The article describes ways to adapt the method of characteristics to solving the problem of pressure pulsation
calculation of a high-pressure plunger water pump considering a complex pipeline network using a CAE
software “SimulationX”. The objective of this adaptation is to increase the stability of the numerical solution and
reduce the calculation time. To verify the accuracy of the simulation, the pressure pulsations were compared
with pulsations in various parts of a real complex pipeline. As a result, a compromise between accuracy and
speed of calculations was achieved, which improves the process of pump development.

The necessity for greener, flexible and more efficient equipment has led OEMs and manufacturers to create
intelligent fluid power systems. The complexity of the design of these integrated solutions, involving many fields
of expertise, provides significant challenges. Control Specialists and system designers have their own knowledge
domains so there is an increasing need to use an integrated simulation platform so they can work together.
Hybrid modelling methods of mechatronics software, integrating equation- or model-based modelling and datamapping
from test results– known as Machine Knowledge Management – have many benefits. By combining
these fields of expertise using co-simulation between software and hardware, control specialists and application
experts will be properly integrated in the design and analysis process.

In this paper, a real-time measurement system of power of incompressible unsteady laminar flow in a pipe using
a Kalman filter is studied and condition for successfully performing the real-time measurement is discussed
focused on the number of element of a pipeline model used for the Kalman filter. The optimized finite element
model of pipeline dynamics is used as a plant model of the Kalman filter. The number of element for finite
approximation may influence on accuracy of the approximation. Large enough number of element to approximate
pipeline dynamics may increase real-time computational task of the Kalman filter. In this paper, the optimized
finite element model integrated with the Kalman filter is briefly introduced. The Kalman filter with the optimized
finite element model is installed in a real-time computing system. Turnaround time of the Kalman filter is measured
for various numbers of element. The turnaround time is a key factor to determine sampling time of the real-time
measurement system.

The movement of a vehicle is determined by the torque acting at the wheel. With the speed-controlled engine in
mobile machines, the torque characteristics at the wheel are determined by the transmission. Traditionally, this is
realized with the inherent mechanic-hydraulic torque behaviour of the components, like a torque converter or an
axial piston pump. The disadvantage of this approach is the missing flexibility, resulting in trade-offs like low
fuel efficiency or high effort for the realization of control functions. In contrast, the ongoing electronification of
mobile machines is the enabler for a new, much more flexible technological approach for hydrostatic drive
trains: Torque Control.