Solutions Of Ordinary Differential Equations Research Articles

Development of an Elementary Reaction-Based Kinetic Model to Predict the Aqueous-Phase Fate of Organic Compounds Induced by Reactive Free Radicals.

ConspectusAqueous-phase free radicals such as reactive oxygen, halogen, and nitrogen species play important roles in the fate of organic compounds in the aqueous-phase advanced water treatment processes and natural aquatic environments under sunlight irradiation. Predicting the fate of organic compounds in aqueous-phase advanced water treatment processes and natural aquatic environments necessitates understanding the kinetics and reaction mechanisms of initial reactions of free radicals with structurally diverse organic compounds and other reactions. Researchers developed conventional predictive models based on experimentally measured transformation products and determined reaction rate constants by fitting with the time-dependent concentration profiles of species due to difficulties in their measurements of unstable intermediates. However, the empirical treatment of lumped reaction mechanisms had a model prediction limitation with respect to the specific parent compound's fate. We use ab initio and density functional theory quantum chemical computations, numerical solutions of ordinary differential equations, and validation of the outcomes of the model with experiments. Sensitivity analysis of reaction rate constants and concentration profiles enables us to identify an important elementary reaction in formating the transformation product. Such predictive elementary reaction-based kinetics models can be used to screen organic compounds in water and predict their potentially toxic transformation products for a specific experimental investigation.Over the past decade, we determined linear free energy relationships (LFERs) that bridge the kinetic and thermochemical properties of reactive oxygen species such as hydroxyl radicals (HO•), peroxyl radicals (ROO•), and singlet oxygen (1O2); reactive halogen species such as chlorine radicals (Cl•) and bromine radicals (Br•); reactive nitrogen species (NO2•); and carbonate radicals (CO3•-). We used literature-reported experimental rate constants as kinetic information. We considered the theoretically calculated aqueous-phase free energy of activation or reaction to be a kinetic or a thermochemical property, and obtained via validated ab initio or density functional theory-based quantum chemical computations using explicit and implicit solvation models. We determined rate-determining reaction mechanisms involved in reactions by observing robust LFERs. The general accuracy of LFERs to predict aqueous-phase rate constants was within a difference of a factor of 2-5 from experimental values.We developed elementary reaction-based kinetic models and predicted the fate of acetone induced by HO• in an advanced water treatment process and methionine by photochemically produced reactive intermediates in sunlit fresh waters. We provided mechanistic insight into peroxyl radical reaction mechanisms and critical roles in the degradation of acetone and the formation of transformation products. We highlighted different roles of triplet excited states of two surrogate CDOMs, 1O2, and HO•, in methionine degradation. Predicted transformation products were compared to those obtained via benchtop experiments to validate our elementary reaction-based kinetic models. Predicting the reactivities of reactive halogen and nitrogen species implicates our understanding of the formation of potentially toxic halogen- and nitrogen-containing transformation products during water treatment processes and in natural aquatic environments.

Computing subgradients of convex relaxations for solutions of parametric ordinary differential equations

A novel subgradient evaluation method is proposed for nonsmooth convex relaxations of parametric solutions of ordinary differential equations (ODEs) arising in global dynamic optimization, assuming that the relaxations always lie strictly within interval bounds during integration. We argue that this assumption is reasonable in practice. These subgradients are computed as the unique solution of an auxiliary parametric affine ODE, analogous to classical forward/tangent sensitivity evaluation methods for smooth dynamic systems. Unlike established subgradient evaluation approaches for nonsmooth dynamic systems, this new method does not require smoothness or transversality assumptions, and is compatible with existing subgradient evaluation methods for closed-form convex functions, as implemented in subgradient evaluation software such as EAGO.jl and MC ++. Moreover, we show that a subgradient for a lower-bounding problem in global dynamic optimization can be directly evaluated using reverse/adjoint sensitivity analysis, which may reduce the overall computational effort for an overarching global optimization method. Numerical examples are presented, based on a proof-of-concept implementation in Julia.

