Numerical Computations Research Articles

Optimal replacement policy for repairable products with multi-attempt minimal repair under the free-repair warranty

ABSTRACT This paper investigates the effects of unsuccessful repairs on the optimal periodic replacement policy for a repairable product with multiple attempts of minimal repair under the free-repair warranty. The motivation for this study is based on the novel notion of ‘multi-attempt minimal repair’, first introduced by Cha and Finkelstein (2024a, 2024b), in which they highlighted various practical reasons and examples to illustrate that minimal repair attempts may sometimes fail and need to be repeated until success is achieved. Their repair model and useful properties served as the motivation for this study. For both the warranted and non-warranted products, cost models are developed from the user’s perspective, and the corresponding optimal replacement policies are derived to minimize the long-run expected cost rates. By comparing these optimal policies, we show that the optimal planned time for preventive replacement should be adjusted toward the end of the warranty period, and the effects of unsuccessful minimal repair produce different degrees of impact, respectively, on the warranted and non-warranted product. Finally, detailed numerical examples and computation results illustrate our findings, and some insightful observations about practical and managerial implications are concluded.

Blockchain-based energy consumption approaches in IoT.

Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT.

Influence of dynamic complexity on serial robots with joint clearance based on a nonlinearity measure

Conditional monitoring and diagnosis of serial robots depend not only on the method used but also on the dynamic complexity of the robotic system itself. Previously, researchers have focused mainly on the analysis of dynamic behaviors with different parameters of a mechanical system with clearance. Although the indicators such as Root Mean Square (RMS), spectrum kurtosis (SK), Entropy have the potentiality to express the specific trend by different parameters, it can be noticed that they mainly depend on the values of the dynamic responses and neglect the information of the nonlinear characteristics in the responses of mechanical systems. In this paper, a nonlinearity measure-based method is proposed to analyze the impact of dynamic complexity on serial robots with joint clearance. The kinetic equations of a serial robot with joint clearance are established by considering the contact forces in the joint. The dynamic model of the robotic system is validated by experiments. A quantization method for dynamic complexity analysis is established based on the nonlinearity measure, and the influences of parameters on the dynamic complexity are analyzed via numerical computations for the validated dynamical models of the robotic system. A relationship is developed among the clearance radius, friction coefficient, and dynamic complexity. This shows that a larger clearance radius will lead to a more complex system and that friction coefficients within a certain range can reduce the dynamic complexity of the system. These results can guide methodological choices for condition monitoring and diagnosis of serial robotic systems.

Numerical simulation of three-dimensional seepage field in a tailing pond under multiple operating conditions.

Establishing strong seepage stability for tailings dams is crucial for ensuring their safety and mitigating the risk of failure. This study developed a three-dimensional seepage numerical model using finite element numerical computation for four different elevation conditions (5070m, 5081m, 5159m, and 5213m) encompassing the pond area and dam body. Seepage calculations were conducted under normal and flooding conditions, and the tailings pond's seepage stability was assessed for various stacking scenarios. The spatial distribution pattern of the infiltration surface and the hydraulic stability of the tailings pond were evaluated, which provides insights into the three-dimensional infiltration stability. Examining the seepage stability under different accumulation conditions revealed distinct spatial distribution patterns of the infiltration surface and hydraulic ratio drop values. The findings indicated that the maximum permeability slope at 5070m elevation ranged from 0.66 to 0.75 at normal operation water level and maximum flood level. Most hydraulic ratio drop values at 5081m were below 0.2, while the anti-seepage lining sections at 5159m and 5213m showed larger values, and maintained the overall hydraulic ratio drop within safe limits. Consequently, the dam body's permeability was deemed secure, and no infiltration damage was anticipated with the proposed design of seepage control and drainage facilities. Moreover, sensitivity analysis of the tailing sand's permeability coefficient demonstrated that variations between 0.2 and 5 times the given parameter align with the seepage control requirements for the tailings dam. Additionally, local geomembrane breakage was found to have minimal impact on the tailing pond's seepage field and the dam body's permeability stability, which provides a scientific foundation for analyzing and designing the seismic static-dynamic stability of the tailings pond.

