Dynamic Effects of Wind Loads on a Gravity Damper

AbstractThe gravity damper is safety device used for the air treatment that prevent overpressure inside the unit through the opening. It is a normally closed valve under the effect of the gravity force, which, under the action of the incident air flow, allows to manage any excess mass. Clearly, although the device is rather simple and therefore reliable, the operating conditions may prove burdensome, especially if the gravity dampers are applied to installations of energy transformation, such as the gas turbines; this is mainly due to the need to develop large masses of air at speeds rather incurred. This article describes an experiment carried out on a gravity damper designed to be installed in a gas turbine. The characterization has been performed in numerical (CFD-FEM), considering both the mode shapes and the natural frequencies of the device in working condition as well as any phenomenon of detachment of the fluid that can trigger vortex shedding and subsequently validated in the wind tunnel facilities of the University of Perugia. In particular, what is wanted to be highlighted is the fact that, after a preliminary analysis, it has been clearly evident that, under the operating conditions, the structure would be affected by phenomena of vortex shedding. The shedding frequency is next to some natural frequencies of the structure, with obvious repercussions on the integrity of the structure. An experimental vibration analysis performed in the wind tunnel at flow regime has in fact allowed to identify the phenomenon of lock-in

Development of a non-contact torque transducer based on the laser speckle contrast method

When a torque measurement is required, torque transducers show many drawbacks during their use: the usual limits are the need for contact and the effects on shaft line parameters. A new approach is proposed in this work: a non-contact torque meter for a machine shaft has been developed. It carries out torque measurements evaluating the torsional displacement between two distinct sections of the shaft, through the monitoring of their roughness. The sensing principle employed is the classical laser speckle contrast method. The outcoming intensity scattered by the rough surface is processed by a cross-correlation function. In this way, the angular torsion of two distinct sections is obtained. It is therefore possible to point out the applied torque knowing the shaft torsional stiffness

Real-Time Oil Leakage Detection on Aftermarket Motorcycle Damping System with Convolutional Neural Networks

In this work, we describe in detail how Deep Learning and Computer Vision can help to detect fault events of the AirTender system, an aftermarket motorcycle damping system component. One of the most effective ways to monitor the AirTender functioning is to look for oil stains on its surface. Starting from real-time images, AirTender is first detected in the motorbike suspension system, simulated indoor, and then, a binary classifier determines whether AirTender is spilling oil or not. The detection is made with the help of the Yolo5 architecture, whereas the classification is carried out with the help of a suitably designed Convolutional Neural Network, OilNet40. In order to detect oil leaks more clearly, we dilute the oil in AirTender with a fluorescent dye with an excitation wavelength peak of approximately 390 nm. AirTender is then illuminated with suitable UV LEDs. The whole system is an attempt to design a low-cost detection setup. An on-board device, such as a mini-computer, is placed near the suspension system and connected to a full hd camera framing AirTender. The on-board device, through our Neural Network algorithm, is then able to localize and classify AirTender as normally functioning (non-leak image) or anomaly (leak image)

Aerodynamic effects in proximity to high-speed trains

The effects of aerodynamic actions acting on antinoise barriers produced in proximity to high-speed trains are considered. Because of certain accidents noticed on the german high-speed railway, the problem has been developed. A mathematical model has been carried out for specific typologies of barriers. The input applied to this model is represented by the Time History furnished by the technical supplemental and temporary prescriptions for the antinoise barriers design. A critical analysis of the results has been conducted

Diagnosis of vortex induced vibration of a gravity damper

A gravity damper is a one-way valve, employed for regulating the airflow rate in ducts, generally constituted by a series of rectangular panels (closure sections), connected to an articulated quadrilateral synchronizing the movements. If the device needs to process large masses of high speed air, as common in the case of energy conversion systems, disadvantageous dynamic effects can occur. In this study, vortexinduced vibration (VIV), occurring on a gravity damper for high values of the Reynolds number, is investigated. The analysis of this work couples numerical methods (Computational Fluid Dynamics with Large-Eddy Simulation turbulence model and Finite Element Method) to experiments: a full-scale accelerometric measurement campaign is actually performed at the wind tunnel facilities of the University of Perugia. VIVs are diagnosed and quantified through the experimental vibration analysis, which is interpreted through numerical simulations. The large amplitude of VIV is interpreted as due to a tendency towards lockin because of the approaching of the vortex shedding frequency to a natural vibration mode of the system. The integrated numerical and experimental framework finally inspires two different design solutions for mitigating the amplitude of VIV: these strategies are tested at the wind tunnel and they are indeed shown to be effective