An efficient uncertainty propagation method for nonlinear dynamics with distribution-free P-box processes

The distribution-free P-box process serves as an effective quantification model for time-varying uncertainties in dynamical systems when only imprecise probabilistic information is available. However, its application to nonlinear systems remains limited due to excessive computation. This work develops an efficient method for propagating distribution-free P-box processes in nonlinear dynamics. First, using the Covariance Analysis Describing Equation Technique (CADET), the dynamic problems with P-box processes are transformed into interval Ordinary Differential Equations (ODEs). These equations provide the Mean-and-Covariance (MAC) bounds of the system responses in relation to the MAC bounds of P-box-process excitations. They also separate the previously coupled P-box analysis and nonlinear-dynamic simulations into two sequential steps, including the MAC bound analysis of excitations and the MAC bounds calculation of responses by solving the interval ODEs. Afterward, a Gaussian assumption of the CADET is extended to the P-box form, i.e., the responses are approximate parametric Gaussian P-box processes. As a result, the probability bounds of the responses are approximated by using the solutions of the interval ODEs. Moreover, the Chebyshev method is introduced and modified to efficiently solve the interval ODEs. The proposed method is validated based on test cases, including a duffing oscillator, a vehicle ride, and an engineering black-box problem of launch vehicle trajectory. Compared to the reference solutions based on the Monte Carlo method, with relative errors of less than 3%, the proposed method requires less than 0.2% calculation time. The proposed method also possesses the ability to handle complex black-box problems.

Review of Adams-Bashforth method for numerical solution of first order ordinary differential equations

This project work presents a comprehensive review of the Adams-Bashforth method for the numerical solution of explicit first-order ordinary differential equations (ODEs). The study begins with a historical overview of the development of ordinary differential equations (ODEs), tracing back to the seminal works of Newton, Leibniz, and their contemporaries. The evolution of differential equations as a distinct mathematical discipline and their wide-ranging applications across various fields are discussed. Furthermore, the paper provides an in-depth analysis of the Adams-Bashforth method, a prominent numerical technique for solving ODEs. The method is derived and analyzed using Mathematica Software, demonstrating its efficacy and accuracy in approximating solutions to ODEs. A comparison with existing methods such as the Euler method and the Runge-Kutta method highlights the advantages of the Adams-Bashforth method, particularly in terms of computational efficiency and accuracy. Moreover, fundamental concepts related to ODEs, including the initial value problem, existence, and uniqueness of solutions, are explored in the context of numerical solution methods. The properties of linear multistep methods and their relevance to solving ODEs efficiently are also discussed. In conclusion, this project work provides valuable insights into the Adams-Bashforth method as a powerful tool for the numerical solution of first-order ODEs. By leveraging modern computational tools such as Mathematica Software, the method offers a practical and efficient approach to solving complex differential equations encountered in various scientific and engineering applications.

On Periodic Solutions of a Second-Order Ordinary Differential Equation

On Periodic Solutions of a Second-Order Ordinary Differential Equation

Numerical analysis of inclined magnetic force impact on cross liquid flowing with widening shallow and heat generating

ABSTRACT The creation, utilisation, conversion, and exchange of thermal energy are all covered under the thermal engineering subject of heat transfer. Heat transfer plays a major role in engineering and industrial sectors like food packaging, food additive manufacturing, electronic cooling, microturbine, etc. Scientists across the world have tried to explore the impact of heat transfer on fluid past an expandable surface due to its exciting applications in industries like polymer production, paper production, crystal glass manufacturing, etc. This article has unique exploration, and it emphasises interpreting the Cross fluid flow along stretching surface with convective heat transport analysis by using the Lie group methodology. We investigated the implication of inclined upper-convected Maxwell fluid magnetohydrodynamic (MHD) movement through a penetrable strained plate. To solve nonlinear differential equations and clearly determine their absolute invariants, the Lie group approach is introduced. For the numerical solution of non-linear ordinary differential equations with boundary conditions, the fourth-order Runge–Kutta procedure with shelling performance is utilised. Plots showing the impacts of motivated and non-motivated MHD are researched and examined for the alterations of many parameters, and statistical graphs are given to explain the physical values. It is evident that opposing behaviour occurs in connection to temperature captivation and generation parameters together with motivated and non-motivated MHD. Under an amplified, heat-generating parameter and Deborah number, the rate of heat transmission increases. The Lie group investigation of Cross liquid in the presence/absence of inclined and non-inclined has not been investigated yet in the available literature.

