Engineering Confidence
How Digital Twins Transform Simulation and Validation in Aerospace
Aerospace Innovation Needs a New Validation Paradigm
Aerospace engineering has always been defined by complexity. However, the challenges facing today’s aircraft, unmanned or manned, and space systems differ in both scale and character. Modern aerospace systems are increasingly software-defined, highly interconnected, and expected to perform advanced functions reliably. Flight controls, avionics, propulsion, and autonomy functions are no longer developed as isolated domains. They interact continuously. A change in one subsystem can affect the behavior of the integrated system and even the overall aerial vehicle. Therefore, aerospace platforms must satisfy an ever-growing list of requirements: functional safety, reliability, cybersecurity, operational efficiency, and autonomy.
Unmanned aerial vehicles (UAV) include autonomous mission systems with AI-supported decision-making functions or are optionally remotely piloted. Therefore, they must be developed for and validated across a vast number of operating scenarios. These include nominal flight conditions, rare edge cases, degraded sensor states, communication losses, environmental disturbances, and combinations of faults that can be too risky, too expensive, or sometimes almost impossible to reproduce in physical testing.
Traditional development, verification, and validation methods are reaching their limits. Physical prototypes and flight tests remain indispensable, but they are costly, capacity-constrained, and typically available only late in the development cycle. By the time all system components are integrated into a physical test bench or an aircraft for flight-testing, design changes can be expensive and time-consuming. For AI-based systems in particular, the challenge is even greater. Before engineers can be confident in the behavior of perception algorithms, decision-making logic, and control functions, the algorithms must be trained with an enormous variety of scenarios and data, which can be provided realistically only by synthetic data generation and virtual flight testing.
This is where digital twins become a key enabler, bridging the gap between simulation of a single system and real-world behavior of the entire aerial vehicle. By combining high-fidelity models, real-time simulation, validated system behavior, and data from development and operations, digital twins allow aerospace engineers to shift validation activities into earlier phases, scale scenario-based testing, and enter real-world testing with more mature systems. The result is not the replacement of physical testing, but a smarter balance between virtual and physical validation to reduce time and cost.
Digital Twins in Aerospace
A digital twin is more than a simulation model. It is a dynamic replica of a physical object or system, including a simulation of the physical environment. A digital twin can represent a single component, such as an actuator or engine subsystem, a complete aircraft system, such as flight control or propulsion, or even an integrated aircraft, UAV, or mission environment. It can include validated simulation of physics-based models, software models, control algorithms, communication networks, sensor models, and environmental models. With these high-fidelity models, a representation of the real-world counterpart is accomplished that is sufficiently realistic for the intended engineering decision, with assumptions, deviations, and validity boundaries made explicit.
The required level of fidelity of a digital twin depends on its purpose. For early concept evaluation, a simplified model can be sufficient to compare architectures. For later stages of controller development, engineers need real-time-capable plant models that capture relevant dynamic behavior. For safety-critical validation, models must be validated against measurements and used within clearly defined limits. This makes digital twins highly practical engineering assets. They create a digital thread between requirements, models, software, hardware, test cases, test results, test automation, and operational data. When connected to simulation platforms, software-in-the-loop (SIL) environments, or real-time hardware-in-the-loop (HIL) systems, they become the backbone of a simulation-driven validation strategy.
Shifting Left: Virtual Development and Early Testing
Digital twins introduce a disruptive change in the development process, because they allow engineers to move a substantial part of testing activities to earlier development phases. Aircraft behavior can be simulated under many different near-real-world scenarios using models of systems, controllers, and the environment, long before a complete physical prototype is available.
This shift-left approach moves testing activities from late-stage integration to earlier phases of the development process. Instead of waiting until hardware, embedded software, and system components are physically available, teams can already begin validating assumptions, interfaces, and control strategies virtually.
The benefits are substantial. Integration issues can be detected earlier. Design alternatives can be iterated on and compared faster, leading to enhanced design accuracy and performance validation. Test benches can be designed and prepared based on digital twins and their interfaces before all components are available. This accelerates the setup of physical test benches once real components are available. Ultimately, the number of design iterations and required physical prototypes can eventually be reduced, improving risk management and accelerating time to market.
In addition, digital twins strongly support continuous integration (CI) and continuous testing (CT). In an agile software development approach, e.g., in software-defined autonomous systems, an automated simulation-based test environment is essential to enable fast software updates between iteration cycles. Every software build can be executed against a defined set of regression tests, continuously and as part of automated workflows. Test results can be traced back to requirements and failures can be reproduced exactly. This creates a more controlled, data-driven development process.
Digital Twins for Safety-Critical Validation
For AI-based systems, digital twins are even more important. Perception and decision-making algorithms require extensive exposure to diverse operational scenarios. Synthetic data generation and virtual flight testing allow teams to create controlled, repeatable datasets for training, testing, and validation. Weather conditions, lighting, sensor noise, air traffic density, terrain, mission profiles, and fault cases can be varied systematically, which is essential for reproducibility and traceability of test results.
