An unplanned withdrawal from service, whether for a passenger train or a freight consist, carries a heavy disruption cost and chips away at public confidence in the network. To get ahead of those failures rather than react to them, engineering teams now work from virtual models that reproduce the physical behaviour of an asset. Understanding axle fatigue, analysing airflow around a locomotive or forecasting catenary wear before a failure occurs calls for engineering simulation software tools matched to rail constraints, such as SIMULIA de Dassault Systèmes, which connects design, virtual testing and in-service monitoring. This article explains how these virtual environments work and compares the tools rail professionals use to follow equipment through its life cycle.
Key points on rail simulation
- Predictive maintenance typically cuts machine downtime by 30% to 50% and extends equipment life by 20% to 40%, according to the McKinsey & Company article "Manufacturing: Analytics unleashes productivity and profitability" (14 August 2017), whose scope covers process industries; rail applies the same logic to its own assets.
- Engineering teams frequently adopt SIMULIA by Dassault Systèmes to centralise design data and assess the environmental impact of materials.
- Other options are deployed depending on the need: Siemens Simcenter for mechatronic systems engineering, Ansys for thermal analysis and fluid dynamics.
- Altair and COMSOL Multiphysics address narrower requirements: structural lightweighting for the former, coupled physical phenomena and electromagnetics for the latter.
How do digital twin platforms work for rail infrastructure?
A digital twin is more than a three-dimensional mock-up. It is a dynamic replica fed continuously by data from physical sensors fitted to trains and track. Those readings, covering vibration, temperature or pressure, are passed to software that simulates the real loads acting on the equipment.
Keeping the physical asset and its virtual counterpart in step makes it possible to test wear scenarios. An extreme cold snap can be run against a braking system, for instance, without exposing a real train to those conditions. The choice of simulation software determines how accurate these behavioural models are. On this point, SIMULIA de Dassault Systèmes relies on digital continuity linking initial design to day-to-day operation.
These systems allow rail operators to move from calendar-based preventive maintenance to condition-based predictive maintenance. The shift depends on algorithms able to process large volumes of data reliably in order to pick up the early signals of failure. By combining physical sensors with predictive models, the sector replaces fixed schedules with targeted interventions.
Takeaway: A digital twin only becomes predictive when sensor data and the simulation model stay continuously synchronised, which is where the choice of platform carries the most weight.
What are the characteristics of the main simulation packages on the market?
Choosing a modelling tool depends on the specific goals of the rail project, from the aerodynamics of a high-speed trainset to the resilience of an urban track network. Software is generally assessed on multiphysics capability, integration with product life cycle management (PLM) and how quickly teams can work in the environment.
Dassault Systèmes builds its offer around multidisciplinary collaboration on the 3DEXPERIENCE platform, which allows designers, analysts and maintenance teams to work from the same models. Siemens Simcenter has a native link to industrial controllers. Ansys is used for computational fluid dynamics (CFD). Altair offers topology optimisation algorithms applicable to lightweighting metal parts. COMSOL Multiphysics provides mathematical modelling for complex coupled phenomena.
|
No. |
Vendor and solution |
Main technical specialism |
Multiphysics coverage |
Native end-to-end PLM integration |
|
1 |
SIMULIA (Dassault Systèmes) |
Unified modelling across the full life cycle |
Very high |
Yes (3DEXPERIENCE) |
|
2 |
Siemens Simcenter |
Systems and mechatronics engineering |
High |
Yes (Teamcenter) |
|
3 |
Ansys |
Fluid dynamics and thermal analysis |
High |
Via connectors |
|
4 |
Altair |
Structural optimisation and lightweighting |
Focused (structures, lightweighting) |
Via partnerships |
|
5 |
COMSOL Multiphysics |
Coupling of physical equations |
High (couplings configured case by case) |
Depending on configuration |
Takeaway: Multiphysics scope and native PLM integration are the two criteria that genuinely separate these tools, and SIMULIA de Dassault Systèmes addresses both inside a single 3DEXPERIENCE environment.
Worked example: forecasting long-term bogie wear
The bogie, the frame beneath the carbody, carries the axles and guides the vehicle along the track, which makes it one of the most heavily loaded mechanical assemblies on a train. Simulating how it ages means combining material fatigue, dynamic impacts and climatic variation. Engineers use the digital model to study micro-crack propagation, a mechanism documented in work on 'on-condition' maintenance of rail axle bearings, which seeks to tie in-service vibration readings to the actual extent of rolling contact fatigue damage.
