System Of Nonlinear ODEs Research Articles

MHD nanofluid flow between porous convergent-divergent channel with velocity slip and nanoparticle aggregation

The aggregation of nanoparticles is a major phenomenon having broad consequences in many fields. In order to fully utilize the capabilities of nanoparticles in a variety of applications and to evaluate the effects that they will have on the environment and biological systems, it is crucial to comprehend and manage aggregation. To develop and improve nanoparticle-based technology, researchers are still learning more about the aggregation processes. Thus, the present study investigates the combined effects of velocity slip and nanoparticle aggregation on Heat transfer (HT) analysis of MHD nanofluid (i.e., TiO2-C2H6O2) flow between porous convergent, divergent channel. The modified Krieger–Dougarty and Maxwell–Bruggeman models were utilized for nanoparticle aggregation. The modeling is based on nonlinear PDEs such as continuity, momentum, and heat equations. These equations are transformed into a system of nonlinear ODEs using similarity transformations and then solved numerically and analytically. The analytical solution has been constructed using the ADM method. The present results in particular cases are compared to results obtained by the HAM- package and by the Runge- Kutta Fehlberg 4th–5th order (RKF-45) for validation. The effects of active parameters on the velocity, temperature, concentration, skin friction, and Nusselt numbers are investigated. It is found that nanoparticle aggregation can limit fluid velocity in converging channels by increasing flow resistance through aggregate formation. Individual nanoparticles generate friction and lower velocity, while aggregated nanoparticles boost fluid density and velocity. In addition, it is found that the magnetic field lowers skin friction and increases HT due to Lorentz force, while porosity increases friction and HT. Nanoparticle concentration inversely affects friction, increasing friction without aggregation and decreasing friction with aggregation, with the HT rate rising with increased nanoparticle concentrations.

Explicit numerical methods for solving singular initial value problems for systems of second-order nonlinear ODEs

Explicit numerical methods for solving singular initial value problems for systems of second-order nonlinear ODEs

Irreversibility of Al2O3-Ag hybrid nanoparticles in mixture base fluid on microchannel with variable viscosity, buoyancy forces, and suction/injection effects: An analytical study

The development of inherent irreversibility in the system is caused by single phase Poiseuille flow considering hybrid nanoparticles (Al2O3-Ag) and mixture fluid (water and ethylene glycol H2O50%-C2H6O250%) in the upright microchannel with unequal viscosity. Taking into account the buoyancy force, suction/injection at the walls, and the form factor and geometry of the nanoparticles. The modeling is based on nonlinear PDEs such as continuity, momentum, and heat equations, which are then transformed to a system of nonlinear ODEs using similarity transformations and solved numerically and analytically. The analytical solution was built using the Differential Transform Method (DTM), and the current results in specific cases are compared to results obtained by the HAM-based Mathematica package, the Runge-Kutta Fehlberg 4th-5th order (RKF45), and those available in literature. The effects of active parameters are investigated on the velocity and temperature, entropy generation and Bejan number. The outcomes of the present analysis reveal that the nanoparticles volume fraction, thermal radiation and Biot number acts to enhance the cooling of the system through the release of thermal energy. in addition, the enhancement in variable viscosity parameter, causes a rise in the irreversibility rate, which in turn boosts the rates of entropy generation and Bejan number. On the other hand, Irreversibility due to heat transfer is dominant in the centerline of the microchannel, while fluid friction irreversibility dominates its walls.

Integrated intelligence of inverse multiquadric radial base neuro‐evolution for radiative MHD Prandtl–Eyring fluid flow model with convective heating

AbstractIn this communication, a new stochastic numerical paradigm is introduced for thorough scrutinization of the magnetohydrodynamic (MHD) boundary layer flow of Prandtl–Eyring fluidic model along a stretched sheet impact on thermal radiation as well as convective heating scenarios by exploiting the accurate approximation knacks of ANNs (artificial‐neural‐networks) modeling by using the inverse multiquadric (IMQ) function optimized/trained with well‐reputed global search with genetic algorithms (GAs) and local search efficacy of sequential quadratic programming (SQP), that is, ANNs‐IMQ‐GA‐SQP. The mathematical model of the Prandtl–Eyring fluid flow is portrayed in the form of PDEs and then converted in the form of a nonlinear system of ODEs by utilizing suitable similarity transformation to calculate/analyze the solution dynamics. The kinematic and thermodynamic properties of MHD Prandtl–Eyring fluid flow are examined/observed by varying the sundry physical parameters. The obtained intelligent computing designed solver‐based numerical results are consistently found in good agreement with reference results obtained through the Adams numerical technique. First and second‐order cumulant‐based statistical assessments are extensively exploited to endorse trustily the efficacy of ANNs‐IMQ‐GA‐SQP solver based on exhaustive simulations for the Prandtl–Eyring fluidic system.

