A Digital Engineering Case Study for Aerospace Webinar

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In today's rapidly evolving aerospace industry, digital engineering is transforming the way we design, analyze, and validate complex systems. Our webinar, “A Digital Engineering Case Study for Aerospace,” showcased a case study centered on a hydrogen fuel cell powertrain for regional aircraft, presented by Sabrina Barm, a PhD student and research associate at the Technical University of Applied Sciences in Augsburg, Germany. 

This case study not only highlights the innovative nature of hydrogen power in aviation but also delves into the methodologies employed in Model-Based Systems Engineering (MBSE), particularly highlighting the role of Innoslate.

Understanding the Context

The primary focus of the research is the development of a hydrogen fuel cell powertrain that utilizes cryogenic hydrogen as an energy carrier. This hydrogen is stored in liquid form at extremely low temperatures (approximately 20 Kelvin or -250 degrees Celsius). 

The powertrain utilizes an axial flux motor, a unique type of electric motor, which also employs this cryogenic hydrogen as a cooling medium. This setup indicates the complexity of the system, particularly in terms of thermal management and operational requirements.

The Challenge of Complexity

As Sabrina notes, building such a sophisticated system comes with its own set of challenges, primarily related to temperature differentials and operational demands. The central question posed is how to design an optimal system that meets these stringent requirements. This complexity necessitates a comprehensive understanding of the system, which is where modeling becomes vital.

The Three-Way Modeling Approach Leveraging Innoslate

To tackle the intricacies involved in designing the cooling system for the hydrogen fuel cell powertrain, a three-pronged modeling approach is employed, facilitated by Innoslate's powerful capabilities:

1. Descriptive Model-Based Systems Engineering (MBSE) Model: This model captures and synchronizes all relevant system information, facilitating effective visualization and understanding of the system’s architecture and functions. Innoslate allows teams to create detailed models of system requirements, designs, and their relationships, improving communication among stakeholders.
   
   
2. Behavioral Model: This component represents the system's functionality and behavior, allowing researchers to assess how well the system will perform under various conditions. Innoslate’s simulation features enable teams to conduct scenario analyses and explore how different configurations can affect performance.
   
   
3. Causal Network Model: This model is essential for exploring causal relationships within the system, helping to establish how different elements influence each other. Innoslate helps in visualizing these relationships, making it easier to analyze system dynamic behavior.

A Focus on Cooling

The cooling system's design becomes critical in ensuring the efficient operation of the powertrain. The team analyzed the aircraft's mission profile to determine the required motor performance. From this, they derived system functional requirements, such as the heat removal necessary to maintain optimal performance.

By employing meaningful abstractions, the modeling shifts away from overly simplistic representations, capturing essential phenomena like heat absorption and transport. The cooling system is designed to function without an explicit pump, relying on the cryogenic properties of hydrogen itself to maintain flow, which further complicates the design process.

Experimental Validation and Test Predictions

The behavioral model developed allows the team to run various test predictions, which inform the design of experiments for component testing. Sabrina described how the model helped identify functional ranges for system parameters such as entry pressure, revealing that pressures below certain thresholds could result in flow stalling.

Using Innoslate's integrated calculations and simulation capabilities, the team could simulate various scenarios and iteratively adjust parameters such as mass flow and entry temperature. This flexibility allowed the researchers to test the system's behaviors without resorting to costly physical tests.

Moving Forward

As the team continues to refine and remodel their causal network, they aim to enhance their predictive accuracy. One significant insight gained was the importance of considering external heat inputs, such as from ambient air, which were previously deemed negligible.

Engaging in this detailed modeling approach has provided the researchers with a robust framework for understanding complex interactions within the cooling system and the broader hydrogen fuel cell powertrain. Innoslate plays a critical role in this process, enabling efficient documentation and analysis.

The case study presented in this webinar serves as a prime example of how digital engineering and MBSE methodologies, enhanced by tools like Innoslate, are reshaping aerospace design. By leveraging sophisticated modeling techniques, researchers can navigate the complexities associated with new technologies like hydrogen fuel cells in aviation.

As the industry moves towards more sustainable and efficient power solutions, the insights garnered from this research are invaluable in paving the way for innovative designs that are both effective and environmentally friendly.

For more insights from this dynamic conversation in aerospace engineering, tune in to future webinars and explore the realm of digital engineering possibilities, powered by Innoslate!