Numerical Computation and Statistical Interpretations of Heat Transfer of Tween-20/ethyl Acetate Nanofluid Flow with Melting Rheological Quality and Activation Energy

Tween-20 plays a significant role in the biological, food, and pharmaceutical industries. Additionally, it plays a vital role in improving the quality of reverse mechanisms of multi-drug resistance. This paper uses artificial neural computing to examine Tween-20 nanoparticles’ behaviors in a base fluid called ethyl acetate to enhance the applications in nanomedicine, drug delivery and biotechnology. The study focuses on the nonlinear model of a viscous fluid at the stagnation point, where mixed convection processes and activation energy are present. The study incorporates slip velocity and melting boundary conditions to examine heat and mass transfer, taking into account thermal and solutal stratification. Various fields of research and technology, specially in industrial engineering that include hydrodynamics, panto-graph systems, and biomedical mathematics, extensively utilize artificial computing. The datasets are based on velocity, temperature, and concentration outlines. The fourth-order Runge-Kutta method is used to generate the datasets. To validate the LMM-ABNNs, we have compared them with a numerical solution, showing a high level of agreement. We utilize the error histogram and mean square error results to validate the performance, scrutinize the training and testing methods, and explore the validity of the approximate answer. Furthermore, the quality characteristics such as skin friction, rate of heat, and mass movement (Nusselt and Sherwood numbers) are statistically analyzed to forecast the model’s durability. This article is the best example of investigating different fluid parameters with the latest artificial neural computing.

Impact of Thermal Energy on Water and Ethylene Glycol (50:50)-Based Rotating Hybrid Nanofluid Past A Riga Surface with Radiation, Homogeneous and Heterogeneous Reactions

Hybrid nanofluid is crucial in improving the efficiency of heat transmission in thermal engineering applications, particularly the cooling of electrical equipment, heat exchangers, fuel cells, printing machines, etc. This study sought to investigate the augmentation of heat and mass transmission by the use of a rotating hybrid nanofluid consisting of a mixture of gold and silver nanoparticles suspended in water and ethylene glycol (50:50) past a heated Riga surface with velocity slip and suction. The energy equation is constructed using nonlinear radiation and heat consumption. The impact of both homogeneous and heterogeneous processes on the hybrid nanofluid is also explored. The governing mathematical model begins with partial differential equations that are converted into ordinary differential equations using a suitable conversion technique. The results of numerical computations are recorded as graphs and tables using the bvp4c scheme in MATLAB. It is noticed that both directional velocities undergo significant negative changes as a result of enhanced values of the rotational parameter. The higher levels of the radiation parameter resulted in significant enhancements in the thermal profile. The falling behavior of the nanoparticle concentration profile is recorded against a greater quantity of both chemical reaction parameters. Based on our analysis, when the Biot number changes from 0.4 to 0.8, the most significant heat transmission gradient occurs.

Numerical PMM test in finite depth

This study conducted a numerical planar motion mechanism (PMM) test for a containership, considering the effects of finite depth. An OpenFOAM-based computational fluid dynamics (CFD) approach was employed to conduct the simulations. Before conducting a series of numerical computations, uncertainty analysis for the grid size and time step was performed to ensure the reliability of the computational results. Two PMM tests, static drift and pure yaw, were conducted, and the results were validated against experimental data. The comparison demonstrated good agreement between forces, moments, and hydrodynamic coefficients when compared with both experimental and other computational results. Furthermore, a comparison between a first- and second-order combination model and a first- and third-order combination model revealed that the latter showed better alignment with experimental data in deep water, while the former performed better in shallow water, emphasizing the role of crossflow. This study contributes to understanding the differences in maneuvering performance between deep and shallow water conditions.

