The COM interface, developed by Microsoft®, provides a standardized interface for programs to communicate with each other. KULI has a set of built-in COM commands, which allow other programs to run and control a KULI simulation.
The current example is an Excel sheet which is created for the control and post processing of the time dependent simulation of a cooling system. The certain benefit of this Excel sheet is, that no Visual Basic programming knowledge of the user is required. Input and output parameters of a KULI file can simply be defined in a list, where the parameters can be chosen from an interactive menu. The sheet allows access to standard KULI COM objects but also to components directly via the KULI Direct Access Interface.Usable from release: KULI 8.0-1.04
This subsystem demonstrates a possibility to model a 4 way valve with which branches of a circuit can be combined or separated.
The attached subsystem has two fluid input ports (in1, in2), two fluid output ports (out1, out2) and one controller input with two possible input values. Depending on the value of the controller input, either in1 is connected to out1 and in2 is connected to out2 (“direct” connection), or in1 is connected to out2 and in2 is connected to out1 (“indirect” connection). In this way two loops/branches of a cooling circuit can either be combined (by using the “indirect” connection) or separated (by using the “direct” connection).Usable from release: KULI 12
This subsystem demonstrates a possibility to model an electric motor with fluid cooling of both stator and rotor.
The built-in model of an electric motor in KULI assumes that the stator is (optionally) fluid-cooled whereas the rotor is not. The rotor is only connected to the stator via conduction. Some e-motor concepts however also include a water cooled rotor. The attached subsystem provides a method with which such a configuration can be modeled. The main idea is to connect the heat conduction connector of the rotor with a point mass in the fluid circuit that models the coolant within the rotor. The point mass itself has a very high heat transfer coefficient. The heat conduction component modeling the heat transfer between the rotor and the water has a heat conductivity coming from CFD analysis.Usable from release: KULI 12
With this model and in particular with this workflow a pareto front diagram can be created in KULI lab.
During the process of optimizing a cooling package, quite often some compromise must be accepted. If you want to improve one value, another one gets worse. So usually it is quite difficult to define what the “optimal” solution really is. The pareto front is a graphical way to illustrate the line of “best compromises” that can be achieved.
The example shows how to generate such a diagram with the help of Monte Carlo Simulation and the 2D diagram features in KULI lab. In this example we vary the oil flow rate and the length of the oil cooler with the aim to reach both a low entry temperature and a low pressure drop in the cooler. Obviously both targets cannot be minimized at the same time, so we would like to see which values can be reached and we want to identify the parameters with which such a good compromise can be achieved.
Usable from release: KULI 12
With KULI you can model cooling systems for railroad applications, which can be diesel or electrical powered. In this specific application you can find a roof mounted cooling system with three coolers. One low temperature circuit for cooling the electric and electronic parts, one high temperature radiator for engine cooling and one intercooler.
One can include different types of fans, hydrostatic, electric or mechanical driven. In the simulation one can control the speed of the fan very flexible depending on the requirements of the cooling system measured by sensors like in the real life. Of course the operating conditions e.g. driving up a high mountain or in hot climate conditions are considered in the cooling system simulation.
At the end of the various variants done in the concept phase to find the best configuration of all components one will get a virtual prototype of the whole cooling system for railroad applications, which you will produce only once in hardware to verify the KULI simulation results on the test bench and in build-in situation.Usable from release: KULI 11.1
One possibility to set a target temperature in the cabin compressors is to control the compressors piston displacement.
This example demonstrates how a subsystem including several calculation controllers can easily be added to an existing HVAC simulation system.
By adjusting the compressors piston displacement, the performance of the compressor is controlled. This controlling strategy is included in a subsystem which mainly consists of calculation controllers.
As a necessary input, the user has to define a required cabin temperature and the upper and lower limit for the piston displacement. The calibration coefficient is a kind of displacement offset for the controller, used in each simulation time step.
If the average cabin temperature exceeds the upper temperature limit plus the temperature tolerance, the max. piston displacement of the compressor is used.
In all other cases the displacement is reduced or increased by the calibration coefficient. Due to the change of the displacement in each simulation time step, a smooth control characteristic is created.Usable from release: KULI 13.1
This example shows how to find a suitable compressor ratio with the help of parameter variation and a stop controller.
The task of this example is to find the lowest compressor rpm that meets the desired performance results. In this example we assume that the task is to find the compressor rpm such that the evaporator air outlet temperature is at or below 5 °C. Since in reality sometimes only a limited number of distinct ratios between engine rpm and compressor rpm is available, parameter variation is used. In addition a stop controller is applied such that the parameter variation stops as soon as the first suitable solution is found. This avoids the unnecessary calculation of operating points when the solution has already been found. It is important to run the parameter variation with increasing ratio such that the first solution that fulfills the stop criterion is really the solution with the lowest possible compressor rpm.Usable from release: KULI 11
This example shows how to automatically stop a transient KULI simulation if a given signal has become stationary.
In this example a stop controller is used to check if a certain signal has converged. The convergence itself is checked with the help of a series of delay controllers and a calculation controller. So the signal value at the current time t and at the time steps t-10, t-20, t-30, t-40, and t-50 seconds are evaluated. If the difference between the maximum and the minimum of these values is smaller than the value specified in the stop controller, then the simulation is stopped. In the given example the stop criterion is 0.1 K.
The big advantage of using the stop controller is that you can specify a very long simulation time (in the simulation parameters) such that it is ensured that convergence will occur, but if the convergence occurs pretty early, then KULI stops and does not produce “unnecessary” simulation output.
The calculation of this convergence value is put into a subsystem such that it can easily be transferred to other models.Usable from release: KULI 11
With this model the porosity coefficients for the air side resistance of a radiator can be calculated. This makes it easier to use the same air side resistance of a radiator in KULI and in CFD.
In standard underhood CFD calculations the heat exchangers (radiator, charge air cooler, condenser, etc.) are modeled as porous media. If a radiator is available as a KULI file, then this KULI model can be used to obtain the required coefficients which define the resistance of the radiator in the CFD model. In StarCCM+© (from cd-adapco) the resistance is defined in the following way:
delta p / L = alpha * v^2 + beta * v
where delta p is the static air side pressure difference, L is the depth of the radiator, v is the velocity perpendicular to the radiator surface, and alpha and beta are the coefficients that need to be evaluated. The KULI model makes a parameter variation for the velocity, where the lower and upper bounds can be specified as constants (they are predefined with 1 and 10 m/s, which is a reasonable range for most applications). The velocity is converted into a volume flow with the help of the width and height of the radiator. An analysis controller is used to calculate the sum of the deviations between simulated pressure difference at the component and the pressure difference calculated with the approximation formula. The sum of all these deviations is then put into an optimization target which is set to be a minimum. The coefficients alpha and beta serve as optimization parameters. (Usually these coefficients are in a range of up to 1000 or at most 1500, so this can be taken as a range for the optimization parameters.)
The example can easily be modified to include different formulas for porosities as used by other CFD codes.Usable from release: KULI 10.1
Thermostat to control the mass flow through two different branches.
With the subsystem Thermostat it is possible to control the mass flows through two different branches. Over the temperature of the 1.PM fluid resistances are increased or decrease over 2D curves.
To adjust the behavior of the thermostat to your own thermostat the following parameters have to be adjusted:
Usable from release: KULI 9.1-0.01