Multi-Disciplinary design and Analysis Framework for Aerial Systems

The outcome should contribute to:

• Enhance operational superiority and lifecycle management.
• Enable breakthroughs such as integrated mission management and systems diagnosis, predictive maintenance facilitating mission planning and mission planning adaptation, simulation and training scenarios, reduced manning and/or autonomous operations.
• Ensure safer platforms, increased equipment reliability, endurance, and reduced maintenance costs.
• Facilitate the validation and incorporation of new technologies throughout the platform lifecycle.
• Enable early risk reduction and digital system maturation to minimise development time and costs.
• Enhance the effectiveness of coordination with Military Airworthiness Authorities.
• Allow the air forces of EU Member States and EDF Associated Countries to remotely configure customised platforms and assess operational effect.
• Revolutionise aerial system design and certification, enabling multi-functional and high-fidelity system design coupled to physical testing infrastructure, leading to improved system efficiency and reduced costs.

Digital Twins (DT) are defined as validated virtual models of physical entities and processes, with the capability to be (seamlessly) connected, in right time throughout their lifecycles, enabling simulation, performance optimisation, and informed decision-making.

A System of Systems (SoS) DT is a model that integrates multiple DTs of individual systems, subsystems, and components, providing a holistic, real-time view of the entire system behaviour, performance, and responses to various scenarios and conditions.

The development of advanced fully coupled DT simulation capabilities is necessary to support the design of complex, high-fidelity aerial systems, and to facilitate the generation of certification-relevant data.

This framework should comprise aerodynamic, structural, flight control system, general system, embedded software, and design capabilities from level zero to high fidelity modelling, offering the possibility to provide close and loosely coupled multi-disciplinary simulations, and provide full design gradients for multi-disciplinary numerical optimisation.

The multidisciplinary analysis and optimisation capability is a design methodology for fast and reliable design space exploration, trade-offs, and requirements sensitivity assessment, hence, a key technology for modern aircraft development.

DTs need to capture the complexity of the system being modelled and its surrounding environment, including the capabilities of Allied Nations.

Potential benefits of DTs for military applications include:

• Increase fleet availability and reliability by enabling better maintenance planning and reducing the occurrence of unanticipated damage findings.
• Improve product development and reduce lead time for the new military systems.
• Ensure fleet safety by providing better information on the condition of each individual asset.
• Incorporate added capabilities to provide operational superiority.
• Reduce maintenance costs by increasing maintenance interval, reducing inspections and maintenance labour.

A key objective of this call topic is to explore the benefits of applying DT technologies across the entire lifecycle of military systems, from design and development to operation and maintenance. This includes investigating how DTs can improve the efficiency, effectiveness, and interoperability of systems throughout their lifespan.

The research also aims to examine the flow of digital data across different stages of the lifecycle, as well as between various domains, such as:

• Lifecycle phases: How digital data can be seamlessly shared and utilised across different stages, e.g., from design to software development to mechanical engineering.
• Application domains: How digital data can be integrated and leveraged across different areas, such as system operation, logistics, and maintenance.
• Information spaces: How digital data can be shared and utilised across different information systems and platforms, ensuring interoperability, and reducing silos.

Specific objective

To address the interoperability challenges of DTs in a global context, it is essential to develop a robust reference architecture that can handle the complexities of exchanging, sharing, and reusing information across diverse systems and nations. The key issues to be addressed are the following:

• Effective Information Exchange is challenging because of use of diverse data formats and standards, variations in modelling techniques and simulations tools (esp. considering sensor networks). This should be addressed through improvement of standardisations, implementation of middleware solutions and data catalogues.
• Coordination and Enrichment of Simulations: the main challenge is the need for faster than real time data integration from multiple sources and ensuring data consistency. These should be addressed through the creation of a federated architecture, data orchestration and cross-domain ontologies.
• Security of Information Exchange to protect against cyber threats. Data should be secured both during transfer and storage. Therefore, following technologies must be considered: encryption, access control, Intrusion Detection Systems or use of block chain technologies.
• Coherent Data Analysis, Storage, and Discovery where the main issues are about managing large volumes of heterogeneous data, handling high-resolution sensor data from multiple sources, and ensuring its quality and consistency. The proposals must address the use of cloud storage for raw data, structured data as well as big data analysis, metadata management, and use of AI and Machine Learning for data cleaning, integration and predictive analytics.
• Development and Validation of Models where the main challenges lie in ensuring accuracy, reliability of physics based and data-driven models and calibration with real-world data. The following technologies and approaches should be considered: Hybrid modelling approaches, model validation frameworks, continuous learning, and collaborative platforms.
• Explore the DT potential in non-technical areas, such as managing cost overruns, reporting progress, and coordinating multi-national capabilities. A common model database, featuring constructive entities and terrain data, can facilitate data sharing and reuse, enhance collaboration, and boost efficiency. This requires understanding data ownership, sovereignty, and sharing concepts to ensure effective management and protection of critical data assets.

