Fast and Accurate Predictor-Corrector Methods Using Feedback-Accelerated Picard Iteration for Strongly Nonlinear Problems

Although predictor-corrector methods have been extensively applied,they might not meet the requirements of practical applications and engineering tasks,particularly when high accuracy and efficiency are necessary.A novel class of correctors based on feedback-accelerated Picard iteration(FAPI)is proposed to further enhance computational performance.With optimal feedback terms that do not require inversion of matrices,significantly faster convergence speed and higher numerical accuracy are achieved by these correctors compared with their counterparts;however,the computational complexities are comparably low.These advantages enable nonlinear engineering problems to be solved quickly and accurately,even with rough initial guesses from elementary predictors.The proposed method offers flexibility,enabling the use of the generated correctors for either bulk processing of collocation nodes in a domain or successive corrections of a single node in a finite difference approach.In our method,the functional formulas of FAPI are discretized into numerical forms using the collocation approach.These collocated iteration formulas can directly solve nonlinear problems,but they may require significant computational resources because of the manipulation of high-dimensionalmatrices.To address this,the collocated iteration formulas are further converted into finite difference forms,enabling the design of lightweight predictor-corrector algorithms for real-time computation.The generality of the proposed method is illustrated by deriving new correctors for three commonly employed finite-difference approaches:the modified Euler approach,the Adams-Bashforth-Moulton approach,and the implicit Runge-Kutta approach.Subsequently,the updated approaches are tested in solving strongly nonlinear problems,including the Matthieu equation,the Duffing equation,and the low-earth-orbit tracking problem.The numerical findings confirm the computational accuracy and efficiency of the derived predictor-corrector algorithms.

Calculation of Mass Concrete Temperature Containing Cooling Water Pipe Based on Substructure and Iteration Algorithm

Mathematical physics equations are often utilized to describe physical phenomena in various fields of science and engineering.One such equation is the Fourier equation,which is a commonly used and effective method for evaluating the effectiveness of temperature control measures for mass concrete.One important measure for temperature control in mass concrete is the use of cooling water pipes.However,the mismatch of grids between large-scale concrete models and small-scale cooling pipe models can result in a significant waste of calculation time when using the finite element method.Moreover,the temperature of the water in the cooling pipe needs to be iteratively calculated during the thermal transfer process.The substructure method can effectively solve this problem,and it has been validated by scholars.The Abaqus/Python secondary development technology provides engineers with enough flexibility to combine the substructure method with an iteration algorithm,which enables the creation of a parametric modeling calculation for cooling water pipes.This paper proposes such a method,which involves iterating the water pipe boundary and establishing the water pipe unit substructure to numerically simulate the concrete temperature field that contains a cooling water pipe.To verify the feasibility and accuracy of the proposed method,two classic numerical examples were analyzed.The results showed that this method has good applicability in cooling pipe calculations.When the value of the iteration parameterαis 0.4,the boundary temperature of the cooling water pipes can meet the accuracy requirements after 4∼5 iterations,effectively improving the computational efficiency.Overall,this approach provides a useful tool for engineers to analyze the temperature control measures accurately and efficiently for mass concrete,such as cooling water pipes,using Abaqus/Python secondary development.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

Another SSOR Iteration Method

Kellogg gave a version of the Peaceman-Radford method. In this paper, we introduce a SSOR iteration method which uses Kellogg’s method. The new algorithm has some advantages over the traditional SSOR algorithm. A Cyclic Reduction algorithm is introduced via a decoupling in Kellogg’s method.

