diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
index 22577cd858c14f2070da6c3ee0094fbeadf93d39..0000000000000000000000000000000000000000
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diff --git a/02_Presentation/documentation/codeStructure.md b/02_Presentation/documentation/codeStructure.md
index 2a148a926041bf41cd5caec4ea073d93736e506a..de6ad5b3e7ce02d7a0469c77563dc1748f497bbe 100644
--- a/02_Presentation/documentation/codeStructure.md
+++ b/02_Presentation/documentation/codeStructure.md
@@ -10,8 +10,8 @@
using Pkg; Pkg.add("PackageName")
```
- The dependencies I am using are:
- - `IJulia`, `LinearAlgebra`, `Plots`, `JSON`, `StaticArrays`, `BenchmarkTools`, `CUDA`
- - More infor to install `CUDA` can be found [here](https://juliagpu.github.io/CUDA.jl/stable/installation/overview/#InstallationOverview)
+ - `IJulia`, `LinearAlgebra`, `Plots`, `JSON`, `StaticArrays`, `BenchmarkTools` and `CUDA` (optional)
+ - More information to install `CUDA` can be found [here](https://juliagpu.github.io/CUDA.jl/stable/installation/overview/#InstallationOverview)
- The first time you use `CUDA` in julia it will take time to download the corresponding [CUDA toolkit](https://developer.nvidia.com/cuda-downloads)
- Node.js
- [Download and install Node.js](https://nodejs.org/en/download/)
@@ -22,8 +22,16 @@
- The dependecies I am using are:
- `three`, `rhino3dm`, `file-system`, `edit-json-file`, `finalhandler`, `http`, `serve-static`, `child_process`
-## GPU Requirements
-- Since I am using CUDA to do the gpu calculations make sure to have an NVIDIA GPU and CUDA toolkit installed (more details [here](https://juliagpu.gitlab.io/CUDA.jl/installation/overview/#InstallationOverview-1)).
+## CPU/GPU Requirements
+- MetaVoxels can run on the CPU or the GPU, more info about the performance of both can be found in [this page.](./../performance/performance.md)
+- For the CPU, I am using `Base.Threads` to run the code in parallel on the CPU. If you run `Threads.nthreads()`. If you want to change the number of threads, you can follow instructions in [this page](https://docs.julialang.org/en/v1/manual/multi-threading/). However, since we are using jupyter to run the code, to add threads you have to create a new kernel using the following command:
+ ```Julia
+ using IJulia
+ IJulia.installkernel("Julia 12 Threads", env=Dict(
+ "JULIA_NUM_THREADS" => "12",
+ ))
+ ```
+- For the GPU, I am using CUDA, so make sure to have an NVIDIA GPU and CUDA toolkit installed (more details [here](https://juliagpu.gitlab.io/CUDA.jl/installation/overview/#InstallationOverview-1)).
- A very helpful introductory on how to write gpu kernels can be found [here](https://juliagpu.gitlab.io/CUDA.jl/tutorials/introduction/#Introduction-1)
- If you don't have a local NVIDIA computer, I found it easy to use the [Satori](https://mit-satori.github.io/satori-basics.html) gpu cluster and use the V100s with jupiter notebook
- go to this [page](https://satori-portal.mit.edu:8443/auth/realms/satori-portal-dev/protocol/openid-connect/auth?response_type=code&scope=openid&client_id=satori-portal.mit.edu&state=YKQ7UEqOcWKOdRD9QlYJcbX2BlM&redirect_uri=https%3A%2F%2Fsatori-portal.mit.edu%2Foidc&nonce=Dho2i4UdTcxRTmlAOtenGodVe-4LnUyaM2gcflFYvjU)
@@ -52,23 +60,28 @@
- html interactive demos of simulation results
- contains all html and styling information
- **Julia**
- - include folder is the most important as it contains the latest julia libraries for the gpu simulation
