Examples¶
Here, we look at example use cases for the library, and show how to use it in python.
It’s also well worth looking at the unit test cases copied straight from the unit test cases, so you can always check there to see how everything hooks up.
[Simple] Equipment installation cost¶
You need to provide your team with an estimate for installation cost of an equipment foundation.
It’s a straightforward calculation for you, but the Logistics Team keeps changing the installation position, to try and optimise the overall project logistics.
Each time the locations change, the GIS team gives you an updated embedment depth, which is what you use (along with steel cost and foundation type), to calculate cost and report it back.
This twine allows you to define to create a wrapper around your scripts that communicates to the GIS team what you need as an input, communicate to the logistics team what they can expect as an output.
When deployed as a digital twin, the calculation gets automatically updated, leaving you free to get on with all the other work!
We specify the steel_cost and foundation_type as configuration values, which you can set on startup of the twin.
Once the twin is running, it requires the embedment_depth as an input_value from the GIS team. A member
of the GIS team can use your twin to get foundation_cost directly.
{
"title": "Foundation Cost Model",
"description": "This twine helps compute the cost of an installed foundation.",
"children": [
],
"configuration_values_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Foundation cost twin configuration",
"description": "Set config parameters and constants at startup of the twin.",
"type": "object",
"properties": {
"steel_cost": {
"description": "The cost of steel in GBP/m^3. To get a better predictive model, you could add an economic twin that forecasts the cost of steel using the project timetable.",
"type": "number",
"minimum": 0,
"default": 3000
},
"foundation_type": {
"description": "The type of foundation being used.",
"type": "string",
"pattern": "^(monopile|twisted-jacket)$",
"default": "monopile"
}
}
},
"input_values_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Input Values schema for the foundation cost twin",
"description": "These values are supplied to the twin asynchronously over a web socket. So as these values change, the twin can reply with an update.",
"type": "object",
"properties": {
"embedment_depth": {
"description": "Embedment depth in metres",
"type": "number",
"minimum": 10,
"maximum": 500
}
}
},
"output_manifest": {
"datasets": []
},
"output_values_schema": {
"title": "Output Values schema for the foundation cost twin",
"description": "The response supplied to a change in input values will always conform to this schema.",
"type": "object",
"properties": {
"foundation_cost": {
"description": "The foundation cost.",
"type": "integer",
"minimum": 2
}
}
}
}
[Simple] Site weather conditions¶
You need to be able to get characteristic weather conditions at a specific location, for a range of reasons including assessing extreme design loads. The values you need are computed in a script, which calls a Weather API (provided by a third party), but also needs a dataset of “Wind Resource” files.
{
"title": "Weather Service Digital Twin",
"description": "Provides a model for design extreme weather conditions given a location",
"notes": "Easily extendable with children to add forecast and historical data of different types.",
"credentials": [
{
"name": "WEATHER_API_SECRET_KEY",
"purpose": "Token for accessing a 3rd party weather API service"
}
],
"input_manifest": {
"datasets": [
{
"key": "wind_resource_data",
"purpose": "A dataset containing Wind Resource Grid files"
}
]
},
"input_values_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Input Values for the weather service twin",
"description": "This is a simple example for getting metocean conditions at a single location",
"type": "object",
"properties": {
"location": {
"description": "Location",
"type": "object",
"properties": {
"latitude": {
"type": "number",
"minimum": -90,
"maximum": 90
},
"longitude": {
"type": "number",
"minimum": -180,
"maximum": 180
},
"srid": {
"description": "The Spatial Reference System ID for the coordinate. Default is 4326 (WGS84)",
"type": "integer",
"default": 4326
}
}
}
}
},
"output_manifest": {
"datasets": [
{
"key": "production_data",
"purpose": "A dataset containing production data",
"tags": {"cleaned": true},
"labels": ["production", "wind"]
}
]
},
"output_values_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Output Values for the metocean service twin",
"description": "The output values strand of an example twine",
"type": "object",
"properties": {
"water_depth": {
"description": "Design water depth for use in concept calculations",
"type": "number"
},
"extreme_wind_speed": {
"description": "Extreme wind speed value for use in concept calculations",
"type": "number"
}
}
}
}