{"id":68845,"date":"2025-09-16T03:16:27","date_gmt":"2025-09-16T07:16:27","guid":{"rendered":"https:\/\/blogs.sw.siemens.com\/simcenter\/?p=68845"},"modified":"2026-03-26T06:47:44","modified_gmt":"2026-03-26T10:47:44","slug":"bob-ransijn-on-system-simulation","status":"publish","type":"post","link":"https:\/\/blogs.sw.siemens.com\/simcenter\/bob-ransijn-on-system-simulation\/","title":{"rendered":"Bob Ransijn on System Simulation, Digital Twins & AI: Insights from the Engineered Mind Podcast"},"content":{"rendered":"\n

In an increasingly complex engineering landscape, the ability to accurately predict and optimize system behavior is paramount. This critical need was the central theme of a recent insightful discussion on the “Engineered Mind by Jousef Murad<\/a>” podcast, featuring Bob Ransijn<\/a><\/strong>, a seasoned System Simulation Specialist and Presales Manager at Siemens Digital Industries Software. Ransijn’s deep dive into the world of system simulation offers a compelling perspective on its evolution, current capabilities, and future trajectory.<\/p>\n\n\n

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\"Bob<\/figure><\/div>\n\n\n

Defining System Simulation: Beyond the Basics<\/h3>\n\n\n\n

Ransijn clarifies that system simulation, particularly “lumped parameter system simulation,” involves representing physical properties as discrete elements\u2014such as a mass, spring, damper, or inductor. This approach allows for the rapid solution of ordinary differential equations, often in real-time. “We take all the different physics\u2014mechanical, electrical, fluids\u2014and create a comprehensive model of a whole system,” Ransijn explains, emphasizing the multi-physics nature of the discipline. He further demystifies concepts like 0D\/1D modeling, noting that 0D models lack spatial dimensions, while 1D models add a single spatial dimension, often used to break down complex systems into manageable, interconnected blocks.<\/p>\n\n\n\n

A Historical Journey: From Manhattan Project to Modern Accessibility<\/h3>\n\n\n\n

The origins of computer simulation, as Ransijn points out, trace back to the 1940s with the Manhattan Project, where it was used to simulate nuclear detonation. However, it wasn’t until 20-30 years ago, with the advent of personal computers, that behavioral simulations became more widespread. Early tools faced significant limitations in computer power, solver technologies, and user-friendliness, often requiring extensive coding by specialists.<\/p>\n\n\n\n

A pivotal shift occurred in the early 2000s when the ability to combine different physics domains\u2014electrical, mechanical, and fluid\u2014became available. Today, Ransijn highlights the “democratization” of simulation, where tools are designed for ease of use, making them accessible to a broader range of engineers. He proudly notes that Siemens’ Simcenter Amesim solver, for instance, now runs 50% faster than it did just a few years ago, allowing engineers to focus more on model development and analysis rather than solver intricacies.<\/p>\n\n\n\n

The Digital Thread, Digital Twin, and the Power of AI<\/h3>\n\n\n\n

The conversation naturally progresses to the intertwined concepts of the Digital Thread and Digital Twin. Ransijn defines the Digital Thread<\/strong> as the seamless connection of data throughout the engineering workflow, ensuring a “single source of truth.” This enables a “shift left” in the product design cycle, allowing simulation to commence as soon as product ideas and requirements are formed, even before physical prototypes exist.<\/p>\n\n\n\n

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Screenshot from the podcast episode on YouTube<\/figcaption><\/figure>\n<\/div>\n\n\n\n
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The\u00a0Digital Twin<\/strong>, Ransijn clarifies, is not a singular entity but a versatile concept. “A digital twin is a concept… There’s many different digital twins available for different purposes,” he states, ranging from a simple CAD environment to complex models running alongside physical machines.<\/p>\n<\/div>\n<\/div>\n\n\n\n

A significant portion of the discussion is dedicated to the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on system simulation. Ransijn explains how AI is used to create “reduced order models” (ROMs). These ROMs are incredibly fast and accurate, capable of capturing complex dynamics without the need for detailed physical models. “You lose all the physical details in there, but you get the right outputs from them,” he explains. These AI-driven ROMs can be deployed in embedded control systems or as “executable digital twins” that run in real-time alongside operational machinery<\/a>. This capability facilitates advanced applications like predictive maintenance, allowing the system to predict unmeasured values or warn of potential issues by comparing simulated data with actual sensor readings.<\/p>\n\n\n\n

Siemens’ Comprehensive Approach and Future Outlook<\/h3>\n\n\n\n

Siemens is at the forefront of this evolution, offering a robust suite of tools within its Simcenter portfolio. Ransijn details key offerings such as:<\/p>\n\n\n\n