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In a real cooling package the air is not always guided through sealed shrouds, so there exist several leakages.

With KULI it is possible to model such leakages and even recirculation paths. 

This example is based on the Tutorial Example ExTruck. 

It shows how to model a leakage and a recirculation path such that the ratio of the air mass flow through radiator / inlet grid varies for different operating points between 70% – 130%. 

The leakage is modeled via an area resistance below the main cooling package and the recirculation path is regulated via a second built in resistance. 

The advantage of this modeling is that there is used the same cp-value and the same built-in-resistance for several operating points.

KULI advanced offers  an optimization toolbox which can be used for  quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand.

The current example is a cooling system with goal seek on two targets, and two parameters respectively. The speed of the mechanical fan and the heat rejection of the radiator are adjusted to hit the target of a given radiator top hose temperature and a given charge air exit temperature. The cooling system is based on tutorial example ExCar.scs from the KULI Setup.

KULI advanced offers  an optimization toolbox which can be used for  quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand. Based on those functions it is possible to perform statistical analysis as well. 

This example shows the application of the Monte Carlo Method in KULI. The basic idea is that some input parameters do have a certain degree of uncertainty. In this example we assume that the charge air temperature has a temperature of 150°C, but we are not sure, so we apply a normal distribution with mean 150°C and a standard deviation of 10 K. Furthermore we assume that the built in resistance has a zeta of 400, again with some uncertainty resulting in a normal distribution with mean 400 and standard deviation of 10. And finally we assume that we don’t really know the exit cp-value; since we can’t even guess a mean value we apply a uniform distribution between -0.4 and -0.2. When we run this model we choose „Monte Carlo Simulation“ with a sample size of e.g. 500. A possible output is mean value and standard deviation of water and charge air temperatures.

KULI advanced offers  an optimization toolbox which can be used for  quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand.

This example shows how to simulate a component test rig preserving constant air pressure drop. Based on this method you can compare the performance of different radiators taking into account that a radiator with lower pressure drop allows more air flow such that the overall performance might be better than in the case of a radiator with better heat transfer characteristic but higher pressure drop. The optimization target is set to a given pressure difference and the fan rpm is optimized such that the demanded delta p will be achieved. Choose “Parameter Variation” after clicking the Analysis button!

The supercharger provides more oxygen for combustion to the engine than it is available for the naturally aspirated engine.

KULI provides the tools to create a calculation model of a supercharger by oneself. Using sensors, actuator and calculation objects the performance of the supercharger can be computed. Based on the input information like piston displacement, engine power, engine speed, the entry pressure, the entry charge air mass flow and the entry temperature into the charge air cooler is calculated. For a better overview of the complete system the model is packed into a KULI subsystem.

KULI advanced offers  an optimization toolbox which can be used for  quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand.

The objective of the current examples is the calibration of the simulation model to measured temperatures by adjusting the Resistance Parameter of the Built-In-Resistance. This adjustment can be done automatically using the KULI optimization toolbox. Two variants for the calibration are presented. Variant 1 shows how to set up the optimization controllers if the user wants to find the optimum parameter for each single operating point. Variant 2 shows how to set up the optimization controllers if the user wants to find one optimum parameter for all operating points. The cooling system is based on tutorial example ExCar.scs from the KULI Setup.

A fin and tube heat exchanger offers a complex refrigerant flow pattern. Usually starting from multiple inlet tubes the refrigerant passes the heat exchanger similar to serpentine flow but in several tube rows.

The KULI model for fin and tube heat exchangers offers a possibility to connect each single tube of the heat exchanger in accordance to the real flow configuration.

In this example a fin and tube evaporator is used instead of a plate evaporator. It is based on the example “Ex_AC.scs” from the KULI installation setup.

Plate Heat Exchangers gain in importance in the field of automotive cooling systems. Especially in EV/HEV application the so-called “Chiller”  removes heat from battery coolant using the refrigerant cycle.

KULI hvac provides a simulation model for plate heat exchangers (“ACPHE”). In proper KULI style input data are kept simple. Basic geometry and flow configuration data as well as test bench data are sufficient to be able to simulate the plate heat exchanger. In the current example a chiller is added to a conventional refrigerant circuit and works parallel to the refrigerant-to-air evaporator. The example is based on “Ex_AC.scs” from the KULI installation setup.

This tutorial demonstrates at the example of a hybrid vehicle:

• How a battery is set up
• How e-components can be included in a cooling system
• How to include a simple thermal Management

Beside a conventional engine model and an oil circuit, also a passenger compartment is included. The electric components like the power electronics and the e-machine are separated from the battery cooling circuit. Additionally, all results for the battery are available at module and cell level (96 cells in 8 modules).

CAE software like KULI supports the engineer in to investigate in arbitrary concepts. A possible concept is the indirect cooling of refrigerant using water instead of air as coolant medium. 

In this case an A/C parallel flow cooler (ACPFC) component can used instead of the standard refrigerant-to-air condenser. Thence the refrigerant can be cooled by low temperature water. In concept phase the decent refrigerant charge for the circuit is not known. KULI enables to calculate the optimum charge for an operating point based on a desired subcooling value.

In real-life cooling packages there will be an non-uniform air flow distribution on heat exchangers, which can have a big effect on heat transfer rate. Using CFD results with KULI it is possible to consider this uneven air flow on heat exchangers. 

Typically, from a given air velocity field over heat exchanger surface at a given operating condition, KULI calculates a system of local resistance correction factors which enables to extrapolate to other operating conditions. This system of correction factors is called “Resistance Matrix”. Nevertheless, often the resistance matrix significantly depends on driving speed, or on the state of a fan or something else. For this reason KULI offers the possibility to use resistance matrices that depend on the driving speed and/or another, user-specified parameter.

Driving Simulation (includes pre-defined cycles) with an optional Gear Shifting Logic

The subsystem Gear Shifting Logic chooses the correct gear dependent on the engine revolutions.

The Shifting strategy is defined via the following input data:

• Upshift RPM (dependent on gear)
• Downshift RPM (dependent on gear)
• Idle Engine Revolution

In this example, the inlet temperature's dependecy on the massflowrate is used for the adjustment of the BiR.
Therefore the subsystem models a calibration routine for a Built-in-resistance.

Inputs:

• Target Temperature
• Optimization region for the dimensionless loss-coefficient zeta
• Position of the Built-in-resistance inside the air-path

This subsystem models the prohibition of repeated activations during a critical time period.

The counter-driven activation is defined via the following input:

• Critical Delay Time (Please enter the delay time in seconds as delay of the delay-controller)

In large KULI systems - containing many controllers, sensors and actuators - a clearly arranged layout is an issue.

The Signal Receiver helps to avoid long and confusing connector lines. A signal receiver is a “wireless” connector which can tap values from sensors, COM-objects  and variables.The example illustrates the use of signal receivers based on the basic example “Ex_Fluid.scs” from the KULI installation setup. In this example a characteristic sensors the temperature of the 3rd tube and outputs a flow for the radiator bypass valve.

KULI advanced offers  an optimization toolbox which can be used for  quick parameter variation on one hand, and automatic minimization, maximization or goal seek on the other hand.

This example shows how to simulate a component test rig preserving constant air pressure drop. Based on this method you can compare the performance of different radiators taking into account that a radiator with lower pressure drop allows more air flow such that the overall performance might be better than in the case of a radiator with better heat transfer characteristic but higher pressure drop. The optimization target is set to a given pressure difference and the fan rpm is optimized such that the demanded delta p will be achieved. Choose “Parameter Variation” after clicking the Analysis button!