Modeling the influence of thermal modification on the electrical conductivity of wood

A model has been developed aiming at the description of the effect of thermal modification on the electrical conductivity of wood. The intention was to calculate the moisture content (MC) of thermally modified timber (TMT) through the parameters electrical resistance R, wood temperature T, and CIE Lab color data, which are known to correlate well with the intensity of a heat treatment. Samples of Norway spruce (Picea abies Karst.) and beech (Fagus sylvatica L.) samples were thermally modified in laboratory scale at 11 different heat treatment intensities and the resistance characteristics of the samples were determined. Within the hygroscopic range, a linear relationship between the resistance characteristics and the mass loss (ML) through the heat treatment was established. Based on this, a model was developed to calculate MC from R, T, and ML. To validate this model, color values of 15 different TMTs from industrial production were determined for estimation of their ML and fed into the model. MC of the 15 arbitrarily heat-treated TMTs was calculated with an accuracy of ± 3.5% within the hygroscopic range. The material-specific resistance characteristics based on experimental data led to an accuracy of ± 2.5%. © 2014 Walter de Gruyter GmbH, Berlin/Boston

Ultra Rapid Data Assimilation Based on Ensemble Filters

The goal of this work is to analyse and study an ultra-rapid data assimilation (URDA) method for adapting a given ensemble forecast for some particular variable of a dynamical system to given observation data which become available after the standard data assimilation and forecasting steps. Initial ideas have been suggested and tested by Etherthon 2006 and Madaus and Hakim 2015 in the framework of numerical weather prediction. The methods are, however, much more universally applicable to general non-linear dynamical systems as they arise in neuroscience, biology and medicine as well as numerical weather prediction. Here we provide a full analysis in the linear case, we formulate and analyse an ultra-rapid ensemble smoother and test the ideas on the Lorentz 63 dynamical system. In particular, we study the assimilation and preemptive forecasting step of an ultra-rapid data assimilation in comparison to a full ensemble data assimilation step as calculated by an ensemble Kalman square root filter. We show that for linear systems and observation operators, the ultra-rapid assimilation and forecasting is equivalent to a full ensemble Kalman filter step. For non-linear systems this is no longer the case. However, we show that we obtain good results even when rather strong nonlinearities are part of the time interval [t0, tn] under consideration. Then, an ultra-rapid ensemble Kalman smoother is formulated and numerically tested. We show that when the numerical model under consideration is different from the true model, used to generate the nature run and observations, errors in the correlations will also lead to errors in the smoother analysis. The numerical study is based on the popular Lorenz 1963 model system used in geophysics and life sciences. We investigate both the situation where the full system forecast is calculated and the situation important to practical applications where we study reduced data, when only one or two variables are known to the URDA scheme