletes. However, the results of the YBT did not correlate strongly with the CAIT scores, suggesting an inability to predict dynamic single-leg balance deficits based on perceived ankle stability alone in this population.

This study examined the effect of age and surface on patellofemoral joint (PFJ) stress magnitude and waveform during stair ascent and descent tasks. A total of 12 young and 12 older adults had knee biomechanics quantified while they ascended and descended stairs on normal, slick, and uneven surfaces. The peak of stance (0–100%) PFJ stress and associated components were submitted to a two-way repeated measures ANOVA, while the PFJ stress waveform was submitted to statistical parametric mapping two-way ANOVA. During stair ascent, older adults exhibited greater PFJ stress waveforms, from 55 to 59% and 74 to 84% of stance (p < 0.001) as well as greater PFJ stress–time integral across stance (p = 0.003), and later peak PFJ stress, than young adults (p = 0.002). When ascending on the uneven surface, participants exhibited smaller PFJ stress from 9 to 24% of stance compared to the normal surface, but greater PFJ stress from 75 to 88% and from 63 to 68% of stance (p < 0.001) as well as greater PFJ stress–time integrals compared to normal and slick surfaces (p < 0.032). During stair descent, older adults exhibited a smaller PFJ contact area range (p = 0.034) and peak knee flexion angle (p = 0.022) than young adults. When descending on the slick surface, participants exhibited smaller PFJ stress from 5 to 18% of stance, but greater stress, from 92 to 98% of stance (both: p < 0.001), compared to the normal surface. Negotiating slick and uneven stairs may produce knee biomechanics that increase PFJ stress, and the larger, later PFJ stress exhibited by older adults may further increase their risk of PFJ pain.

Musculoskeletal disorders are common among older adults, affecting mobility and quality of life. Effective rehabilitation is essential, but the implementation of programs faces challenges. Traditional methods often necessitate in-person assessments, which can be difficult for older adults with mobility limitations. Telerehabilitation offers a solution, bringing therapy closer to patients. However, the accurate remote monitoring of health and performance remains a challenge. This study addresses this gap by developing and validating the System for Tracking and Evaluating Performance (STEP). STEP is a hardware-software system that automates physical performance tests, eliminating the need for constant expert supervision. The system focuses on three standard tests: the Six-Minute Walking Test (6MWT), the Ten-Meter Walking Test (10MWT), and the 30-s Sit-to-Stand Test (30STS). Validation compared results from the STEP app with in-person assessments by physicians for patients undergoing rehabilitation after knee or hip arthroplasty. The study found strong positive correlations between the app’s results and the physicians’ assessments for all tests. These findings demonstrate the STEP system’s potential as a reliable tool for remote physical performance assessment. Further research is needed to explore its integration into clinical practice and cost-effectiveness in reducing the need for operator assistance in monitoring patients with physical limitations.

This paper explores the adaptation of pseudo-rigid-body models (PRBMs) for simulating large geometric nonlinear deflections in passive exoskeletons, expanding upon their traditional application in small compliant systems. Utilizing the AnyBody modeling system, this study employs force-dependent kinematics to reverse the conventional simulation process, enabling the calculation of forces from the deformation of PRBMs. A novel approach, termed “Constraint Force”, is introduced to facilitate this computation. The approach is thoroughly validated through comparative analysis with laboratory trials involving a beam under bending loads. To demonstrate the functionality, the final segment of this study conducts a biomechanical simulation incorporating motion capture data from a lifting test, employing a novel passive exoskeleton equipped with flexible spring elements. The approach is meticulously described to enable easy adaptation, with an example code for practical application. The findings present a user-friendly and visually appealing simulation solution capable of effectively modeling complex mechanical load cases. However, the validation process highlights significant systematic errors in the direction and amplitude of the calculated forces (20% and 35%, respectively, in the worst loading case) compared to the laboratory results. These discrepancies emphasize the inherent accuracy challenges of the “Constraint Force” approach, pointing to areas for ongoing research and enhancement of PRBM methods.

