Unknown Boundary Research Articles

Identification of Boundary Conditions in a Spherical Heat Conduction Transmission Problem

Although numerous analytical and numerical methods have been developed for inverse heat conduction problems in single-layer materials, few methods address such problems in composite materials. The following paper studies inverse interface problems with unknown boundary conditions by using interior point observations for heat equations with spherical symmetry. The zero degeneracy at the left interval 0<r<R1 leads to solution difficulties in the one-dimensional interface problem. So, we first investigate the well-posedness of the direct (forward) problem in special weighted Sobolev spaces. Then, we formulate three groups of unknown boundary conditions and inverse problems upon internal point measurements for the heat equation with spherical symmetry. Second-order finite difference scheme approaches for direct and inverse problems are developed. Computational test examples illustrate the theoretical statements proposed.

Transport of intensity phase retrieval in the presence of intensity variations and unknown boundary conditions

The so-called Transport of Intensity Equation (TIE) phase retrieval technique is widely applied in light, X-ray and electron optics to reconstruct, e.g., refractive indices, electric and magnetic fields in solids. Here, we present a largely improved TIE reconstruction algorithm, which properly considers intensity variations as well as unknown boundary conditions in a finite difference implementation of the Transport of Intensity partial differential equation. That largely removes reconstruction artifacts encountered in state-of-the-art Poisson solvers of the TIE, and hence significantly increases the applicability of the technique.

Reversible esterification process in Casson nanofluids: A numerical analysis of non-newtonian hydromagnetic flow

A computational mechanism has been formulated to scientifically analyze the phenomenon of combined convection in the occurrence of magnetohydrodynamic flow behavior of Casson nanofluid. This study emphasizes the trajectory of the nanofluid along a stretching sheet with nonlinear permeability, while considering additional physical effects such as reversible chemical reaction, thermal radiation, energy source/sink, suction and viscous dissipation. In this study, we have additionally integrated the nanofluid paradigm proposed by Buongiorno, which encompasses the repercussion of thermophoresis along with Brownian motion. The utilization of appropriate similarity renovations serves the purpose of recasting the dominant multivariate differential equations to a collection of nonlinear differential equations in single variable. The Runge–Kutta shooting mechanism is implemented for the intention of numerically determining the unknown boundary conditions. The solution to the dominant equations was obtained by employing the fourth-order Runge–Kutta technique and to ensure the dependability of the outcomes, the bvp4c method was employed. The graphical and tabulated observations are presented herein to facilitate a comprehensive analysis of the underlying physical characteristics inherent in the problem at hand. The utilization of the Casson parameter has been found to be advantageous in the reduction of shear stress rate, along with the enhancement of mass and energy transfer rates. Additionally, the application of suction has been observed to be beneficial in the enhancement of Sherwood number and energy transfer rate. Within the scope of the context of the esterification process, the purpose of this proposal is to evaluate the impact that numerous arithmetical values exert on the temperature field, the velocity profile and the volumetric concentration. An in-depth graphical analysis that takes into account the relevant physical consequences is used to evaluate the most important factors that relate to the physical quantities that describe the contours of temperature, velocity and concentration. In addition to being accompanied by their respective explanations, the tabular portrayal of the local Sherwood number, shear stress rate along with local Nusselt number all feature in this paper. There is a clear distinction that can be made between irreversible and reversible flows in the evaluation of the local Sherwood number, rate of shear stress and local Nusselt number. This differentiation is brought about by taking into analysis thermophoresis parameter, Brownian motion parameter and suction parameter. The results that were produced from the theoretical simulations have significant repercussions for a variety of fields that are related to energy engineering. The findings derived from the analysis indicate a negative correlation between the Brownian motion parameter and both irreversible and reversible flow in terms of rate of energy transfer and shear stress rate. It is imperative to highlight that reversible flow holds greater significance in comparison to irreversible flow.

Evolution of linear internal waves over large bottom topography in three-layer stratified fluids

Evolutions of internal waves of different modes, particularly mode 1 and mode 2, passing over variable bathymetry are investigated based on a new numerical scheme. The problem is idealized as interfacial waves propagating on two interfaces of a three-layer density stratified fluid system with large-amplitude bottom topography. The Dirichlet-to-Neumann operators are introduced to reduce the spatial dimension by one and to adapt the three-layer system and significant topographic effects. However, for simplicity, nonlinear interactions between interfaces are neglected. Numerical techniques such as the Galerkin approximation, proven effective in previous works, are applied to save computational costs. Shoaling of linear waves on an uneven bottom is first studied to validate the proposed formulation and the corresponding numerical scheme. Then, for two-dimensional numerical experiments, mode transition phenomena excited by locally confined bottom obstacles and quickly varying topographies, including the Bragg resonance, mode-2 excitation, wave homogenization, etc., are investigated. In three-dimensional simulations, internal wave refraction by a Luneberg lens is considered, and good agreement is found in comparison with the ray theory. Finally, in the limiting case, when the top layer can be negligible (for example, a gas layer of extremely small density), the problem is reduced to a two-and-a-half-layer fluid system, where an interface and a surface are unknown free boundaries. In this situation, the surface signature of an internal wave is simulated and verified by introducing the realistic bathymetry of the Strait of Gibraltar and qualitatively compared with the satellite image.