Hall current effect on molecules motion in viscous non-Newtonian power law fluid with Joule heating and fluxing model of Cattaneo-Christov

This study examines the molecules motion influences and thermal attributes in power-law non-Newtonian dissipative fluids' magnetohydrodynamic behavior, Joule heating, electromigration, and Cattaneo-Christov effects. Effects of Hall on both flow velocity and temperature, as well as the heat transport are considered. The investigation looks at heat transference that occurs when there is a slippage consequence, how the temperature influences the viscosity of the fluid, and how the potential parameters influence the exponential non-Newtonian flow of fluid via an extensible sheet. Runge-Kutta-Fehlberg's approach, which is based on the shooting procedure, was used to develop computational solutions for nonlinear ordinary differential equations. When exposed to a deformation that is generated by expansion, pressure, or agitation, a dilatant fluid is characterized by a phenomenon in which its viscosity rises and may harden. This phenomenon is referred to as the dilatation phenomenon. High shearing rates provide the impression that it is stable. When expanded, subjected to pressure, or agitated, dilatant fluids become more viscous and transform into solids. Both the non-equilibrium that occurs within the fluid and the reduction of the irreversibility processes that occur within it are caused by the acquired charges that are a result of Hall effect of fluid molecules.

Unraveling Drug Delivery from Cyclodextrin Polymer-Coated Breast Implants: Integrating a Unidirectional Diffusion Mathematical Model with COMSOL Simulations.

Breast cancer ranks among the most commonly diagnosed cancers worldwide and bears the highest mortality rate. As an integral component of cancer treatment, mastectomy entails the complete removal of the affected breast. Typically, breast reconstruction, involving the use of silicone implants (augmentation mammaplasty), is employed to address the aftermath of mastectomy. To mitigate postoperative risks associated with mammaplasty, such as capsular contracture or bacterial infections, the functionalization of breast implants with coatings of cyclodextrin polymers as drug delivery systems represents an excellent alternative. In this context, our work focuses on the application of a mathematical model for simulating drug release from breast implants coated with cyclodextrin polymers. The proposed model considers a unidirectional diffusion process following Fick's second law, which was solved using the orthogonal collocation method, a numerical technique employed to approximate solutions for ordinary and partial differential equations. We conducted simulations to obtain release profiles for three therapeutic molecules: pirfenidone, used for preventing capsular contracture; rose Bengal, an anticancer agent; and the antimicrobial peptide KR-12. Furthermore, we calculated the diffusion profiles of these drugs through the cyclodextrin polymers, determining parameters related to diffusivity, solute solid-liquid partition coefficients, and the Sherwood number. Finally, integrating these parameters in COMSOL multiphysics simulations, the unidirectional diffusion mathematical model was validated.

A Chebyshev polynomial based block integrator for the direct numerical solution of fourth order ordinary differential equations

This paper introduces an innovative method for numerically integrating fourth-order initial value problems by utilizing Chebyshev polynomials as the fundamental basis function. The block integrator base on Chebyshev polynomial demonstrates significant improvements in accuracy and stability, rendering it a valuable tool across various scientific and engineering fields. By leveraging the characteristics of Chebyshev polynomials, this approach accurately estimates solutions for fourth-order differential equations without reducing it to a system of first order Ordinary Differential Equations while at the same time effectively managing error accumulation within a block integration framework and thereby enhancing its accuracy over extended intervals. Through rigorous numerical experiments, the effectiveness and reliability of the new integrator are demonstrated and compared with existing methods. The new method is consistent, zero stable and convergent. The method also shows an appreciable error constants. The new method performed better in terms of accuracy than the existing methods in the literature in both linear and nonlinear problems.

On the Solution of Blasius Boundary Layer Equations of Prandtl-Eyring Fluid Flow Past a Stretching Sheet

Objective: This paper investigates velocity profile for two-dimensional, incompressible, laminar forced convection flow of the fluid model for Prandtl-Eyring fluid past a stretching sheet in the presence of fluid parameters. Methods: The governing partial differential equation for the flow was transformed into non-linear ordinary differential equation by using the deductive one parameter group theoretic method and numerical solution of non-linear ordinary differential equation (ODE) is solved by MATLAB bvp4c solver. Findings: The solution of velocity profile obtained as a function of parameter and . The effect of the fluid parameter was discussed graphically. Novelty: The main goal of this article is to analyze boundary layer flow of Prandtl-Eyring fluid over a stretching surface. The conservation equations of mass, momentum are converted into non-linear ordinary differential equations along with boundary conditions using deductive one parameter group theoretic method and solved by MATLAB ODE solver. Comparisons with previously published works are made, and results show a high level of agreement. This type of research is applicable to extrusion, paper production, fiber glass production, hot rolling, condensation process, crystal growing, polymer sheets etc. Keywords: Boundary layer, laminar flow, Deductive one parameter Group theoretic method, Absolute invariant, Stretching Sheet, Prandtl-Eyring fluid