Extending nonstandard finite difference scheme rules to systems of nonlinear ODEs with constant coefficients

In this paper, we present a reformulation of Mickens' rules for nonstandard finite difference (NSFD) schemes to adapt them to systems of ODEs. We want to compare several methods within the class of NSFD schemes. These methods are expressed as exponential integrators which treat exactly the linear part of the equations. While the classical methods address each equation of a system separately, we propose here to address them as a whole, which involves a reformulation of Mickens' rules. This leads to enhance the results compared to the classical case, even if approximations of the matrix exponentials are used. Precisely, using Cayley–Hamilton theorem we obtain two correction factors, one of which is linked to the size of the system and the other to the nonlinear term, we are thus able to use test cases which show the impact of one or another corrector. We prove the consistency and the zero-stability of the method in the nonlinear case to conclude to the convergence of the method.

Neuro-Heuristic Computational Intelligence Approach for Optimization of Electro-Magneto-Hydrodynamic Influence on a Nano Viscous Fluid Flow

In this investigative study, the electro-magneto hydrodynamic (EMHD) influence on a nano viscous fluid model is scrutinized by designing an artificial neural network (ANN) paradigm using a neuro-heuristic approach (NHA) through the combination of GAs (genetic algorithms) and one of the most efficient locally searching solver SQP (sequential quadratic programming), i.e., NHA-GA-SQP. The fluid flow for the proposed problem is initially interpreted in the form of PDEs and then utilization of suitable similarity transformation on these PDEs yields in terms of a stiff nonlinear system of ODEs. The numerical results of the suggested fluidic model based on the variation of its physically existing parameters are calculated through the NHA-GA-SQP solver to detect the variation in velocity, thermal gradient, and concentration during the fluid flow. A detailed analysis of obtained outcomes through the NHA-GA-SQP algorithm and their comparison with the reference results estimated via the Adams method are presented. The calculation of the proposed solver’s accuracy, stability, and consistency through various statistical operators is also involved in the current inspection.

Intelligent computing technique to study heat and mass transport of Casson nanofluidic flow model on a nonlinear slanted extending sheet

AbstractThis paper explains the importance and benefits of using AI in fluid mechanics, emphasizing its capability of finding the solution of fluid flow systems through iterative optimization techniques. It explores how AI can enhance the understanding of fluid dynamics to improve engineering processes. In the presented studies, the heat and mass transport of the Casson nanofluidic flow model on a nonlinear slanted extending sheet (HMT‐CNFM) via AI‐based Levenberg Marquard methodology with backpropagated trained neural networks (TNN‐BLMM) is studied. By applying an appropriate transformation, the governing PDEs representing HMT‐CNFM are converted into a system of nonlinear ODEs. The dataset for Levenberg Marquard methodology with backpropagated trained neural network (TNN‐BLMM) for all six scenarios of this proposed model via computational power of the Lobatto IIIA scheme using the “bvp4c” package in MATLAB and then graphically illustrate all these six scenarios through nftool to attain mean square error, regression, error histogram, performance, and fit curve. Training, testing, and validation processes of NN‐BLMM are organized for the investigation of the HMT‐CNFM model.

Analytical solution of an Ill-posed system of nonlinear ODE’s

In this paper, we discuss a solution strategy for an overdetermined ODE system that uses elliptic functions. In particular, we show how an apparently ill-posed ODE system can be made amenable to treatment by Jacobian elliptic functions by introducing additional free parameters and still obtain a solution that corresponds well to the physically expected behaviour.

