@article {bnh-7922, title = {Improving the resilience of existing housing to severe wind events - final project report}, number = {656}, year = {2021}, month = {03/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

This BNHCRC project titled:\  Improving the resilience of existing housing to severe wind events prescribes practical structural retrofits that will make improvements to the performance of Pre-80s (Legacy) houses in windstorms as well as measures to reduce damage and loss to contemporary houses.

Damage investigations carried out by the Cyclone Testing Station (CTS) following severe windstorms have typically shown that houses built prior to the mid-1980s in Australia perform worse than houses constructed to contemporary building standards, during windstorms. Given that these older houses are a significant proportion of the housing stock, practical structural upgrading based on the latest research may improve performance of housing and the economic and social wellbeing of a community.

Some details for structural retrofitting currently exist, but their uptake is limited, and there is also evidence that these are not carried out when houses require repairs following severe storms. Therefore, the issues of retrofitting legacy housing, including feasibility and benefit-cost are analysed in this project.

The primary objective of this study was to identify vulnerable legacy house types across Australia and develop cost-effective retrofits for mitigating damage during windstorms. These evidence-based strategies will (a) aid policy formulation and decision making by Government and industry, and (b) provide guidelines detailing various options and benefits to homeowners and the industry for retrofitting typical at-risk houses in Australia. The main aims were to:

This report presents an overview of the research approach used for this project including the selection of house types, the development of the VAWS software and the Internet-based guidelines. A case study is presented of the vulnerability and benefit cost assessment of one of the selected house types, with the complete set of results presented in the Appendices. These results show that tile roofed houses in cyclonic regions of Australia benefit the most from retrofitting for severe wind events. The benefit-cost ratios for these tile roof houses and other house types are expected to improve when accounting for intangible costs, which are currently not included in the analyses presented in this report. In addition, examples of the impacts and utilisation of this project including the Queensland Government Housing Resilience Program are also presented.

}, keywords = {cyclone, events, existing housing, resilience, severe, wind}, issn = {656}, author = {John Ginger and Korah Parackal and David Henderson and Martin Wehner and Hyeuk Ryu and Mark Edwards} } @article {bnh-8284, title = { Studying leaf area density based wind adjustment factor in Spark}, number = {713}, year = {2021}, month = {11/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In some operational fire prediction models such as the McArthur model [1] or Vesta model [2], a static (constant) wind reduction factor (WRF) is used to account for subcanopy wind profile. However, the WRF is far more complicated than what can be described by a constant value. A dynamic WRF depends on wind velocity well above the forest canopy, canopy height and canopy density which may vary from location to location. In our earlier work, we developed a simpler model considering uniform vertical canopy density, known as the Harman-Finnigan model [3].\  However, Moon et al. [4] and Sutherland et al. [5] demonstrated that vertically heterogonous leaf area density (LAD) can significantly change the WRF. Massman et al [6] presented a set of equations accounting for vertically heterogonous LAD,\  plant area index (PAI) and above-canopy wind profiles towards calculating subcanopy wind adjustment factor (WAF). WAF is basically the inverse of WRF. In this study, we implement this LAD based WRF (by inversing Massman{\textquoteright}s WAF) model in CSIRO{\textquoteright}s Spark platform within its Vesta fire propagation model [2].

This research was initially tested using a synthetic dataset to apply canopy parameters that represent Australian vegetation properties. The preliminary Spark simulation results show some variation in fire rate of spread based on various vegetation types. Further, we assess the implementation using three case studies based on three fire incidents, Mount Cooke Fire 2003 of Western Australia, Kilmore Fire 2009 of Victoria, and Lithgow Fire (State Mine) 2013 of New South Wales. Taking PAI as half of leaf area index (LAI), testing four different LAD configurations, it is found that for Tall Open Eucalyptus LAD, best results are obtained in relation to actual final fire perimeters

}, keywords = {adjustment, area density, factor, leaf, spark, wind}, issn = {713}, author = {Mahmood Rashid and Khalid Moinuddin} } @article {bnh-8191, title = {Wind speed Reduction Factors (WRFs): utilities for WRF assessment and communication - Black Summer final report}, number = {688}, year = {2021}, month = {08/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {MELBOURNE}, abstract = {

This project commenced in March 2021 as a Bushfire and Natural Hazards CRC Black Summer funded initiative between Queensland Fire and Emergency Services and the School of Earth and Environmental Sciences at the University of Queensland. The purpose of the project was to undertake an evaluation of WRFs used in Australia (including during Black Summer) to quantify the reduction of open space wind speed by Australian fuel types.

