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SIKKA GROUP

Capability 01

Digital Engineering

Engineering confidence through simulation and test.

Advanced design, simulation, test and validation, with growing capabilities in digital twins, AI and Scientific Machine Learning (SciML).

Delivered throughNFOTEC Digital Engineering(opens in a new tab)

Drag to compare the digital model and the hardware.

What it includes

  • 01Design & engineering
  • 02Simulation
  • 03Systems engineering
  • 04Electrical systems engineering
  • 05Test & virtual validation
  • 06Digital twins
  • 07AI & Scientific Machine Learning (SciML)

Representative applications

  • Aircraft & UAV development
  • Thermal-fluid systems
  • Structural design
  • Propulsion & energy systems
  • Electrical architectures

Digital Twins

Models that stay connected to the asset.

A digital twin is a validated simulation model kept in step with a physical system through live sensor and operating data. It is used to monitor condition, predict performance and evaluate changes before they are made on the real system.

  • Physics-based system models
  • Live data integration
  • Condition monitoring
  • Performance prediction
  • What-if evaluation

Digital twin loopSensors on a physical asset stream live data to its digital twin, a validated simulation model, which returns monitoring, prediction and optimised settings to the asset.PHYSICAL ASSETDIGITAL TWINMONITORPREDICTOPTIMISELIVE SENSOR DATAINSIGHT AND SETTINGSTHE MODEL IS VALIDATED AGAINST TEST AND KEPT IN STEP WITH THE ASSET
Digital twin loop · schematic

AI & Scientific Machine Learning

Simulation, AI and machine learning.

Scientific Machine Learning (SciML) applies AI to engineering by combining physics-based simulation with machine learning. Models trained on simulation and test data allow a wider range of design options to be evaluated earlier, while remaining grounded in physics.

  • Physics-informed models
  • Surrogate models
  • Reduced-order models
  • Design-space exploration
  • Simulation-to-test calibration
Scientific Machine Learning workflowPhysics-based simulation and test data train a SciML model constrained by physics, which then evaluates many design options rapidly.PHYSICS-BASED SIMULATIONSciML MODELRAPID DESIGN EVALUATIONPHYSICS CONSTRAINTSCALIBRATION AGAINST SIMULATION AND TEST DATA
Scientific Machine Learning workflow · schematic
Exploded view of an airborne jammer pod with a thermal and power simulation result. Representative visualisation.

Related technology domains

  • Siemens Digital IndustriesAccelerating digital enterprises, powered by AI.
  • PACEAerospace engineering and digital aviation technologies.
  • J2 Aircraft DynamicsAircraft design, flight physics, modelling and simulation.
Technology Ecosystem

From the Insights library

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Inside SIKKA · 3 min read

One Group. Two Engineering Worlds.

Engineering rarely fails for lack of tools; the harder task is connecting design, manufacturing, integration and testing without losing engineering intent. This article introduces how SIKKA Group is bringing NFOTEC Digital Engineering and MICRONEL Global Engineers together around three complementary business areas, and the Design, Integrate, Validate philosophy guiding its continuing development.

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