{"id":697,"date":"2026-04-27T11:27:55","date_gmt":"2026-04-27T11:27:55","guid":{"rendered":"https:\/\/blogs.aalto.fi\/fire\/?page_id=697"},"modified":"2026-06-19T04:16:15","modified_gmt":"2026-06-19T04:16:15","slug":"finwui","status":"publish","type":"page","link":"https:\/\/blogs.aalto.fi\/fire\/finwui\/","title":{"rendered":"FinWUI &#8211; Tools for managing the Wildland-Urban Interface\/mix fires in Finland"},"content":{"rendered":"\n<div class=\"wp-block-cover aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"540\" height=\"540\" class=\"wp-block-cover__image-background wp-image-744\" alt=\"AI-generated image of a wildfire behind a white family house.\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/05\/FinWUI-1_05.png\" data-object-fit=\"cover\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/05\/FinWUI-1_05.png 540w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/05\/FinWUI-1_05-362x362.png 362w\" sizes=\"auto, (max-width: 540px) 100vw, 540px\" \/><span aria-hidden=\"true\" class=\"wp-block-cover__background has-background-dim\"><\/span><div class=\"wp-block-cover__inner-container is-layout-flow wp-block-cover-is-layout-flow\">\n<p class=\"has-text-align-center has-large-font-size\"><strong>FinWUI<\/strong><\/p>\n<\/div><\/div>\n\n\n\n<p><\/p>\n\n\n\n<p>The <strong>goal <\/strong>of this research is to improve wildfire risk management at WUI and to enhance preparedness to climate change consequences by developing the fire simulation technologies.<\/p>\n\n\n\n<p><strong>Target 1<\/strong>: To create situation picture of available data sources and competences in Finland. Plan for future.<\/p>\n\n\n\n<p><strong>Target 2<\/strong>: To develop capability to simulate Finnish forest fuels ignition and combustion now and in future climate scenarios. Focus is on CFD (<a href=\"https:\\\\github.com\\firemodels\\fds\">FDS<\/a>) simulations.<\/p>\n\n\n\n<p><strong>Target 3<\/strong>: Capability to generate models of fuel mass and class distributions from the remote sensing data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Project members<\/mark><\/h2>\n\n\n\n<p class=\"has-text-align-left\"><strong>1) <mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Aalto University<\/mark><br><\/strong><em><span style=\"text-decoration: underline\">a) Fuel characterization and development of fire spread model<\/span><\/em><strong><br><\/strong>Prof. Simo Hostikka <br>Dr. D. Shanmugasundaram<br>Mr Aki Pakarinen<br>b) <em><span style=\"text-decoration: underline\">Particle cloud model creation based on LiDAR data<\/span><\/em> <br>Prof. Miina Rautiainen<br>Dr. Aarne Hovi<\/p>\n\n\n\n<p><strong>2) <mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">H\u00e4me University of Applied Sciences &#8211; HAMK:<\/mark> <em><span style=\"text-decoration: underline\">Fuel selection<\/span><\/em> <\/strong><br>Dr. Henrik Lindeberg<\/p>\n\n\n\n<p><strong>3) <mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Natural Resources Institute Finland &#8211; LUKE:<\/mark> <span style=\"text-decoration: underline\"><em>Fuel selection and sample collection <\/em><\/span><\/strong><br>Dr. Ilkka Vanha-Majamaa<br>Dr. Ekaterina Shorohova<\/p>\n\n\n\n<p>4) External Collaboration: <mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>Western Norway University of Applied Sciences<\/strong>:<\/mark> <br>Prof. Maria de Las Nieves Fernandez Anez<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Fuel characterization- TGA and cone calorimeter experiments<\/mark><\/h2>\n\n\n\n<p>The goal of the research project is to characterize five different fuel particulates from Scandinavian forests, formulate pyrolysis models and validate them using laboratory-scale cone calorimeter and fire spread experiments. Model development also includes the creation of a particle cloud description from remote sensing data. To develop a pyrolysis model, fuel characterization (TGA and cone experiments) were carried out for the following live and fresh fuels:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/fuels_all.jpeg\"><img