Analysing wind turbine state dynamics for fault diagnosis

Supervisory Control And Data Acquisition (SCADA) systems have recently become ubiquitous in wind energy technology. SCADA data analysis actually can provide considerable performance improvement at low cost. This also boosts wind energy exploitation, because it enlarges short and long term economic sustainability of investments. Nevertheless, SCADA data analysis poses several scientific and technological challenges, mostly related to the vastness of the data sets required for significant analysis. Separating the signal from the noise is therefore a complex task. In the present work, this issue is tackled by the point of view of state dynamics of wind turbines. SCADA control systems often record superabundant and ambiguous information. Therefore, in this work it is shown that hierarchical classification of information and time discretization of the continuous motion of states are powerful tools. The time-discretized state dynamics is processed in the formulation of several indices for performance evaluation and fault diagnosis. The method is tested on the data set of a wind farm owned by Renvico s.r.l. and sited in Italy

A new data mining approach for power performance verification of an on-shore wind farm

Monitoring wind energy production is fundamental to improve the performances of a wind farm during the operational phase. In order to perform reliable operational analysis, data mining of all available information spreading out from turbine control systems is required. In this work a SCADA (Supervisory Control And Data Acquisition) data analysis was performed on a small wind farm and new post-processing methods are proposed for condition monitoring of the aerogenerators. Indicators are defined to detect the malfunctioning of a wind turbine and to select meaningful data to investigate the causes of the anomalous behaviour of a turbine. The operating state database is used to collect information about the proper power production of a wind turbine and a number map has been codified for converting the performance analysis problem into a purely numerical one. Statistical analysis on the number map clearly helps in detecting operational anomalies, providing diagnosis for their reasons. The most operationally stressed turbines are systematically detected through the proposal of two Malfunctioning Indices. Results demonstrate that a proper selection of the SCADA data can be very useful to measure the real performances of a wind farm and thus to define optimal repair/replacement and preventive maintenance policies that play a major role in case of energy production

Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

This paper presents a comparison of different methodologies for monitoring the plants growth in a greenhouse. A 2D measurement based on Computer Vision algorithms and 3D shape measurements techniques (Structured light, LIDAR and photogrammetry) are compared. From the joined 2D and 3D data, an analysis was performed considering health plant indicators. The methodologies are compared among each other. The acquired data are then fed into Deep Learning algorithms in order to detect anomalies in plant growth. The final aim is to give an assessment on the image acquisition methodologies, selecting the most suitable to be used to create the Deep Learning model inputs saving time and resources

Hyperthermic antiblastic perfusion with alpha tumor necrosis factor and doxorubicin for the treatment of soft tissue limb sarcoma in candidates for amputation: result of a phase I study.

To improve the therapeutic effectiveness of hyperthermic antiblastic perfusion (HAP), the association of recombinant tumor necrosis factor alpha (rTNF alpha), doxorubicin, and true hyperthermia (41 degrees C) was employed for the treatment of soft tissue limb sarcoma. A dose-escalation study according to Fibonacci's modified scheme was conducted, starting with a rTNF alpha dose of 0.5-3.3 mg. The doxorubicin doses (0.7 and 1.4 mg for the upper and lower limbs, respectively) and temperature level (41 degrees C) remained unchanged. Eighteen patients have been treated thus far: 9 males and 9 females of a mean age of 33 years (range: 24-71 years). The tumor was located in the upper limb in one patient and in the lower limbs in seventeen. Only 16 patients were evaluable, as 2 refused further treatment after the perfusion. In terms of local toxicity, a grade I limb reaction was observed in 3 patients, a grade II or III in 10 patients, and a grade IV in 5 patients, showing a strict correlation between the TNF dose and the grade of limb reaction. In fact, a grade III-IV limb reaction was observed in 66.6% of the patients treated with >1 mg of rTNF alpha. The maximum tolerable dose in association with doxorubicin and true hyperthermia (41 degrees C) was 2.4 mg. Eleven patients showed a good pathological response (>75%) and five patients showed a partial response (>25%-<75%). In no case was stable or progressive disease observed. The postperfusional tumor shrinkage permitted limb-sparing surgery in 75% of the patients, all of whom were candidates for amputation before HAP. No recurrences have been observed thus far. Two patients developed regional disease: one presented with a skip femur metastasis that disappeared after radiotherapy and systemic chemotherapy; the second developed regional node involvement, requiring a radical node dissection. Another patient had pulmonary metastases, 2 months after the HAP, which were resected. At a median follow-up of 12 months, all the patients are living without disease. The results of this phase I study suggest that the association of rTNFa, doxorubicin, and true HAP (41 degrees C) by regional perfusion is feasible and safe at a maximum tolerable rTNF alpha dose of 2.4 mg. However, because no correlation was found between the amount of rTNF alpha and the tumor response, 1 mg is recommended as the dose able to provide a high tumor necrosis rate and low local and systemic toxicity. This association appears to play an important role in the neoadjuvant treatment of soft tissue limb sarcoma