Computational Analysis of Fire Dynamics Inside a Wind Turbine

Rengel, B.; Pastor, Elsa; Hermida, D.; Gómez, E; Molinelli, L.; Planas, Eulàlia

doi:10.1007/s10694-017-0664-0

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Infrared rays are sent to a receiver that sends you back. It has algorithms to avoid false alarms and can monitor 5–100 m. As seen from their wide operating range, these detectors are more suitable for warehouses and places where the ceiling is too high and normal infrared sensors are unreliable [ 28 , 32 ].…”

Section: Wind Turbine Fire Prevention Methodsmentioning

confidence: 99%

Fire risk assessments and fire protection measures for wind turbines: A review

You,

Shaik,

Rokonuzzaman

et al. 2023

Heliyon

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Section: Wind Turbine Fire Prevention Methodsmentioning

confidence: 99%

Fire risk assessments and fire protection measures for wind turbines: A review

You,

Shaik,

Rokonuzzaman

et al. 2023

Heliyon

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“…4,8,[12][13][14] A more recent work was pointed to modelling a typical wind turbine fire by using Computational Fluid Dynamics (CFD), but the fire scenario design is simple (ie, only consider the electrical cabinet burning). 15 In the United States, NFPA 30 "Flammable and Combustible Liquids Code" has been a nationally recognized consensus standard for the safe storage and handling of flammable and combustible liquids. It defines "flammable liquid" and "combustible liquid" and classifies liquid fuels into various classes based on their flash point and boiling point.…”

Section: Funding Informationmentioning

confidence: 99%

“…All the experiments were conducted by placing the sample fuels horizontally in a modified circular pan made of stainless steel with a diameter of 100 mm, a height of 15 mm, and a wall thickness of 1.5 mm. Tests were performed at 5 radiant heat fluxes (15,25,35,50, and 75 kW/m 2 ). A radiant heat flux meter was placed at the initial location of the fuel surface to calibrate the radiant heat fluxes.…”

Section: Cone Calorimetermentioning

confidence: 99%

“…Some work only focused on fire causes and relevant fire protection systems from the viewpoint of engineering, such as the selection of proper and a high‐efficiency automatic fire‐fighting system . A more recent work was pointed to modelling a typical wind turbine fire by using Computational Fluid Dynamics (CFD), but the fire scenario design is simple (ie, only consider the electrical cabinet burning) . In the United States, NFPA 30 “Flammable and Combustible Liquids Code” has been a nationally recognized consensus standard for the safe storage and handling of flammable and combustible liquids.…”

Section: Introductionmentioning

confidence: 99%

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Flammability hazards of typical fuels used in wind turbine nacelle

et al. 2018

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Summary This study aims to develop a complete methodology for assessing flammability hazards of typical fuels (ie, transformer oil, hydraulic oil, gear oil, and lubricating grease) used in a wind turbine nacelle by combining different experimental techniques such as thermogravimetric analysis and cone calorimetry. Pyrolysis properties (onset temperature, temperature of maximum mass loss rate, and mass residue) and reaction‐to‐fire properties (ignition time, heat release rate, mass loss rate, and smoke release rate) were determined and used for a preliminary assessment of thermal stability and flammability hazards. Additional indices, for ignition and thermal behavior (effective heat of combustion, average smoke yield, and smoke point height, heat release capacity, fire hazard parameter, and smoke parameter, were calculated to provide a more advanced assessment of the hazards in a wind turbine. Results show that pyrolysis of transformer oil, lubricating grease, hydraulic oil, and gear oil occur in the range of 150°C to 550°C. Lubricating grease and transformer oil show the higher and lower thermal stabilities with maximum pyrolysis rate temperatures of 471°C and 282°C, respectively. The measured relation between ignition time and radiant heat flux agrees well with Janssens method (a power of 0.55). The aforementioned indices appear to provide a reasonable prediction of performance under real fire conditions according to a full‐scale fire test documented by Declercq and Van Schevensteen. The results of the study indicate that transformer oil is the easiest to ignite while lubricating grease is the most difficult to ignite but also has the highest smoke production rate; that transformer oil has the highest heat release rate while gear oil has the lowest; and that the fire hazard parameter is the highest for transformer oil and the smoke parameter is the highest for lubricating grease. The potential of this type of work to design safer wind turbines under performance‐based approaches is clearly clarified.

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“…Compared with onshore wind farms, offshore wind farms are mostly located in remote coastal areas, and some offshore wind farms are even situated tens of kilometers away from the coast, with extremely inconvenient traffic, which is not conducive to timely firefighting and disaster protection for wind turbines. The fire accident rate of wind turbines has also gradually increased in recent years, becoming the second largest disaster after lightning strikes [10]. Due to difficulties in firefighting, multiple fire hazards, and complex fire types in wind turbines, once a wind turbine catches fire, it will cause the loss of the whole unit, which easily causes great economic losses, and the rescue possibility is almost zero [11].…”

Section: Introductionmentioning

confidence: 99%

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

Wan,

Du,

Gong

et al. 2024

Energies

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To improve the power generation efficiency of offshore wind turbines and address the problem of high fire monitoring and warning costs, we propose a data-driven fire warning method based on transfer learning for wind turbines in this paper. This paper processes wind turbine operation data in a SCADA system. It uses an extreme gradient-boosting tree (XGBoost) algorithm to build an offshore wind turbine unit fire warning model with a multiparameter prediction function. This paper selects some parameters from the dataset as input variables for the model, with average cabin temperature, average outdoor temperature, average cabin humidity, and average atmospheric humidity as output variables. This paper analyzes the distribution information of input and output variables and their correlation, analyzes the predicted difference, and then provides an early warning for wind turbine fires. This paper uses this fire warning model to transfer learning to different models of offshore wind turbines in the same wind farm to achieve fire warning. The experimental results show that the prediction performance of the multiparameter is accurate, with an average MAPE of 0.016 and an average RMSE of 0.795. It is better than the average MAPE (0.051) and the average RMSE (2.020) of the prediction performance of a backpropagation (BP) neural network, as well as the average MAPE (0.030) and the average RMSE (1.301) of the prediction performance of random forest. The transfer learning model has good prediction performance, with an average MAPE of 0.022 and an average RMSE of 1.469.

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Computational Analysis of Fire Dynamics Inside a Wind Turbine

Cited by 6 publications

References 4 publications

Fire risk assessments and fire protection measures for wind turbines: A review

Fire risk assessments and fire protection measures for wind turbines: A review

Flammability hazards of typical fuels used in wind turbine nacelle

Using Transfer Learning and XGBoost for Early Detection of Fires in Offshore Wind Turbine Units

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