Imagine yourself in the midst of designing a new product – say, a new kind of aerial working platform for the construction industry. After months or years of work, you’re nearing the finish line, and just need to validate the design with some physical prototyping to make sure everything works as intended. It’s only at the point of physical prototyping that you realize your team has made a crucial oversight – the new system seems so prone to vibrations that would be prohibitive for operator safety. To fix design issues associated with this problem, you need a solution – and fast.
Even worse – what if you didn’t catch this mistake until several of these new units were manufactured and shipped to the customer?
In both scenarios, there’s a big rush to fix design issues as fast as possible. You might consider upgrading the hardware at key points of the aerial working platform, in order to reduce the vibrations. This approach would eventually be successful, but the cost of oversizing components might quickly start eating up profit margins.
This kind of scenario is where dynamic, system simulation – otherwise known as a Digital Twin – might offer a better solution. By taking a system-level approach to simulating the crane’s dynamics, you could identify the specific movement patterns that give rise to the vibrations, and eliminate them by tweaking the motion profiles themselves – no new hardware required. If this kind of solution sounds too good to be true, then perhaps you only need to learn a bit more about the technology and process behind dynamic system simulations.
What is Dynamic System Simulation?
System simulation is a broad term that refers to the simulation of multiple subsystems of a given design, considered together in a single, overarching model. Dynamic simulation, in this context, is exploring just how all the subsystems actually operate together over time. The subsystems included can vary, allowing you to consider the impacts of multiple domains (mechanical, electric, hydraulic, and so on) in a single simulation environment.
With these kinds of simulations, you’ll get a powerful tool that allows you to spot all kinds of cross-system interactions that you may have otherwise missed during product development, allowing you to fix design issues you may have missed. While you might end up catching some of these issues during physical prototyping, it would be far more efficient to catch these issues before needing to build the physical prototypes. If you’ve already got a product in operation at a customer’s site, you could also stand to save both time and money by identifying issues via simulation, rather than expensive testing on the physical product.
Building the System-Level Model – The Digital Twin
The key component of a successful, simulation-based solution is the dynamic, system-level model. You’ll need to create this in a system-level simulation tool (such as MapleSim). Tools like MapleSim offer hundreds of built-in components that you can drag-and-drop into place, creating a dynamic representation of your product. These components span several domains of engineering, allowing you to build your model to include the effects from your overall design. For each of these components, you can customize their parameters to match the specifics of your design – for example, the frictions on a ball joint.
System-level modeling may not be a skillset your team has internally, but the demand for these skills is growing. As such, system-level modeling tools have become increasingly user-friendly, and tool vendors now offer services for both training and model development.
System-level modeling tools are often expandable with domain-specific libraries, allowing you to include dynamics from various domains in the same model. With MapleSim, there are a variety of toolboxes offered to expand the kinds of abilities you have when creating a digital twin.
Here are just a few examples of the toolboxes available within MapleSim, as an example of other domains you might want to include when building your digital twin:
MapleSim Tire Library – Use high-performance, pneumatic tire components in your models, including industry-standard tire force models like Fiala, Calspan, and Pacejka’s magic tire formula.
MapleSim Battery Library – Incorporate physics-based, predictive models of battery cells into your overall system model to understand loading effects, battery heat flow, and more.
MapleSim Driveline Library – A collection of components, transmission subassemblies, and powertrain examples that help you understand your system from the engine to the differential, wheels, and road loads.
MapleSim Heat Transfer Library – Simulate the effects of heat transfer across a multidomain system, allowing users to diagnose heat transfer impacts much earlier than typical FEA-based workflows.
MapleSim Ropes and Pulleys Library – Create winch and pulley systems as part of overall machine development, including both 2-D and 3-D systems that can be easily visualized as 3-D models.
Graham Jackson is a contributor to VirtualCommissioning.com.
A nanotechnology engineer by education, he’s got a broad background in new technologies across domains. Graham has spent most of his career communicating technical concepts to those outside of the R&D department, and building support for system-level approaches to modeling and simulation.
Alex Beilby is the Editor-in-Chief of VirtualCommissioning.com.
With a career spanning supply chain operations and designing IT data systems, Alex is the Technical Marketing Manager at Maplesoft, building support for system-level approaches to modeling and simulation. Alex has a Masters degree in Mathematical Engineering and enjoys staying up to date with the ever-changing use of math and engineering software tools in automation and design.
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