Iteration and evaluation of shale oil development technology for continental rift lake basins

By benchmarking with the iteration of drilling technology,fracturing technology and well placement mode for shale oil and gas development in the United States and considering the geological characteristics and development difficulties of shale oil in the Jiyang continental rift lake basin,East China,the development technology system suitable for the geological characteristics of shale oil in continental rift lake basins has been primarily formed through innovation and iteration of the development,drilling and fracturing technologies.The technology system supports the rapid growth of shale oil production and reduces the development investment cost.By comparing it with the shale oil development technology in the United States,the prospect of the shale oil development technology iteration in continental rift lake basins is proposed.It is suggested to continuously strengthen the overall three-dimensional development,improve the precision level of engineering technology,upgrade the engineering technical indicator system,accelerate the intelligent optimization of engineering equipment,explore the application of complex structure wells,form a whole-process integrated quality management system from design to implementation,and constantly innovate the concept and technology of shale oil development,so as to promote the realization of extensive,beneficial and high-quality development of shale oil in continental rift lake basins.

Comparative Studies between Picard’s and Taylor’s Methods of Numerical Solutions of First Ordinary Order Differential Equations Arising from Real-Life Problems

To solve the first-order differential equation derived from the problem of a free-falling object and the problem arising from Newton’s law of cooling, the study compares the numerical solutions obtained from Picard’s and Taylor’s series methods. We have carried out a descriptive analysis using the MATLAB software. Picard’s and Taylor’s techniques for deriving numerical solutions are both strong mathematical instruments that behave similarly. All first-order differential equations in standard form that have a constant function on the right-hand side share this similarity. As a result, we can conclude that Taylor’s approach is simpler to use, more effective, and more accurate. We will contrast Rung Kutta and Taylor’s methods in more detail in the following section.

修正的Picard算子在Orlicz空间中的指数加权逼近

本文研究修正的Picard算子在Orlicz空间内指数加权逼近的收敛性和逼近性质.通过建立Orlicz空间内指数加权逼近的相关引理,利用H?lder不等式,Korovkin定理,凸函数的Jensen不等式,Minkowski不等式及相关分析技巧得出该算子在Orlicz空间中指数加权逼近的正定理及相关性质.

A Unification of the Concepts of the Variational Iteration, Adomian Decomposition and Picard Iteration Methods;and a Local Variational Iteration Method

This paper compares the variational iteration method(VIM),the Adomian decomposition method(ADM)and the Picard iteration method(PIM)for solving a system of first o rder n onlinear o rdinary d ifferential e quations(ODEs).A unification of the concepts underlying these three methods is attempted by considering a very general iterative algorithm for VIM.It is found that all the three methods can be regarded as special cases of using a very general matrix of Lagrange multipliers in the iterative algorithm of VIM.The global variational iteration method is briefly reviewed,and further recast into a Local VIM,which is much more convenient and capable of predicting long term complex dynamic responses of nonlinear systems even if they are chaotic.

Multistage Numerical Picard Iteration Methods for Nonlinear Volterra Integral Equations of the Second Kind

Using the Picard iteration method and treating the involved integration by numerical quadrature formulas, we propose a numerical scheme for the second kind nonlinear Volterra integral equations. For enlarging the convergence region of the Picard iteration method, multistage algorithm is devised. We also introduce an algorithm for problems with some singularities at the limits of integration including fractional integral equations. Numerical tests verify the validity of the proposed schemes.

Integration of the Coupled Orbit-Attitude Dynamics Using Modified Chebyshev-Picard Iteration Methods

This paper presents Modified Chebyshev-Picard Iteration(MCPI)methods for long-term integration of the coupled orbit and attitude dynamics.Although most orbit predictions for operational satellites have assumed that the attitude dynamics is decoupled from the orbit dynamics,the fully coupled dynamics is required for the solutions of uncontrolled space debris and space objects with high area-to-mass ratio,for which cross sectional area is constantly changing leading to significant change on the solar radiation pressure and atmospheric drag.MCPI is a set of methods for solution of initial value problems and boundary value problems.The methods refine an orthogonal function approximation of long-time-interval segments of state trajectories iteratively by fusing Chebyshev polynomials with the classical Picard iteration and have been applied to multiple challenging aerospace problems.Through the studies on integrating a torque-free rigid body rotation and a long-term integration of the coupled orbit-attitude dynamics through the effect of solar radiation pressure,MCPI methods are shown to achieve several times speedup over the Runge-Kutta 7(8)methods with several orders of magnitudes of better accuracy.MCPI methods are further optimized by integrating the decoupled dynamics at the beginning of the iteration and coupling the full dynamics when the attitude solutions and orbit solutions are converging during the iteration.The approach of decoupling and then coupling during iterations provides a unique and promising perspective on the way to warm start the solution process for the longterm integration of the coupled orbit-attitude dynamics.Furthermore,an attractive feature of MCPI in maintaining the unity constraint for the integration of quaternions within machine accuracy is illustrated to be very appealing.