Background: Individuals may exhibit altered foot pronation during gait when fatigue sets in. Therefore, a more evidence-based understanding of these fatigue-induced changes may be helpful for future gait analysis and return-to-play tests since fatigue can provide new insights that might explain a person’s complaints. Methods: A total of 25 healthy individuals (12♂, 13♀; 24.3 ± 2.7 years; 174.9 ± 9.09 cm; 70 ± 14.2 kg; BMI: 22.7 ± 2.8) participated in this controlled non-randomized study of unilateral fatigue of the right foot’s stabilizing muscles with regard to the pronation of the foot, measured by navicular drop (ND) in static (statND; standing) and dynamic (dynND; walking) states. The left foot served as the control. Surface electromyography was used to verify fatigue. Results: While the statND did not change, the dynND increased significantly by 1.44 ± 2.1 mm (=22.3%) after the foot-stabilizing muscles experienced fatigue. No correlation was found between the statND and dynND. Conclusions: Muscular fatigue can affect foot pronation. The dynND appears to be more representative of the loads in everyday life, whereby most studies use the statND.

Using proposed system psychologists can use technology to make decisions which can provide ease for both patients and psychologists. Psychologists can check the progress of patients by analysing emotions reports of patient over time. Using historical data and emotion detection technology psychologists can make more accurate decisions. Using proposed system patient and psychologists don’t have to go to anywhere they only need a device and internet. Based on the characteristics of patient emotion psychologist only need report generated by system and prescribe medicine in emergency situation. Proposed system improves consultancy method by using machine learning emotion detection algorithm. Proposed system detects facial emotion of patient by using CNN with HAAR cascade classifier. We use FER 2013 dataset to train our model. We use VGG 19 architecture to train our model for optimization function to enhance the accuracy of model. We use RELU. We use DJANGO framework for integration with frontend. Result of our model on dataset 82.3% after find tuning the accuracy goes to 82.3% to 92%. We use recall and F1 method to check the performance of model. We trained model on the testing dataset which have gray scale images and 48*48pixel images to achieve his performance. To achieve our accuracy goal, we split dataset into trainee validation and testing dataset. We use CNN and achieve 93% accuracy in our system which help patient to get feedback only selected question and psychologist. Patients select psychologist to answer questions of psychologist system stores emotions of patient against every question to generate emotion report. Psychologist can analyze emotion report to provide better prescription to patient.

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks.

The mechanical properties such as tensile behavior of a 3D printed object can be influenced by various printing parameters, including printing temperature, orientation, infill density, and printing speed. This study focuses on investigating the effects of infill density and printing speed. Thirty dog-bone specimens were 3D printed using Fused Deposition Modelling (FDM) technique with Polylactic Acid (PLA) filament. Three different infill density settings (40%, 60%, and 80%) and three printing speed settings (30 mm/s, 60 mm/s, and 90 mm/s) were used. Tensile tests were performed on each specimen using a Universal Testing Machine. The experimental results indicate a clear trend of tensile behaviour with infill density. Increasing the infill density leads to improved tensile behaviour in the specimen. The highest Young’s Modulus and ultimate tensile strength (UTS) were achieved at 541.67 MPa and 24.3 MPa, respectively, with an infill density of 80%. On the other hand, printing speed showed an inverse relationship with tensile behaviour. As the printing speed increased, the Young’s Modulus and UTS decreased. However, the effect of printing speed on the mechanical properties was not as significant as that of infill density. When increasing the printing speed from 30 mm/s to 90 mm/s, the UTS only decreased by 5.61%. In contrast, increasing the infill density from 40% to 80% resulted in a UTS increase of 35.23%.

Adult-acquired flatfoot has been considered to arise from tibialis posterior tendon deficiency. Recent evidence shows that arch stability is mainly maintained by structures such as plantar fascia and spring ligament. The dysfunction of these ’passive’ stabilizers results in loss of arch integrity that causes forefoot pronation and reactive tendon overload, especially in the tibialis posterior tendon and peroneus longus tendon. The peroneus longus tendon (PLT) spans several midfoot joints and overloads with arch lengthening. The biomechanical stress/changes that occurs in this tendon are not well recognized. This study evaluates the biomechanical consequences that fusions have on peroneus longus tendon stresses in soft-tissue deficiencies associated with flatfoot deformity. A complete computational human foot model was used to simulate different scenarios related to the flatfoot deformity and associated common midfoot/hindfoot fusions, to quantify the biomechanical changes in the peroneus longus tendon. The results showed that the stress of the peroneus longus tendon is especially affected by the fusion of hindfoot joints and depends on the soft tissue types that fail, causal in generating the flatfoot. These results could be useful to surgeons when evaluating the causes of flatfoot and the secondary effects of surgical treatments on tissues such as the peroneus longus tendon.

