Statistical approach to compute a surrogate input for building physics CFD simulations through experimental measurements

Abstract

Computational Fluid Dynamics (CFD) simulations are sensitive to input uncertainties and human errors. Using real-world data as an input for CFD simulations is not rare for building physics simulations and it is still an open topic. In most cases, computer simulations using CFD are for design purposes and they aim to represent the situations occur in the real-world. The real-world parameters are commonly set based on experimental measurements. However, it is known that experimental measurements are affected by uncertainties; hence the experimental values have to be processed to calibrate numerical simulations. This paper investigates the CFD simulation calibration through experimental measurements. In particular, a statistical approach is employed to study the experimental data of inlet-outlet velocities for ten non successive days in a test-room with the purpose of assuming a surrogate day input that is the most significant of the dataset. Moreover, five different input methods on the boundary conditions of inlet velocity obtained from the experimental measurements are implemented and the accuracy of the predicted results through CFD simulations is presented. For the first input, the actual measurements of one particular day were chosen among the ten available days. For the other four, the numerical input relating to each second of the synthetic day was constructed by means of a statistical assessment of the actual measures obtained at each corresponding second of the ten actual days. The Hermite polynomial chaos expansion was selected for the last approach. Results have shown a significant variability of airflow for both experimentally measured input and output signals. By using the experimental signal expansion through Hermite polynomials the experimental and numerical values give satisfactory results.