Heat transport and storage processes in differential scanning calorimeter: Computational analysis and model validation

•3-D computational model of a differential scanning calorimeter is constructed.•Five reference processes are used for experimental and computational calibration.•Model is validated using two different materials.•Physical origins of measurement uncertainties can be identified.•Systematic errors involved in raw experimental data can be eliminated. Experimental results provided by differential scanning calorimetry (DSC) can be affected by systematic errors, which are difficult to identify and quantify correctly by the end-users, as a DSC device is commonly used as a gray box. The signal delay due to thermal inertia and the effects of sample size or heating rate present the most common sources of uncertainties. In this paper, a 3-D computational model of a differential scanning calorimeter is constructed, calibrated and validated. Five reference standards are used for both experimental and computational calibration, resulting in a very good agreement (R2 > 0.999794) of the computational model with experimental outputs. Model is validated using two different materials and processes. The analysis of melting of aluminum, as one of the standards not used at the calibration, shows a maximum difference of 0.279 mW·mg−1 at the peak top, which is well within the accuracy limits. The application of the model for the determination of effective specific heat capacity of quartz, as a representative of commonly studied materials which are though not standardized for DSC, reveal a good agreement with both the measured data and the results of independent experiments reported by several other investigators. The main advantage of the model consists in the detailed analysis and separation or quantification of particular heat evolving/consuming mechanisms in both the DSC device and the studied materials. Therefore, contrary to the empirical calibration, it can identify exactly the physical sources of measurement uncertainties. The raw experimental data provided by the DSC device can then be corrected in a straightforward way and the systematic errors can be eliminated.