Considering that people with Parkinson’s disease (PD) experience challenges in the control of both balance and eye movements, this study investigated the effects of saccadic eye movements on body sway in people with PD in two bases of support positions (side-by-side and tandem stances). Ten people with PD and 11 healthy individuals performed (a) fixation; (b) horizontal saccadic eye movements to the right and left; and (c) vertical saccadic eye movements up and down. The protocol for each postural task consisted of one block of six trials, making a total of 12 trials. Body sway and gaze parameters were measured during the trials. In both people with PD and healthy individuals, anterior–posterior body sway was significantly reduced in horizontal saccadic eye movements in contrast to fixation, regardless of the body position (side-by-side and tandem stances). Furthermore, vertical saccadic eye movements increased the area of sway in contrast to horizontal ones (and not to fixation) in people with PD. In addition, people with PD showed a higher number of fixations in all experimental conditions, without changes in the mean duration of fixations in both body positions. In conclusion, individuals with PD can improve body sway by coupling eye movements and postural sway when performing horizontal saccadic eye movements but not when performing vertical saccadic eye movements.

The purpose of this study was to compare average rate of oxygen consumption (VO2), slow component of oxygen consumption (VO2 drift), heart rate (HR) and rating of perceived exertion (RPE) while wearing compression pants vs. a control garment during long-distance running. Methods: Nine injury-free and recreationally active participants (32 ± 11 years) were recruited for this study. Participants ran in full-leg compression pants (COMP) and a loose-fitting control garment (CON). Participants ran in each condition for 40 min at a preferred submaximal speed. The rate of oxygen consumption (VO2) was measured continuously via a metabolic cart throughout each condition. Both HR and RPE were recorded every 5 min during each condition. Oxygen consumption was averaged across the entirety of the steady state during the 40 min conditions for analysis. Additionally, the average from the first five minutes of the steady state was subtracted from the average of the last five minutes to assess VO2. A paired t-test was used to assess for differences for both variables. Both HR and RPE were each compared between conditions using 2 (garment) × 8 (time) repeated measure ANOVAs (α = 0.05). Results: There were no differences between VO2 or VO2 drift while running with full-leg compression pants vs. the control garment (p > 0.05). Neither RPE nor HR were influenced by the garments (p > 0.05) or time (p > 0.05) during each condition. Conclusion: Wearing compression pants did not result in reduced VO2, VO2 drift, HR or RPE during a long-distance run. Although measured performance variables were not aided using compression pants, there were no negative effects to the use of compression pants.

Purpose: Short-track speed skating results in high-energy crashes with an elevated risk of head injury. The goal of this study was to evaluate the resulting kinematics of an anti-rotation helmet technology for speed skating. Methods: Two traditional rigid foam speed-skating helmets (BT and ST) were compared with one anti-rotation speed skating helmet (MIPS). Each helmet was impacted with a pneumatic device across three locations. The resulting linear or rotational accelerations (PLA or PRA) and rotational velocities (PRV) were measured with accelerometers placed on a Hybrid III head form. Additionally, the head impact criterion (HIC) was calculated from accelerations and the brain injury criterion (BrIC) was obtained from rotational velocities. Results: MIPS showed significantly higher values of accelerations (PLA = 111.24 ± 9.21 g and PRA = 8759.11 ± 2601.81 rad/s2) compared with the other helmets at all three impact locations (p < 0.01, ES = 3.00 to 4.11). However, velocities were lowest, but not significantly different, for the MIPS helmet (25.77 ± 1.43 rad/s). Furthermore, all resulting kinematics except peak linear accelerations were significantly different across impact locations. Conclusion: Helmet designs specific to the collision characteristics of speed skating may still be lacking, but would decrease the risk of sport-related concussions.

