In the realm of model verification, ensuring traceability and repeatability is paramount for achieving Modeling & Simulation (M&S) credibility and development efficiently. This paper explores challenges and solutions for expressing complex model Verification & Validation (V&V) workflows that ensures readability while still enables automation, parallelization and advanced customization of verification activities. This paper contributes a practical solution that is evaluated through a detailed implementation within an in-house developed multipurpose automation and Verification & Validation (V&V) framework.

Complex aerospace systems require extensive simulation capabilities, but current practices rely on proprietary interfaces requiring manual integration effort that limits scalability and interoperability. This paper evaluates the Functional Mock-up Interface (FMI) 2.0 and System Structure & Parameterization (SSP) standards for legacy based aerospace components through practical implementation and industrial assessment. A two-layered wrapper architecture was developed where legacy components was successfully converted to FMI-compliant Functional Mock-Up Units (FMUs) while preserving original simulation logic. The implementation addresses complex data structure interoperability, type system mapping, and coordination for components operating at different frequencies. An evaluation using OMSimulator demonstrated successful FMU integration within SSP packages, though execution pattern mismatches revealed several tool limitations. Furthermore, workshops with aerospace engineers indicated FMI 2.0 and SSP could satisfy domain specific simulation requirements for cross-departmental exchange of models and moderate-complexity applications, while identifying performance overhead concerns for high-performance scenarios.

This article proposes a method for improved model verification within Large-Scale Simulators (LSS). The approach relies on machine-interoperable traceability of model verification information, such as model Operational Domains (ODs). This enables automated evaluation of model relevance and facilitates the combination of models for a broader evaluation of credible simulation results. The paper introduces a proof-of-concept testbed for verification of black-box models against model requirements. Furthermore, the results also include a proposal for a machine-readable format to capture model requirement Verification & Validation (V&V) results, along with the resulting model and updated model OD information.