Case Studies For Website

_config.yml

@@ -239,13 +239,6 @@ compress_html:
     envs: development
 
 
-# Collections
-collections:
-  projects:
-    output: true
-    permalink: /:collection/:path/
-
-
 # Defaults
 defaults:
   # default page (where no layout is specified)
@@ -263,19 +256,19 @@ defaults:
         overlay_color: "#4d4d4d"
         overlay_particles: true
       classes: wide
-  # _projects
+  # models
   - scope:
-      path: "_projects"
-      type: projects
+      path: "models"
     values:
       layout: single
       author_profile: false
-      read_time: true
-      comments: # true
+      read_time: false
+      comments: false
       share: false
       related: false
+      toc: false
       sidebar:
-        nav: "projects"
+        nav: "models"
 
 # scripts to include in head    
 head_scripts:

_data/benchmarks.yml

@@ -0,0 +1,20 @@
+- name: "Circles Benchmark"
+  description: "Performance benchmarking of the Circles model using the C++ and C++ RTC interfaces and different communication strategies at various scales"
+  image_url:
+  video_url:
+  github_url: https://github.com/flamegpu/FLAMEGPU2-circles-benchmark
+  language: C++
+
+- name: "Concurrency Benchmark"
+  description: "Performance benchmarking of concurrency within FLAME GPU 2"
+  image_url:
+  video_url:
+  github_url: https://github.com/flamegpu/FLAMEGPU2-concurrency-benchmark
+  language: C++
+
+- name: "Ensemble Benchmark"
+  description: "Demonstration of the ensembles feature of FLAME GPU 2"
+  image_url:
+  video_url:
+  github_url: https://github.com/flamegpu/FLAMEGPU2-ensemble-benchmark
+  language: C++

_data/models.yml

@@ -1,34 +1,29 @@
-- name: "Circles Benchmark"
-  description: "Performance benchmarking of the Circles model using the C++ and C++ RTC interfaces and different communication strategies at various scales"
-  image_url:
-  video_url:
-  github_url: https://github.com/flamegpu/FLAMEGPU2-circles-benchmark
-  language: C++
+- name: "Digital Twinning of Urban Transportation Systems with Fujitsu Research Europe"
+  description: "A road network simulation model of vehicles and junctions to create a digital twin of the Isle of Wight."
+  image_url: /assets/images/models/digital-twinning-tile.png
+  video_url: https://www.youtube.com/watch?v=hZPDgZZP5TM
+  more_url: /models/digital-twinning
 
-- name: "Concurrency Benchmark"
-  description: "Performance benchmarking of concurrency within FLAME GPU 2"
-  image_url:
-  video_url:
-  github_url: https://github.com/flamegpu/FLAMEGPU2-concurrency-benchmark
-  language: C++
+- name: "Simulating Plastic Strain Accumulation in Rail Steel"
+  description: "A model of plastic strain accumulation in rail steels to understand structural failures."
+  image_url: /assets/images/models/rail-steel-tile.png
+  video_url: https://www.youtube.com/watch?v=_jRZMfsbwsw
+  more_url: /models/rail-steel
+
+- name: "Patient-specific Multi-Scale Tumor Growth Modelling"
+  description: "Cellular level modelling as part of a multi scale simulation approach for studying neuroblastoma"
+  image_url: /assets/images/models/primage-tile.png
+  video_url: https://vimeo.com/607559004/2f944666ad?fl=pl&fe=vl
+  more_url: /models/tumor-modelling
 
-- name: "Ensemble Benchmark"
-  description: "Demonstration of the ensembles feature of FLAME GPU 2"
-  image_url:
-  video_url:
-  github_url: https://github.com/flamegpu/FLAMEGPU2-ensemble-benchmark
-  language: C++
+- name: "Testing Interventions for Infectious Disease Spread within Buildings"
+  description: "Large-scale epidemiological models of infectious disease spread within buildings"
+  image_url: /assets/images/models/forge4flame-tile.png
+  video_url: https://www.youtube.com/watch?v=H_RMj3LmD18
+  more_url: /models/forge4flame
 