This is a clear advantage compared to physical tests, where environmental conditions, hardware states, timing, and operator inputs can vary. In a digital twin environment, the same scenario can be executed repeatedly with precisely controlled parameters. This allows engineers to isolate root causes of failures, compare software versions, and verify whether a design change has resolved an issue.
Moreover, when failures occur, teams need to know which scenario was executed, which model version was used, which software build was tested, and which requirement was affected. A validation environment based on digital twins enables the linking of test cases, requirements, model versions, software versions, configuration data, and results to provide this traceable chain of evidence.
Scenario-based testing is another major advantage. Aerospace systems must be validated across enormous operational envelopes. For autonomous or AI-supported systems, the number of relevant scenarios can grow exponentially. Digital twins make it possible to define, execute, and manage large scenario catalogs, including rare edge cases and even catastrophic scenarios. These may include sensor dropouts, actuator faults, GPS degradation, unexpected obstacles, severe weather conditions, communication latency, or simultaneous subsystem failures. This virtual fault injection is particularly valuable, since faults that would be dangerous, expensive, or impractical to create in physical testing can be introduced safely in simulation. In aerospace, testing is not just about proving that a system works under ideal conditions. It is about building confidence that the system behaves safely and predictably under expected, unexpected, and degraded conditions. This makes virtual validation with digital twins a critical and powerful complement to real-world data collection and testing, because it enables engineers to deepen their understanding before entering costly physical test campaigns.
Simulation-Based Integration of Complex Systems
To realize the potential of digital twins, aerospace organizations need the appropriate simulation and test architecture. Two key approaches are software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing which can also be combined in a hybrid setup.
In SIL testing, embedded software or application code, referred to as the system under test (SUT), is tested in a fully virtual environment without requiring the final target hardware (see figure 1). Because the test setup is independent of dedicated simulation hardware, test cases can be executed as fast as possible and scaled efficiently using standard computing infrastructure. VEOS, a PC-based platform for SIL simulation from dSPACE, integrates a wide range of simulation models, including functional algorithms, bus systems, virtual electronic control units, and vehicle models. To accelerate project setup, developers can incorporate highly accurate and realistic models from the dSPACE ASM suite. The ASM suite contains open models that are implemented in MATLAB/Simulink® and can easily be extended by developers, for example, with aircraft-specific dynamics models. As a result, SIL testing is especially well-suited for early software verification, regression testing, virtual electronic control unit validation, controller validation, and large-scale scenario execution.
HIL testing puts physical devices into a closed-loop simulation (see figure 2) by stimulating electrical I/O, network and bus interfaces in real time. Actual Line-Replaceable Units (LRU), electronic control units, avionics computers, sensors, actuators, or network components are connected to real-time HIL simulators, such as SCALEXIO from dSPACE, to simulate the environment. The hardware interacts with simulated aircraft dynamics, loads, signals, buses, and environmental conditions as if it were operating in the real system. As with SIL testing, high-fidelity virtual worlds with realistic environmental modeling, including weather dynamics and terrain, can be provided by dSPACE AURELION. It delivers pixel-accurate ground-truth data for all sensor types and supports AI use cases with synthetic data generation. Furthermore, easy parameterization and positioning of camera, radar, and lidar sensors, combined with real-time simulation, enable cost-effective generation of realistic sensor data. HIL testing is essential when timing behavior, electrical interfaces, communication protocols, and embedded hardware constraints must be validated.
For aerospace systems, distributed and networked testing is increasingly important. Modern aircraft architecture includes multiple interconnected systems communicating over avionics buses, including Ethernet-based networks such as AFDX® / ARINC 664. Test environments must therefore simulate not only individual components, but also communication behavior, timing, latency, bus loads, and fault conditions. With digital twins, a flight control computer can be connected to a simulated aircraft and environment model. Avionics software can interact with virtual sensors and communication buses. A propulsion controller can be validated against a real-time engine model. Autonomy software can process synthetic sensor inputs from a simulated environment. Different test stages – from desktop simulation to SIL, HIL, and hybrid benches – can reuse models and test scenarios where appropriate.
This reuse is one of the most important practical benefits. Organizations can avoid rebuilding models and tests for every phase. Instead, models can mature over time, becoming increasingly validated and connected to real system behavior.
Maximize Use of Digital Twins
Digital twins do not deliver much value in isolation. They require an infrastructure that supports performance, scalability, automation, interoperability, and collaboration.
First, high-performance simulation environments are essential. Some use cases benefit from faster-than-real-time execution, for example, when running thousands of virtual test scenarios in parallel in a SIL environment. Others require deterministic real-time simulation, particularly for HIL testing, where real hardware expects accurate timing. The infrastructure must therefore support both scalable cloud or server-based simulation and real-time test systems.
Second, automation of test workflows, test system orchestration, and data management matters. Digital twin environments can generate large amounts of data: model parameters, simulation outputs, test results, scenario definitions, software versions, signal traces, sensor data, and operational feedback. Without automation and structured data management, the value of virtual validation is quickly lost. Teams need clear versioning, metadata, result comparison, and traceability.