loading=\"lazy\" decoding=\"async\" width=\"1140\" height=\"356\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/fuels_all-1140x356.jpeg\" alt=\"\" class=\"wp-image-714\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/fuels_all-1140x356.jpeg 1140w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/fuels_all-788x246.jpeg 788w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/fuels_all.jpeg 1151w\" sizes=\"auto, (max-width: 1140px) 100vw, 1140px\" \/><\/a><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Thermogravimetric analysis (TGA) was conducted in an air environment at a small scale to investigate the thermal decomposition behaviour at three different heating rates of 10, 25 and 50 K\/min.<\/li>\n\n\n\n<li><strong>Arrhenius parameters for pyrolysis and char oxidation reactions are estimated using FDS tool<\/strong><\/li>\n<\/ul>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/TGA_all_f.jpeg\"><img loading=\"lazy\" decoding=\"async\" width=\"1056\" height=\"247\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/TGA_all_f.jpeg\" alt=\"\" class=\"wp-image-715\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/TGA_all_f.jpeg 1056w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/TGA_all_f-788x184.jpeg 788w\" sizes=\"auto, (max-width: 1056px) 100vw, 1056px\" \/><\/a><figcaption class=\"wp-element-caption\">TGA mass loss data measured at a heating rate of 10 K\/min<\/figcaption><\/figure>\n<\/div>\n\n\n<p>Cone calorimeter experiments are essential for modelling the pyrolysis of porous forest fuels because they provide heat release, mass loss and ignition data at the material scale under well-defined external heat fluxes that are representative of fire conditions. These measurements are crucial for calibrating and validating the sub-models of pyrolysis used in physics-based fire simulations, including reaction rates, the heat of pyrolysis, and char yields. <\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/holder.jpg\"><img loading=\"lazy\" decoding=\"async\" width=\"926\" height=\"387\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/holder.jpg\" alt=\"\" class=\"wp-image-734\" style=\"aspect-ratio:2.392842836159701;width:462px;height:auto\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/holder.jpg 926w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/holder-788x329.jpg 788w\" sizes=\"auto, (max-width: 926px) 100vw, 926px\" \/><\/a><figcaption class=\"wp-element-caption\">Photographic images of conventional sample holder and new porous sample holder with ash tray<\/figcaption><\/figure>\n<\/div>\n\n\n<p>Using conventional solid holders in cone calorimeter experiments can suppress gas transport and distort how highly porous forest fuels burn. This results in non-representative pyrolysis parameters. Therefore, it is particularly important to use a porous sample holder for the selected fuels, as this preserves natural aeration, permeability, and internal heat transfer pathways, which have a strong influence on devolatilization. Consequently, all the cone calorimeter experiments were carried out in this work using a new porous stainless steel sample holder with an opening area of 50%. The length and width of the sample holder are 10 cm, whereas the height is 3 cm.<\/p>\n<\/div>\n<\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1140\" height=\"305\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image-1140x305.png\" alt=\"\" class=\"wp-image-708\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image-1140x305.png 1140w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image-788x211.png 788w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image-1536x411.png 1536w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Cone_image.png 1552w\" sizes=\"auto, (max-width: 1140px) 100vw, 1140px\" \/><\/a><figcaption class=\"wp-element-caption\">Snapshots of burning of fresh and live leaves and needles under cone heater<\/figcaption><\/figure>\n<\/div>\n\n\n<ul class=\"wp-block-list\">\n<li>Experiments conducted at two different external heatfluxes (ranging from 35 to 65 kW\/m<sup>2<\/sup>) and two bulk densities (ranging from 30 to 65 kg\/m<sup>3<\/sup>)<\/li>\n\n\n\n<li>Measured Heat Release Rate Per Unit Area (HRRPUA) and Mass loss rate per unit area (MLRPUA) were used to validate developed pyrolysis and char oxidation models.