diff --git a/_datasets/chung2024.md b/_datasets/blastnet_momentum.md
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diff --git a/_datasets/poludnenko.md b/_datasets/ch4air_forced_hit_flame.md
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diff --git a/_datasets/chung2022.md b/_datasets/compressible_inert_ch4o2_hit.md
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diff --git a/_datasets/shantanu.md b/_datasets/decaying_hit.md
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diff --git a/_datasets/sharma2024.md b/_datasets/diluted_partially_premixed_h2air_lifted_flame.md
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diff --git a/_datasets/jung2021.md b/_datasets/diluted_partially_premixed_h2air_lifted_slot_flame.md
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diff --git a/_datasets/wang2024.md b/_datasets/firebench_wildfire_les.md
similarity index 98%
rename from _datasets/wang2024.md
rename to _datasets/firebench_wildfire_les.md
index 4c8eeb2..f1bbc15 100644
--- a/_datasets/wang2024.md
+++ b/_datasets/firebench_wildfire_les.md
@@ -1,156 +1,156 @@
----
-layout: datapage
-excerpt: (117 cases)
-title: FireBench data above ground level
-description: LES of an ensemble of wildfire spread
-header:
- image: /assets/img/wang2024.png
- teaser: /assets/img/ico_wang2024.png
-categories:
-- reacting
-- environmental
-- turbulent
-- numerical
-- benchmark
----
-
-
-
-## Description
-The propagation of wildfires is a complex, dynamic process that is influenced by various factors, such as fuel, wind, terrain, and other environmental conditions. Accurately and reliably predicting the rate-of-spread of wildfires is of critical importance for fire management, rapid fire response, and fire mitigation. The [Google FireBench dataset](https://sites.research.google/gr/wildfires/firebench/) [1] aims to provide high-fidelity data to tackle these issues by providing an ensemble of large-eddy simulations that capture the three-dimensional wildfire-spread behavior and coupling with the atmospheric environment.
-
-The spatial and temporal evolution of the combustion of solid fuel coupled with the
-atmospheric flow is described by a two-phase model [2]. The gas-phase is described by
-the Favre-filtered conservation equations for mass, momentum, oxygen-fraction, and potential temperature [3]:
-{::nomarkdown}
-$$
-\partial_t \overline{\rho} + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}}) = S_\rho,
-$$
-$$
-\partial_t (\overline{\rho} \widetilde{\boldsymbol{u}} ) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \otimes \widetilde{\boldsymbol{u}}) = - \nabla \overline{p_d} + \nabla \cdot \overline{\tau} + [\overline{\rho} - \rho(z)] g \boldsymbol{\hat{k}_z} + \boldsymbol{f}_D + \boldsymbol{f}_C,
-$$
-$$
-\partial_t (\overline{\rho} \widetilde{Y_O}) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \widetilde{Y_O}) = \nabla \cdot \overline{\boldsymbol{j}_O} + \overline{\rho} \widetilde{\dot{\omega}_O},
-$$
-$$
-\partial_t (\overline{\rho} \widetilde{\theta}) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \widetilde{\theta}) = \nabla \cdot \overline{\boldsymbol{q}} + \frac{\overline{\rho} \widetilde{\theta}}{c_p \widetilde{T}} [h a_v (T_s - \widetilde{T}) + \dot{q}_r + (1-\Theta) H_f \widetilde{\dot{\omega}}],
-$$
-where $\widetilde{\cdot}$ denotes Favre-filtering and $\overline{\cdot}$ denotes Reynolds filtering. $\rho$ is the density, $\boldsymbol{u}$ is the velocity vector, $p_d$ is the hydrodynamic pressure, $\tau$ is the shear stress tensor, $g$ is the gravitational acceleration, $\boldsymbol{\hat{k}_z}$ is the unit vector along the gravitational direction, $f_D = - \overline{\rho} c_d a_v \boldsymbol{|\widetilde{u}| \widetilde{u}}$ is the drag force due to surface vegetation, $\boldsymbol{f}_C = f \boldsymbol{\hat{k}_z} \times \overline{\rho} (\widetilde{\boldsymbol{u}} - \boldsymbol{U}_\infty)$ is the Coriolis force, $Y_O$, $\boldsymbol{j}_O$, and $\dot{\omega}_O$ are the mass fraction, species diffusion, and source term of the oxidizer, $\theta$ is the potential temperature, $\boldsymbol{q}$ is the heat flux vector, $T$ is the gas-phase temperature, and $H_f$ is the heat of combustion.