Enhancements to Modified Chebyshev-Picard Iteration Efficiency for Perturbed Orbit Propagation

Modified Chebyshev Picard Iteration is an iterative numerical method for solving linear or non-linear ordinary differential equations.In a serial computational environment the method has been shown to compete with,or outperform,current state of practice numerical integrators.This paper presents several improvements to the basic method,designed to further increase the computational efficiency of solving the equations of perturbed orbit propagation.

Efficient Orbit Propagation of Orbital Elements Using Modified Chebyshev Picard Iteration Method

This paper focuses on propagating perturbed two-body motion using orbital elements combined with a novel integration technique.While previous studies show that Modified Chebyshev Picard Iteration(MCPI)is a powerful tool used to propagate position and velocity,the present results show that using orbital elements to propagate the state vector reduces the number of MCPI iterations and nodes required,which is especially useful for reducing the computation time when including computationally-intensive calculations such as Spherical Harmonic gravity,and it also converges for>5.5x as many revolutions using a single segment when compared with cartesian propagation.Results for the Classical Orbital Elements and the Modified Equinoctial Orbital Elements(the latter provides singularity-free solutions)show that state propagation using these variables is inherently well-suited to the propagation method chosen.Additional benefits are achieved using a segmentation scheme,while future expansion to the two-point boundary value problem is expected to increase the domain of convergence compared with the cartesian case.MCPI is an iterative numerical method used to solve linear and nonlinear,ordinary differential equations(ODEs).It is a fusion of orthogonal Chebyshev function approximation with Picard iteration that approximates a long-arc trajectory at every iteration.Previous studies have shown that it outperforms the state of the practice numerical integrators of ODEs in a serial computing environment;since MCPI is inherently massively parallelizable,this capability is expected to increase the computational efficiency of the method presented.

Variational Iteration Method for Solving Time Fractional Burgers Equation Using Maple

The Time Fractional Burger equation was solved in this study using the Mabel software and the Variational Iteration approach. where a number of instances of the Time Fractional Burger Equation were handled using this technique. Tables and images were used to present the collected numerical results. The difference between the exact and numerical solutions demonstrates the effectiveness of the Mabel program’s solution, as well as the accuracy and closeness of the results this method produced. It also demonstrates the Mabel program’s ability to quickly and effectively produce the numerical solution.

Policy Iteration for Optimal Control of Discrete-Time Time-Varying Nonlinear Systems

Aimed at infinite horizon optimal control problems of discrete time-varying nonlinear systems,in this paper,a new iterative adaptive dynamic programming algorithm,which is the discrete-time time-varying policy iteration(DTTV)algorithm,is developed.The iterative control law is designed to update the iterative value function which approximates the index function of optimal performance.The admissibility of the iterative control law is analyzed.The results show that the iterative value function is non-increasingly convergent to the Bellman-equation optimal solution.To implement the algorithm,neural networks are employed and a new implementation structure is established,which avoids solving the generalized Bellman equation in each iteration.Finally,the optimal control laws for torsional pendulum and inverted pendulum systems are obtained by using the DTTV policy iteration algorithm,where the mass and pendulum bar length are permitted to be time-varying parameters.The effectiveness of the developed method is illustrated by numerical results and comparisons.

