General Electric (Italy)

This paper presents WoTEdu, a multimodal interactive storytelling system in the Nautical Cultural Heritage domain. The storytelling system is based on the Web of Things (WoT) paradigm and facilitates the museum visitors’ interaction with museum artefacts. Multiple interaction modalities supported by the system allow people with motor impairment to experience the story through alternative input channels.

Abstract Loss analysis is a valuable technique for improving the thermodynamic performance of turbomachines. Analyzing loss in terms of the “mechanical work potential” (Miller, R.J., ASME Turbo Expo 2013, GT2013-95488) provides an instantaneous and local account of the thermal and aerodynamic mechanisms contributing to the loss of thermodynamic performance. This study develops the practical application of mechanical work potential loss analysis, providing the mathematical formulations necessary to perform loss analysis using practical Reynolds-averaged Navier–Stokes (RANS) or large eddy simulations (LES). The analysis approach is demonstrated using RANS and LES of a linear compressor cascade, both with and without incoming wakes. Spatial segmentation is used to attribute loss contributions to specific regions of the flow, and phase-averaging is performed in order to associate the variation of different loss contributions with the periodic passage of wakes through the cascade. For this un-cooled linear cascade, viscous dissipation is the dominant source of loss. The analysis shows that the contribution of the viscous reheat effect depends on the operating pressure of the compressor stage relative to the ambient “dead state” pressure—implying that the optimal blade profile for a low-pressure compressor stage may be different from the optimal profile for a high-pressure compressor stage in the same engine, even if the operating conditions for both stages are dynamically similar.

Avalanche occurrences are unambiguous indicators of unstable snow conditions. Information on past and current avalanche activity is therefore crucial for avalanche forecasting. To continuously assess avalanche activity, automatic detection systems are required. In recent years, technological and signal processing advances have led to the development of operational infrasound avalanche detection systems. We evaluated the detection performance of four operationally running infrasound detection systems installed at three different sites in the Swiss Alps during two entire winter seasons. To this end, we collected a comprehensive data set of avalanche activity using a network of automatic cameras and supplementary field observations by local observers. The events automatically identified by the systems were then compared to the data set of visually observed avalanches. Only 3% of the 839 observed avalanches were associated with automatic detections and 21% of the automatic detections were confirmed by field observations. However, the majority of observed avalanches were small and most automatic detections occurred during periods of poor visibility. Furthermore, the probability of detection (POD) increased with avalanche size, decreased with distance, and varied with avalanche type. Dry-snow avalanches were generally better detected than wet-snow and mixed-type avalanches. Large avalanches (on the order of 300 m wide and 1000 m long) within a distance of 3 km from the array were typically well detected (POD ≈ 90%). The false alarm ratio was estimated to 13–30%.

Abstract Design of gas turbine packages is subjected to safety issues and the related guidelines are provided by ISO-21789. According to this code, the ventilation system shall guarantee a good and safe dilution in case of an unexpected gas leakage from components of the fuel gas system inside the enclosure. The evaluation of the dilution is commonly carried out by CFD simulations and the ISO-21789 indicates the criteria to evaluate the danger of a gas leak by estimating the cloud volume of the explosive mixture. To follow this prescription and to properly calculate the exact volume cloud, it is fundamental to accurately reproduce the fuel gas leak, which is always a supersonic jet of fuel gas into an air-ventilated domain. The main criticality is to simulate a supersonic jet into a complex domain such as the gas turbine package, considering the industrial goals in terms of accuracy and time constraints. The complexity is due to the geometry of the package and to the multiple locations where the leakage could occur. In such context, it is preferable to develop an advanced modeling of the phenomenon rather than simply improve the detail of the CFD, that could turn out to be unfeasible for industrial goals. For this reason, the authors present a series of simulations of under-expanded jets at high pressure ratios carried out to investigate the applicability of the sonic source approach to not-round jets.

A form of supervised machine learning was applied to highly resolved large-eddy simulation (LES) data to develop non linear turbulence stress and heat flux closures with increased prediction accuracy for trailing-edge cooling slot cases. The LES data were generated for a thick and a thin trailing-edge slot and shown to agree well with experimental data, thus providing suitable training data for model development. A Gene Expression Programming (GEP) based algorithm was used to symbolically regress novel nonlinear Explicit Algebraic Stress Models (EASM) and heat-flux closures based on either the gradient diffusion or the generalized gradient diffusion approaches. Following a-priori assessment, the new models were used for steady RANS calculations of both thin and thick trailing-edge slot geometries, testing their performance and robustness. Overall, the best agreement with LES data was found when training the RANS model in the near wall region where high levels of anisotropy exist and using the mean squared error of the anisotropy tensor as cost function. In the case of the thin lip geometry, combining an improved EASM model with the standard eddy-diffusivity model predicted the adiabatic wall effectiveness in good agreement with the LES and experimental data. Crucially, the obtained model was also applied to different blowing ratios of the thin lip geometry and a significant improvement in the predictive accuracy of adiabatic wall effectiveness was observed for those cases not previously seen in the training process. For the thick lip case the match with reference values deteriorated due to the presence of large-scale, relative to the slot height, vortex shedding. The machine-learning algorithm was therefore also used to ‘learn’ an appropriate closure for the turbulent heat flux vector. The constructed scalar flux model, in conjunction with a trained RANS model, was found to have the capability to further improve the prediction of the adiabatic wall effectiveness.

This paper presents a Neural Network (NN) approach for steam turbines modelling. NN models can predict generated power as well as different steam features that cannot be directly monitored through sensors, such as pressures and temperatures at drums outlet and steam quality. The investigated models have been trained and validated on a dataset created through the internal sizing design tool and tested by exploiting field data coming from a real-world power plant, in which a High Pressure and a Low Pressure turbines are installed. The proposed approach is applied to identify the variation of the characteristics from data measurable on the operating field, by means of suitable monitoring and control algorithms that are implemented directly on the PLC.

Structural topology optimization is an innovative approach in turbomachinery to satisfy the increasing demand for higher rotational speeds, light components and optimized natural frequencies, with a remarkable economic impact. Although this approach has never been extensively applied before to rotating machines, it is very promising for the mechanical optimization of rotor and stator components. This approach enables the creation of complex three-dimensional geometries, which are usually difficult or impossible to be built using traditional manufacturing methods. Thanks to innovative technologies and to the use of innovative materials, it is now possible to effectively exploit topology optimization. It allows to change the topology of the structures, significantly improving material distribution within a given design space for a given set of boundary conditions and loads. In this work, the authors have deeply investigated the applicability of topology optimization to the fields of turbomachinery and rotordynamics.

Bladed wheel dynamic characterization is a crucial issue to avoid resonance excitations. The test bench presented in this paper was designed to independently excite the wheel sectors with one electromagnetic shaker each blade. Since a wide frequency range (1–10 kHz) is usually considered for bladed wheels, custom electromagnetic devices were designed, and then a closed-loop control software was also implemented. The global mode shapes of the wheel were then reconstructed through subsequent accelerometer measurements on all sectors to evaluate the harmonic response. The main target of the test rig is the reproduction of any operational condition by experimentally simulating an arbitrary number of stator vanes. In this way the response levels of the differently excited modes are measured and the modal damping is optimally quantified by providing a selective excitation of any number of nodal diameters. Preliminary results showed how the test setup actually allows to excite those modes with a specific number of nodal diameters, however, also exposed some difficulties to avoid small load components with different numbers of nodal diameters.

The purpose of the present study is to evaluate the temperature increase due to radio-frequency (RF) exposure in Magnetic Resonance (MR) examinations, by combining electromagnetic/thermal simulations and B1 maps.