Wind is a key driver of fire behaviour and can be highly variable and difficult to predict, particularly within the lowest 1-2km of the atmosphere where it interacts in complex ways with topography and vegetation. Operational fire spread modelling quantifies the impact of vegetation or fuel type on wind speed using Wind speed Reduction Factors (WRFs) or Wind Adjustment Factors (WAFs). Specifically, these factors quantify the impact of vegetation on reducing the speed of the open space prevailing wind. WRF is typically the ratio of 10m open wind speed to 2m wind speed, whereas WAF is the ratio of {\textquoteleft}midflame{\textquoteright} wind speed to 20 ft open wind speed.

To date, single or static WRFs have been assigned to 62 Queensland Broad Vegetation Groups (BVGs) for use within the operational fire simulation application PHOENIX Rapidfire. These WRFs have been derived via approximation. Specifically, the 10m open wind speed has been approximated by the closest Bureau of Meteorology (BOM) Automatic Weather Station (AWS) to the field site, and the 2m near-surface wind speed has been approximated via the use of a handheld anemometer raised to eye-level at a suitable location within the field site.

The key research aims were to:

The review found that the use of approximated static WRFs has caused minor to significant error accumulation in the fire spread model outputs produced by fire simulation applications, including PHOENIX Rapidfire.

To reduce error in fire spread modelling, the review concluded that the development of dynamic WRF modelling capabilities should be a priority. These dynamic WRFs should respond to key wind, fuel, fire and topography parameters that change over time and space.

However, a dynamic WRF model should not require such high levels of computation so as to delay real-time fire spread modelling outputs. At its simplest, a dynamic WRF model is a discrete, empirically derived WRF profile, illustrating the change in WRF at specific heights measured within a fuel type in the field. A more advanced dynamic WRF model might be a mathematical model for which wind, fuel, fire and topography parameters act as inputs and a mathematically idealised continuous WRF profile is the output. This model should be validated by empirical data. Overall, each fuel type should have its own WRF profile. The end goal should be to replace all static WRFs with dynamic WRF profiles in fire spread models.

A WRF test site was established in the priority fuel type {\textquoteleft}moist to dry eucalypt woodland on coastal lowlands and ranges{\textquoteright} at the Queensland University of Technology (QUT) Samford Ecological Research Facility (SERF), located on the outskirts of Samford Valley in Southeast Queensland. Installed at the site is a 15m instrumented tower using 3D sonic anemometers to record mean 3D wind speed, vertical wind direction and sonic air temperature. A discrete WRF profile was derived by taking the ratio of the average 10m open wind speed measured by the nearest BOM AWS in Brisbane and the average wind speed measured at heights of 2.5m, 4.5m, 10.5m and 15.5m. This WRF profile is preliminary as it is based on 23 hours of data collected outside the southeast Queensland fire season (August {\textendash} December).

Preliminary investigations of relationships between variables related to WRF were also conducted. The overall wind profile was compared to the Plant Area Density (PAD) profile of the vegetation obtained via terrestrial LiDAR (Light Detection and Ranging). A weak to moderate relationship was identified (R2 = 0.22) between mean wind speed and PAD, which may be due to the calm conditions experienced over the short length of the data collection period. Additionally, the day-time and night-time subcanopy temperature profiles were compared. The day-time profile was found to be slightly more constant with height, which may indicate that the subcanopy environment is more mixed and turbulent throughout the day. This result was supported by increased measurements of vertical mixing throughout the day. Nevertheless, data collection over a longer period under more varied conditions is recommended to investigate these relationships further.

The WRF test site at the QUT SERF has provided a preliminary insight into the relationships between vegetation and meteorology in the Australian context, which is essential for the development of empirically based dynamic WRF profiles for all fuel types. The methodology used is transferable and will be applied to other sites containing other priority fuel types. Anemometer measurements and LiDAR scans may then be used as key datasets for underpinning and validating the development of advanced dynamic WRF modelling capabilities in the next generation of fire spread models. Until this capability is developed, the new quick-reference WRF profile assessment resource developed by this project will enable FBANs near the fire ground to quickly identify the WRF values most relevant to the ensuing fire spread. These values may then be communicated to fire spread modellers.