Current-vortex-sheet model of the magnetic Rayleigh-Taylor instability

This study investigates the Rayleigh-Taylor instability in the magnetic field applied parallel to the interface. The motion of the interface is described using a current-vortex-sheet model. The growth rate of the interface is obtained from a linear stability analysis of the model. The interface of a single mode k=1 is linearly stable for RA≤1, where RA denotes the Alfvén number. Further, we conduct numerical computations for the evolution of the interface from the model for both regimes of RA≤1 and RA>1. For RA≤1, the interface oscillates vertically but does not intrude into the opposite phase. The amplitude of the interface and the oscillation period decrease with decrease in RA. For 1<RA≲1.5, the interface undergoes some growth at early times and then decreases. This oscillation is repeated, and no roll-ups are observed at the interface. Therefore, the nonlinear growth of the Rayleigh-Taylor instability is stabilized by a sufficiently strong magnetic field. For RA≳3 the interface is unstable and the pattern of the interface evolution differs from the density ratio. In addition, the magnetic field is greatly amplified as RA increases. Published by the American Physical Society 2024

SH‐waves in a complex fluid composite structure with spring membrane imperfect interfaces

AbstractComplex fluids are of nobleness due to their unique mechanical responses to applied stress, which are different from simple fluids. Therefore, the current study encapsulated the behavior of SH‐wave in a complex fluid layer overlying a fiber‐reinforced half‐space with six different cases considered at the common interface. These cases involve various interface conditions, including perfect bonding, a spring layer, an infinitely thin membrane layer, loose bonding, a combined spring‐membrane layer, and a membrane layer with loose bonding. Precisely, the effect of loose bonding, membrane strength, and spring stiffness at the common interface on the propagation of SH (Shear Horizontal) wave is studied in detail. The problem is mathematically formulated for all the aforementioned six models followed by their solution with the aid of appropriate boundary conditions. Numerical computations are performed with a view to study the effect of stiffness of spring and membrane, as well as loose bonding at the common interface of the media. It is demonstrated that membrane strength impedes wave propagation, while increased spring stiffness supports it. Notably, the combined spring‐membrane interface has a more pronounced effect than a spring layer alone. The findings of this study may be utilized in the development of membrane technology, including the study of fluid membranes and it has significant importance in seismic engineering and vibration isolation systems.

Investigation and Optimization of Wave Suppression Baffles in Automobile Integrated Water Tanks

The risk of liquid agitation in pump-driven tanks within integrated tanks has significantly escalated due to the growing demands for tank integration in new-energy vehicles. In order to solve the problem of liquid sloshing in integrated tanks, this paper presents the design of a baffle structure aimed at reducing waves in integrated water tanks. The numerical simulation method of combining the level-set function with the volume of fluid (CLSVOF) has been employed, significantly enhancing the accuracy of numerical calculations related to a two-phase flow field inside an integrated tank. A comparison was made by analyzing different factors, notably baffle length (L), baffle depth (H), and baffle angle (θ), to investigate their influences in suppressing liquid agitation within the integrated water tank. Numerical computations were conducted utilizing design points acquired by the Latin hypercube sampling technique. The Kriging approximation modelling method was employed to hold down computing time. The Pareto solution was obtained by means of the non-dominated sorting genetic algorithm II, while the optimal solution set was evaluated and ranked using the multi-criteria decision-making algorithm (MCDM). The results show that increasing the baffle depth within a certain range can effectively suppress the wave height in the tank. When the baffle depth is increased to a certain value, the effect on wave-height suppression in the water tank is limited. When the baffle length and angle of the baffle exceed a certain value, it will also have the effect of suppressing the wave height in the tank. After comparing various factors of the baffle, it was ultimately found that the wave suppression effect is maximal when the length of the baffle is 13 millimeters, the depth of the baffle is 49 millimeters, and the angle of the baffle is -20 degrees. The main contribution of this study is the proposed wave-suppressing baffle structure, which provides new insights for the future structural design of integrated water tanks.