<\/li>\n<\/ul>\n\n\n\n\t\t\t\t<div class='panopto-iframe'>\n\t\t\t\t\t<iframe\n\t\t\t\t\t\tsrc='https:\/\/aalto.cloud.panopto.eu\/Panopto\/pages\/embed.aspx?id=dc862698-5f14-4c54-aa14-b444010cbdfe'\n\t\t\t\t\t\twidth='720'\n\t\t\t            height='405'\n\t\t\t            frameborder='0'\n\t\t\t            allowfullscreen='true'\n\t\t\t            allow='autoplay'\n\t\t\t\t\t><\/iframe>\n\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"has-text-align-center\">Burning of fresh and live Pinus Sylvesteris needles under cone heater<\/p>\n\n\n\n\t\t\t\t<div class='panopto-iframe'>\n\t\t\t\t\t<iframe\n\t\t\t\t\t\tsrc='https:\/\/aalto.cloud.panopto.eu\/Panopto\/pages\/embed.aspx?id=096add42-aaa7-4cb3-bcca-b44401094a89&amp;start=0'\n\t\t\t\t\t\twidth='720'\n\t\t\t            height='405'\n\t\t\t            frameborder='0'\n\t\t\t            allowfullscreen='true'\n\t\t\t            allow='autoplay'\n\t\t\t\t\t><\/iframe>\n\t\t\t\t<\/div>\n\t\t\t\n\n\n<p class=\"has-text-align-center\">FDS modelling video on burning of fresh and live Pinus Sylvesteris needles under cone heater<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\"><div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Pine_needle_FDS-1.jpeg\"><img loading=\"lazy\" decoding=\"async\" width=\"576\" height=\"476\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/Pine_needle_FDS-1.jpeg\" alt=\"\" class=\"wp-image-710\" style=\"aspect-ratio:1.2101023890784983;width:370px;height:auto\" \/><\/a><figcaption class=\"wp-element-caption\">FDS model validation against measured HRRPUA and MLRPUA data for pine needles<\/figcaption><\/figure>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/grass.jpg\"><img loading=\"lazy\" decoding=\"async\" width=\"797\" height=\"500\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/grass.jpg\" alt=\"\" class=\"wp-image-735\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/grass.jpg 797w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/04\/grass-788x494.jpg 788w\" sizes=\"auto, (max-width: 797px) 100vw, 797px\" \/><\/a><figcaption class=\"wp-element-caption\">FDS model validation against measured HRRPUA and MLRPUA data for Norwegian grass<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Particle cloud model<\/mark><\/h3>\n\n\n\n<p>The goal is to utilize airborne laser scanning (ALS) data to derive particle clouds that represent forest canopies. These particle clouds, together with information on forest floor composition, will then be used in fire simulations in the FDS model.<\/p>\n\n\n\n<p>The procedure for modeling forest canopies is as follows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Produce high-resolution voxel representations of plant area density (square meter of leaves and woody material per cubic meter of canopy volume) based on transmission of ALS pulses through the canopy<\/li>\n\n\n\n<li>Convert the plant area densities to bulk densities (kilograms of leaves and woody material per cubic meter of canopy volume), using tree species-specific coefficients derived from literature<\/li>\n\n\n\n<li>Model the leaves and woody material as Lagrangian particles in the FDS model<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_A-4.png\"><img loading=\"lazy\" decoding=\"async\" width=\"715\" height=\"692\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_A-4.png\" alt=\"\" class=\"wp-image-777\" style=\"width:592px;height:auto\" \/><\/a><figcaption class=\"wp-element-caption\">Left: Hemispherical photographs simulated using voxelized plant area densities (1 m resolution) estimated from ALS data in birch and pine forests. Right: Corresponding real hemispherical photographs taken in the field.