}, keywords = {assessment, black summer, communication, factors, reduction, speed, wind}, issn = {688}, author = {Hamish McGowan and Katherine Rosenthal and Raymond Bott and John Myles} } @mastersthesis {bnh-6825, title = {Development of an interface using penalisation method for improving computational performance of bushfire simulation tools}, volume = {Master of Engineering}, year = {2019}, month = {05/2019}, pages = {350}, school = {Victoria University}, address = {Melbourne}, abstract = {

Wind is the dominant environmental factor affecting wildland fire intensity and spread. Previously, fire analysts and managers have relied on local measurements and site-specific forecasts to determine how winds influence fire. The advancements in computer hardware, increased availability of electronic topographical and experimental data, and advances in numerical methods for computing winds, have led to the development of new tools capable of simulating wind flow. Several numerical models have been developed for fire prediction and forecasting. Modelling wind in physics-based models like Fire Dynamics Simulator (FDS) has been shown to produce promising results, but at an inordinate cost. Because of the high computational expense, physics-based models are not suitable for operational use. Little research has been conducted to improve the computational speed of these models. The current study intends to decrease the computational cost of physics-based fire simulations and improve physics-based models by including more complicated driving winds.\ 

Physics-based wildfire simulations are driven by inlet boundary conditions which model the atmospheric boundary layer. Various inlet conditions, such as the 1/7-powerlaw or the log law models with artificial turbulence (e.g. the synthetic eddy method, [SEM]) can be used as an inlet to generate a statistically steady wind field for a fire simulation. The power-law inlet is the default inlet condition used in FDS where the wind develops turbulence as it sweeps through the domain, and is often used with wall-of-wind\ type methods. The log-law inlet generates a log wind profile similar to Atmospheric Boundary Layer (ABL). Development of techniques for imposing inlet conditions and initial conditions for flow simulations have been topics of interest for the past few decades. CUrrent inlet and initial conditions requires time in a scale order of 100s of CPU hours, for generating an appropriate condition to start a fire simulation, hence resulting in increased computational expense. A novel nesting method has been implemented, which involves two regions: penalisation and blending, named as the PenaBlending method. The initial conditions of the fire simulations in FDS are set to the initial condition prescribed by an external model or simulation. This is achieved by a one-way coupling method. External wind data, for which u,v,w can vary in space and time, can be obtained. The precursor data can be generated either from any reduced wind model such as Windninha, which gives terrain\ modified wind data, or by using analytical methods such as generating logarithmic windfield using Matlab.\ These external data can be introduced into the FDS domain\ through a penalization region at the inlet/outlet. A blending region has also been\ implemented near the specified inlet/outlet which allows a smooth mixing of a precursor\ wind field to that in the simulation domain. This new inlet condition allows complicated\ terrain modified temporally and spatially varying wind fields, obtained from precursor\ simulations or any other models, to be implemented relatively easily in the FDS domain. To test the implementation of this method, a flat terrain is considered in the current study.\ However, this method could also be used for complicated terrain structures, as a part of\ future studies. The PenaBlending method provides appropriate flow conditions with reduced\ computational effort (up to  80\%), to start a fire simulation, and, hence, reduces\ the computational expense of physics-based models.

The results obtained using the PenaBlending method have been compared with that obtained using the existing inlet conditions of FDS, like the SEM method, wall-of-wind method and mean-forcing methods, using the 1/7 power-law or log-law inlets. To test these three methods, a set of fire simulations have been conducted and tested against the PenaBlending method. It was found that the results of the PenaBlending methods agree well with that of existing methods, with small variations for both the wind and fire cases.\ 

FDS 6.6.0 (the version used in this study) requires a very fine grid to obtain grid convergence. This is not feasible in the case of a large-scale simulation because of very high computation cost. FDS 6.2.0, with a reaction-rate-limiter combustion model, needs less fine grids to obtain grid convergence. Therefore, this combustion model is re-introduced into FDS 6.6.0, providing an option\ \ of choosing between two different combustion models,\ as a part of this study. For all the simulations, the reaction-rate-limiter combustion\ model has been used. The simulations are carried out in a neutral-atmospheric stability\ condition. However, the PenaBlending method can apply any general driving wind, and\ the effect of atmospheric stability, could be included, as part of future studies. The PenaBlending\ method could be extended in conjunction with Monin-Obukhov Similarity\ Theory (introduced in FDS 6.6.0) to model fire in various atmospheric stability conditions.