-The heat exchange between the solid and gas phase is modeled with $h$ as the convective heat transfer coefficient, $a_v$ as the bulk fuel area-to-volume ratio, and $\dot{q}_r$ is the radiation source term. $\Theta = 1 - \rho_f/\rho_{f,0}$ is the fraction of the heat release that contributes to the increase of the solid phase temperature.
-$\dot{\omega}$ is the gas-phase combustion source term.
-{:/}
-
-The dataset consists of 117 cases with 9 velocities and 13 slopes with data extracted 1.5 m and 10 m above ground level. In addition, data was extracted at a streamwise location of 100 m < x < 1000 m.
-Specifically, the cases span a range of mean inlet velocity at 10 m above ground level of 2 to 10 m/s with a step of 1 m/s, and a range of slopes from 0 to 30 degrees with steps of 2.5 degrees.
-
-## Quick Info
-* Contributors: Qing Wang, Matthias Ihme, Cenk Gazen, Yi-Fan Chen, John Anderson, Jen Zen Ho, Bassem Akoush
-* Nx = 900, Ny = 252
-* DOI
-* .bib
-
-## Links to different cases
-
-
-
-
- | 0 |
- u10 = 2 m/s |
-
- 68 |
-
- Kaggle
- |
-
-
- | 1 |
- u10 = 3 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 2 |
- u10 = 4 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 3 |
- u10 = 5 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 4 |
- u10 = 6 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 5 |
- u10 = 7 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 6 |
- u10 = 8 m/s |
-
- 60 |
-
- Kaggle
- |
-
-
- | 7 |
- u10 = 9 m/s |
-
- 42 |
-
- Kaggle
- |
-
-
- | 8 |
- u10 = 10 m/s |
-
- 51 |
-
- Kaggle
- |
-
-
-
-## References
-[1]. Q. Wang, M. Ihme, C. Gazen, Y. F. Chen, J. Anderson. A high-fidelity ensemble simulation framework for interrogating wildland-fire behaviour and benchmarking machine learning models. International journal of wildland fire (2024).
-
-[2]. R. R. Linn. A transport model for prediction of wildfire behavior (No. LA-13334-T). PhD thesis. Los Alamos National Lab., NM, United States (1997).
-
-[3]. Q. Wang, M. Ihme, R. R. Linn, Y. F. Chen, V. Yang, F. Sha, C. Clements, J. S. McDanold, J. Anderson. A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow. International journal of wildland fire (2023).
+---
+layout: datapage
+excerpt: (117 cases)
+title: FireBench data above ground level
+description: LES of an ensemble of wildfire spread
+header:
+ image: /assets/img/wang2024.png
+ teaser: /assets/img/ico_wang2024.png
+categories:
+- reacting
+- environmental
+- turbulent
+- numerical
+- benchmark
+---
+
+
+
+## Description
+The propagation of wildfires is a complex, dynamic process that is influenced by various factors, such as fuel, wind, terrain, and other environmental conditions. Accurately and reliably predicting the rate-of-spread of wildfires is of critical importance for fire management, rapid fire response, and fire mitigation. The [Google FireBench dataset](https://sites.research.google/gr/wildfires/firebench/) [1] aims to provide high-fidelity data to tackle these issues by providing an ensemble of large-eddy simulations that capture the three-dimensional wildfire-spread behavior and coupling with the atmospheric environment.