Third, interoperability is critical. Aerospace programs involve OEMs, system suppliers, software suppliers, tool providers, and certification stakeholders. No single tool environment covers every discipline. Standards are therefore essential for exchanging models, connecting simulation components, and protecting intellectual property.
Collaborative Ecosystems and Standardization
The full value of digital twins emerges when they become part of collaborative engineering ecosystems. Aerospace platforms are rarely developed by a single organization. Alongside OEMs and Tier-1 suppliers, specialists contribute to the final system. Therefore, models must be exchanged, interfaces must be agreed upon, and test scenarios must be reused, so that results become usable across organizational boundaries. However, suppliers cannot simply hand over detailed internal models or proprietary know-how. Standardized interfaces and packaged “black-box” simulation components make it possible to share behavior while protecting implementation details.
This is crucial for building integrated digital twins across OEM and supplier boundaries.
The Functional Mock-up Interface (FMI) is a prominent example of how cross-organizational work can succeed. FMI is a free, tool-independent standard for exchanging dynamic simulation models, distributed as Functional Mock-up Units (FMU), and is supported by more than 280 tools. FMI is maintained as a Modelica Association Project. It supports model exchange and co-simulation, enabling models from different tools and suppliers to be integrated more easily, either as open or as black-box containers.
Additionally, in virtual and hybrid testing, EUROCAE standard ED-247 is highly relevant. It envisions new possibilities for designing future test benches architectures and advances test strategies. ED-247 aims to standardize virtual and hybrid testing by defining virtual component integration and interface rules for aircraft signals and buses. It further addresses interoperability between virtual and real avionics components, hybridization, command, and notification methods, and exchange processes between component providers and integrators, thereby also enabling distributed testing.
Standards reduce friction and make simulation assets reusable across projects, platforms, and lifecycle phases. Open and industry-accepted standards play a central role. FMI supports model exchange and co-simulation across tools. ED-247 supports virtual and hybrid avionics testing. Other aerospace and systems engineering standards can contribute to requirements traceability, data exchange, communication simulation, and model-based systems engineering.
Standardization is a central element of scalability, because it unlocks the full potential of digital twins. If every supplier delivers models in a clearly defined and easily usable format, integration becomes fast and affordable. If every test bench uses agreed interfaces, test reuse becomes virtually unlimited. Continuous validation becomes realistic if every simulation component is ready to use without manual adaptation. This ultimately leads to a collaborative digital twin ecosystem, which helps improve quality. When models from different domains are integrated earlier, interface issues become visible earlier. When suppliers can test against OEM-provided virtual components, integration risk decreases. When OEMs can validate supplier components in system context, confidence increases. The result is a more mature system entering physical testing.
Beyond Testing: Digital Twins Across the Lifecycle
While simulation and validation are among the most compelling use cases, the application of digital twins does not stop at testing. Their long-term potential lies in connecting development, production, operation, and maintenance, while serving as the central pillar of an end-to-end digitalization strategy. This lifecycle perspective is important because every stage generates data that can improve the next and provides information in digital form.
This feedback loop is strategically important. Operational data can be used to refine models. Refined models can improve future validation. Future validation can improve the next aircraft generation. In this way, digital twins become not only a development tool, but an engine of continuous learning.
For AI-based aerospace systems, this lifecycle feedback is particularly relevant. Operational data and edge cases observed in the field can be fed back into simulation environments. New scenarios can be generated. Updated algorithms can be tested virtually before deployment. This creates a closed-loop validation process that supports continuous improvement while maintaining control over safety-critical behavior.
Towards Simulation-Driven Aerospace Validation
Digital twins are transforming aerospace development and testing because they address one of the industry’s central challenges: How to build confidence in systems that are becoming too complex, too software-defined, and too scenario-dependent for traditional methods alone. The enablement of earlier testing, faster design iterations, automated workflows, scalable scenario execution, reproducible validation, virtual fault injection, and better integration across domains helps teams bridge the gap between simulation and real-world behavior by allowing systems to enter physical testing at a higher level of maturity.
In a simulation-driven era, digital twins will become increasingly strategic for next-generation aircraft, UAVs, autonomous platforms, and space systems. Aerospace organizations need high-performance simulation environments, including SIL and HIL, automated test workflows, robust data management, and open standards for model and system integration. This is where an end-to-end SIL/HIL tool chain becomes essential, supporting both classical control systems and AI-based functions – from early virtual development to system-level validation. By combining validated models, real-time simulation, automated testing, scenario management, and hardware integration, companies such as dSPACE can support aerospace organizations in setting up digital twins for the development and validation of both classical and AI-based systems.
As aerospace enters a new era of autonomy, connectivity, and software-defined functionality, validation strategies must evolve. Digital twins provide the foundation for that evolution – helping engineers manage complexity, reduce risk, and accelerate innovation with confidence.
By dSPACE