+ - `MetaVoxels.jl` will attach all the external and internal libraries
+ - `include` folder is the most important as it contains the latest julia libraries for the gpu simulation
- `vector.jl`
- utils for vectors and quaternions
- `material.jl`
- utils for node and edge material
- - `export.jl`
- - export simulation data to json
- `run.jl`
- turn json setup to cuda arrays and run simulation
- `updateEdges.jl`
- edges properties update
+ - `updateNodes.jl`
+ - nodes properties update
- `externalForces.jl`
- external forces applied to the system
- `forces.jl`
- force integration
- - `updateNodes.jl`
- - nodes properties update
+ - `export.jl`
+ - export simulation data to json
+ - `plotViz.jl`
+ - plotting and getting data out for analysis
+ - `cuda_calls.jl`
+ - CUDA related calls
- **json**
- folder containing structures that are precomputed (either geometry or fea)
- example of json `setup`:
@@ -210,7 +223,7 @@
- useful if needed to create the geometry using python or any other software
- **lib**
- - contains all external libraries used
+ - contains all external libraries used (javascript)
- **visualization**
- Contains all 3D visualization and design code
- `draw.js`
diff --git a/02_Presentation/hierarchy/c1.png b/02_Presentation/hierarchy/c1.png
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diff --git a/02_Presentation/hierarchy/c2.png b/02_Presentation/hierarchy/c2.png
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diff --git a/02_Presentation/hierarchy/c3.png b/02_Presentation/hierarchy/c3.png
new file mode 100644
index 0000000000000000000000000000000000000000..1982d27b1b30d8bfad151c668d9cf2cd990f7aa8
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diff --git a/02_Presentation/hierarchy/hierarchy.md b/02_Presentation/hierarchy/hierarchy.md
index 2ca449ffff5515d4f64c585bb595ee39fa58f09e..fec993beeb470390897a22ddf790c1ceeb591dce 100644
--- a/02_Presentation/hierarchy/hierarchy.md
+++ b/02_Presentation/hierarchy/hierarchy.md
@@ -6,30 +6,26 @@ Going from thousands of elements (FEA/DEM) to hundred of elements (Beam Theory)
<img src="./comp.png" width="70%" /> <br>
<img src="./hier.gif" width="70%" /> <br>
-### Example
-<img src="./h.png" width="100%" /> <br>
+### Example:
+
+<img src="./h.png" width="50%" /> <br>
3292 Nodes, 11476 edges
-<img src="./h1.png" width="100%" /> <br>
+<img src="./h1.png" width="50%" /> <br>
830 Nodes, 2111 edges > **75% less nodes and 82% less edges**
-<img src="./h2.png" width="100%" /> <br>
+<img src="./h2.png" width="50%" /> <br>
264 Nodes, 1034 edges > **92% less nodes and 91% less edges**
---
## Hierarchy Implementation
-<img src="./homogenized _cuboct.gif" width="70%" /> <br>
+<img src="./homogenized _cuboct.gif" width="40%" /> <br>
- Automated workflow to extract the elasticity tensor from voxel units
-- References:
- - [Homogenization Research PDF](./homogenization.pdf)
- - [Numerical coarsening of inhomogeneous elastic materials](https://dl.acm.org/doi/10.1145/1531326.1531357)
- - [Elastic textures for additive fabrication](https://cims.nyu.edu/gcl/papers/panetta2015et.pdf)
- - [Two-scale topology optimization with microstructures](http://people.csail.mit.edu/desaic/pdf/tto.pdf)
- - [Tailoring materials for specific needs](https://www.semanticscholar.org/paper/Tailoring-materials-with-prescribed-elastic-Sigmund/05a6bee1ea068482482f9cfd86627d13de006e8d)
+- Detailed explanation [here](./../top_opt/inverse_hom/inverse_homogenization.md).