-- name: "CUDA C++ Model Template"
-  description: "The template repository which can be used as a starting point for CUDA C++ FLAME GPU 2 models"
-  image_url:
-  video_url:
-  github_url: https://github.com/flamegpu/FLAMEGPU2-example-template
-  language: C++
-
-- name: "Python Model Template"
-  description: "The template repository which can be used as a starting point for python FLAME GPU 2 models"
-  image_url:
-  video_url:
-  github_url: https://github.com/FLAMEGPU/FLAMEGPU2-python-example-template
-  language: Python
+- name: "Improving Passenger Boarding Rate and Reducing Risk at the Platform Train Interface "
+  description: "Evaluating how rolling stock characteristics such as door width and carriage layout affect passenger movement."
+  image_url: /assets/images/models/ratesetter-tile.png
+  video_url: https://www.youtube.com/watch?v=pvhDlSd1gyo
+  more_url: /models/ratesetter

_data/navigation.yml

@@ -4,12 +4,19 @@ main:
     url: /download/
   - title: "About"
     url: /about/
-  # - title: "Projects"
-  #  url: /projects/
   - title: "Models"
     url: /models/
   - title: "Documentation"
     url: /documentation/
   - title: "Contact"
     url: /contact/
-
+
+models:
+  - title: Models
+    children:
+     - title: "Case Studies"
+       url: /models/
+     - title: "Benchmarks"
+       url: /models/benchmarking
+     - title: "Templates"
+       url: /models/templates/   

_data/templates.yml

@@ -0,0 +1,13 @@
+- name: "CUDA C++ Model Template"
+  description: "The template repository which can be used as a starting point for CUDA C++ FLAME GPU 2 models"
+  image_url:
+  video_url:
+  github_url: https://github.com/flamegpu/FLAMEGPU2-example-template
+  language: C++
+
+- name: "Python Model Template"
+  description: "The template repository which can be used as a starting point for Python FLAME GPU 2 models"
+  image_url:
+  video_url:
+  github_url: https://github.com/FLAMEGPU/FLAMEGPU2-python-example-template
+  language: Python

models/benchmarking/index.md

@@ -0,0 +1,36 @@
+---
+title: "FLAME GPU 2 Models"
+permalink: "models/benchmarking"
+---
+
+In order to evaluate the performance of FLAME GPU a number of benchmarking models exist which evaluate different scaling aspects of FLAME GPUs features. 
+
+<!-- Completely custom html / css, because undoing feature-row is a lot of effort.  -->
+<div class="flex_feature_container small-2-col medium-3-col">
+  {% for m in site.data.benchmarks %}
+    <div class="flex_feature_item">
+      <div class="flex_feature_item_teaser">
+        {% if m.image_url %}
+          <img src="{{ m.image_url | relative_url }}" alt="Image of {{ m.name }} Model in FLAME GPU 2">
+        {% else %}
+          {% if m.language == "Python" %}
+            <img src="{{ 'assets/images/pyfgpu2_icon_512.png' | relative_url }}" alt="Default Python model image in FLAME GPU 2">
+          {% else %}
+            <img src="{{ 'assets/images/fgpu2_icon_512.png' | relative_url }}" alt="Default C++ model image in FLAME GPU 2">
+          {% endif %}
+        {% endif %}
+      </div>
+      <div class="flex_feature_item_body">
+        <h3>{{ m.name }}</h3>
+        <div class="">
+          {{ m.description | markdownify }}
+        </div>
+      </div>
+      <div class="flex_feature_item_footer">
+        {% if m.github_url %}
+          <a href="{{ m.github_url }}" class="btn btn--primary">GitHub</a>
+        {% endif %}
+      </div>
+    </div>
+  {% endfor %}
+</div>