}, keywords = {computational fluid dynamics, Fire, fire dynamics simulator, Physics-based modelling, Wildfire, wind}, url = {http://vuir.vu.edu.au/40023/}, author = {Sesa Singha Roy} } @conference {bnh-6407, title = {Modelling the vulnerability of a high-set house roof structure to windstorms using VAWS }, booktitle = {Bushfire and Natural Hazards CRC Research Day AFAC19}, year = {2019}, month = {12/2019}, address = {Melbourne}, abstract = {

Modelling the vulnerability of houses in windstorms is important for insurance pricing, policy-making, and emergency management. Vulnerability models for Australian house types have been developed since the 1970s and have ranged from empirical models to more advanced reliability based structural engineering models, which provide estimates of damage for a range of wind speeds of interest. This paper describes recent developments in the engineering based vulnerability modelling software: {\textquoteleft}Vulnerability and Adaption to Wind Simulation{\textquoteright} (VAWS), which uses probability based reliability analysis and structural engineering for the loading and response coupled with an extensive test database and field damage assessments to calculate the damage experienced by selected Australian house types. A case study is presented to demonstrate the program{\textquoteright}s ability to model progressive failures, internal pressurization and debris impact.

}, keywords = {debris, simulation, structures, VAWs, Vulnerability, wind}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Korah Parackal and Martin Wehner and Hyeuk Ryu and John Ginger and Daniel Smith and David Henderson and Mark Edwards} } @mastersthesis {bnh-4426, title = {Statistical Characterisation Of Wind Fields Over Complex Terrain With Applications In Bushfire Modelling}, year = {2017}, month = {11/2017}, pages = {274}, school = {University of New South Wales}, address = {Canberra}, abstract = {

The propagation of bushfire across the landscape is dependent on a variety of environmental factors, but the wind, in particular, has a major effect on both the speed and direction of fire propagation. As such, bushfire spread models, which underpin successful bushfire management, require accurate knowledge of the pattern of winds across the landscape. This can be problematic over complex terrain where winds exhibit considerable spatial variability due to wind-terrain interactions, and where detailed measurements of wind characteristics are comparatively rare.

This thesis contributes two new wind datasets to address the previous lack of data available to develop and validate wind models over complex terrain. It also details analyses that focus on the statistical characterisation of wind as joint wind direction distributions, which represent the directional wind response to changing topography and surface roughness. A novel method for toroidal surface fitting is introduced and implemented to estimate the true continuous response from discrete observed data. This new method, which relies on a conceptually simple adaptation of planar techniques, is compared to the limited range of available toroidal surface estimation techniques and is shown to perform as well as, if not better than, these more sophisticated methods.

Monte Carlo simulations are employed to highlight the sensitivity of statistical comparison tests to alternative distribution structures, and to validate bivariate and circular extensions of the Kolmogorov-Smirnov test. These tests are applied to directional wind response pairs, showing that vegetation regrowth has a significant but varying impact across complex terrain.

Finally, this thesis demonstrates how statistical approaches can be used to complement current physics-based wind modelling methods. The resulting probabilistic representations provide more accurate predictions of wind direction variability, and are better suited to emerging ensemble-based bushfire prediction frameworks. As such, they provide a superior characterisation of uncertainty across the fire modelling process; ultimately enabling fire managers to make more informed decisions.

}, keywords = {Bushfire, bushfire management, propagation, spatial variability, toroidal surface, wind, wind-terrain}, url = {https://www.researchgate.net/profile/Rachael_Quill/publication/322212511_Statistical_characterisation_of_wind_fields_over_complex_terrain_with_applications_in_bushfire_modelling/links/5a4c20cf458515a6bc6bf931/Statistical-characterisation-of-wind-fields-ov}, author = {Rachael Quill} }