- Steps
- Get Microstructure
- Apply 6 main harmonic displacements
@@ -137,13 +133,32 @@ Going from thousands of elements (FEA/DEM) to hundred of elements (Beam Theory)
| # Nodes | 320 | 62 | 41 |
| Memory | 100% | 14% | 9% |
| # Timesteps | 50000 | 1000 | 1000 |
-| Speedup | 1x | -- | -- |
+| Speedup | 1x | 124x | 160x |
| Accuracy | 100% | -- | -- |
-<img src="./comp_graph.PNG" width="80%" />
+dt ∝ particle mass, the smaller the particle the smaller dt, ∴ more timesteps needed
+
+<br><br/>
+<img src="./comp_graph.png" width="80%" />
+
----
+## MADCAT
+
+<img src="./m1.png" width="33%" />
+<img src="./m2.png" width="33%" />
+<img src="./m3.png" width="33%" />
+
+<br><br/>
+
+<img src="./c1.png" width="33%" />
+<img src="./c2.png" width="33%" />
+<img src="./c3.png" width="33%" />
+
+
+---
## Future Steps
+
- [x] Detailed and hierarchical model comparison
- [x] Choose interesting case study
- [x] Madcat (working with Ben)
diff --git a/02_Presentation/hierarchy/m1.png b/02_Presentation/hierarchy/m1.png
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diff --git a/02_Presentation/hierarchy/m2.png b/02_Presentation/hierarchy/m2.png
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diff --git a/02_Presentation/hierarchy/m3.png b/02_Presentation/hierarchy/m3.png
new file mode 100644
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diff --git a/02_Presentation/performance/performance.md b/02_Presentation/performance/performance.md
index 3283d0e90be0ed38159721f58de0855ddd96ffe6..8f9b6d115bcbc8a20ab5307bd8e3675e2cea631c 100644
--- a/02_Presentation/performance/performance.md
+++ b/02_Presentation/performance/performance.md
@@ -20,10 +20,15 @@ each timeStep
```
Fetching the current node positions in the ``updateEdges`` kernel as well as fetching the internal forces/moments from the edges ``updateNodes`` is the step that takes much of the time as allow scalar access and operations on GPU arrays which is relatively slow.
-### Benchmarks
+### Benchmark
+
+
+
+### Older Benchmarks
+
| Lattice Size| #nodes | #links |#steps | Total CPU (sec)| GPU Init (sec) | Total GPU (sec)| Total GPU V100|
| :---------: | :-------: | :------: |:---------: | :-------: | :------: | :------: | :------: |
| 4 | 300 | 960 |10 | 3.014437901 | 12.72 | 0.0124470 | 0.0028345 |
@@ -40,6 +45,7 @@ Fetching the current node positions in the ``updateEdges`` kernel as well as fet
| 15 | 11,520 | 43,200 |200 | | 15.56 | 0.4721120 | 0.0975230 |
<!-- | 20 | 26,460 | 979,102 |200 | | 13.89 | 0.9551915 | 0.1593967 | 88,594,485 -->
+<br></br>
diff --git a/02_Presentation/performance/speed.png b/02_Presentation/performance/speed.png
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diff --git a/02_Presentation/robotics/tendon/Tendon Validation.ipynb b/02_Presentation/robotics/tendon/Tendon Validation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3c0588a3003ebeaf79e34ee9cd169d94c96187a5
--- /dev/null
+++ b/02_Presentation/robotics/tendon/Tendon Validation.ipynb
@@ -0,0 +1,1741 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Amira Abdel-Rahman\n",
+ "# (c) Massachusetts Institute of Technology 2021\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Tendon physical Testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "using CSV\n",
+ "using DataFrames;\n",
+ "using Plots;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ "source": [
+ "i=1\n",
+ "plot(nodes[1:5,i,1],nodes[1:5,i,2],title=\"Beam\",label=\"\", aspect_ratio=:equal)\n",
+ "plot!(nodes[6:10,i,1],nodes[6:10,i,2],label=\"\")\n"
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+ },
+ "execution_count": 150,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot(nodes[1:5,1,1],nodes[1:5,i,2],title=\"Physical Test\",label=\"\", aspect_ratio=:equal)\n",
+ "plot!(nodes[6:10,1,1],nodes[6:10,i,2].-0,label=\"\", aspect_ratio=:equal)\n",
+ "for i=2:12\n",
+ " plot!(nodes[1:5,i,1],nodes[1:5,i,2],label=\"\", aspect_ratio=:equal)\n",
+ " plot!(nodes[6:10,i,1],nodes[6:10,i,2],label=\"\", aspect_ratio=:equal)\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 126,
+ "metadata": {},
+ "outputs": [
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+ },
+ "execution_count": 126,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot(nodes[1,:,1],title=\"x\",label=\"\")\n",
+ "for i=2:10\n",
+ " plot!(nodes[i,:,1],label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
