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Weather in Shoreline Design

How Shoreline Design uses weather in a simulation run

Shoreline simulates sequences and timings of operations through time. This is why our apps require weather input in the time domain instead of statistical input such as e.g., weather windows or weather downtime. 

Weather data time series consist of one or more weather parameters, e.g., significant wave height and wind speed (required parameters in Shoreline) and a timestamp. In Shoreline, you can use any weather parameter for which you have time series data available. 

Based on these time series, it is possible to generate weather window statistics and traditional weather downtime statistics. 

To understand how Shoreline does this, the following definitions are useful: 

  • Weather state: One or more weather parameters taken together, e.g., `wind speed` and `wave height`. 
  • Weather window statistics: A histogram of weather window lengths for a specified weather state.  
  • Shoreline calculates the weather distribution from one data point to another data point (so from one hour to the next hour) using the "linear distribution". 

When generating weather window statistics, the weather windows for a weather state with 1 hour, 2 hours, 3 hours, … 30 hours, etc. lengths in a weather data time series are identified. 

The weather window algorithm uses the weather data time series to check if the weather criteria set on an activity are met for the required weather window length. 

Example 

Shoreline Design case: 100 WTG installations. 

For all 100 wind turbine installations, an HLV will go through a work process to assemble the constituent parts. This work process has three steps: 

  1. Lift the tower in place. 
  • Duration: 7 hours 
  • Required weather window: 9 hours 
  • Wind speed: 14 m/s 
  • Wind speed reference height: 100 m. 
  1. Lift the nacelle in place. 
  • Duration: 7 hours 
  • Required weather window: 9 hours 
  • Wind speed: 14 m/s 
  • Wind speed reference height: 100 m. 
  1. Attach the three blades. 
  • Duration: 7 hours 
  • Required weather window: 9 hours 
  • Wind speed: 12 m/s 
  • Wind speed reference height: 100 m. 

Before each step beings, the weather-checking algorithm runs to verify whether there is a suitable weather window. 

For step 1, if the wind speed is less than 14 meters per second at a 100-meter height for the next 9 hours, the activity can start. 

When step 1 is complete, a new weather window check for the nacelle installation starts. For this, a weather window of 9 hours with a wind speed below 14 m/s at 100-meter height is necessary. 

Following completion of step 2, the same weather window for the blade installation but with a lower wind speed limitation (12 m/s) is required. 

Any delay in step 1 can cause a knock-on delay to step 2 due to missing a weather window. This is called a negative feedback loop in simulation theory. This interdependence between operations and vessels is not easily calculated with the statistical approach. 

Estimating weather downtime through a simulation is more accurate as it includes the variability of weather in a month and does not assume it is constant like the traditional weather window statistics approach. 

Estimating project plans 

Weather is the main source of uncertainty in offshore construction projects and maintenance activities. 

A weather downtime assessment provides input for a project plan estimation. 

There are two approaches to master schedule estimation: 

  • The traditional approach: 
  • Use weather window statistics. 
  • Estimate weather downtime for different operations per month and multiply this workability percentile by the corresponding operation’s duration to get the weather-adjusted duration. 
  • Shoreline’s simulation modelling: 
  • Use modeling software to estimate a probability weighted master schedule, accounting for the variability of weather within a month, in addition to estimating the weather downtime per month. 

Running simulations with Shoreline Design estimates a master schedule and weather downtime, including weather variability within a month and the interdependence of all the operations. The traditional approach has a smooth curve because no variability in weather the whole month is assumed. The chart below shows the difference between weather downtime estimated traditionally (orange) and with simulation (blue).