This archive contains data files from spark-ignited homogeneous combustion internal combustion engine experiments. Included are high-resolution two-dimensional two-component velocity fields acquired at two 5 x 6 mm regions, one near the head and one near the piston. Crank angle resolved heat flux measurements were made at a third location in the head. The engine was operated at 40 kPa, 500 and 1300 RPM, motor and fired. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations.

This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields from various measurement planes with maximized field of view, in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Fired operation was with stoichiometric propane air, 40kPa MAP, at 1300 RPM.

This is the data used for the results in the paper "Ignition and combustion study of premixed ammonia using GDI pilot injection in CI engine" (Førby et al, 2022, Fuel). https://doi.org/10.1016/j.fuel.2022.125768

Experiments: In the experiments, premixed ammonia was ignited using a pilot fuel in a CI-engine. The pilot fuel was injected with a GDI nozzle. Very small pilot fuel sprays were used succesfully to ignite the ammonia.

The data available here is the in-cylinder pressure pick-up traces -- collected with LabView-software -- and overviews of the corresponding test conditions.

The data can be loaded and used with Open-Source software freely available through e.g. Python.

Please consult the ReadMe-file for using the data.

For any questions or enquiries, please contact: nilfor@dtu.dk

This archive contains data files from motored internal combustion engine experiments. Included are two-dimensional two-component velocity fields from four measurement planes with maximized field of view. in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Motored operating conditions include 40kPa and 90kPa MAP, 800 and 1300 RPM.

This archive contains data files from spark-ignited homogenous combustion internal combustion engine experiments. Included are two-dimensional two-component velocity fields acquired in a small, high-resolution field of view near the spark plug, and images of hydroxyl radical chemiluminescence recording the early flame-kernel growth. Included are in-cylinder pressure measurements, external pressure and temperature data, as well as details on the geometry of the optical engine to enable setups of simulation configurations. Included are tables of one-per-cycle parameters for each test with methane or propane at stoichiometric, dilute limit, lean limit, and rich limit, operation conducted at 40kPa and 1300 RPM.

The dataset was created to simulate data related to the predictive maintenance of critical components in construction machinery, such as cranes, excavators, and bulldozers. It contains 1,000 records, each representing a unique component, with the following attributes:

1) Component_ID: A unique identifier for each component, formatted as CMP0001 to CMP1000. Component_Type: The type of component, categorized into three types: Engine, Hydraulic Cylinder, and Gear. 2) Vibration: A numerical feature indicating the vibration level of the component, measured in arbitrary units between 0.1 and 5.0. 3) Temperature: The operating temperature of the component, recorded in degrees Celsius within a range of 40 to 100. 4) Pressure: The pressure exerted on the component, measured in psi, ranging from 50 to 300. Operating_Hours: The total number of hours the component has been in operation, ranging from 0 to 5,000. 5) Remaining_Useful_Life (RUL): The estimated number of hours left before the component fails, randomly assigned within a range of 50 to 1,000.

This dataset aims to support the development and testing of machine learning models for Remaining Useful Life (RUL) prediction. It mimics real-world scenarios where sensor data is collected and analyzed to optimize maintenance schedules, reduce downtime, and improve operational efficiency. The features are designed to allow exploratory data analysis and advanced feature engineering for predictive maintenance tasks.