An Inverse recursive algorithm to retrieve the shape of the inaccessible dielectric objects

A regularized electromagnetic iterative inverse algorithm is formulated and implemented to reconstruct the shape of 2D dielectric objects using the far-field pattern of the scattered field data. To achieve this, an integral operator that maps the unknown boundary of the object onto the far-field pattern of the scattered field is defined and solved for the unknown boundary. The addressed inverse problem has an ill-posed nature and inherits nonlinearity. To overcome these, the proposed solution is linearized via Newton and regularized by Tikhonov in the sense of least squares. Besides, the dominance of the shadow region in the inverse-imaging process is exceeded by considering the superposition of multi-incoming plane waves, leading to less computational cost and a very fast inversion process. Comprehensive numerical analyses are carried out to ascertain the algorithm's feasibility, revealing that it is very efficient and promising.

Disturbance observer based adaptive predefined‐time sliding mode control for robot manipulators with uncertainties and disturbances

AbstractThis article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

Simplification and improvement of cable tension identification based on effective vibration length estimated from multiple sensors located near one support

Effective vibration length (EVL) has been proposed and found able to accurately identify tension force in short cables with unknown boundaries. However, EVL estimation accuracy is degraded when measurements are distributed only near the cable-deck end. This study investigates the EVL and modal characteristics of cables under different factors using a finite element model. The corresponding conclusions are then utilized to simplify and improve the original method. Method 1 is proposed by simplifying the original method using a fixed EVL for different modes. Method 2 is developed by enhancing Method 1 with additional tests where masses are attached on the cable near the sensors. In numerical experiments with 1 % random noise, the proposed methods achieve 70 %–80 % of results with errors less than 5 % while 1 % of results from the original method achieve this level of accuracy. Full-scale cable experiments have further validated the effectiveness of the proposed methods.

Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode

Simultaneous identification of groundwater contamination source information, model parameters, and boundary conditions under an unknown boundary mode

Vibration control of 2-D variable-length flexible riser systems with unknown boundary disturbance

The riser system is subject to vibration due to marine environmental loads, which can affect the efficiency of the system or even destroy it. This paper proposes a solution for the vibration control problem of a 2-D variable-length flexible riser based on PDE model. Firstly, a disturbance observer is developed to estimate and offset the unknown disturbance in the feedback loop. Based on the above method, an effective boundary controller is designed to realize the elastic suppression goal of the 2-D variable-length flexible riser. Secondly, when considering the asymmetric output constraints, a time adjustment function is introduced to develop a new controller based on the barrier Lyapunov function, which realizes the control objective of suppressing the boundary vibration of the flexible riser within specified constraints in a finite time. Finally, the effectiveness of the controller is verified by simulation examples.

Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data

Masonry arches and vaults are common historic structural elements that frequently experience asymmetric loading due to seismic action or abutment settlements. Over the past few decades, numerous studies have sought to enhance our understanding of the structural behavior of these elements for the purpose of preventive conservation. The assessment of the structural performance of existing constructions typically relies on effective numerical models guided by a set of unknown input parameters, including geometry, mechanical characteristics, physical properties, and boundary conditions. These parameters can be estimated through deterministic optimization functions aimed at minimizing the discrepancy between the output of a numerical model and the measured dynamic and/or static structural response. However, deterministic approaches overlook uncertainties associated with both input parameters and measurements. In this context, the Bayesian approach proves valuable for estimating unknown numerical model parameters and their associated uncertainties (posterior distributions). This involves updating prior knowledge of model parameters (prior distributions) based on current measurements and explicitly considering all sources of uncertainties affecting observed quantities through likelihood functions. However, two significant challenges arise: the likelihood function may be unknown or too complex to evaluate, and the computational costs for approximating the posterior distribution can be prohibitive. This study addresses these challenges by employing Approximate Bayesian Computation (ABC) to handle the intractable likelihood function. Additionally, the computational burden is mitigated through the use of accurate surrogate models such as Polynomial Chaos Expansions (PCE) and Artificial Neural Networks (ANN). The research focuses on setting up numerical models for simple structural systems (tie-rods) and inferring unknown input parameters, such as mechanical properties and boundary conditions, through Bayesian updating based on observed structural responses (modal data, strains, displacements). The main novelties of this research regard, on the one hand, the proposal of a methodology for obtaining a reliable estimate of the axial force in ancient tie-rods accounting for different sources of uncertainty and, on the other hand, the application of ABC to obtain the structural identification inverse problem solution.

