Advancing the Energy Transition through Computational Design and Additive Manufacturing
Siemens Energy Harnesses Additive Manufacturing and nTop for Enhanced Energy Solutions
Interview with Bradley Rothenberg of nTop & Markus Lempke of Siemens Energy
In this interview with Markus Lempke of Siemens Energy and Bradley Rothenberg of nTop, we explore how computational design and additive manufacturing can accelerate the transition to sustainable energy.
Set against the backdrop of their forthcoming joint presentation at the CDFAM Computational Design Symposium in Berlin, the conversation underscores the tangible benefits of computational methodologies in refining and mass-producing advanced metal components. It highlights nTop's role in driving efficiencies and fostering innovation within the energy sector.
The conversation transitions from nTop's inception to its application in tangible manufacturing contexts, illuminating the progressive adoption of novel design strategies. It stresses the importance of feedback from clients and the synergy between different software tools in propelling the domain forward.
Highlighting their upcoming joint presentation at CDFAM in Berlin, Lempke and Rothenberg discuss the practical impacts of computational approaches for the optimization and serial production of high-performance metal components, underscoring the crucial role of software like nTop in enhancing efficiency and innovation within the energy industry.
Key highlights from our discussion are outlined below. For the complete interview, please visit CDFAM.com
ORIGINS OF NTOP
Brad, could you provide a brief overview of the motivation behind founding nTop and the specific challenges it aims to address?
I started nTop almost a decade ago because I was frustrated by mainstream CAD software’s ability to model complex shapes really easily and quickly. From working with 3D printing, I knew that this introduced a level of complexity that made the current tools grind to a halt.
I had this eye-opening on-site mtg in the early days of nTop at GE Aviation: I asked one of their top mechanical designers to walk me through their process of modeling a heat exchanger: he had this complex model chopped up and split into 12 different sub-models running on 12 different machines.
He would go to each machine, punch in a few numbers to the parametric model defined as a BREP, and his CAD system would crunch away, sequentially updating the model on a single CPU core for hours at a time on each machine.
Due to rebuild errors, the models on several of the machines would fail to update. This was a process that would go on for weeks at a time in order to get new geometry, suitable to 3D print and test.
That same afternoon, I sat with the same engineer, and in an hour, we built a computational model of that same heat exchanger in nTop that we could then punch in new parameters to watch the entire geometry generate in real time, ready to get sliced for 3D printing.
ADOPTION OF NTOP AT SIEMENS ENERGY
Markus, could you share the journey of Siemens Energy’s adoption of nTop? What is your role, how did you get started with nTop, and how has it aligned with and supported the initial objectives that led to its integration into your workflows?
I am a heat transfer design engineer within the central metal Additive Manufacturing (AM) organization serving both our own internal, mostly gas turbine market, as well as supporting our growing external customer base.
One product group that is of high interest right at this moment are customized and highly efficient heat exchangers, as well as other heat management applications where high surface to volume ratios are paramount.
My initial interest in nTop was in the exploration and application of TPMS structures within thermal management applications.
The ability of nTop to utilize implicit modeling to develop lattice structures is so strong. Indeed, nTop has helped us explore a variety of conceptual heat exchanger designs over a large range of boundary conditions as well as modules for the direct capture of CO2 from the atmosphere.
Thanks to their kind support of CDFAM Berlin you can use the code NTOP for a 20% discount on tickets at checkout
NTOP’S COLLABORATION WITH SIEMENS ENERGY
Brad, could you describe how nTop collaborates with companies like Siemens Energy to expedite their realization of value from using nTop?
Computational design is a new paradigm for engineers – i.e. it’s a different process to design a model in nTop than to design a part in CAD.
