No abstract

Gas turbine packages require a ventilation system in order to keep temperatures under acceptable limits and to dilute any hazardous accumulation of gas due to unexpected leakages. As part of the design phase, a detailed CFD (Computational Fluid Dynamics) analysis is performed on the complete system to assess the fluid dynamic behavior of the flow in terms of flow path, temperature distributions and velocity field. In this work, as additional approach, a detailed experimental 3D assessment of an entire aero-derivative gas turbine (GT) package was performed by creating a scale model (1:8) of the real configuration. The original package can be as much as 60 m3 in volume where details from pipes to valves can create severe flow distortions and the 3D CFD study might not necessarily include all details up to such level during the design phase. The scale model, built using sintered plastic material through rapid prototyping, was used for a test campaign reproducing the operation of the ventilation system, copying the dynamic similarity of the real scale. The model was equipped with a set of instruments to acquire measurements of pressure and velocity in several locations and at different flow rates. A significant benefit of using a scale model with transparent plexiglass for the external structure of the enclosure and ventilation ducts walls, was that it allowed to carry out a smoke test. This has been done by injecting a visible gas from several locations allowing the visualization of the streak lines of the local flow field. The aim of this approach was to find a fast and reliable way to investigate in detail complex phenomena such as gas leakage dilution and local flow distribution. A good agreement between experimental and computational data was found confirming that the CFD studies currently performed during the standard design phase are accurate and reliable enough to provide a proper prediction of the flow field inside the entire package even when a high level of details is included.

Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material.

Torque and force signals data were acquired from a load-cell sensor during a robotic welding process, in presence of collisions between the tool and the workpiece edges outlined in part in "Haptic-based touch detection for collaborative robots in welding applications" [1] . The dataset is composed from 15 tests captured during a tele-operated welding robot performing a 1G ASME/AWS (i.e., PA ISO) welding process. The raw data files have been provided. These data can be used to correlate torque signal features with collision events, to improve algorithms of collision detection/avoidance and to develop reliable real-time haptic feedback to the welder. This dataset can also be used to study the torque signal variation in different welding positions (e.g., 2G, 3G, 2F, etc.). Dataset is provided as raw data and in MATLAB files.