Open AccessDevelopment and evaluation of a novel 3D simulation software for modelling wood stacks
Author(s) -
Felipe de Miguel-Díez,
Philippe Guigue,
Tim Pettenkofer,
Eduardo Tolosana,
Thomas Purfürst,
Tobias Cremer
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264414
Subject(s) - volume (thermodynamics) , sensitivity (control systems) , computer science , bark (sound) , set (abstract data type) , software , simulation modeling , stack (abstract data type) , statistics , simulation software , data set , calibration , data mining , simulation , mathematics , engineering , artificial intelligence , biology , ecology , physics , mathematical economics , quantum mechanics , electronic engineering , programming language
Assessing the solid wood content is crucial when acquiring stacked roundwood. A frequently used method for this is to multiply determined conversion factors by the measured gross volume. However, the conversion factors are influenced by several log and stack parameters. Although these parameters have been identified and studied, their individual influence has not yet been analyzed using a broad statistical basis. This is due to the considerable financial resources that the data collection entails. To overcome this shortcoming, a 3D-simulation model was developed. It generates virtual wood stacks of randomized composition based on one individual data set of logs, which may be real or defined by the user. In this study, the development and evaluation of the simulation model are presented. The model was evaluated by conducting a sensitivity and a quantitative analysis of the simulation outcomes based on real measurements of 405 logs of Norway spruce and 20 stacks constituted with these. The results of the simulation outcomes revealed a small overestimation of the net volume of real stacks: by 1.2% for net volume over bark and by 3.2% for net volume under bark. Furthermore, according to the calculated mean bias error (MBE), the model underestimates the gross volume by 0.02%. In addition, the results of the sensitivity analysis confirmed the capability of the model to adequately consider variations in the input parameters and to provide reliable outcomes.