This computational model represents not just one design, but all possible designs within a given set of a parameters – for example, you might have a model of a lower level design feature, like an isogrid structure that can be applied to any surface to add additional stiffness to it, or you might have a computational model for a part-level design, like a 3-domain heat exchanger that can be sized to a specific set of requirements / locations of inlets & outlets, all the way to a computational model for the entire structure of a robotic assembly, like the Ocado series 600 warehouse robot.
NTOP + SIEMENS ENERGY CDFAM PRESENTATION OVERVIEW
Could you provide a preview of the topics you’ll be covering in your joint presentation at the CDFAM Symposium in Berlin?
Markus: My intent is to highlight that computational design and implicit modeling have very practical uses within the world of additive manufacturing. nTop is so much more than a software tool to create fancy prototypes for exhibitions.
At Siemens Energy our focus is the serial production of high-performance metal components. Typical manufactured batch sizes range from dozens to thousands of parts per year.
In this context I want to highlight how nTop helps us create robust parametric models for optimization and how we leverage field-driven design to simplify and “tune” our heat exchanger designs for end-customer use.
I will showcase how computational design can help to reduce cost in the AM manufacturing process, by optimizing support structures based on process simulation data.
SELECTING IMPACTFUL APPLICATIONS AT SIEMENS ENERGY
Markus, when considering computational design and advanced manufacturing techniques such as metal 3D printing, how do you identify the applications where they can deliver the most significant impact and provide return on investment?
Markus: There are certain factors that are already a good first indication for the likelihood of success:
Is it a clean-sheet design?
While supply chain resilience and lead time topics can also be a major motivation to shift to additive manufacturing, the additional design freedom that AM brings to the table usually remains untapped in these situations. Accordingly, the necessity for computational design of legacy parts is often quite limited. However, with full design authority (within conventional constraints such as bolt holes, connections, etc.), you can unleash the full potential of AM. This extends beyond the AM component itself to the overall assembly, e.g. because that enables more flexibility in the type and position of the internal interfaces.
Is part performance / functionality paramount?
At Siemens Energy our focus area in metal AM is the Laser Powder Bed Fusion (LPBF) process. This process is ideally suited for parts with intricate details under high thermomechanical loads. But AM can come at a cost. Therefore, the additional cost must be justified by an improvement in the functionality and performance of the part. To achieve this, it is also paramount to leverage computational design to quickly create and digitally assess design variants so we can print right first time.
Is the cost for the conventionally manufactured part also high?
As mentioned earlier, AM in most cases comes at a premium. However, some components are expensive to produce even with traditional manufacturing techniques. A good example here are complex assemblies with tight tolerances. Here the additional cost for the manufacturing part can very often be offset by savings in assembly cost or also by reliability due to the reduction of complexity. AM has the potential to achieve all of this whilst maintaining or even improving the functionality of the part.
TAKEAWAYS AND EXPECTATIONS FROM CDFAM
Finally, what are the key takeaways you both hope attendees will gain from your presentation, and what do you aim to learn or achieve at CDFAM?
…I really want to share examples with the community that show how we are using computational design and implicit modeling on real parts to solve problems with tremendous impact on the speed and success of the energy transition.
I think this can vastly contribute to the sense of purpose that a lot of us feel.
With so many software vendors being present, I would also like to use the “voice of the customer” to advocate for increased standardization and interoperability around implicit modeling.
And then last but not least I can’t wait for all the inspiration and creative ideas I will soak up from the community and that will help me tremendously in existing design challenges and those yet to come.
Brad: I hope to share with the community my experience of and learning what it takes to build industrial parts using new implicit modeling tools and I hope others can learn from what we’ve built and improve on / speed up current bottlenecks in our customer workflows.
I’m most excited to spend time with close friends and make new friends while all of us are sharing advanced new software and algorithms to solve design and engineering challenges.
For the complete interview, please visit CDFAM.com and register to attend CDFAM Berlin to learn from, and network with experts in engineering, architectures and computational design. With over 30 presentations from industry, academia and software developers such as nTop, the event brings together leaders in their field to share ideas and build connections.