The final outcome of this proposal should be a demonstration of a system of system DT that showcases the functional requirements listed further below on one or a few fully described use case(s) for which the consortium can demonstrate to have access to all the necessary data.

The proposals must address the study and design of a SoS DT, in a modular way (sub-systems level), in order to enable a gradual and progressive development.

Priority should be the development of modules related to the modelling of aerial systems and their integration in a digital rig, with a view to certifying these integrations of systems and subsystems. Further modules should be developed and coupled towards reaching the goal of obtaining a DT at the aerial system level. This modular building up could benefit complementary developments for other weapons systems.

Different levels of interdisciplinary coupling strategies are required depending on the relevant involved disciplines. Depending on the aerial system type, exploiting the interaction between aerodynamics, structure, control laws, general systems, control software, manufacturing and performance analysis (mission and point performance) is crucial, in order to achieve an optimal design and significantly de-risk the programme overall.

The DT should be able to couple with physical tests and easily integrate derived data-driven models. Furthermore, the DT should be able to estimate platform behaviour when stimulated in a virtual environment, as to forecast possible integration issues and evaluate different architecture and solutions to de-risk development of system-details and physical testing.

Simulation data management must ensure data consistency across the design cycle. Any needed High-Performance Computing (HPC) architectures should be accessible.

In order to achieve a DT that is highly realistic and has a robust predictive capability, the underlying modelling and computational technologies must be developed, tested and validated against representative data.

The focus of this activity should encompass the entire spectrum of the systems development life cycle. Therefore, a demonstration of a concept for real-time interconnection of individual DTs of military assets with a monitoring and diagnostic dashboard is essential. This should include a framework for data transfer and feedback loops.

Types of activities

The following types of activities are eligible for this topic:

Accordingly, the proposals must cover at least the following tasks as part of mandatory activities:

• Studies:
  • Creating a concept of operations (CONOPS) for a fully coupled DT system for a multi-functional and high-fidelity aerial system design, consider the following aspects:
    • System Architecture.
    • Modelling and Simulation.
    • Data Management.
    • Cybersecurity.
    • Information Exchange.
    • Virtual Environment.
    • User interfaces.
    • Certification and Validation.
    • Interdisciplinary Collaboration.
    • Scalability and Extensibility accommodating new generation assets (manned or unmanned) and technological novelties.
    • Non-technical Applications: Investigate the DT's usefulness for non-technical aspects, such as cost-overrun and progress reporting, and consider the benefits of a common model database for constructive entities, terrain data, and addressing data ownership, sovereignty, and sharing concerns.
  • Exploring use cases beyond those identified in the proposal covering the lifecycle of weapon systems, such as Product Development, Acceptance Test, Supply Chain, Maintenance (PLM), Training, Fleet Supervision, Operational Support, Mission Planning, Product Improvement, Lifetime Evaluations and Extensions, Retrofits, and the benefits of these use cases, while overcoming current challenges in implementing DTs in the military air domain, and issues related to accessibility of information, such as data ownership, intellectual property rights (IPR), concurrency, and synchronisation between subsystems.
• Design:
  • The architecture of the digital and fully coupled DT system for a multi-functional and high-fidelity aerial system design must focus on the following aspects:
    • Common Assets: Implement a SoS DT with a modular, scalable, and standardised approach to ensure interoperability across various aerial system components and subsystems.
    • Modularity: Design the DT system to be modular, enabling gradual and progressive development, focusing on the modelling of aerial systems and their integration.
    • Scalability: Ensure the DT system can accommodate new generation assets (manned or unmanned) and technological novelties like Big Data, AI, and XR, cloud architecture solutions, tactical data links, and LVC interoperability solutions.
    • Standardisation: Establish standardised information exchange, data formats, and ontologies to facilitate faster-than-real-time data integration from multiple sources and ensure data consistency.
    • Communications Backbone System: Develop a communications backbone system model that supports real-time data transfer between the DT and other system components.
  • A comprehensive data storage and transfer concept addressing:
    • Data Compression: Establish a data compression system that supports various data formats, resolutions, and compression concepts based on sensor accuracy and use case.
    • Data Model Definition: Define a data model that covers not only metadata but also data governance, including capturing, logging, and datatype (e.g., type: Date) to enable big data fleet analytics.
    • Data toolsets: such as data recorders, data players, application launchers, and scripting interpreters.
  • An initial demonstrator of the system of system DT which includes:
    • A simulation communications backbone system demonstrator, serving as the basis for a European Defence Standard of communications systems.
    • A set of high-fidelity generic models that demonstrate the feasibility and benefits of a generic DT concept, across the systems development life cycle and for users in operation and lifecycle evaluation.
    • The ability to work across the systems development life cycle and provides benefits for users in operation and lifecycle evaluation.
    • 2 independent tests of the demonstrator using real operation data from military assets, focusing on its effectiveness and performance.