Exploring the impact of Hall and ion slip effects on mixed convective flow of Casson fluid Model: A stochastic investigation through non-Fourier double diffusion theories using ANNs techniques

The analysis of Non-Newtonian Casson fluid flow using Artificial Neural Networks (ANNs) has enticed the attention of researchers and scientists due to their tremendous role in modern technologies and development. The generalised Ohm law with mass and thermal transport is employed. The mechanism of mass and energy transmission is based on generalised Fick’s and Fourier laws. An incompressible magnetohydrodynamics (MHD) boundary layer flow of Darcy-Forchheimer Casson fluid across a stretching sheet is elaborated in the current study. The flow incorporates the effects of Hall and ion slip phenomena and occurs steadily over a stretchable linear surface through porous medium. The Casson fluid flow has been expressed in form of system of PDEs, which are further simplified to non-linear system of ODEs through similarity replacements. The ANN-LMBOA algorithm is used to crack these equations (ODEs). To validate the ANN-LMBOA results, the dataset is produced by employing the (FDM) finite difference method (Lobatto IIIA) in Matlab package using bvp4c-solver. The dataset is generated for diverse scenarios of flow constraints, testing, and validation of the neural network. The accurateness of the ANN-LMBOA (Artificial Neural Network- Levenberg Marquardt Backpropagation Optimization Algorithm) is evaluated via several statistical neural network tools, i.e., RP, MSE, EH and CF graphs.

Intelligent computing Levenberg Marquardt paradigm for the analysis of Hall current on thermal radiative hybrid nanofluid flow over a spinning surface

The aim of this work is to construct a supervised learning neural network algorithm termed as LM-SLNNAs (Levenberg Marquardt) for the influence of Hall current on Radiation Nanofluidic flow on a spinning Disk (IHC-RNF-OSD). The effects of a magnetic field and heat radiation have also been considered in order to better understand of the flow and energy details. Hybrid nanofluid, comprising Copper (Cu) and Titanium Dioxide (TiO2) in water, is utilized. The intended IHC-RNF-OSD is originally represented as a system of PDEs, which may be transformed into a system of non-linear ODEs using the appropriate transformation. To construct a dataset of the proposed model for six scenarios, the bvp4c numerical technique is used. These scenarios may produce by adjusting the physical variations of the model such as hall parameter, thermal radiation parameter, Prandtl number, etc. LM-SLNNAs executed operations on training, testing, and certification to data-samples of IHC-RNF-OSD model. Comparing the conventional result and the outputs of the suggested solver for the proposed model, the statistical analysis such as M.S.E (mean squared error), histograms plots, regression and absolute error analysis has used which confirmed the efficiency of the LM-SLNNAs. Furthermore, the result demonstrates that the radial skin frictional factor is increased for the Hall parameter; however, the transverse frictional component displays the inverse situation of this. The major goal of this research is to use artificial intelligence to model and predict the properties of nanofluid/hybrid nanofluid, choose the optimal ANN structure from a group of predicted structures, and control time and cost by predicting the ANN with the least error.

Solitary Wave Propagation of the Generalized Rosenau–Kawahara–RLW Equation in Shallow Water Theory with Surface Tension

This paper addresses a numerical approach for computing the solitary wave solutions of the generalized Rosenau–Kawahara–RLW model established by coupling the generalized Rosenau–Kawahara and Rosenau–RLW equations. The solution of this model is accomplished by using the finite difference approach and the upwind local radial basis functions-finite difference. Firstly, the PDE is transformed into a nonlinear ODEs system by means of the radial kernels. Secondly, a high-order ODE solver is implemented for discretizing the system of nonlinear ODEs. The main advantage of this technique is its lack of need for linearization. The global collocation techniques impose a significant computational cost, which arises from calculating the dense system of algebraic equations. The proposed technique estimates differential operators on every stencil. As a result, it produces sparse differentiation matrices and reduces the computational burden. Numerical experiments indicate that the method is precise and efficient for long-time simulation.

