This paper introduces the F3ORNITS non-iterative co-simulation algorithm in which F3 stands for the 3 flexible aspects of the method: flexible polynomial order representation of coupling variables, flexible time-stepper applying variable co-simulation step size rules on subsystems allowing it and flexible scheduler orchestrating the meeting times among the subsystems and capable of asynchronousness when subsystems constraints requires it. The motivation of the F3ORNITS method is to accept any kind of co-simulation model, including any kind of subsystem, regardless on their available capabilities. Indeed, one the major problems in industry is that the subsystems usually have constraints or lack of advanced capabilities making it impossible to implement most of the advanced co-simulation algorithms on them. The method makes it possible to preserve the dynamics of the coupling constraints when necessary as well as to avoid breaking C1 smoothness at communication times, and also to adapt the co-simulation step size in a way that is robust both to zero-crossing variables (contrary to classical relative error-based criteria) and to jumps. Two test cases are presented to illustrate the robustness of the F3ORNITS method as well as its higher accuracy than the non-iterative Jacobi coupling algorithm (the most commonly used method in industry) for a smaller number of co-simulation steps.

中文翻译：

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F3ORNITS：灵活的可变步长非迭代协同仿真方法，用于处理具有混合高级功能的子系统

本文介绍了F3ORNITS非迭代协同仿真算法，其中F3表示该方法的3个灵活方面：耦合变量的灵活多项式顺序表示，在子系统上应用变量协同仿真步长规则的灵活时间步长允许和灵活的调度程序，用于安排子系统之间的会议时间，并在子系统约束要求时具有异步性。F3ORNITS方法的动机是接受任何种类的协同仿真模型，包括任何种类的子系统，而不论其可用功能如何。确实，工业上的主要问题之一是子系统通常具有约束条件或缺乏高级功能，从而使得无法在其上实现大多数高级协同仿真算法。该方法不仅可以在必要时保留耦合约束的动态性，而且还可以避免在通讯时破坏C1的平滑度，并且还可以通过对零交叉变量都具有鲁棒性的方式来调整协同仿真步长（违反经典的基于相对误差的标准）并跳转。提出了两个测试案例，以说明F3ORNITS方法的鲁棒性以及与非迭代Jacobi耦合算法（工业上最常用的方法）相比，在较少的协同仿真步骤下具有更高的准确性。并以对零交叉变量（与经典的基于相对误差的标准相反）和跳跃均具有鲁棒性的方式来调整协同仿真步长。提出了两个测试案例，以说明F3ORNITS方法的鲁棒性以及与非迭代Jacobi耦合算法（工业上最常用的方法）相比，在较少的协同仿真步骤下具有更高的准确性。并以对零交叉变量（与经典的基于相对误差的标准相反）和跳跃均具有鲁棒性的方式来调整协同仿真步长。提出了两个测试案例，以说明F3ORNITS方法的鲁棒性以及与非迭代Jacobi耦合算法（工业上最常用的方法）相比，在较少的协同仿真步骤下具有更高的准确性。