Quantifying voyage optimisation with wind propulsion for short-term CO2 mitigation in shipping.

Data availability description This study quantifies the interaction between wind propulsion and voyage optimisation to reduce carbon emissions in the global shipping sector. The repository contains the following folders: “Model code”, “Panamax ship performance files” and “Results and figure data”. First, the model uses the “create_batch_input_files.py” to split the simulations by month, route, ship type (wind-assist/reference) and simulation type (GCR/optimised), defines the constants for each batch of simulations and saves to a text file. Main.py then runs the sequential steps of the model as follows: 1. Load input variables from the batch text files (uses “my_constants.py”) 2. Loop across the month in the specified time steps 3. Load the u and v components of wind from the ECMWF ERA-Interim dataset and filter using GSHHS data (uses “wind_data_file_name.py”, “load_wind_data.py” and “filter_grid.py”) 4. Create the GCR grid and the three-dimensional spatial grid (if using) and calculate the wind speed and wind angle at each grid point (add the weather) (uses “gcr_calc.py”, “dp_grid_three_d.py”, “add_wind_data.py” and “wind_class.py”) 5. Calculate the fuel consumption of the reference ship with no sails at 12 knots and the fuel consumption of the wind-assisted ship on the out and return legs of EITHER a. the GCR or b. the optimised voyage for the input route and all arrival times. Also tracks the currents using NOAA current data (uses “route_calcs.py”, “dp_algorithm_three_d.py”, “ship_model_integration.py”, “land_avoidance.py”, “functions.py” and “load_current_data.py”) 6. Calculate the fuel savings of the wind-assisted ship relative to the reference ship at 12 knots on either the GCR or the optimised voyage 7. Save the data The Panamax ship performance files folder stores the data for the Panamax ship performance. Data is stored for ship speeds from 8 to 13 knots in intervals of 1 knot. Data is stored for both the wind-assisted ship with four Flettner rotors installed, and a reference ship with no Flettner rotors installed. A flag file is also included for each, which describes the speed state of the ship (whether the sails are providing full power and need to depower, or whether the ship can’t physically maintain the speed). The Results and figure data files contains the source data for the results, in Excel format.数据可用性说明 本研究量化了风力推进与航线优化之间的交互作用，以降低全球航运业的碳排放。本数据存储库包含以下三类文件夹："模型代码"、"巴拿马型船舶性能文件（Panamax ship performance files）"与"结果与图表数据（Results and figure data）"。 首先，本模型通过"create_batch_input_files.py"按月份、航线、船舶类型（风力辅助/基准型）与模拟类型（GCR/优化航线）拆分模拟任务，定义每批模拟的常量参数并保存至文本文件。随后，Main.py按以下顺序执行模型的核心步骤： 1. 从批量文本文件加载输入变量（调用"my_constants.py"） 2. 按指定时间步长遍历目标月份 3. 从欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）的ERA-Interim数据集加载风场的u、v分量，并通过GSHHS地理数据集进行滤波处理（调用"wind_data_file_name.py"、"load_wind_data.py"与"filter_grid.py"） 4. 创建GCR网格与（若启用则创建）三维空间网格，计算每个网格点处的风速与风攻角并叠加气象数据（调用"gcr_calc.py"、"dp_grid_three_d.py"、"add_wind_data.py"与"wind_class.py"） 5. 计算基准型无帆船舶以12节航速航行时的燃油消耗量，以及风力辅助船舶在指定航线的去程与返程航段（可选a. GCR航线；或b. 优化航线）、所有抵达时刻下的燃油消耗量。同时结合美国国家海洋和大气管理局（National Oceanic and Atmospheric Administration，NOAA）的海流数据追踪海流工况（调用"route_calcs.py"、"dp_algorithm_three_d.py"、"ship_model_integration.py"、"land_avoidance.py"、"functions.py"与"load_current_data.py"） 6. 计算风力辅助船舶相对于以12节航速航行的基准型船舶，在GCR航线或优化航线下的燃油节省量 7. 保存计算所得数据 巴拿马型船舶性能文件夹存储巴拿马型船舶的性能数据：数据涵盖航速8至13节、间隔1节的航速工况，分别对应安装4个弗莱特纳转子（Flettner rotors）的风力辅助船舶与未安装转子的基准型船舶。两类船舶均附带标记文件，用于描述船舶的航速运行状态（例如帆体是否满功率出力并需要减载，或船舶无法维持目标航速）。 结果与图表数据文件夹包含以Excel格式存储的研究结果源数据。