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Bobbins

The Experts below are selected from a list of 3867 Experts worldwide ranked by ideXlab platform

Atsuhiko Yamanaka – 1st expert on this subject based on the ideXlab platform

  • Mechanical Loss and Bobbin Material in Double Pancake AC Superconducting Coils
    IEEE Transactions on Applied Superconductivity, 2013
    Co-Authors: Tomoaki Takao, S Fukui, Takayuki Goto, Orie Sakamoto, K. Nishimura, T. Takagi, S. Sakai, Atsuhiko Yamanaka

    Abstract:

    We fabricated nonimpregnated HTS superconducting coils with a BSCCO tape. Bobbin materials in the coils are a Dyneema(R) fiber reinforced plastic (DFRP) and a glass fiber reinforced plastic (GFRP). We excited those coils with ac currents, and estimated mechanical losses. According to the measured data, the mechanical loss decreased with increase of winding tension of the coils, because strong winding tension fixed the coils tightly. Also, the mechanical loss occurred in the DFRP coil was smaller than that occurred in the GFRP coil. A thermal expansion coefficient of the DFRP is a negative value, that is, the DFRP expands with cooling down from room temperature to cryogenic temperature. The expansion of the DFRP bobbin made the winding of the coil fix tightly, and the mechanical loss decreased. From those experimental results, we think that the DFRP bobbin is useful for decreasing of mechanical losses of the ac coils.

  • High Thermal Conduction Bobbin and Thermal Stability of Conduction Cooled Superconducting Pancake Coils
    IEEE Transactions on Applied Superconductivity, 2013
    Co-Authors: Tomoaki Takao, Shunsuke Asano, Kohei Ishikawa, Yuzuru Kawahara, Orie Sakamoto, Arata Nishimura, Atsuhiko Yamanaka

    Abstract:

    We have proposed a Dyneema fiber reinforced plastic (DFRP) as a coil bobbin material. The DFRP has some properties such as high thermal conduction, easy mechanical processing, and expansion with cool down. We fabricated superconducting coils having an YBCO tape, cooled the coils with a refrigerator, and supplied a dc current to the conduction cooled coils. Thermal stability of the coils was also estimated. The DFRPs, glass fiber reinforced plastics, and AlN blocks were used as the bobbin materials for the coils. From the experimental results, the thermal stability of the coils increased with increasing of the winding tension of the coils, and the coil having the DFRP bobbin showed better performance than the coils with the glass fiber reinforced plastic and the AlN Bobbins. We think that contact force between the superconducting tape and the bobbin became large due to the thermal expansion of the DFRP bobbin. These results showed that DFRP can represent a viable opportunity as bobbin material for conduction cooled high-temperature superconducting coils.

  • Increase of Cooling Performance of Conduction Cooled Superconducting Coils Using High Thermal Conduction Plastic
    IEEE Transactions on Applied Superconductivity, 2012
    Co-Authors: Tomoaki Takao, Atsuhiko Yamanaka, Takayuki Goto, Daigo Hachisuka, Syunsuke Asano, Takuro Yuhara, Arata Nishimura

    Abstract:

    We experimentally study thermal stability of conduction cooled superconducting coils from a viewpoint of structural materials of the coils. The materials are Dyneema fiber reinforced plastics (DFRPs). The DFRP has properties of high thermal conduction and negative thermal expansion. We evaluated the stability of the conduction cooled coils having DFRP Bobbins with changing the DFRP’s thermal expansion. According to the experimental results, when the expansion became large, the stability also became better. It is expected that the DFRP is the effective heat sink bobbin material.

Ahmet Cihan – 2nd expert on this subject based on the ideXlab platform

  • drying kinetics of cotton based yarn Bobbins in a pressurized hot air convective dryer
    Proceedings of the Institution of Mechanical Engineers Part E: Journal of Process Mechanical Engineering, 2017
    Co-Authors: Dinçer Akal, Ugur Akyol, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, the drying kinetics of cotton bobbin drying process in a pressurized hot-air convective bobbin dryer was investigated, and a drying model was introduced for the simulation of drying. Tests were conducted for drying temperatures of 70℃, 80℃, and 90℃; effective drying air pressures of 1, 2, and 3 bars; three volumetric flow rates of 42.5, 55, and 67.5 m3/h; and for three different bobbin diameters of 10, 14, and 18 cm. Optimum drying conditions were specified in terms of drying time and energy consumption. Results indicate that the total drying time depends significantly on the drying temperature, pressure, and volumetric flow rate. Results show that the minimum energy consumption is obtained for low values of drying air temperatures and pressures, and for moderate and high values of drying air volumetric flow rates. It was also found that the Page model is suitable for simulating the drying behavior of cotton yarn Bobbins. Finally, results show that effective diffusion coefficient values are…

  • A model for predicting drying time period of wool yarn Bobbins using computational intelligence techniques
    Textile Research Journal, 2015
    Co-Authors: Ugur Akyol, Pınar Tüfekci, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn Bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn Bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally, the best performance has been found by the REP tree method, which achieved minimum error and time taken to build the model.

  • A model for predicting drying time period of wool yarn Bobbins using computational intelligence techniques
    Textile Research Journal, 2014
    Co-Authors: Ugur Akyol, Pınar Tüfekci, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn Bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn Bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally,…

Ugur Akyol – 3rd expert on this subject based on the ideXlab platform

  • drying kinetics of cotton based yarn Bobbins in a pressurized hot air convective dryer
    Proceedings of the Institution of Mechanical Engineers Part E: Journal of Process Mechanical Engineering, 2017
    Co-Authors: Dinçer Akal, Ugur Akyol, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, the drying kinetics of cotton bobbin drying process in a pressurized hot-air convective bobbin dryer was investigated, and a drying model was introduced for the simulation of drying. Tests were conducted for drying temperatures of 70℃, 80℃, and 90℃; effective drying air pressures of 1, 2, and 3 bars; three volumetric flow rates of 42.5, 55, and 67.5 m3/h; and for three different bobbin diameters of 10, 14, and 18 cm. Optimum drying conditions were specified in terms of drying time and energy consumption. Results indicate that the total drying time depends significantly on the drying temperature, pressure, and volumetric flow rate. Results show that the minimum energy consumption is obtained for low values of drying air temperatures and pressures, and for moderate and high values of drying air volumetric flow rates. It was also found that the Page model is suitable for simulating the drying behavior of cotton yarn Bobbins. Finally, results show that effective diffusion coefficient values are…

  • A model for predicting drying time period of wool yarn Bobbins using computational intelligence techniques
    Textile Research Journal, 2015
    Co-Authors: Ugur Akyol, Pınar Tüfekci, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn Bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn Bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally, the best performance has been found by the REP tree method, which achieved minimum error and time taken to build the model.

  • A model for predicting drying time period of wool yarn Bobbins using computational intelligence techniques
    Textile Research Journal, 2014
    Co-Authors: Ugur Akyol, Pınar Tüfekci, Kamil Kahveci, Ahmet Cihan

    Abstract:

    In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn Bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn Bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally,…