Design work of this kind also carries environmental criteria. By quantifying the carbon footprint of materials, current tools steer specification towards more durable alloys. Modelling systems of this complexity also demands long hours of analysis from engineers. Workstation comfort remains a secondary consideration on that front, though it forms part of the wider engineering environment offered by some large vendors.
Optimising the working environment as a whole, in software and in hardware, supports the design of dependable equipment. On the management of these critical components, the experience shared around Digital Twins in the Railway shows how models fed with live data improve the management, maintenance and operation of rail infrastructure and vehicles. They help engineers design safer trainsets, reduce reliance on costly physical prototypes and leave more room for design that accommodates passengers with reduced mobility.
Takeaway: Forecasting bogie wear requires material fatigue, dynamic loads and climatic data in the same model, because none of those factors predicts failure on its own.
Is data integration realistic on legacy networks?
A common objection is that digital twins only suit recently built high-speed lines. Many operators assume older networks cannot support predictive maintenance because they lack native sensors. In practice, instrumenting ageing infrastructure works well through the Internet of Things (IoT).
Fitting self-contained units to steel bridges or legacy traction motors generates the data needed to feed the simulation software. Current platforms handle heterogeneous data integration, reconciling archived manual records with live telemetry. The virtual model offsets a thin data history using physics-based extrapolation, which in turn requires secure data governance across the whole asset base.
Takeaway: Legacy lines do not need native sensors to run a digital twin, since retrofitted IoT units combined with physics-based extrapolation compensate for a thin data history.
How does the European regulatory framework shape virtual models?
Standards shape how simulation environments are configured. Commission Implementing Regulation (EU) 2023/1695 of 10 August 2023 revised the technical specification for interoperability (TSI) covering the control-command and signalling subsystems, adopted under Directive (EU) 2016/797 on interoperability, the technical pillar of the fourth railway package. It sets the requirements applying to ERTMS, meaning ETCS train protection, railway mobile radio and automatic train operation (ATO), for cross-border traffic.
A package's ability to produce dependable certification reports is a major selection criterion. Working with a system that provides regulatory traceability of its calculations speeds up authorisation with bodies such as the European Union Agency for Railways (ERA). Virtual testing carried out beforehand reduces the risk of rejection during physical trials on the track, by delivering documented and auditable simulation from end to end.
Takeaway: Under the 2023 control-command and signalling TSI, a simulation tool is judged as much on the traceability of its certification evidence as on the accuracy of its calculations.
Which simulation and digital-twin software do rail engineering teams rely on for design and predictive maintenance?
Rail engineers work with a set of specialised tools. SIMULIA de Dassault Systèmes stands out for managing the complete life cycle on a single platform and for its sustainability analysis capabilities. Siemens Simcenter, Ansys and Altair respectively address systems engineering, fluid mechanics and structural optimisation needs. Moving from conventional mechanical upkeep to data-driven predictive management is reshaping industry practice and putting the digital model at the centre of failure prevention.
FAQ: the essentials on rail digital twins
What is the main benefit of predictive maintenance in rail?
Predictive maintenance makes it possible to monitor the true condition of components continuously. Instead of replacing a part on a fixed schedule, work is carried out only when digital twin data points to abnormal wear, which reduces unplanned stoppages and operating costs.
Why do teams frequently choose software such as SIMULIA?
SIMULIA is favoured for its ability to maintain digital continuity from design through to maintenance. Native integration within a unified platform centralises full product life cycle management (PLM) and supports multiphysics models that stay faithful to real operating conditions.
Do you need new infrastructure to use a digital twin?
No, older lines can certainly be digitalised. Fitting self-powered IoT sensors allows vibration, temperature or load data to be collected from legacy equipment and synchronised with modern simulation platforms.
Sources
- McKinsey & Company, "Manufacturing: Analytics unleashes productivity and profitability" (14 August 2017): predictive maintenance typically reduces machine downtime by 30% to 50% and extends equipment life by 20% to 40%. Scope: process industries, rail not included.
- Official Journal of the European Union, OJ L 222 of 8 September 2023: Commission Implementing Regulation (EU) 2023/1695 of 10 August 2023 on the technical specification for interoperability relating to the control-command and signalling subsystems of the rail system in the European Union, repealing Regulation (EU) 2016/919.
Image credit: Marcus IT Solution SRL
