The Experts below are selected from a list of 648 Experts worldwide ranked by ideXlab platform
Tore Vernersson - One of the best experts on this subject based on the ideXlab platform.
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Thermally Induced Roughness of tread-braked railway wheels. Part 1: Brake rig experiments
Wear, 1999Co-Authors: Tore VernerssonAbstract:Roughness (corrugation, waviness) on the tread of a rolling railway wheel leads to vibrations in wheels, bogies and superstructure and also in rails, sleepers and roadbed. These vibrating components radiate noise to the surroundings. A lowering of the noise levels from tread-braked freight cars is of utmost importance for the future of rail traffic. The thermomechanical interaction between brake block and wheel tread during braking has been found to cause hot spots on the wheel tread. This phenomenon is believed to be a main contributor to the development of a wavy tread surface. Due to thermal expansion of the rim material, the hot spots will protrude from the wheel tread and be more exposed to wear during the wheel/block contact than the rest of the tread surface. The non-even wear results in Roughness on the tread of the wheel after cooling. In the present paper, full-scale tread braking experiments on an inertia dynamometer are reported. Mainly cast iron but also composition and sinter material brake blocks are tested. A stationary pattern of temperature rises (hot spots) on the wheel tread is recorded which is found to correlate well with the measured Roughness. In a companion paper results of modelling and field measurements are presented.
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Thermally Induced Roughness of tread braked railway wheels. Part 2: Modelling and field measurements
Wear, 1999Co-Authors: Tore VernerssonAbstract:Roughness (corrugation, waviness) on the thread of a rolling railway wheel leads to vibrations in wheels, bogies and superstructure and also in rails, sleepers and roadbed. These vibrating components radiate noise to the surroundings. A lowering of the noise levels from tread braked freight cars is of utmost importance for the future of rail traffic. The thermomechanical interaction between brake block and wheel tread during braking has been found to cause hot spots on the wheel tread. This phenomenon is believed to be a main contributor to the development of a wavy tread surface. Due to thermal expansion of the rim material, the hot spots will protrude from the wheel tread and be more exposed to wear during the wheel/block contact than the rest of the tread surface. The non-even wear results in Roughness on the thread of the wheel after cooling. In the present paper, a theoretical and numerical model of the interaction between wheel rim and brake block has been developed. Simulations with this model demonstrate the principal phenomena occurring at the wheel/block contact. Furthermore, results from field measurements of wheel Roughness are presented.
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Thermally Induced Roughness of tread braked railway wheels
1998Co-Authors: Tore Vernersson, Martin Petersson, Martin HienschAbstract:Roughness (corrugation, waviness) having circumferential wavelengths of 20 to 200 mm on the tread surface of a running railway wheel leads to vibrations in wheels, bogies and superstructure and also in rails, sleepers and roadbed. The vibrations radiate noise to the surroundings. The most efficient way to reduce the rolling noise would be to bring down the Roughness on wheel tread and rail head. In the present work mechanisms causing wheel Roughness are investigated. Results from full-scale tread braking experiments on an inertia dynamometer are presented. In these tests the influence of various parameters such as block material, wheel speed and braking force is investigated. The temperatures of the wheel tread and the temperatures and vibrations of the block are measured during braking. Tread surface temperatures are measured using IR techniques. The evolution of wheel tread Roughness is measured after each braking cycle. Finally, the hardness of the tread is registered and the tread microstructure is determined by use of metallographic methods. In parallel to the experiments, a mathematical model of the interaction between brake block and wheel has been developed. Thermal loading, contact mechanics and surface wear are considered. A detailed understanding of the braking phenomena causing the growth of wheel Roughness is aimed at. Countermeasures could then be developed.
Nadir Dagli - One of the best experts on this subject based on the ideXlab platform.
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propagation loss study of very compact gaas algaas substrate removed waveguides
Optics Express, 2009Co-Authors: Jaehyuk Shin, Yuchia Chang, Nadir DagliAbstract:Very compact GaAs/AlGaAs optical waveguides with propagation loss as low as 0.9 dB/cm at λ=1.55 μm were demonstrated using substrate removal and evaporated Si for index loading. Loss components were identified and minimized through process and waveguide design. Process Induced Roughness contributed significantly to overall propagation loss. Therefore a low damage process in addition to proper waveguide design is needed for loss minimization.