On the solution of evolution p-Laplace equation with memory term and unknown boundary Dirichlet condition

On the solution of evolution p-Laplace equation with memory term and unknown boundary Dirichlet condition

A Robust Numerical Simulation of a Fractional Black–Scholes Equation for Pricing American Options

After the discovery of fractal structures of financial markets, fractional partial differential equations (fPDEs) became very popular in studying dynamics of financial markets. Available research results involves two key modelling aspects; firstly, derivation of tractable asset pricing models, those that closely reflects the actual dynamics of financial markets. Secondly, the development of robust numerical solution methods. Often times, most the effective models are of a nonlinear nature, and as such, reliable analytical solution methods are seldomly available. On the other hand, the accurate value of American options strongly lies on the unknown free boundaries associated with these types of derivative contracts. The free boundaries emanates from the flexibility of the early exercise features with American options. To the best of our knowledge, the approach of pricing American options under the fractional calculus framework has not been extensively explored in literature, and an obvious wider research gap still exist on the design of robust solution methods for pricing American option problems formulated under the fractional calculus framework. Therefore, this paper serve to propose a robust numerical scheme for solving time-fractional Black–Scholes PDEs for pricing American put option problems. The proposed scheme is based on the front-fixing algorithm, under which the early exercise boundaries are transformed into fixed boundaries, allowing for a simultaneous computation of optimal exercise boundaries and their corresponding fair premiums. Results herein indicate that, the proposed numerical scheme is consistent, stable, convergent with order O(h2,k)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(h^2,k)$$\\end{document}, and also does guarantee positivity of solutions under all possible market conditions.

On the identification of the elastic modulus and axial force of beam members with unknown boundary conditions using modal information

The axial force of beam members with unknown boundary conditions can be estimated by using vibration measurements, and this method necessitates the real elastic modulus of the members to produce accurate estimations. However, in engineering practice, the value of the elastic modulus of such members differs from the designed value owing to material degradation and structural damage. In addition, the elastic modulus of the beam structure may have a significant impact on the identification accuracy of axial force. Therefore, this study develops a methodology for simultaneously estimating axial force and elastic modulus within axial force members of structural systems, based on measurements of multiple natural frequencies and mode shapes. First, an improved vibration formula for the Timoshenko beam under axial load is obtained using the energy approach. Then, based on the proposed dynamic equation, the axial force and elastic modulus of the beam are discovered to have a linear relationship when the modal information of the structure is known. Further, using this linear relationship, a method to identify the axial force and elastic modulus of the beam member under unknown boundary constraints is proposed. Finally, the applicability and accuracy of the proposed method are validated by many numerical and experimental tests, and excellent estimates of the axial forces and elastic modulus are achieved.

Adaptive observer based controls for a flexible wing system under unknown output disturbances

This paper considers a wing system under unknown output disturbances, references, distributed disturbances and boundary disturbances. These signals have unknown frequencies, amplitudes and phases, and are then supposed to be from an exosystem with an unknown matrix. In this sense, we firstly find the wing system merely under the references and output disturbances, for which the PDE observer can be easily designed based on the measurable outputs. Then, an adaptive observer is designed for a detectable exosystem so that the references have known coefficients and approximately known states. Adaptive observer based controls are then proposed for the closed-loop system so that the convergence of tracking error and the boundedness of the states are guaranteed. Two simulation examples are compared to show the robustness of the designed controls.

Estimating uncertainty of mean water table depth in the contiguous United States using highly parameterized linear inverse method

Because of spatiotemporal observation gaps and prohibitive computational cost, estimating uncertainty of mean water table depth in the continental scale is a challenging problem. Based on the groundwater flow of mean water table, we first analyzed uncertainty of the mean water table depth in the contiguous US using a highly parameterized linear inverse method with unknown boundary condition and source/sink terms. To estimate uncertainty of the mean heads, the total relative errors of temporal heads and mean heads for all observed wells with more than one head instead of the relative error of temporal heads and mean head for each observed well was proposed to obtain t Location-scale and Gaussian distributions of the total relative errors in the synthetic cases. The results indicate that uncertainties of the mean heads by t Location-scale distribution are more accurate than those by Gaussian distribution. Then, we applied the inverse method and t Location-scale distribution of the total relative errors in the contiguous US to estimate uncertainty of the mean water table depth during 1900–2016. The results demonstrate that uncertainty in the K values did not result in large uncertainty of the mean water table depth. Uncertainty of the mean water table depth in the contiguous US was first estimated based on variation of hydraulic conductivity and the observed mean water table, where 100 realizations of the mean water table depth were obtained by t Location-scale distribution. The results demonstrate both the feasibility and high precision of the inverse method.