+
+The spatial and temporal evolution of the combustion of solid fuel coupled with the
+atmospheric flow is described by a two-phase model [2]. The gas-phase is described by
+the Favre-filtered conservation equations for mass, momentum, oxygen-fraction, and potential temperature [3]:
+{::nomarkdown}
+$$
+\partial_t \overline{\rho} + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}}) = S_\rho,
+$$
+$$
+\partial_t (\overline{\rho} \widetilde{\boldsymbol{u}} ) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \otimes \widetilde{\boldsymbol{u}}) = - \nabla \overline{p_d} + \nabla \cdot \overline{\tau} + [\overline{\rho} - \rho(z)] g \boldsymbol{\hat{k}_z} + \boldsymbol{f}_D + \boldsymbol{f}_C,
+$$
+$$
+\partial_t (\overline{\rho} \widetilde{Y_O}) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \widetilde{Y_O}) = \nabla \cdot \overline{\boldsymbol{j}_O} + \overline{\rho} \widetilde{\dot{\omega}_O},
+$$
+$$
+\partial_t (\overline{\rho} \widetilde{\theta}) + \nabla \cdot (\overline{\rho} \widetilde{\boldsymbol{u}} \widetilde{\theta}) = \nabla \cdot \overline{\boldsymbol{q}} + \frac{\overline{\rho} \widetilde{\theta}}{c_p \widetilde{T}} [h a_v (T_s - \widetilde{T}) + \dot{q}_r + (1-\Theta) H_f \widetilde{\dot{\omega}}],
+$$
+where $\widetilde{\cdot}$ denotes Favre-filtering and $\overline{\cdot}$ denotes Reynolds filtering. $\rho$ is the density, $\boldsymbol{u}$ is the velocity vector, $p_d$ is the hydrodynamic pressure, $\tau$ is the shear stress tensor, $g$ is the gravitational acceleration, $\boldsymbol{\hat{k}_z}$ is the unit vector along the gravitational direction, $f_D = - \overline{\rho} c_d a_v \boldsymbol{|\widetilde{u}| \widetilde{u}}$ is the drag force due to surface vegetation, $\boldsymbol{f}_C = f \boldsymbol{\hat{k}_z} \times \overline{\rho} (\widetilde{\boldsymbol{u}} - \boldsymbol{U}_\infty)$ is the Coriolis force, $Y_O$, $\boldsymbol{j}_O$, and $\dot{\omega}_O$ are the mass fraction, species diffusion, and source term of the oxidizer, $\theta$ is the potential temperature, $\boldsymbol{q}$ is the heat flux vector, $T$ is the gas-phase temperature, and $H_f$ is the heat of combustion.
+The heat exchange between the solid and gas phase is modeled with $h$ as the convective heat transfer coefficient, $a_v$ as the bulk fuel area-to-volume ratio, and $\dot{q}_r$ is the radiation source term. $\Theta = 1 - \rho_f/\rho_{f,0}$ is the fraction of the heat release that contributes to the increase of the solid phase temperature.
+$\dot{\omega}$ is the gas-phase combustion source term.
+{:/}
+
+The dataset consists of 117 cases with 9 velocities and 13 slopes with data extracted 1.5 m and 10 m above ground level. In addition, data was extracted at a streamwise location of 100 m < x < 1000 m.
+Specifically, the cases span a range of mean inlet velocity at 10 m above ground level of 2 to 10 m/s with a step of 1 m/s, and a range of slopes from 0 to 30 degrees with steps of 2.5 degrees.
+
+## Quick Info
+* Contributors: Qing Wang, Matthias Ihme, Cenk Gazen, Yi-Fan Chen, John Anderson, Jen Zen Ho, Bassem Akoush
+* Nx = 900, Ny = 252
+* DOI
+* .bib
+
+## Links to different cases
+
+
+
+
+ | 0 |
+ u10 = 2 m/s |
+
+ 68 |
+
+ Kaggle
+ |
+
+
+ | 1 |
+ u10 = 3 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 2 |
+ u10 = 4 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 3 |
+ u10 = 5 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 4 |
+ u10 = 6 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 5 |
+ u10 = 7 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 6 |
+ u10 = 8 m/s |
+
+ 60 |
+
+ Kaggle
+ |
+
+
+ | 7 |
+ u10 = 9 m/s |
+
+ 42 |
+
+ Kaggle
+ |
+
+
+ | 8 |
+ u10 = 10 m/s |
+
+ 51 |
+
+ Kaggle
+ |
+
+
+
+## References
+[1]. Q. Wang, M. Ihme, C. Gazen, Y. F. Chen, J. Anderson. A high-fidelity ensemble simulation framework for interrogating wildland-fire behaviour and benchmarking machine learning models. International journal of wildland fire (2024).
+
+[2]. R. R. Linn. A transport model for prediction of wildfire behavior (No. LA-13334-T). PhD thesis. Los Alamos National Lab., NM, United States (1997).
+
+[3]. Q. Wang, M. Ihme, R. R. Linn, Y. F. Chen, V. Yang, F. Sha, C. Clements, J. S. McDanold, J. Anderson. A high-resolution large-eddy simulation framework for wildland fire predictions using TensorFlow. International journal of wildland fire (2023).
diff --git a/_datasets/pkyeung2025.md b/_datasets/forced_hit_passive_scalars.md
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diff --git a/_datasets/ho2024.md b/_datasets/h2ch4_turbulent_jet_flows.md
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diff --git a/_datasets/coulon.md b/_datasets/nh3h2air_premixed_slot_flame.md
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diff --git a/_datasets/roshan2024.md b/_datasets/rayleigh_benard_convection.md
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