Hidden convexity in the heat, linear transport, and Euler’s rigid body equations: A computational approach

A finite element based computational scheme is developed and employed to assess a duality based variational approach to the solution of the linear heat and transport PDE in one space dimension and time, and the nonlinear system of ODEs of Euler for the rotation of a rigid body about a fixed point. The formulation turns initial-(boundary) value problems into degenerate elliptic boundary value problems in (space)-time domains representing the Euler-Lagrange equations of suitably designed dual functionals in each of the above problems. We demonstrate reasonable success in approximating solutions of this range of parabolic, hyperbolic, and ODE primal problems, which includes energy dissipation as well as conservation, by a unified dual strategy lending itself to a variational formulation. The scheme naturally associates a family of dual solutions to a unique primal solution; such ‘gauge invariance’ is demonstrated in our computed solutions of the heat and transport equations, including the case of a transient dual solution corresponding to a steady primal solution of the heat equation. Primal evolution problems with causality are shown to be correctly approximated by noncausal dual problems.

Fitting parameters and therapies of ODE tumor models with senescence and immune system

This work is devoted to introduce and study two quasispecies nonlinear ODE systems that model the behavior of tumor cell populations organized in different states. In the first model, replicative, senescent, extended lifespan, immortal and tumor cells are considered, while the second also includes immune cells. We fit the parameters regulating the transmission between states in order to approximate the outcomes of the models to a real progressive tumor invasion. After that, we study the identifiability of the fitted parameters, by using two sensitivity analysis methods. Then, we show that an adequate reduced fitting process (only accounting to the most identifiable parameters) gives similar results but saving computational cost. Three different therapies are introduced in the models to shrink (progressively in time) the tumor, while the replicative and senescent cells invade. Each therapy is identified to a dimensionless parameter. Then, we make a fitting process of the therapies’ parameters, in various scenarios depending on the initial tumor according to the time when the therapies started. We conclude that, although the optimal combination of therapies depends on the size of initial tumor, the most efficient therapy is the reinforcement of the immune system. Finally, an identifiability analysis allows us to detect a limitation in the therapy outcomes. In fact, perturbing the optimal combination of therapies under an appropriate therapeutic vector produces virtually the same results.

Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics

We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of C^{infty } bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy.

Analysis and simulations of the HANDY model with social mobility, renewables and nonrenewables

We expand the HANDY (Human And Nature DYnamics) model for the socioeconomic dynamics of a large stratified society. The basic model was introduced in Motesharrei (Dissertation 2014) and Motesharrei et al. (2016). It is a nonlinear system of ODEs for a `simple society' of Elites, Workers, Wealth, and Natural Resources. Following Ali Kadhim (Dissertation 2021), we add social mobility between the classes and split natural resources into renewables and nonrenewables. We establish the existence, boundedness and positivity of the solutions, and investigates the stability of the steady states. The model admits stable steady states, and there is numerical evidence of stable periodic solutions and limit cycles. Simulations depict the different qualitative types of model behavior: convergence to steady states, periodic oscillations, collapse.
 For more information see https://ejde.math.txstate.edu/Volumes/2023/59/abstr.html

Significance of nanoparticle radius on EMHD Casson nanomaterial flow with non-uniform heat source and second-order velocity slip

For its application in cancer therapy, targeted drug delivery, radiofrequency ablation, and magnetic resonance imaging, the dynamics of electro-magnetohydrodynamic flow of blood-gold nanomaterial over a nonlinearly stretching surface utilizing the Casson model has been elucidated numerically. The impact of second-order hydrodynamic-slip, nanoparticle radius, first-order thermal-slip, inter-particle spacing and non-uniform heat source are also accounted. The modeled flow equations are transmuted into a nonlinear system of first-order ODEs (with the aid of apposite similarity variables) which are then resolved numerically utilizing the bvp5c scheme. The thermal field augments with an increase and a decrease in the inter-particle spacing and radius of gold-nanoparticles, respectively. However, a reverse trend is noted for the velocity profile when the radius and inter-particle spacing of gold-nanoparticles are altered. The trend and magnitude of the change in the drag rate and heat transfer rate under the influence of effectual parameters have been demonstrated statistically using slope of linear regression. It is noticed that per unit increase in the volume fraction of gold nanoparticles augments the heat transfer rate by 81.71% and reduces the surface drag by 163.51%. Further, per unit increase in the inter-particle spacing of gold nanoparticles augments the drag coefficient by 85.92% whereas per unit increase in the radius of gold nanoparticles reduces the skin friction coefficient by 49.71%.