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+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot(nodes[1,:,2],title=\"y\",label=\"\")\n",
+ "for i=2:10\n",
+ " plot!(nodes[i,:,2],label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
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+ "source": [
+ "edges=fill(0.0,8,12,1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i= 1:12\n",
+ " edges[1,i,1]=sqrt((nodes[2,i,1]-nodes[1,i,1])^2+(nodes[2,i,2]-nodes[1,i,2])^2)\n",
+ " edges[2,i,1]=sqrt((nodes[3,i,1]-nodes[2,i,1])^2+(nodes[3,i,2]-nodes[2,i,2])^2)\n",
+ " edges[3,i,1]=sqrt((nodes[4,i,1]-nodes[3,i,1])^2+(nodes[4,i,2]-nodes[3,i,2])^2)\n",
+ " edges[4,i,1]=sqrt((nodes[5,i,1]-nodes[4,i,1])^2+(nodes[5,i,2]-nodes[4,i,2])^2)\n",
+ " edges[5,i,1]=sqrt((nodes[7,i,1]-nodes[6,i,1])^2+(nodes[7,i,2]-nodes[6,i,2])^2)\n",
+ " edges[6,i,1]=sqrt((nodes[8,i,1]-nodes[7,i,1])^2+(nodes[8,i,2]-nodes[7,i,2])^2)\n",
+ " edges[7,i,1]=sqrt((nodes[9,i,1]-nodes[8,i,1])^2+(nodes[9,i,2]-nodes[8,i,2])^2)\n",
+ " edges[8,i,1]=sqrt((nodes[10,i,1]-nodes[9,i,1])^2+(nodes[10,i,2]-nodes[9,i,2])^2)\n",
+ "end"
+ ]
+ },
+ {
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+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
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+ ],
+ "source": [
+ "plot(edges[1,:,1],title=\"lower edge lengths\",label=\"\")\n",
+ "for i=2:4\n",
+ " plot!(edges[i,:,1],label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
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+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot((edges[1,:,1].-edges[1,1,1])./edges[1,1,1].*100,title=\"lower edge lengths\",label=\"1\")\n",
+ "for i=2:4\n",
+ " plot!((edges[i,:,1].-edges[i,1,1])./edges[i,1,1].*100,label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [
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+ ]
+ },
+ "execution_count": 57,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot(edges[5,:,1],title=\"upper edge lengths\",label=\"5\")\n",
+ "for i=5:8\n",
+ " plot!(edges[i,:,1],label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [
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+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot((edges[5,:,1].-edges[5,1,1])./edges[5,1,1].*100,title=\"lower edge lengths\",label=\"5\")\n",
+ "for i=6:8\n",
+ " plot!((edges[i,:,1].-edges[i,1,1])./edges[i,1,1].*100,label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
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+ "source": [
+ "i=1\n",
+ "plot(edges[i,:,1],title=\"edge lengths\",label=\"\")\n",
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+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot((edges[1,:,1].-edges[1,1,1])./edges[1,1,1].*100,title=\"Edges Lengths\",label=\"1\",ylabel=\"change %\",xlabel=\"time\", legend = :outertopright)\n",
+ "for i=2:8\n",
+ " plot!((edges[i,:,1].-edges[i,1,1])./edges[i,1,1].*100,label=\"$i\")\n",
+ "end\n",
+ "plot!()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 156,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "12-element Array{Float64,1}:\n",
+ " 0.0\n",
+ " -1.5843128158723532\n",
+ " -2.132414919738861\n",
+ " -2.823692814734942\n",
+ " -3.834120839271824\n",
+ " -4.955014397985033\n",
+ " -5.393057331917063\n",
+ " -6.278516272895473\n",
+ " -6.699645639430331\n",
+ " -7.900963333404645\n",
+ " -8.686516653106786\n",
+ " -8.03972135932008"
+ ]
+ },
+ "execution_count": 156,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "i=4\n",
+ "(edges[i,:,1].-edges[i,1,1])./edges[i,1,1].*100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 158,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "8-element Array{Float64,1}:\n",
+ " -28.216928592014963\n",
+ " -15.535579801040738\n",
+ " -9.723469721300651\n",
+ " -8.03972135932008\n",
+ " 16.743465126188728\n",
+ " 9.4650672769009\n",
+ " 7.694733049416276\n",
+ " 7.146582838102479"
+ ]
+ },
+ "execution_count": 158,
+ "metadata": {},
+ "output_type": "execute_result"
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+ ],
+ "source": [
+ "(edges[:,12,1].-edges[:,1,1])./edges[:,1,1].*100"
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+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
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+ "source": []
