A transient surface heating or cooling process of a solid is considered. A procedure for the determination of surface temperature and surface heat flux density during such a process is presented using a submersed temperature sensor in the solid. From this measured temperature the surface temperature and surface heat flux density are calculated by inverse process modelling. This method is prone to errors since measurement errors are amplified in the inverse process modelling and can thus easily become unacceptably large. The LSQR regularisation algorithm is optimised for fast performance as well as less memory requirement and applied to the inverse problem solution. The proposed method allows to simulate an experimental setup and to determine the accuracy of the results gained from the simulated experiment. This is essential for the determination of the accuracy of a planned or existing test facility. The influence of process parameters like sensor depth, sensor noise level, sampling rate, heat flux density amplitude and cooling/heating process duration is investigated. In most cases it is very important to carefully adjust the process parameters in order to obtain reliable and accurate results. Additionally the proper selection of the regularisation parameter required for the inverse problem solution is analysed.

In secondary cooling of continuous casting, it is very important to know the cooling heat flux for the actual spray cooling situation with respect to various parameters like the local position, the nozzle types, distances, and the water and air flow rates, to be able to control the cooling conditions precisely. As heat flux measurements on a casting machine are too challenging, experimental laboratory test rigs are designed and used for measurements by different research groups. Therefore, metal probes of different dimensions and materials are heated up to the desired temperature and then exposed to spray nozzles. The heat flux is usually measured by temperature sensors immersed in the probe body, and then determined from the measured temperature using inverse modelling methods. Herein, the differences between the real and laboratory conditions are focused on using a mathematical heat transfer simulation model. The influence of strand surface temperature, nozzle spray water flow conditions, and Leidenfrost effect are pointed out. A procedure to use heatflux data measured on a test rig for cooling control on a real caster despite the different conditions is proposed.

ZusammenfassungBei hohen Temperaturgradienten in einem Festkörper ist die exakte Kenntnis der Messposition selbst innerhalb der Perle eines Thermoelements wichtig. Anhand einer numerischen thermoelektrischen Simulation wird gezeigt, wie die thermoelektrischen Effekte im Thermoelement im Detail zusammenwirken, und wo sich die daraus resultierende Messposition innerhalb des Thermoelements befindet.