Adaptive sliding mode and RBF neural network based fault tolerant attitude control for spacecraft with unknown uncertainties and disturbances

Taking the external disturbance, model uncertainty, actuator failure, and actuator saturation into consideration, this paper investigates the nonlinear fault-tolerant attitude control problem for the spacecraft. First, to deal with the disturbance and uncertainty with unknown boundaries, an adaptive sliding mode control method is proposed to stabilize the state variables of the spacecraft attitude control system. Then the actuator failure and saturation are further considered, and the second spacecraft fault-tolerant attitude controller is derived based on adaptive sliding mode control and radial basis function (RBF) neural networks. The adaptive control gains reduction technique is applied in the design process, which can weaken the chattering phenomenon of the controller to some extent, and the stability of the attitude control system is analyzed through Lyapunov theory. In the proposed controller, model information and external disturbance are not required and only the system states are needed. Furthermore, the boundaries of the model uncertainty, external disturbance, actuator fault and saturation are assumed to be existing but unknown by the proposed controllers. These features make the proposed attitude controllers need few modeling information of the spacecraft, or maintain strong robustness even if the model or external environment occurs significant changes. Finally, numerical simulations demonstrate the great performance of the proposed fault-tolerant attitude control method.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

Neural Network Boundary Approximation for Uncertain Nonlinear Spatiotemporal Systems and Its Application of Tracking Control.

This brief addresses the neural network (NN) approximation problem for uncertain nonlinear systems with time-varying parameters (that is, unknown nonlinear spatiotemporal systems). Due to the fact that the unknown spatiotemporal functions cannot be directly approximated by NNs, a so-called time-varying parameter extraction is given to separate time-varying parameters from uncertain nonlinear spatiotemporal functions. By using the supremum of Euler norm of the extracted time-varying parameters, the nonlinear spatiotemporal function is mapped to an unknown state-based boundary function, which can be approximated by NNs. Based on the time-varying parameter extraction, an adaptive neural tracking control law is designed for uncertain strict-feedback nonlinear spatiotemporal systems, which guarantees the convergence of the tracking error with a trajectory performance. The effectiveness of the designed method is verified by simulations.

Bridge cable tension estimation using the vibration method

Accurate cable tension assessment is essential for cable-stayed bridges in the construction and health monitoring phases. Accordingly, several approaches, have been presented over the last decades. The vibration method has become one of the most relevant methods. Throughout the years, several practical formulas, and approaches for cable tension estimation from measured natural frequencies have been presented. The paper demonstrates in-situ measurements of natural frequencies of cables and subsequent cable tension calculation, on a cable-stayed bridge. Problem of unknown bending stiffness was solved by using multiple frequencies. On the other hand, problem of unknown boundary stiffness is much more difficult to solve. Therefore, fixed and hinged ends were considered as two boundary situations. Moreover, the design of the bridge allows to measure the tension of each individual rope. This allows the unique comparison of the Cable tensions and individual rope tensions. Cable tension was also estimated as a sum of rope tensions what allows to verify the cable tension results. The measurement of individual ropes allowed the authors to highlight the most stressed ropes. The paper also provides a unique insight to the distribution and variation in the rope tension within the cables. Possible causes of tension differences in ropes and differences between the rope and cable tensions are discussed.

On parametric semidefinite programming with unknown boundaries

In this paper, we study parametric semidefinite programs (SDPs) where the solution space of both the primal and dual problems change simultaneously. Given a bounded set, we aim to find the a priori unknown maximal permissible perturbation set within it where the semidefinite program problem has a unique optimum and is analytic with respect to the parameters. Our approach reformulates the parametric SDP as a system of partial differential equations (PDEs) where this maximal analytical permissible set (MAPS) is the set on which the system of PDEs is well-posed. A sweeping Euler scheme is developed to approximate this a priori unknown perturbation set. We prove local and global error bounds for this second-order sweeping Euler scheme and demonstrate the method in comparison to existing SDP solvers and its performance on several two-parameter and three-parameter SDPs for which the MAPS can be visualized.