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1608\" height=\"400\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2.png\" alt=\"\" class=\"wp-image-770\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2.png 1608w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2-788x196.png 788w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2-1140x284.png 1140w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_B-2-1536x382.png 1536w\" sizes=\"auto, (max-width: 1608px) 100vw, 1608px\" \/><\/a><figcaption class=\"wp-element-caption\">Preliminary results on validation of ALS based plant area density estimates in 40 field plots in Finland and Estonia. The y-axis represents canopy interceptance (1 \u2013 transmittance) calculated by ray tracing from ALS-based plant area densities (Figure A), and the x-axis shows corresponding reference estimates derived from real hemispherical photographs taken in the field. Results with original ALS pulse density (40\u201360 pulses per m<sup>2<\/sup>), as well as using operational pulse densities in Finland (20 and 5 pulses per m<sup>2<\/sup>) are shown.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><a href=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_C.png\"><img loading=\"lazy\" decoding=\"async\" width=\"930\" height=\"658\" src=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_C.png\" alt=\"\" class=\"wp-image-768\" style=\"aspect-ratio:1.4133978336312967;width:505px;height:auto\" srcset=\"https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_C.png 930w, https:\/\/blogs.aalto.fi\/fire\/files\/2026\/06\/Figure_C-788x558.png 788w\" sizes=\"auto, (max-width: 930px) 100vw, 930px\" \/><\/a><figcaption class=\"wp-element-caption\">Particle cloud representing forest canopy in the FDS model.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Visible research output<\/mark><\/h3>\n\n\n\n<p><strong>Conferences<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Shorohova, E., Vanha-Majamaa, I., Lindberg, H., Shanmugasundaram D., Hostikka, S., The burning question of a boreal biodiversity hotspot- coarse woody debris, IBFRA, Canada, August 2026 <strong>(Accepted)<\/strong>.<\/li>\n\n\n\n<li>Shanmugasundaram D., Shorohova, E., Vanha-Majamaa, I., Lindberg, H., Hovi, A., Miina Rautiainen, Hostikka, S., Modelling of Scandinavian forest fuels for wildfire simulations, NFSD, Denmark, August 2026 <strong>(Accepted)<\/strong>.<\/li>\n<\/ol>\n\n\n\n<h3 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Work in progress<\/mark><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Design and fabrication of lab scale fire spread apparatus<\/li>\n\n\n\n<li>Pyrolysis modelling and validation of forest fuels using FDS<\/li>\n\n\n\n<li>Validation of developed particle cloud model<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><mark style=\"background-color:#ffffff\" class=\"has-inline-color has-luminous-vivid-orange-color\">Funding<\/mark><\/h3>\n\n\n\n<p>The research is funded by the Finnish Fire Protection Fund (Palosuojelurahasto).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"http:\/\/palosuojelurahasto.fi\"><img loading=\"lazy\" decoding=\"async\" width=\"166\" height=\"117\" src=\"http:\/\/blogs.aalto.fi\/fire\/files\/2016\/05\/psr.png\" alt=\"\" class=\"wp-image-88\" \/><\/a><\/figure>\n<p><span class=\"author vcard\">Posted by Simo Hostikka<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>The goal of this research is to improve wildfire risk management at WUI and to enhance preparedness to climate change consequences by developing the fire simulation technologies. Target 1: To create situation picture of available data sources and competences in &hellip; <a href=\"https:\/\/blogs.aalto.fi\/fire\/finwui\/\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2422,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-697","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/pages\/697","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/users\/2422"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/comments?post=697"}],"version-history":[{"count":29,"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/pages\/697\/revisions"}],"predecessor-version":[{"id":778,"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/pages\/697\/revisions\/778"}],"wp:attachment":[{"href":"https:\/\/blogs.aalto.fi\/fire\/wp-json\/wp\/v2\/media?parent=697"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}