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https://github.com/friesischscott/resiliencedecisionmaking.jl
https://github.com/friesischscott/resiliencedecisionmaking.jl
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/friesischscott/resiliencedecisionmaking.jl
- Owner: FriesischScott
- License: mit
- Created: 2022-08-30T12:37:05.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-18T12:32:26.000Z (over 1 year ago)
- Last Synced: 2024-10-09T20:18:31.836Z (2 months ago)
- Language: Julia
- Size: 29.3 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ResilienceDecisionMaking.jl
[](https://doi.org/10.5281/zenodo.7034998)
This package contains the code and examples for the resilience decision-making method presented in [Salomon et al. (2022)](https://doi.org/10.1016/j.rcns.2022.10.005).
## Installation
The package is registered in the Julia General registry and can be installed from Pkg with
```julia
] add ResilienceDecisionMaking
```
## Getting started
The main interface of the resilience decision making method is the `grid_search` method which returns the computed resilience values as a `Dict` of the endowment configurations, the coefficient of variation and the pareto front. To explain we use the example of the multistage high-speed axial compressor as presented in the paper. First, we define a system representation in this case the adjacency matrix of the system as
```julia
A = [
0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
]
```
We further define the maximum values of the endowment configurations we want to explore and the time as a tuple of minimum and maximum as well as the number of time steps
```julia
endowments = [20, 20, 20, 20]
t = (0.0, 10.0)
tₙ = 200
```
Next, we define a function `failure_rates` which returns the current failure rates of all components for all time steps `times` for the current endowment configuration `η`
```julia
λ = 0.8
function failure_rates(times::Vector{T} where {T<:Real}, η::Vector{T} where {T<:Integer})
rates = [
(λ - 0.03 * η[1]) * ones(2)...,
(λ - 0.03 * η[2]),
(λ - 0.03 * η[3]),
(λ - 0.03 * η[4]) * ones(4)...,
]
return hcat([rates for _ in 1:length(times)]...)
end
```
Finally, we define the recovery behaviour of the components through the `recovery!` function. This function must update the `components` and `recovery_times` vectors in place (!) and return `nothing`. Note, that in this example recovery is independent of `η`. See the other `demo` example for how to use `η` to influence the recovery of components.
```julia
r_max = 21
function recovery!(components, recovery_times, η)
for (i, (c, r)) in enumerate(zip(components, recovery_times))
if ~c
if r >= r_max - 11
components[i] = true
recovery_times[i] = 1
else
recovery_times[i] = recovery_times[i] + 1
end
end
end
return nothing
end
```
Finally, we can run the `grid_search` where configurations are accepted if the resilience value is above `α = 0.85`. We also pass a structure function evaluating of the system is currently working (s-t-connectivity) and the number of samples (500) to use per resilience computation.
```julia
α = 0.85
res, σ, front = grid_search(
A,
endowments,
t,
tₙ,
failure_rates,
recovery!,
ResilienceDecisionMaking.s_t_connectivity([1, 3], [5, 6, 7, 8]),
500,
α,
true, # only return the "dominant" entries of the pareto front
)
```
## References
Salomon, J., Behrensdorf, J., Winnewisser, N., Broggi, M., & Beer, M. (2022). Multidimensional resilience decision-making for complex and substructured systems. Resilient Cities and Structures, 1(3), 61-78, [10.1016/j.rcns.2022.10.005](https://doi.org/10.1016/j.rcns.2022.10.005).