Wood Fuel Procurement to Bioenergy Facilities: Analysis of Moisture Content Variability and Optimal Sampling Strategy

Moisture content is the most relevant quality parameter for wood fuels. Effective and fast determination of moisture of incoming feedstock is essential in the management of bioenergy facilities. The availability of fast and reliable moisture meters based on innovative technologies simplifies this task. However, in Mediterranean conditions the inherent variability of wood fuels calls for a careful sampling strategy if representative results are required while facing acceptable analytic costs. The present study is aimed at measuring the fuel heterogeneity and defining accordingly the appropriate number of samples to be analyzed in order to get reliable moisture-content results. A total of 70 truckloads (about 2270 t of woodchips) were sampled during commercial operations in two different seasons. Five samples were collected from each load and measured with standard method and magnetic resonance gauge. Results show that the variability of moisture content is influenced by mixing of species and storage of biomass. Heterogeneity can vary greatly also within single truckloads, to the point that three samples are needed to achieve about 90% of estimates within the desired precision limits. In the case of larger lots, such as barge or ship loads, 20 samples can provide sufficient precision in most scenarios.

ON THE FLY PREDICTION OF TH-DEPENDENT SPATIAL MACROSCOPIC CROSS-SECTIONS USING FFT

Monte Carlo (MC) codes can accurately model neutron transport in nuclear reactors. However, the efficient inclusion of thermal-hydraulic (TH) feedback within the MC calculation sequence is still an open problem, particularly when burnup’s time-evolution must be included in the analysis. For this reason, deterministic codes, leveraging the use of macroscopic cross-sections generated with higher order methods from 2D lattice calculations, are still widely used to perform reduced-order multiphysics analyses. However, traditional cross-sections generation procedures typically decompose the large core problem into multiple assembly-level problems; thus not having the ability to capture inter-nodal effects. Moreover, the pre-generation procedure requires additional pre-computational time to perturb/branch the problem for various operational conditions (e.g. fuel temperature), which, again, is decoupled from the core. In this paper, we propose a new method leveraging the use of Fourier transfer functions to predict the cross-sections distribution due to a variation in TH conditions. The method was tested against a 3D BWR unit-cell problem with realistic density profile and axial fuel heterogeneity. The method was able to compute the mono-energetic cross-sections distribution with maximum error lower than 2%. Insights on the influence of the statistics used to generate the cross-sections on the accuracy of the results is also provided.

Effects of canopy midstory management and fuel moisture on wildfire behavior

Increasing trends in wildfire severity can partly be attributed to fire exclusion in the past century which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk, while restoring fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a simple fire line and propagating through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a dense midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

Effects of canopy midstory management and fuel moisture on wildfire behavior

Wildfires burning more and more areas in North America can partly be attributed to fire exclusion activities in the past few decades which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk as well as to restore fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a fire starting from a simple fire line and moving through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a thick midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

High-resolution observations of combustion in heterogeneous surface fuels

In ecosystems with frequent surface fires, fire and fuel heterogeneity at relevant scales have been largely ignored. This could be because complete burns give an impression of homogeneity, or due to the difficulty in capturing fine-scale variation in fuel characteristics and fire behaviour. Fire movement between patches of fuel can have implications for modelling fire spread and understanding ecological effects. We collected high resolution (0.8×0.8-cm pixels) visual and thermal imaging data during fire passage over 4×4-m plots of mixed fuel beds consisting of pine litter and grass during two prescribed burns within the longleaf pine forests of Eglin Air Force Base, FL in February 2011. Fuel types were identified by passing multi-spectral digital images through a colour recognition algorithm in ‘Rabbit Rules,’ an experimental coupled fire-atmosphere fire spread model. Image fuel types were validated against field fuel types. Relationships between fuel characteristics and fire behaviour measurements at multiple resolutions (0.8×0.8cm to 33×33cm) were analysed using a regression tree approach. There were strong relationships between fire behaviour and fuels, especially at the 33×33-cm scale (R2=0.40–0.69), where image-to-image overlap error was reduced and fuels were well characterised. Distinct signatures were found for individual and coupled fuel types for determining fire behaviour, illustrating the importance of understanding fire-fuel heterogeneity at fine-scales. Simulating fire spread at this fine-scale may be critical for understanding fire effects, such as understorey plant community assembly.

Simulated western spruce budworm defoliation reduces torching and crowning potential: a sensitivity analysis using a physics-based fire model

The widespread, native defoliator western spruce budworm (Choristoneura occidentalis Freeman) reduces canopy fuels, which might affect the potential for surface fires to torch (ignite the crowns of individual trees) or crown (spread between tree crowns). However, the effects of defoliation on fire behaviour are poorly understood. We used a physics-based fire model to examine the effects of defoliation and three aspects of how the phenomenon is represented in the model (the spatial distribution of defoliation within tree crowns, potential branchwood drying and model resolution). Our simulations suggest that fire intensity and crowning are reduced with increasing defoliation compared with un-defoliated trees, regardless of within-crown fuel density, but torching is only reduced with decreasing crown fuel density. A greater surface fire intensity was required to ignite the crown of a defoliated compared with an un-defoliated tree of the same crown base height. The effects of defoliation were somewhat mitigated by canopy fuel heterogeneity and potential branchwood drying, but these effects, as well as computational cell size, were less pronounced than the effect of defoliation itself on fire intensity. Our study suggests that areas heavily defoliated by western spruce budworm may inhibit the spread of crown fires and promote non-lethal surface fires.

Linking complex forest fuel structure and fire behaviour at fine scales

Improved fire management of savannas and open woodlands requires better understanding of the fundamental connection between fuel heterogeneity, variation in fire behaviour and the influence of fire variation on vegetation feedbacks. In this study, we introduce a novel approach to predicting fire behaviour at the submetre scale, including measurements of forest understorey fuels using ground-based LIDAR (light detection and ranging) coupled with infrared thermography for recording precise fire temperatures. We used ensemble classification and regression trees to examine the relationships between fuel characteristics and fire temperature dynamics. Fire behaviour was best predicted by characterising fuelbed heterogeneity and continuity across multiple plots of similar fire intensity, where impacts from plot-to-plot variation in fuel, fire and weather did not overwhelm the effects of fuels. The individual plot-level results revealed the significance of specific fuel types (e.g. bare soil, pine leaf litter) as well as the spatial configuration of fire. This was the first known study to link the importance of fuelbed continuity and the heterogeneity associated with fuel types to fire behaviour at metre to submetre scales and provides the next step in understanding the complex responses of vegetation to fire behaviour.