Error Analysis and Error Estimates for Co-simulation in FMI for Model Exchange and Co-Simulation v2.0

Abstract

Complex multidisciplinary models in system dynamics are typically composed of subsystems. This modular structure of the model reflects the modular structure of complex engineering systems. In industrial applications, the individual subsystems are often modelled separately in different mono-disciplinary simulation tools. The Functional Mock-Up Interface (FMI) provides an interface standard for coupling physical models from different domains and addresses problems like export and import of model components in industrial simulation tools (FMI for Model Exchange) and the standardization of co-simulation interfaces in nonlinear system dynamics (FMI for Co-Simulation), see [10]. The renewed interest in algorithmic and numerical aspects of co-simulation inspired some new investigations on error estimation and stabilization techniques in FMI for Model Exchange and Co-Simulation v2.0 compatible co-simulation environments. In the present paper, we focus on reliable error estimation for communication step size control in this framework.

Concept of an advanced simulation-based design for engineering support of offshore plant equipment industries and its realization method

“…Co-simulation methods for combining various analysis tools have been actively studied using computer-aided engineering (CAE) technological advances (Haitao et al, 2013;Arnold et al, 2013;Sicklinger et al, 2015). The methods are classified according to the method used, either the conventional co-simulation method or the FMI method as shown in Fig.…”

An empirical survey on co-simulation: Promising standards, challenges and research needs

“…Impact of coupled error controlled algorithms [12,28] 5.7 6.0 5.8 Uncertainty quantification/propagation [7,39] 5.6 6.0 5.8 Impact of updating inputs (and the discontinuity it introduces) in the subsystems [10,20]. 5.6 6.0 5.7 Acausal approaches for co-simulation [59] 5.6 6.0 5.7 Impact of using different tolerances in a sub-component on the overall simulation [3] 5.3 6.0 5.5 Numerical stability [11,22,21] 5. Most research needs (all except simulator black boxing and IP protection) are assessed by the experts with a interpolated median value greater 4.5, corresponding to at least "Somewhat agree".…”

“…The three circles per group indicate the global factor priorities; the longer the distances between the respective group/factor and the origin, the higher the overall importance assigned this group/factor. (7) Sc: Every sub-system can be implemented in a tool that meets the particular requirements for the domain, the structure of the model and the simulation algorithm 0.44 (1) 0.148 (2) Weaknesses (internal) 0.013 0.16 Wa: Computational performance of co-simulation compared to monolithic simulation 0.34 (2) 0.056 (9) Wb: Robustness of co-simulation compared to monolithic simulation 0.41 (1) 0.067 (8) Wc: Licenses for all programs are required to couple different simulation programs 0.24 (3) 0.039 (12) Opportunities (external) 0.003 0.33 Oa: Growing co-simulation community/growing industrial adoption 0.29 (2) 0.094 (4) Ob: User-friendly tools (pre-defined master algorithms, integrated error estimation, sophisticated analysis to determine best parametrization of solvers and master algorithms) 0.47 (1) 0.153 (1) Oc: Better communication between theoretical/numerical part, implementation and application/industry 0.25 (3) 0.080 (6) Threats (external) 0.003 0.18 Ta: Insufficient knowledge/information of user in co-simulation may lead to improper use 0.28 (3) 0.088 (5) Tb: Incompatibility of different standards and co-simulation approaches 0.41 (1) 0.043 (11) Tc: Lack of exchange/cooperation between theoretical/numerical part, implementation and application/industry. 0.31 (2) 0.044 (10) The results of the SWOT-AHP analysis indicate that factors for strengths and opportunities predominate.…”

Convergence of explicitly coupled simulation tools (co-simulations)

“…With the h ij being solved for u ij inside the S j (let solvability be given), for systems with more than two subsystems it is more convenient to write output variable y j and now redefine u ij as the input of S i , consisting of some components of the outputs y j [2]. This structure is defined as kind of a standard for connecting simulators for cosimulation by the Functional Mockup Interface Standard [1].…”