A systematic approach to obtain the analytical solution for linear second order ordinary differential equations: part I

A systematic approach to obtain the analytical solution for linear second order ordinary differential equations: part I

Taylor Polynomials in a High Arithmetic Precision as Universal Approximators

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing partial sums of the Taylor series in a high arithmetic precision. In particular, the proposed approach is capable of interpolation, extrapolation, numerical differentiation, numerical integration, solution of ordinary and partial differential equations, and system identification. The method employs Taylor polynomials and hundreds of digits in the computations to obtain precise results. Interestingly, some well-known problems are found to arise in the calculation accuracy and not methodological inefficiencies, as would be expected. In particular, the approximation errors are precisely predictable, the Runge phenomenon is eliminated, and the extrapolation extent may a priory be anticipated. The attained polynomials offer a precise representation of the unknown system as well as its radius of convergence, which provides a rigorous estimation of the prediction ability. The approximation errors are comprehensively analyzed for a variety of calculation digits and test problems and can be reproduced by the provided computer code.

Solving Nonlinear Fractional Differential Equations by Using Shehu Transform and Adomian Polynomials

The current article provides a detailed analysis of the solution of non-linear ordinary differential equations of fractional and non-fractional order in series forms using the Shehu transform (ST) and the Adomian decomposition method (ADM), also known as the Shehu transform Adomian decomposition method (STADM). Previously, these methods were used to solve differential equations of integer order as well as a very small number of ordinary and fractional differential equations (FDEs). The Caputo's operator is used in a number of well-known FDEs, including the logistic equation, the Van der Pole equation, and other non-fractional order differential equations like the nonlinear Bratu type equation. It is noted that all of the example issues had series solutions thanks to STADM. Plotting the graph for several series terms of the series solution demonstrates how the approximate solution tends to the closed form solution. In some example problems the impact of α is shown.

Qualitative analysis, exact solutions and symmetry reduction for a generalized (2＋1)-dimensional KP–MEW-Burgers equation

The objective of this manuscript is to examine the non-linear characteristics of the modified equal width-Burgers equation, known as the generalized Kadomtsive–Petviashvili equation, and its ability to generate a long-wave with dispersion and dissipation in a nonlinear medium. We employ the Lie symmetry approach to reduce the dimension of the equation, resulting in an ordinary differential equation. Utilizing the newly developed generalized logistic equation method, we are able to derive solitary wave solutions for the aforementioned ordinary differential equation. In order to gain a deeper understanding of the physical implications of these solutions, we present them using various visual representations, such as 3D, 2D, density, and polar plots. Following this, we conduct a qualitative analysis of the dynamical systems and explore their chaotic behavior using bifurcation and chaos theory. To identify chaos within the systems, we utilize various chaos detection tools available in the existing literature. The results obtained from this study are novel and valuable for further investigation of the equation, providing guidance for future researchers.

Probabilistic locked mode predictor in the presence of a resistive wall and finite island saturation in tokamaks

We present a framework for estimating the probability of locking to an error field in a rotating tokamak plasma. This leverages machine learning methods trained on data from a mode-locking model, including an error field, resistive magnetohydrodynamics modeling of the plasma, a resistive wall, and an external vacuum region, leading to a fifth-order ordinary differential equation (ODE) system. It is an extension of the model without a resistive wall introduced by Akçay et al. [Phys. Plasmas 28, 082106 (2021)]. Tearing mode saturation by a finite island width is also modeled. We vary three pairs of control parameters in our studies: the momentum source plus either the error field, the tearing stability index, or the island saturation term. The order parameters are the time-asymptotic values of the five ODE variables. Normalization of them reduces the system to 2D and facilitates the classification into locked (L) or unlocked (U) states, as illustrated by Akçay et al., [Phys. Plasmas 28, 082106 (2021)]. This classification splits the control space into three regions: L̂, with only L states; Û, with only U states; and a hysteresis (hysteretic) region Ĥ, with both L and U states. In regions L̂ and Û, the cubic equation of torque balance yields one real root. Region Ĥ has three roots, allowing bifurcations between the L and U states. The classification of the ODE solutions into L/U is used to estimate the locking probability, conditional on the pair of the control parameters, using a neural network. We also explore estimating the locking probability for a sparse dataset, using a transfer learning method based on a dense model dataset.