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+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Julia 1.5.2",
+ "language": "julia",
+ "name": "julia-1.5"
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+ "language_info": {
+ "file_extension": ".jl",
+ "mimetype": "application/julia",
+ "name": "julia",
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+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02_Presentation/robotics/tendon/physical_tests/anisotropic_beam_steps.xlsx b/02_Presentation/robotics/tendon/physical_tests/anisotropic_beam_steps.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..35da2bf5564a3aacfce24567031dcebb6c30c0ad
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diff --git a/02_Presentation/robotics/tendon/physical_tests/data/ImageProcessed/compliant10mmstrain.png b/02_Presentation/robotics/tendon/physical_tests/data/ImageProcessed/compliant10mmstrain.png
new file mode 100644
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diff --git a/02_Presentation/top_opt/search.md b/02_Presentation/top_opt/search.md
index ec9f147ed94b0a88a303855c4a8e9ccad50d6773..db1a5ea8c0941901f966614c8f77ec078bf94893 100644
--- a/02_Presentation/top_opt/search.md
+++ b/02_Presentation/top_opt/search.md
@@ -106,9 +106,15 @@ Macroscale & Microscale
<img src="./micr_3d/macro_1.gif" width="40%" />
<img src="./micr_3d/micro_1.gif" width="40%" /><br></br>
+<img src="./micr_3d/macro_2.gif" width="40%" />
+<img src="./micr_3d/micro_2.gif" width="40%" /><br></br>
+
### 3D
Macroscale & Microscale
+<img src="./micr_3d/macro_4.png" width="40%" />
+<img src="./micr_3d/micro_4.png" width="40%" /><br></br>
+
<img src="./micr_3d/macro_3.png" width="40%" />
<img src="./micr_3d/micro_3.png" width="40%" /><br></br>
diff --git a/02_Presentation/validationAndConvergence/FINAL_VAL.png b/02_Presentation/validationAndConvergence/FINAL_VAL.png
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diff --git a/02_Presentation/validationAndConvergence/validationAndConvergenceStudies.md b/02_Presentation/validationAndConvergence/validationAndConvergenceStudies.md
index 2720dc7c854d2fef6cca2c0cda982e00a1956f92..8b31ef722ecbcb3dc2e810fbc722bc2f9f0449b8 100644
--- a/02_Presentation/validationAndConvergence/validationAndConvergenceStudies.md
+++ b/02_Presentation/validationAndConvergence/validationAndConvergenceStudies.md
@@ -6,15 +6,24 @@
<img src="./dyn_conv.gif" width="300" />
-The graphs below show that given enough time steps (because of damping and propagation effects) the dynamic model converges to the linear fea model.
+The graphs below show that given enough time steps (because of damping and propagation effects) the dynamic model converges to the linear FEA model.
-
-
+
-The convergence rate is based on three parameters: the size (depth) of the structure, the youngs modulus (the more stiff the faster it converges) and the mass of the nodes. That's because, in order to ensure stability, the time step and damping factor calculation are based on the youngs modulus and node mass [(see the structural mechanics models explanation)](https://amiraa.pages.cba.mit.edu/metavoxels/03_Research/structureModel.html). Below is the same voxel structure with a much higher edge stiffness which increases the damping force and makes it converge much faster.
+The three graphs below show the final node displacement in the x,y,z directions for the the 7*7*7 lattice. One can see how accurate the simulation is for all 1400 nodes.
-
+
+
+
+
+<!-- 
+
+ -->
+
+The convergence rate is based on three physical parameters: the size (depth) of the structure, the youngs modulus (the more stiff the faster it converges) and the mass of the nodes. That's because, in order to ensure stability, the time step and damping factor calculation are based on the youngs modulus and node mass [(see the structural mechanics models explanation)](https://amiraa.pages.cba.mit.edu/metavoxels/03_Research/structureModel.html). Below is the same voxel structure with a much higher edge stiffness which increases the damping force and makes it converge much faster.
+
+<!--  -->
Even though a large amount of timesteps is needed for the convergence, the simulation speed is still orders of magnitude faster than the FEA analysis for given that for large structures. That's because:
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