models/digital_twinning.md

@@ -0,0 +1,15 @@
+---
+title: "Digital Twinning of Urban Transportation Systems with Fujitsu Research Europe "
+permalink: "models/digital-twinning"
+---
+
+![Digital Twinning of Urban Transportation Systems Road Network Model Close-up]({{ "/assets/images/models/digital-twinning.png" | relative_url }}){: .align-right .no-shadow} 
+
+In collaboration with Fujitsu Research of Europe, the FLAME GPU team undertook a collaborative development project to assist in applying FLAME GPU to the simulation of urban transportation systems in order to create a digital twin of the Isle of Wight. The focus of model development was on transportation micro-simulation and road networks (junctions). The developed model aims to replicate a subset of the individual and junction models from the open source Simulation of Urban MObility (SUMO) within FLAME GPU. Computational experiments have indicated that speedups over SUMO of ~68x are achievable on desktop GPUs (Intel Core i7-5930K CPU, an NVIDIA GeForce RTX 3090 (24 GiB) GPU). This is equivalent to simulating ~100x faster than real time with a maximum load of 95k vehicles.
+
+
+## More Information
+
+ - Smilovitskiy et al. "FLAME-GPU for Traffic Systems: A Scalable Agent-Based Simulation Framework." *Systems 2025, 13, 376. https://doi.org/10.3390/systems13050376* [Research Paper](https://www.mdpi.com/2079-8954/13/5/376) 
+
+<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/hZPDgZZP5TM?si=MVQ6LhFkrcweuYay" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

models/forge4flame.md

@@ -0,0 +1,22 @@
+---
+title: "Testing Interventions for Infectious Disease Spread within Buildings "
+permalink: "models/forge4flame"
+---
+
+![Testing Interventions for Infectious Disease Spread within Buildings ]({{ "/assets/images/models/forge4flame.png" | relative_url }}){: .align-right .no-shadow} 
+
+The [QBio group](https://qbio.di.unito.it/) in the Department of Computer Science at the University of Turin, with support from the FLAME GPU team, developed Forge4Flame, a user-friendly interface that makes it easier to harness the full power of FLAME GPU. Specifically, the tool streamlines the design, execution, and analysis of models by automatically generating the necessary FLAME GPU code and incorporating valuable visualisation and post-processing features. This makes the tool more accessible to a broader audience of researchers and public health professionals.
+
+By running large-scale epidemiological models of infectious disease spread within buildings on GPUs, FLAME GPU enables researchers to capture complex interactions in detail and deliver results dramatically faster than traditional CPU-based approaches. This performance opens up new opportunities for exploring dynamic behaviours in epidemiology and beyond. In the COVID-19 case study, FLAME GPU made it possible to model disease spread in an Italian middle school at unprecedented detail and speed, allowing rapid testing of interventions and vaccination strategies. While Forge4FLAME helped to streamline access, the true impact came from FLAME GPU’s ability to combine scale, speed, and accuracy. This brought advanced agent-based modelling within the reach of public health researchers, demonstrating its potential to influence decision-making in real-world crises.
+
+## Testimonial
+
+*[The Quantitative Biology group of the Department of Computer Science from the University of Turin (Italy)](https://qbio.di.unito.it/) collaborated closely with the FLAME GPU developers in the creation of the F4F tool, a graphical interface designed to simplify the modelling process and make agent-based modelling more accessible to a wider audience of researchers and public health professionals. The collaboration was highly synergistic, characterized by virtual meetings, open and constructive discussions. The FLAME GPU team’s expertise, responsiveness, and commitment were instrumental in guiding the technical development and ensuring seamless integration with the FLAME GPU framework. This collaboration not only advanced our project but also led to the joint preparation of a scientific paper.* - Simone Pernice, University of Turin (Italy)
+
+
+
+## More Information
+
+ - Baccega, Daniele, et al. "Forge4Flame: An Intuitive Dashboard for Designing GPU Agent-Based Models to Simulate Infectious Disease Spread." Available at SSRN 5194584. https://doi.org/10.1016/j.simpat.2025.103205* [Research Paper](https://doi.org/10.1016/j.simpat.2025.103205) 
+
+<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/H_RMj3LmD18?si=ifs1Wrjfm_wAVa1D" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

models/index.md

@@ -1,40 +1,25 @@
 ---
-title: "FLAME GPU 2 Models"
+title: "FLAME GPU 2 Case Studies"
+permalink: "models"
 ---
 
-FLAME GPU 2 models can be contained in their own source code repositories. 
-This page provides a selection of models implemented in FLAME GPU 2, which can be built following the instructions provided with each repository.
+FLAME GPU has been used within a wide range of industrial and research applications. Some examples are presented below.
 