Ramanujan systems of Rankin–Cohen type and hyperbolic triangles

Abstract In the first part of the paper, we characterize certain systems of first-order nonlinear differential equations whose space of solutions is an s ⁢ l 2 ⁢ ( C ) \mathfrak{sl}_{2}(\mathbb{C}) -module. We prove that such systems, called Ramanujan systems of Rankin–Cohen type, have a special shape and are precisely the ones whose solution space admits a Rankin–Cohen structure. In the second part of the paper, we consider triangle groups Δ ⁢ ( n , m , ∞ ) \Delta(n,m,\infty) . By means of modular embeddings, we associate to every such group a number of systems of nonlinear ODEs whose solutions are algebraically independent twisted modular forms. In particular, all rational weight modular forms on Δ ⁢ ( n , m , ∞ ) \Delta(n,m,\infty) are generated by the solutions of one such system (which is of Rankin–Cohen type). As a corollary, we find new relations for the Gauss hypergeometric function evaluated at functions on the upper half-plane. To demonstrate the power of our approach in the non-classical setting, we construct the space of integral weight twisted modular form on Δ ⁢ ( 2 , 5 , ∞ ) \Delta(2,5,\infty) from solutions of systems of nonlinear ODEs.

Micropolar Fluid Flow Through a Porous Stretching/Shrinking Sheet with Mass Transpiration: An Analytical Approach

In this paper, the flow of a micropolar fluid over a stretching or shrinking sheet is investigated under magnetohydrodynamic (MHD) conditions. Such a flow is described by highly nonlinear PDEs. Using the similarity transformation technique, the PDEs governing the flow are reduced to a system of nonlinear ODEs, which further allows a closed-form analytical solution. The effect of the microrotation on the skin friction coefficient, the dimensionless forms of the velocity, and the temperature flow fields in the neighborhood of the stretching or shrinking sheet are discussed for various combinations of the dimensionless parameters. The numerical results reveal that the micropolar flow may accelerate or deaccelerate depending upon the numerical values of the mass transpiration and the permeability of the porous sheet. An increase in the tangential and the angular flow velocities is found to occur with an increase in the microrotation. Further, it is observed that the increase in the microrotation increases the skin friction coefficient.

Pre-twisted calculus and differential equations

In this paper we introduce the φA-differentiability for functions f:U⊂Rk→Rn, where U is an open set, A is the linear space Rn endowed with a unital associative commutative algebra product, and φ:U⊂Rk→A is a differentiable function in the usual sense. We call it pre-twisted differentiability. With respect to the φA-differentiability we introduce: (a) a type Cauchy–Riemann equations, which serve as φA-differentiability criteria, (b) a Cauchy-integral theorem, and (c) φA-differential equations, which can be used to solve linear and nonlinear ODE systems. It has recently been shown that the φA-differentiable functions define a complete solutions for the PDEs of the form Auxx+Buxy+Cuyy=0, which is used in this paper for solving the corresponding Cauchy problems. Furthermore, solutions of φA-differential equations define solutions for linear and nonlinear PDE systems.

Mathematical Analysis of Mixed Convective Peristaltic Flow for Chemically Reactive Casson Nanofluid

Nanofluids are extremely beneficial to scientists because of their excellent heat transfer rates, which have numerous medical and industrial applications. The current study deals with the peristaltic flow of nanofluid (i.e., Casson nanofluid) in a symmetric elastic/compliant channel. Buongiorno’s framework of nanofluids was utilized to create the equations for flow and thermal/mass transfer along with the features of Brownian motion and thermophoresis. Slip conditions were applied to the compliant channel walls. The thermal field incorporated the attributes of viscous dissipation, ohmic heating, and thermal radiation. First-order chemical-reaction impacts were inserted in the mass transport. The influences of the Hall current and mixed convection were also presented within the momentum equations. Lubricant approximations were exploited to make the system of equations more simplified for the proposed framework. The solution of a nonlinear system of ODEs was accomplished via a numerical method. The influence of pertinent variables was examined by constructing graphs of fluid velocity, temperature profile, and rate of heat transfer. The concentration field was scrutinized via table. The velocity of the fluid declined with the increment of the Hartman number. The effects of thermal radiation and thermal Grashof number on temperature showed opposite behavior. Heat transfer rate was improved by raising the Casson fluid parameter and the Brownian motion parameter.