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Accelerating the pace of space engineering through AI
In the rapidly evolving space industry, engineering speed and efficiency are critical. As space vehicles and missions become more complex, so do the systems used to model and test their performance and safety. This involves a wide range of factors, including aerodynamics, stability and control, structural integrity, propulsion, fluid dynamics, maneuverability and more.
Yet many aerospace companies have analysis systems that aren’t keeping up with the daily needs of their engineers. This is where machine learning (ML) and generative AI (GenAI) can help. They can help propel aerospace engineering into a new era of speed and efficiency, while also enabling quality and regulatory compliance.
Existing challenges: too much data, not enough time
Consider the challenges faced by space engineers today. A team of engineers at a leading aerospace manufacturer is working to analyze reflective airframe loads data for a new spacecraft. Their goal is to determine necessary changes in structural design to make the craft faster, safer and more efficient. However, they are relying on outdated software and data from siloed flight sciences teams. The process is slow and frustrating, with the new spacecraft sitting idle on the tarmac for weeks or even months. It’s detrimental not only from a revenue generation and shareholder perspective, but results in wasted resources, potentially running into millions of dollars.
At another workstation, an engineer is using the latest software suite to design a new wing for vertical lift. Because there is no workable solution for parts rationalization or recommendations, the engineer has to create something from scratch. This results in new parts and potentially a new supplier, adding to parts proliferation.
No company should accept such limitations. There are new tools available that can guide companies through incremental stages of analysis, each building on the ones before, and much faster than you might imagine. These tools can help companies overcome the challenges of traditional engineering and analysis, such as:
- Reliance on siloed tools and workflows: Connecting engineering teams levels up speed, efficiency and transparency.
- Delays caused by systemic human latency: Engineers get the data they need without having to rely on others.
- Lack of integration with design and manufacturing processes: Connecting engineering and testing to design and manufacturing saves time, money and effort.
- High computational cost and time requirements: Time is money – even for automated processes.
- Difficulty incorporating real-time data: Successful integration of real-time data from testing teams creates a positive feedback loop that speeds product development.
- Regulatory and compliance hurdles that result from nonintegrated and mixed data classifications (ITAR, non-ITAR, CUI, etc.): Engineering processes can build in regulatory compliance from the get-go.
- Parts and supplier proliferation: Managing the parts lifecycle prevents unnecessary proliferation before it even begins.
Emerging trends and technologies to the rescue
Today the multiple promising tools, trends and technologies that are revolutionizing flight sciences analysis include:
- Digital twins and multiphysics simulation
- ML for predictive analytics and improvement
- Computer-aided design (CAD) software
- Big-data analytics
- GenAI aids and plug-ins to augment existing software suites and workflows
- Government cloud services that provide high performance computing (HPC), AI studios for advanced modeling, and support for dynamic, integrated classified data with real-time insights
- High-performance computing and advanced modeling techniques
- Collaborative digital engineering environments with digital thread integration
Together, these capabilities and platforms can support a deep transformation of your business operations from siloed to fully integrated engineering pods. They can help you reduce your engineering development cycle time, streamline production transitions and manage parts registry efficiently. They not only help solve existing problems but also provide new capabilities for working faster and at scale.
Space manufacturing firms reluctant to adopt these technologies run the risk of falling behind. Many industry-leading US manufacturers are beginning to explore how GenAI can transform the maintenance, repair, and overhaul industry.
Transformative tech power
Here’s one example. A PwC space industry client was struggling to manage and reduce the large amounts of data generated by their analysis models — from around 600,000 data points to a few hundred, to produce a two-dimensional design. Running their models on individual, siloed workstations was not getting the job done. They were also working with poorly ingested, unclean data.
PwC worked with vendors to provide the client with a new end-to-end solution. This solution comprised a cloud-based data pipeline complete with ingestion, validation, metadata, data lineage, orchestration and analytics. It also included an integrated AI studio for modeling at scale — all on a government cloud platform.
This solution allowed the client’s analysis processes to operate faster, more reliably and more efficiently. The client was no longer forced to discard valuable data due to lack of computational power and could edit their models in real time. The new cloud-based model library, equipped with GenAI code generation plugins, facilitated the creation of new models — in any programming language preferred by the engineers.
A time to lead
Modernizing aerospace engineering and flight sciences analysis can seem like a daunting challenge, especially given that companies can’t pause their work to focus on it. At PwC, we understand the need for innovation on the go. Our experience lies in helping you expedite the necessary changes to move the needle on your data analysis, both predictive and reflective. We can introduce new capabilities to transform your modeling processes. And we can guide you in creating a clean, modernized data pipeline. This includes improving standardization and validation rules to provide data you can trust. It has never been more crucial to balance technological advancements with strong validation and compliance procedures.
The time is now to invest in digital transformation initiatives that can enable you to:
- Implement up-to-date collaborative platforms and data management strategies
- Embrace AI for advanced analysis and process augmentation, freeing up time for engineers to focus on innovation
- Use cloud computing and high-performance computing resources
- Prioritize integration across the engineering lifecycle
- Foster a culture of innovation and continuous improvement
Modernizing aerospace engineering could impact the aerospace industry far beyond enhancing efficiency. All stakeholders should consider how they can embrace change and lead the way in guiding innovation and renewal in this critical field. Throughout history, ventures into space have relied on bold acts of imagination above all. Giving space engineers the tools they need now can free them to dream big.
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