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By blurring the boundaries between industry domains, engineering teams can use the latest solutions to build the innovative products of tomorrow, today.
April 20, 2022
6 Min Read
Jean-Claude Ercolanelli, SVP of Simulation and Test at Siemens
Engineers, today, are innovating products and processes that have the potential to make the world a better place for us by improving the way people live, travel, connect, and treat diseases. As they design new products or improve existing ones to meet the demand for more personalized, lighter, and safer products, they must adhere to updated specifications and requirements to reduce emissions, improve energy efficiency, and extend operational performance.
One of the toughest challenges that engineers are facing is to design reliable and sustainable products while managing complexity, reducing cost, and shortening time to market. How does an engineer turn these seemingly contradictory requirements into opportunities?
Adoption of a comprehensive digital twin is the key enabler to sustainable industrial innovation success. By applying a digital twin, engineering teams can manage complexity with design decision trade-offs that result in product innovation. The combination of simulation and test is crucial in bringing the digital twin to life. I see this as the “beating heart” of such comprehensive digital twins. Digital simulation and test applications help harness design complexity and accelerate innovation over the entire lifecycle, delivering insights into the real-world performance of products and processes.
There are four key investment imperatives that can accelerate innovation through simulation and test solutions:
Model the Complexity
Solving complex industrial problems requires an approach that involves multiple disciplines and physics methodologies to capture all of the complexities influencing product performance. Although engineers can now evaluate performance for a wide range of physics phenomena, each domain has historically required a specific set of software tools. Interfaces, processes, data files, and vocabulary may be completely different and hinder collaboration. In this situation, full integration between multiple analysis tools is required to better predict how products will perform. A model-based approach that enables multi-domain simulation and verification is essential for developing today’s advanced products.
Let’s take a look at an incredible example of complexity: the human body. Vyaire Medical is a leading manufacturer of solutions for treating and monitoring respiratory conditions at every stage of life. Their commitment represents a considerable engineering challenge. After all, patients come in various shapes and sizes (morphologies), and each patient has a unique breathing profile.
Historically, they used simplified models, and although they were able to extract some meaningful insights from them, they were limited in their use due to a lack of realism. Gradually the team started introducing patient-specific data into their computational fluid dynamics (CFD) simulations provided by Siemens Simcenter software. Today, they use scans of real human heads that represent the morphologies of features for all patient populations: adult, pediatric, and infant.
Explore the Possibilities
Exploring designs requires using different technologies. Using artificial intelligence (AI) and machine learning can generate hundreds of system architectures, which can be evaluated quickly to identify the most promising ones through a process called generative engineering.
That’s how NEVS, a Swedish electric car manufacturer, designs premium electric vehicles and mobility experiences that are simple, engaging, and distinctive but that also shape a brighter, cleaner future for all. They used flow-based topology optimization in Simcenter and then coupled it with Siemens NX Additive Manufacturing solution. The result was a 400% increase in demisting capability, 80% higher flows around passengers' feet and 16% lower volume for potential cost savings. They are a great example of sustainable industrial innovation in action.
By leveraging models in development and extending their use in other phases of the product lifecycle, speed and agility can be achieved. For example, reduced-order models enable using digital twins in many applications by only conveying their core attributes, including design, controls, and condition monitoring.
Artificial intelligence is a ubiquitous technology today, and engineering is just scratching the surface of what is possible with AI. For instance, it can help engineers by enabling faster setup processes and predicting the next command they are likely to use, allowing both new and expert engineers to work with the same tool. Neural network models can accelerate by a few orders of magnitudes early designs performance predictions while capturing the trends of the changes. AI can turn digital twin models created during the design engineering phase into executable digital twins that run real-time on the edge device as smart virtual sensors. Digital twin models can be used to continuously predict performance while monitoring the product in real time.
Simulation and test tools in the cloud permit engineering teams to analyze larger problems with higher fidelity, conduct more complex simulations, run multiple design exploration studies concurrently, and even adapt to changes in simulation on demand. The goal of these solutions is to help engineering teams make better design decisions faster with performance data provided by simulation and test solutions.
Connecting relevant activities during the product development process ensures that the right person can access the right information at the right time. Traditional CAD to CAE processes can be error-prone because they are disconnected, involving multiple users from various teams with different technical backgrounds. For today’s complex products, an integrated toolset that combines design with multidisciplinary simulation allows engineering teams to stay aligned, work much faster, and eliminate rework.
Today, the role of physical testing is evolving. In this new digital era, it is crucial to test, validate, and optimize real-world designs. Test departments are feeling the effects of this evolution in their work, both in volume and technical content. To achieve maximum productivity, innovative testing solutions are crucial.
Princess Yachts is a great example of combining simulation and testing. They are one of the world’s leading luxury motor yacht manufacturers and offer a range of sports yachts, flybridge motor yachts, and superyachts. Every yacht is unique, so noise and vibration are different for each build. Rigorous testing is needed to perfect the overall acoustic performance, inside and out. They take advantage of advanced noise, vibration, and harshness (NVH) digital testing methods. Physical tests on new materials guarantee that their initial simulation models are accurate, and they use automated test templates and batch reporting to certify dozens of configurations efficiently.
By combining test with simulation, the physical and virtual worlds interact intimately to complement each other in multiple scenarios. Test data is used to build, validate, improve, and drive simulation models. Simulation models expand test capabilities and improve and confirm test data. As product complexity increases, simulation and test teams cannot afford to operate in isolation from the rest of the organization. Traceability and standardization of processes become critical. An investment in a managed environment can pay huge dividends.
Looking beyond the typical applications, one prime example of an extra layer of complexity is electrification and autonomous driving applications that increasingly require more complex wiring design. Staying integrated in this case means that teams designing wire harness and routing systems need to be aligned with teams performing system simulation, mechanical simulation, and electromagnetic analysis to avoid interference.
For years, Siemens has been investing in the fidelity, accuracy, and comprehensiveness of what’s commonly called a digital twin, essentially a digitalized model of a product or system. The concept is used widely across many industries to design and build products that range from the largest chemical processes plants to the smallest mobile devices, and everything in between, whether it’s aircraft, ships, cars, engines, medical devices, home appliances, and batteries.
The Siemens Xcelerator portfolio of software, services, and low-code application development tools are contributing to the digital transformation. By blurring the boundaries between industry domains, today’s leading engineering teams can use the latest solutions to build their innovative products of tomorrow, today.
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