Bioconvection flow of Carreau nanomaterial invoking Soret and Dufour impacts

Background and objectiveThermal transport enhancement through nanomaterial flow has gained much attention of the investigators recently. Such attention is for applications in engineering, petroleum industries, geothermal engineering and pharmaceutical developments such as X-ray imaging, welding process, semiconductor material production, thermal storage system, electric cooling devices, drug delivery, magma solidification etc. In view of such interest here we analyze the bioconvective stretched flow of Carreau nanoliquid. Thermal, microorganism and mass convective constraints are considered. Gyrotactic microorganisms within presence of chemical reaction is considered. Variable thermal conductivity is considered. Dufour and Soret features are under consideration. Thermophoresis diffusion and random movement features are considered. Energy relation comprises heat generation and radiation. MethodologyNonlinear dimensionless expressions are developed by employing suitable transformations. Numerical computations are obtained by implementation of Newton built in-shooting technique. ResultsPerformance of liquid flow, concentration, microorganism field, and thermal field is studied. Numerical values of quantities of engineering performance via influential parameters are explored. Larger Weissenberg number yield velocity increase. Reduction in velocity through bioconvection Rayleigh number is witnessed. Concentration and thermal field through solutal thermal Biot numbers have similar effect. An increment in thermal distribution and Nusselt number for Dufour number is detected. Increasing values of Soret number and chemical reaction correspond to decay in concentration. An augmentation in thermal transfer rate and temperature through radiation is noticed. An increment in density number against Peclet and bioconvection Lewis numbers is noticed.

Special Volume on Numerical Analysis and Scientific Computation with Applications

Special Volume on Numerical Analysis and Scientific Computation with Applications

Numerical Methods and Computation in Financial Mathematics: Solving PDEs, Monte Carlo Simulations, and Machine Learning Applications

Abstract. This paper delves into three fundamental numerical methods and computational techniques in financial mathematics: Finite Difference Methods (FDM), Monte Carlo Simulations (MCS), and Machine Learning (ML) applications. Finite Difference Methods are widely utilized for solving partial differential equations (PDEs) in option pricing, with various schemes offering different stability and convergence properties. Monte Carlo Simulations provide a powerful approach for pricing complex derivatives and risk management, addressing the challenges of high-dimensionality and computational complexity. Machine Learning has revolutionized predictive modeling in finance, enabling sophisticated analysis of large datasets to uncover hidden patterns and enhance trading strategies. Through a detailed examination of these methods, including specific examples and data, this paper highlights their theoretical foundations, practical implementations, and the advancements they bring to computational finance. By bridging theoretical approaches with practical applications, we aim to offer insights into the future directions and challenges in financial mathematics.

2S-SGCN: A two-stage stratified graph convolutional network model for facial landmark detection on 3D data

Facial Landmark Detection (FLD) algorithms play a crucial role in numerous computer vision applications, particularly in tasks such as face recognition, head pose estimation, and facial expression analysis. While FLD on images has long been the focus, the emergence of 3D data has led to a surge of interest in FLD on it due to its potential applications in various fields, including medical research. However, automating FLD in this context presents significant challenges, such as selecting suitable network architectures, refining outputs for precise landmark localization and optimizing computational efficiency. In response, this paper presents a novel approach, the 2-Stage Stratified Graph Convolutional Network (2S-SGCN), which addresses these challenges comprehensively. The first stage aims to detect landmark regions using heatmap regression, which leverages both local and long-range dependencies through a stratified approach. In the second stage, 3D landmarks are precisely determined using a new post-processing technique, namely MSE-over-mesh. 2S-SGCN ensures both efficiency and suitability for resource-constrained devices. Experimental results on 3D scans from the public Facescape and Headspace datasets, as well as on point clouds derived from FLAME meshes collected in the DAD-3DHeads dataset, demonstrate that the proposed method achieves state-of-the-art performance across various conditions. Source code is accessible at https://github.com/gfacchi-dev/CVIU-2S-SGCN.