Approximate Solution of Linear Fuzzy Random Ordinary Differential Equations Using Laplace Variational Iteration Method

In this article, the Laplace transformation method in connection with the variational iteration method will be used to solve approximately fuzzy random ordinary differential equations. After that, the sequence of approximated closed form iterated solutions is derived based on the general Lagrange multiplier evaluated using the well-known convolution theorem of the Laplace transformation method. In addition, two examples are given and solved to illustrate the reliability, efficiency and applicability of the proposed method, they are simulated using computer programs with two different generations of stochastic processes, namely the Wiener process or Brownian motion, which are 1000 and 10000, respectively.

Euler State Networks: Non-dissipative Reservoir Computing

Inspired by the numerical solution of ordinary differential equations, in this paper, we propose a novel Reservoir Computing (RC) model, called the Euler State Network (EuSN). The presented approach makes use of forward Euler discretization and antisymmetric recurrent matrices to design reservoir dynamics that are both stable and non-dissipative by construction.Our mathematical analysis shows that the resulting model is biased towards a unitary effective spectral radius and zero local Lyapunov exponents, intrinsically operating near the edge of stability. Experiments on long-term memory tasks show the clear superiority of the proposed approach over standard RC models in problems requiring effective propagation of input information over multiple time steps. Furthermore, results on time-series classification benchmarks indicate that EuSN can match (or even exceed) the accuracy of trainable Recurrent Neural Networks, while retaining the training efficiency of the RC family, resulting in up to ≈464-fold savings in computation time and ≈1750-fold savings in energy consumption. At the same time, our results on time-series modeling tasks show competitive results against standard RC when the architecture is complemented by direct input-readout connections.

Implicit Block and Runge- Kutta type Methods for Solution of Second-Order Ordinary Differential Equations.

In this research, implicit discrete schemes which form our block integrators were developed for solving Initial Valued Problems of Ordinary Differential Equations. The equivalent second order Runge-Kutta type Methods (RKTM) were also constructed for the same purpose. Both methods were demonstrated on linear and nonlinear problems of Ordinary Differential Equations. Numerical results obtained from RKTM show that the method is competitive with the existing one.

Heat generation and melting heat transfer effects on MHD flow of Carreau fluid in a porous medium

AbstractWe consider heat and mass transfer analysis of magnetohydrodynamic (MHD) Carreau fluid flow along a stretching sheet in a permeable medium with impacts of melting condition, heat generation, double diffusion, and variable thermal conductivity. The novelty of the present work is to study the effects of melting heat transfer and heat generation on MHD flow of Carreau fluid along a stretched sheet in a porous medium with influence of Dufour and Soret effects, and variable thermal conductivity. Similarity transformations procedure is used which transform the partial differential equations (PDEs) of fluid flow model into nonlinear dimensionless ordinary differential equations (ODEs). We obtained numerical solutions of ODEs and the effects of assorted governing parameters are investigated. The results showed that boosting melting parameter increases velocity and concentration fields and reduces temperature profile. Enhancing heat source parameter increases temperature profile, concentration profile observing decline for modified Dufour parameter, Schmidt number, and Soret number, and velocity of the fluid decline for magnetic and porosity parameters. Impact of governing parameters on physical quantities comprising Nusselt number, skin friction, and Sherwood number is evaluated numerically. The accuracy of our results are established in comparison with previous research for skin friction coefficient and Nusselt number.

A Bayesian Collocation Integral Method for Parameter Estimation in Ordinary Differential Equations

Inferring the parameters of ordinary differential equations (ODEs) from noisy observations is an important problem in many scientific fields. Currently, most parameter estimation methods that bypass numerical integration tend to rely on basis functions or Gaussian processes to approximate the ODE solution and its derivatives. Due to the sensitivity of the ODE solution to its derivatives, these methods can be hindered by estimation error, especially when only sparse time-course observations are available. We present a Bayesian collocation framework that operates on the integrated form of the ODEs and also avoids the expensive use of numerical solvers. Our methodology has the capability to handle general nonlinear ODE systems. We demonstrate the accuracy of the proposed method through simulation studies, where the estimated parameters and recovered system trajectories are compared with other recent methods. A real data example is also provided. Supplementary materials for this article are available online.