棒束子通道两流体全隐式Picard Krylov算法

为提升压水堆堆芯棒束通道热工水力计算的精度和效率,本文提出一种Picard算法和Krylov子空间算法相结合的全隐式Picard Krylov算法。利用Picard算法完成棒束子通道两流体方程组全隐式求解,并使用Krylov子空间方法提升Picard迭代计算效率。研究表明:全隐式Picard Krylov算法在压水堆子通道及棒束通道基准题稳态和瞬态计算中,数值结果与实验结果吻合较好,且相较于Picard算法稳态计算效率最高提升89.7%,瞬态计算效率最高提升20.68%。相较于传统的算符分解及Picard迭代方法,全隐式Picard Krylov算法具有良好的计算精度和效率。

Iteration dependent interval based open‐closed‐loop iterative learning control for time varying systems with vector relative degree

For linear time varying(LTV)multiple input multiple output(MIMO)systems with vector relative degree,an open‐closed‐loop iterative learning control(ILC)strategy is developed in this article,where the time interval of operation is iteration dependent.To compensate the missing tracking signal caused by iteration dependent interval,the feedback control is introduced in ILC design.As the tracking signal of many continuous iterations is lost in a certain interval,the feedback control part can employ the tracking signal of current iteration for compensation.Under the assumption that the initial state vibrates around the desired initial state uniformly in mathematical expectation sense,the expectation of ILC tracking error can converge to zero as the number of iteration tends to infinity.Under the circumstance that the initial state varies around the desired initial state with a bound,as the number of iteration tends to infinity,the expectation of ILC tracking error can be driven to a bounded range,whose upper bound is proportional to the fluctuation.It is revealed that the convergence condition is dependent on the feed-forward control gains,while the feedback control can accelerate convergence speed by selecting appropriate feedback control gains.As a special case,the controlled system with integrated high relative degree is also addressed by proposing a simplified iteration dependent interval based open‐closed‐loop ILC method.Finally,the effectiveness of the developed iteration dependent interval based open‐closed‐loop ILC is illustrated by a simulation example with two cases on initial state.

常微分方程欧拉折线法的Picard迭代改进

考虑常微分方程数值解法,对欧拉折线法加以改进。主要方法是,对每个节点处局部线性近似解进行一次Picard迭代修正,用局部修正解估算下一节点处解值。证明了算法局部截断误差比欧拉折线法高一阶,分析了算法的收敛性与稳定性。通过方程实例数值计算进一步验证了本文算法所得近似解比欧拉折线法近似解更接近精确解。

A Novel Method to Enhance the Inversion Speed and Precision of the NMR T_(2) Spectrum by the TSVD Based Linearized Bregman Iteration

The low-field nuclear magnetic resonance(NMR)technique has been used to probe the pore size distribution and the fluid composition in geophysical prospecting and related fields.However,the speed and accuracy of the existing numerical inversion methods are still challenging due to the ill-posed nature of the first kind Fredholm integral equation and the contamination of the noises.This paper proposes a novel inversion algorithmto accelerate the convergence and enhance the precision using empirical truncated singular value decompositions(TSVD)and the linearized Bregman iteration.The L1 penalty term is applied to construct the objective function,and then the linearized Bregman iteration is utilized to obtain fast convergence.To reduce the complexity of the computation,empirical TSVD is proposed to compress the kernel matrix and determine the appropriate truncated position.This novel inversion method is validated using numerical simulations.The results indicate that the proposed novel method is significantly efficient and can achieve quick and effective data solutions with low signal-to-noise ratios.

A novel three-step implicit iteration process for threeΦ-hemicontractive mapping in the intermediate sense

In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.We prove that our proposed iteration process converges to the common fixed point of three finite family of asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.Our results extends,improves and complements several known results in literature.

一阶非线性时标动态方程的Hyers-Ulam-Rassias稳定性

利用Picard算子和动态不等式,探讨了一类形式更普遍的一阶非线性时标动态方程的Hyers-Ulam-Rassias稳定性,并且提供三个例子说明这些结论的应用.