Isolator shape transition impact on hypersonic internal waverider intake flow distortion

This paper examines the impact of scramjet isolator shape transition on hypersonic internal waverider (IWR) intake. The IWR intake is designed using the osculating axisymmetric flows and streamline-tracing methods. The new Internal Conical Flow “M” basic flowfield is utilized to provide the flow information for the design method. The intake is equipped with three isolators: one with a constant cross section and two with variable cross sections with circular and rectangular exits. The entrance shape and area of the three isolators are fixed to the intake throat shape and area. The exit area of the three isolators is maintained as the entrance one. Numerical computations of three-dimensional configurations reveal that the isolators with variable cross section shapes demonstrate a higher uniformity index than those with constant cross section shape. Thus, the isolator shape transition has decreased the flow distortion of the hypersonic IWR intake system. The three isolators exhibit varied wall pressure distribution depending on the isolator cross section shape, and the total pressure recovery ratios at the three isolators' exit planes are similar. The wall pressure distributions and key performance parameters at the intake throat section, including total pressure recovery, compression ratio, and Mach number, remained consistent across the first part of the intakes. Therefore, changing the cross section shape of the isolator while keeping the area constant could enhance the flow uniformity of compressed air without negatively impacting the intake system's performance. This allows a separate shape selection of the IWR intake throat and the scramjet combustor entrance to fulfill their special requirements.

Algorithms and Data Structures for Numerical Computations with Automatic Precision Estimation

We introduce data structures and algorithms to count numerical inaccuracies arising from usage of floating numbers described in IEEE 754. Here we describe how to estimate precision for some collection of functions most commonly used for array manipulations and training of neural networks. For highly optimized functions like matrix multiplication, we provide a fast estimation of precision and some hint how the estimation can be strengthened.

Numerical computations of MHD mixed convection flow of Bingham fluid in a porous square chamber with a wavy cylinder

This work examines the behavior of Bingham fluids in porous media under magnetohydrodynamic (MHD) conditions, addressing a significant issue in fluid dynamics and heat transport. The authors study mixed convection in a porous square chamber with twisted cylinders using COMSOL Multiphysics and finite element techniques. Compared to other research, this one offers a more complicated and realistic model since it integrates Bingham fluid flow and MHD effects in a porous chamber with wavy cylinders. Plotting of streamlines, isotherms, and Nusselt numbers is done for several parameters: Numbers Reynolds (1, 5, 10, 50), Hartmann (0, 25, 50, 100), Bingham (0, 1, 5, 10), Darcy (10−4, 10−3, 10−2, 10−1), and Grashof (102,103,104,105) are among those assigned numbers. According to findings, larger Bn values promote thermal stratification and decrease flow mobility, which results in dominating conductive heat transmission. The nusselt number falls with Ha and Bn but rises with Re, Da, and Gr. The study aims to provide useful insights into several industrial disciplines, such as nuclear power plant architecture and petroleum engineering, by enhancing heat transfer in non-Newtonian fluids under magnetic fields. It also boosts the efficiency of chemical reactors and filters. Based on our findings, for lower Re = 1, the lowest value of Numean is 2.602, while at Re = 50, its values rise to 6.425.

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Charge Transport Characteristics in Doped Organic Semiconductors Using Hall Effect

Numerical computations through the finite element method (FEM) are used to determine the impact of doping on carrier concentration and recombination between charges in time for organic semiconductor diodes having low mobility. The Hall effect is used to determine the effects of doping on the performance and reliability of organic semiconductor devices by accurately modeling these processes. In this work, the number density of charge carriers and Hall voltages are computed for n-type doped semiconductors with two different recombination processes, such as non-Langevin and Langevin-type. The findings reveal that in the Langevin system with β′=1, the number density of charge carriers is almost five and four times lower compared with the non-Langevin system with β′=0.01 for increasing dopant concentrations of Npd = 1 and 3, respectively. The Langevin system also had lower Hall voltages than the steady-state and non-Langevin systems for different magnetic fields with dopants, and the non-Langevin system had nearly identical Hall voltages as the steady-state case. The outcome of the current work provides insights into charge transportation mechanisms in low-mobility doped organic semiconductors with Hall effect measurements to improve device efficiency.

Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease

Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO.