Evaluating the quality of steady-state multivariate experimental data relative to various ORC experimental setups

Abstract Today, an increasing amount of experimental data is being released in the ORC field. This data is required to assess and compare the performance of different machines, to point out the main sources of losses, or to calibrate and to validate models. Experimental data is subject to different sources of disturbance and errors whose importance should be assessed. The level of noise, the presence of outliers, or a measure of the ”explainability” of the key variables with respect to the externally imposed operating condition are important indicators, but are not straightforward to obtain, especially if the data is sparse and multivariate. Starting from recent experimental campaigns on two different ORC test rigs, this paper proposes a methodology and a suite of tools implementing Gaussian Processes for quality assessment of steady-state experimental data. The aim of the proposed tool is to (1) provide a smooth (de-noised) multivariate operating map of the measured variable with respect to the inputs; (2) determine which inputs are relevant to predict a selected output; (3) provide a sensitivity analysis of the measured variables with respect to the inputs; (4) provide a measure of the accuracy (confidence intervals) for the prediction of the data; (5) detect the observation that are likely to be outliers. In this paper, the ORC test rigs and the obtained experimental data are described. The results are then analysed with the proposed tool, and compared with the results of traditional modelling techniques. It is demonstrated that GP regression provides insightful numerical indicators for these purposes, and that the obtained performance is higher or comparable to alternative modelling techniques. Finally, the datasets and tools developed in this work are provided within the GPexp open-source package.