In order to predict the static deformations and free vibration behaviour of thin and thick cross-ply laminated plates, we integrate Carrera’s unified formulation with a radial basis function collocation technique on a pseudospectral framework, using Reissner’s mixed variational theorem. Numerical examples illustrate the precision and effectiveness of this collocation technique for static and vibration problems.

The present study develops a new finite element (FE) formulation for the lateral torsional buckling (LTB) analyses of steel beams exteriorly bonded with orthotropic fiber-reinforced polymer (FRP) layers. The formulation considers shear deformations, partial material interaction, local and global warping deformations, and orthotropic FRP properties. The buckling responses of multispan FRP-bonded steel beams predicted by the present solutions are excellently validated against experiment and numerical solutions. As observed, the FRP strengthening is highly effective for the LTB resistance of steel beams. However, the LTB responses are strongly dependent on the orthotropic properties and strengthening lengths of FRP layers. The effects of shear deformations, span ratios, and loading conditions on the LTB responses are also quantified in the present study.

Abstract Microchannel heat exchangers have become the preferred choice in contemporary technologies like electronics, refrigeration, and thermal management systems. Their popularity stems from their compact design and exceptional efficiency, which outperform traditional heat exchangers (HE). Despite ongoing efforts, the optimal microchannels for enhancing heat management, minimizing pressure drop, and boosting overall performance have yet to be identified. This study seeks to deepen our understanding of heat transmission and fluid dynamics within a cross-flow microchannel heat exchanger (CFMCHE). Utilizing numerical modeling, it examines how various physical aspects—such as channel geometry, spacing between channels, the number of channels, and the velocity at the inlet—affect key performance indicators like pressure drop, effectiveness, Nusselt number, and overall efficiency. To enhance the design, we analyze six unique shapes of crossflow microchannel heat exchangers: circular, hexagonal, trapezoidal, square, triangular, and rectangular. We employ the Galerkin-developed weighted residual finite element method to numerically address the governing three-dimensional conjugate partial differential coupled equations. The numerical results for each shape are presented, focusing on the surface temperature, pressure drop, and temperature contours. Additionally, calculations include the efficacy, the heat transfer rate in relation to pumping power, and the overall performance index. The findings reveal that while circular shapes achieve the highest heat transfer rates, they underperform compared to square-shaped CFMCHEs. This underperformance is largely due to the increased pressure drop in circular channels, which also exhibit a 1.03% greater reduction in effectiveness rate than their square-shaped counterparts. Consequently, square-shaped channels, boasting a performance index growth rate of 53.57%, emerge as the most effective design among the six shapes evaluated. Additionally, for the square-shaped CFMCHE, we include residual error plots and present a multiple-variable linear regression equation that boasts a correlation coefficient of 0.8026.

Abstract Computational modeling of the melt pool dynamics in laser-based powder bed fusion metal additive manufacturing (PBF-LB/M) promises to shed light on fundamental mechanisms of defect generation. These processes are accompanied by rapid evaporation so that the evaporation-induced recoil pressure and cooling arise as major driving forces for fluid dynamics and temperature evolution. The magnitude of these interface fluxes depends exponentially on the melt pool surface temperature, which, therefore, has to be predicted with high accuracy. The present work utilizes a diffuse interface finite element model based on a continuum surface flux (CSF) description of interface fluxes to study dimensionally reduced thermal two-phase problems representative for PBF-LB/M in a finite element framework. It is demonstrated that the extreme temperature gradients combined with the high ratios of material properties between metal and ambient gas lead to significant errors in the interface temperatures and fluxes when classical CSF approaches, along with typical interface thicknesses and discretizations, are applied. It is expected that this finding is also relevant for other types of diffuse interface PBF-LB/M melt pool models. A novel parameter-scaled CSF approach is proposed, which is constructed to yield a smoother temperature field in the diffuse interface region, significantly increasing the solution accuracy. The interface thickness required to predict the temperature field with a given level of accuracy is less restrictive by at least one order of magnitude for the proposed parameter-scaled approach compared to classical CSF, drastically reducing computational costs. Finally, we showcase the general applicability of the parameter-scaled CSF to a 3D simulation of stationary laser melting of PBF-LB/M considering the fully coupled thermo-hydrodynamic multi-phase problem, including phase change.