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.Usable from release: KULI 8.0-1.04
This tutorial demonstrates at the example of a hybrid vehicle:
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).Usable from release: KULI 9.1-0.01
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.Usable from release: KULI 8.0-1.04
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.Usable from release: KULI 8.0-1.04
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:
Usable from release: KULI 9.1-0.01
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.
This subsystem models the prohibition of repeated activations during a critical time period.
The counter-driven activation is defined via the following input:
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.Usable from release: KULI 8.0-1.04
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!Usable from release: KULI 8.0-1.04