Simulation & Testing

Modern products consist of a large number of mechanical, hydraulic and electronic subsystems.
For successful market introduction, it is necessary to test the systems as a whole: both mechanical and control systems. As the degree of automatic control increases, the range of design loads expands.
Proper computational load generation (test program) is a separate task for the designers. The system and unit design team needs to be able to simulate different operating scenarios.

  1. System modelling compound is a complex solution from Urartu Systems for model-based system calculations of dynamic characteristics for components and assemblies in real-time. To increase efficiency and reduce risk, engineers can test ECUs virtually, using software models to simulate various components. All components present in the simulator can be tested both independently and in combination with each other.

 

The designers can load mathematical models of physical objects, parametrically adjust the model. Then the model is tested on the HIL simulator considering design loads and impacts that are close to operational ones.

 

Below, you can see our system modeling solutions for various industries

GTE Simulation Library
GTE Toolkit allows to create a GTE model for execution virtual tests when solving design problems and refinement of gas turbine engines, development of their systems management and diagnostics. GTE models are created on the basis of mathematical models, describing thermogasdynamic processes, occurring in the main components of the engine. Complied conditions for the joint operation of all components in the engine, including equations of continuity, balance power and pressure balance.
Fuel System Modelling
Fuel Systems Toolkit solution enables creating models of fuel systems of different aircrafts and conducting their virtual testing.Tasks to be solved:
- design calculation,
- performance calculation,
- сalculation of characteristics of individual assemblies and units,
- simulation under various operation modes and flight conditions.
Landing Gear Modelling
Shock absorber model of landing gear support for testing loads and test program. The model allows to simulate dynamic processes of hydraulic shock absorber drive and select optimal coefficients of PID controller, with increasing and staggered setting of shock absorber rod speed. The model has a flexible parametric structure that allows to create models of the landing gear of various aircrafts.The model allows real-time calculations.
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Modelling of hydraulic suspension
The created mathematical model allows controlling the following parameters:
- hydraulic spring stiffness,
- clearance,
- damping for rebound and compression.

This solution makes it possible to modify car suspension performance: by controlling the hydraulic spring stiffness we can provide wide suspension travel or limit its travel.
Modelling the vehicle braking system
A model of the braking system with ABS for a car with fixed calipers, each containing one pair of brake cylinders is created. This model allows to estimate the parameters of ABS unit operation.
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Autopilot developing
The software design, test, validation and certification for autopilot system satisfying with the aviation standard RTCA/ Do 178B Level B. The model-based development is followed to design autopilot software. The model blocks in Autopilot System Software are developed and verified according to the DO331 standard, which includes all the aspects that address “Model-Based Design” in aviation. The main characteristic of the Autopilot Software is reliability, flexibility and scalability.
Copter modelling
The simulation of the quadcopter and its controller was performed. The control system was selected and PID controller settings were made. For a more realistic model, the threshold conditions for the radio signals and signal filtering were set. The dynamics of the drone was simulated, taking into account the air resistance and the gyroscopic effect of the propeller. The signals sent from the remote control were considered as input data in the model.
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