-If you would like to contribute your own model to this page you should use the FLAME GPU 2 example template and then [contact us](../contact) to include your model.
-
-FLAME GPU 1.x models are included with the FLAME GPU 1.x SDK, which can be found via the  [download](../download) page.
-
-<!-- Compeltely custom html / css, because undoing feature-row is a lot of effort.  -->
+<!-- Completely custom html / css, because undoing feature-row is a lot of effort.  -->
 <div class="flex_feature_container small-2-col medium-3-col">
   {% for m in site.data.models %}
     <div class="flex_feature_item">
       <div class="flex_feature_item_teaser">
         {% if m.image_url %}
-          <img src="{{ m.image_url | relative_url }}" alt="Image of {{ m.name }} Model in FLAME GPU 2">
-        {% else %}
-          {% if m.language == "Python" %}
-            <img src="{{ 'assets/images/pyfgpu2_icon_512.png' | relative_url }}" alt="Default Python model image in FLAME GPU 2">
-          {% else %}
-            <img src="{{ 'assets/images/fgpu2_icon_512.png' | relative_url }}" alt="Default C++ model image in FLAME GPU 2">
-          {% endif %}
+          <a href= "{{ m.more_url }}"><img src="{{ m.image_url | relative_url }}" alt="Image of {{ m.name }} Model in FLAME GPU 2"></a>
         {% endif %}
       </div>
       <div class="flex_feature_item_body">
-        <h3>{{ m.name }}</h3>
+        <h3><a href="{{ m.more_url }}">{{ m.name }}</a></h3>
         <div class="">
           {{ m.description | markdownify }}
         </div>
       </div>
-      <div class="flex_feature_item_footer">
-        {% if m.github_url %}
-          <a href="{{ m.github_url }}" class="btn btn--primary">GitHub</a>
-        {% endif %}
-      </div>
     </div>
   {% endfor %}
 </div>

models/rail_steel.md

@@ -0,0 +1,21 @@
+---
+title: "Simulating Plastic Strain Accumulation in Rail Steel"
+permalink: "models/rail-steel"
+---
+
+![Plastic Strain Accumulation in Rail Steel 3D Render]({{ "/assets/images/models/rail-steel.png" | relative_url }}){: .align-right .no-shadow} 
+
+The modelling of plastic strain accumulation in rail steels, a process that is the root cause of a number of rail failure mechanisms, is generally a complex and computationally expensive task. Finite element techniques are usually limited to simulating only a few hundred wheel passes, which is representative of only a few hours of the in-service life of a rail. Thus, there exists a demand for methods that reduce the computation time required in order to better understand the long-term behaviour of rail materials. 
+
+The Dynarat technique, an established method of simulation which represents rail response by discretising a section of rail material into bricks which accumulate strain in response to the maximum orthogonal shear stress experienced each cycle, was translated into an agent-based materials model using FLAMEGPU. 
+
+Each material agent accumulates strain, in response to each wheel pass, in parallel. This allows for faster simulations of tens of thousands of cycles. With the reduction in computational expense, larger scale, 3d simulations with microstructurally distributed properties, can be introduced. Moreover, agent-based modelling facilitates communication between material agents, such that they can exchange information about their respective strain states and any discontinuities between them. 
+
+This has facilitated the introduction of a novel failure mechanism which represents the effect of strain partitioning between microstructural constituents which could not have been represented before.
+
+
+## More Information
+
+ - [Featured on Page67 of “Rail Engineer magazine](https://www.railengineer.co.uk/rail-engineer-march-april-2025-rules-do-they-keep-track-workers-safe-better-possession-protection-hs2s-delta-junction/)
+
+<iframe width="560" height="315" src="https://www.youtube-nocookie.com/embed/_jRZMfsbwsw?si=wS36YXPbkb9YIkxW" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>