In addition, the proposals should cover the following tasks:

• Studies:
  • Development of a technology maturation roadmap for DTs in Aerial Systems, investigating cost-saving potentials due to Model-Based System Engineering (MBSE) approach and DT technologies. This should include future mission scenarios of the lifecycle military assets.
• Design:
  • Elaboration of a proposal for DT standards:
    • Standardise event lists (log files).
    • Standardise data formats.
  • A concept for design evaluation based on data production, covering a design model for lifecycle evaluation/lifetime evaluation of a component.
  • A concept to evaluate the required numbers of measurements and locations for operation supervision and lifetime evaluation of Aerial Systems.
  • A complete list of components and sub-components, a measurement list and Process & Instrumentation Diagram for the use case of the proposal, including all sensors.
  • Develop a comprehensive data catalogue structure for aerial systems, aligned with international standards ISO/TS 16952, EN 61346-2 and EN 81346-2, which covers:
    • A hierarchical structure with multiple aggregation levels, including:
      • Nation level.
      • Fleet level.
      • System level.
      • Functional overall system level.
      • Sub-system level.
      • Signal identification level.
    • A unique measurement identification system for each sensor or tag at the nation level, ensuring precise tracking and organisation of data from individual sensors/tags to the national level.
  • a concept for DT models that integrate multiple approaches, including:
    • Maths and Physics-Based Models
    • Data-Driven and Calibrated Models

The concept should also include a validation strategy to ensure the reliability and effectiveness of both types of models in operational environments.

In addition, the proposal may cover:

• Studies:
  • Elaboration of recommendations for a European Defence (Aircraft Simulation) DT Model Office in order to share generic models which are not subject to security or export control but improve the speed of development new simulators/prototypes or DT creation. This should include the definition of functional requirements of a number of high-fidelity generic models that could be reused by the defence industries in order to be cost effective and improve the interoperability.
  • Guidelines for:
    • Architecture of DT and the designation of all measurements on fleet perspective.
    • DTaaS concept (DT as a Service).

The proposals must substantiate synergies and complementarity with foreseen, ongoing or completed activities in the field of simulation, notably those described in the call topic EDF-2022-DA-SIMTRAIN-MSSI related to Modelling, simulation and simulator integration contributing to decision-making and training.

Functional requirements

The proposed product and technologies should meet the following functional requirements:

• • Detect and assess damage and optimise aerial systems capabilities by
    • Identifying key features in data.
    • Integrating on-board diagnostic, prognostic, and early-warning functions to ensure reliable performance in various operational, loading and environmental conditions.
    • Achieving high accuracy and low false alarm rates.
  • Consider human factors in multi-level data analysis for various users, including:
    • Aerial Systems or crew.
    • Onshore/Air Force base maintenance services.
    • Fleet management teams.
  • Use Model-Based Systems Engineering (MBSE) to:
    • Simulate system behaviour.
    • Analyse the impact on:
      • Requirements.
      • Functional domain.
      • Logical domain.
      • Physical domain.
  • The system should integrate the following components and capabilities to enable real-time platform health monitoring and predictive maintenance:
    • Industrial IoT Technologies.
    • Sensor Integration.
    • Operator interfaces.
    • Data Lake/Cloud infrastructure.
    • AI-Based Analysis Capabilities.
    • Condition Monitoring.
    • Component/System Failure Modelling.
  • The system should be upgradable and flexible, demonstrated through:
    • Modular Design and DTs: Showcase the ability to upgrade and modify the system using DT technology and modular design principles, ensuring that changes can be made quickly and efficiently.
    • Continuous Deployment: Demonstrate the capability to continuously deploy software systems in both the DT environment and on-board, ensuring that updates and new features can be added seamlessly.
    • Easy Integration of New Functionalities: Show that new digital capabilities and functionalities can be easily added to the system without disrupting existing operations, thanks to the use of shared standards for data and interfaces.
• The system should provide a collaborative environment that enables the following data management capabilities:
  • Comprehensive Data Storage: Store all-time series data from the deployment system, including operational data such as raw and processed measurement data, high-frequency data (>1000 Hz).
  • User-Adaptable Data Management covering prioritisation, resolution and adaptation to changing requirements.
  • Time Series Data Storage: Store data in time series format, taking into account physical constraints up to measurement accuracy.
  • Data Compression: Enable data compression for efficient storage.
  • Unified Data Backbone: Transfer all-time series data on a single, unified data backbone.
  • Big Data Analytics and DT Enablement: Store all data, including raw time series data, from Aerial Systems to enable:
    • Big data analytics.
    • Future DT technologies.
  • Data Transfer to Global Storage Centre: Enable the transfer of all stored data from the deployment system to a global data storage centre, such as a Cloud or Data lake.
  • Real-Time Data Transfer: Enable the state-of-the-science transfer of real-time data from the deployment system to a global data storage centre.
• The digital architecture of the DT solutions should meet the following functional requirements:
  • Design
    • Modular design principle to ensure flexibility and scalability.
    • Use open standards for:
      • Hardware.
      • Software.
      • Infrastructure.
    • Implement modern Service-Oriented Architecture (SOA) principles.
    • Employ Modelling and Simulation as a Service (MSaaS) standards.
    • Adopt DT as a Service (DTaaS) approach.
    • Ensure open standards for interoperability with NATO and national systems (e.g., HLA/DIS/BOM), both military and civilian.
    • Provide state-of-the-art, intuitive graphical user interfaces (GUI) to support:
      • Analytics.
      • Operational needs.
      • Training.
      • Decision-making.
  • Resilience
    • Identify architectural principles to mitigate the impact of undesirable events (e.g., combat damage, loss of power, cyber-attacks) and ensure:
      • Fast recovery.
      • Core functions in degraded mode.
      • Minimal disruption to operations.
  • Security
    • Define and select common architectural principles to maximise security against:
      • Cyber threats.
      • Physical threats.
    • Ensure the safety of the infrastructure for the asset itself.
  • Sustainability
    • Operational Availability: Maintain the architecture's operational availability at reasonable costs through:
      • Maintainability.
      • Obsolescence management.
    • Resource Optimisation: Optimise resource usage through:
      • Lean architecture.
      • Energy optimisation.
    • Ensure the architecture can evolve and integrate future technologies and architectural patterns.
• To ensure the quality and reliability of the DT solutions, incorporate the Verification and Validation (V&V) concept to:
  • Define the V&V process and quality standards at the proposal outset.
  • Standardise the V&V process and documentation across industry members.
  • Conduct the V&V process independently of developers.
  • Ensure transparent and comprehensive documentation of model validation.
• Artificial Intelligence (AI) Requirements
  • Artificial Intelligence (AI) capabilities that enhance decision-making speed, develop behavioural models, and analyse time series data:
    • AI-enhanced Decision-Making.
    • AI-based Behavioural Models (civilian and military).
    • AI-based Time Series Data Analysis:
      • Leverage modern AI analytics to analyse the complete history of time series data.

These AI-based capabilities are to extend the surveillance and operational capabilities of each individual system, allowing for:

• • • Sub-component specific analytics: Analyse the performance of individual components within a system.
    • Fleet-specific analytics: Analyse the performance of entire fleets of systems.