Sentence examples for models for wood from inspiring English sources

Equations (1), (2), (3) and (4) represent the estimated fuel consumption models for wood (W), charcoal (C), kerosene (K) and LPG (L), respectively.

Regarding computational approaches, presented developments include multiscale models for prediction of elastic limit states and creep compliances of wood, macroscopic phenomenological models for wood plasticity and the time and moisture-dependent behavior, and their applications to investigations of dowel-joints and glued-laminated timber beams.

In the last 30 years, there have been significant advances made in the areas of probabilistic modeling of timber mechanical properties, structural analysis models for wood-frame structural systems, and stochastic modeling of structural and environmental loads.

We have created a numerical model for wood which can be used in the design and validating phase of new containers by numerical modelling.

In this review, we address lingering doubts regarding the utility of Arabidopsis as a model for wood development by highlighting studies that provide new biochemical and biophysical evidence that extend support for the Arabidopsis inflorescence stem as a model for wood development beyond what is currently thought.

As a comparison of the potential outcomes, we assess the connectivity resulting from the various policies using a landscape permeability model for wood frogs (Rana sylvatica), a vernal pool-breeding species.

A linear mixed-effects model for wood density was constructed for mean density data measured with high frequency densitometry on stem discs from 72 beech trees sampled from two different aspects (northeast -NE and southwest -SW) of a valley in southwestern Germany.

This implied that, biologically, the GEI was not strong enough to warrant the more complex (bivariate) model for wood traits (less so for growth traits) and that data might be pooled across sites and considered as a single trait.> -wrap-foot> *S1: Asselin Township (S1-C); S2: St. Casimir (S2-D).

Models for predicting wood properties, specifically stress-wave velocity and wood density, also require stand density to be taken into account.

These models for different wood properties are uncoupled, so, for example, increasing wood density will not increase modulus of elasticity or stress wave velocity (a non-destructive measure of wood stiffness) when it is known that density is a major driver of wood stiffness (Evans and Ilic 2001).

The Forecaster modelling system also includes models for other wood properties, such as modulus of elasticity and stress wave velocity (West et al. 2013).