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Propagation loss study of very compact GaAs/AlGaAs substrate removed waveguides
Optics Express, 2009Co-Authors: Jaehyuk Shin, Yuchia Chang, Nadir DagliAbstract:Very compact GaAs/AlGaAs optical waveguides with propagation loss as low as 0.9 dB/cm at λ=1.55 μm were demonstrated using substrate removal and evaporated Si for index loading. Loss components were identified and minimized through process and waveguide design. Process Induced Roughness contributed significantly to overall propagation loss. Therefore a low damage process in addition to proper waveguide design is needed for loss minimization.
Maria Marta Jacob - One of the best experts on this subject based on the ideXlab platform.
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rain Induced near surface salinity stratification and rain Roughness correction for aquarius sss retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Wenqing Tang, Simon H. Yueh, Akiko Hayashi, Alexander G. Fore, Linwood W. Jones, Andrea Santosgarcia, Maria Marta JacobAbstract:The effect of rain on surface salinity stratification is analyzed to develop a rain Roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1–2 cm within the surface and the calibration reference SSS from HYCOM ( $\text{SSS}_\text{HYCOM}$ ) a few meters below the surface. We used the rain impact model (RIM) to adjust $\text{SSS}_\text{HYCOM}$ to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements ( $\text{SSS}_\text{RIM}$ ). When calibrated with $\text{SSS}_\text{RIM}$ , the residuals, i.e., the difference between measured and model predicted brightness temperature ${{\text T}_{\text B}}$ , are considered as rain-Induced Roughness. It was found that rain-Induced Roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with ( $\text{SSS}_\text{{CAP}\_{RC}}$ ) or without ( $\text{SSS}_\text{CAP}$ ) rain Roughness correction. We find that the simultaneously retrieved wind speed with rain Roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-Induced Roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of $- {0}.{184}\;{\text{psu}}$ ${\left({{\text{mm}}\;{\text{h}^{ - \mathbf{1}}}} \right)^{ - \mathbf{1}}}$ , representing the salinity stratification between the two depths (1–2 cm versus 45 cm).
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Rain-Induced Near Surface Salinity Stratification and Rain Roughness Correction for Aquarius SSS Retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Wenqing Tang, Simon H. Yueh, Akiko Hayashi, Alexander G. Fore, Linwood W. Jones, Andrea Santos-garcia, Maria Marta JacobAbstract:The effect of rain on surface salinity stratification is analyzed to develop a rain Roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1-2 cm within the surface and the calibration reference SSS from HYCOM (SSSHYCOM) a few meters below the surface. We used the rain impact model (RIM) to adjust SSSHYCOM to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements (SSSRIM). When calibrated with SSSRIM, the residuals, i.e., the difference between measured and model predicted brightness temperature TB, are considered as rain-Induced Roughness. It was found that rain-Induced Roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with (SSSCAP_RC) or without (SSSCAP) rain Roughness correction. We find that the simultaneously retrieved wind speed with rain Roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-Induced Roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of -0.184 psu (mm h-1)-1, representing the salinity stratification between the two depths (1-2 cm versus 45 cm).
Jaehyuk Shin - One of the best experts on this subject based on the ideXlab platform.
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propagation loss study of very compact gaas algaas substrate removed waveguides
Optics Express, 2009Co-Authors: Jaehyuk Shin, Yuchia Chang, Nadir DagliAbstract:Very compact GaAs/AlGaAs optical waveguides with propagation loss as low as 0.9 dB/cm at λ=1.55 μm were demonstrated using substrate removal and evaporated Si for index loading. Loss components were identified and minimized through process and waveguide design. Process Induced Roughness contributed significantly to overall propagation loss. Therefore a low damage process in addition to proper waveguide design is needed for loss minimization.
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Propagation loss study of very compact GaAs/AlGaAs substrate removed waveguides
Optics Express, 2009Co-Authors: Jaehyuk Shin, Yuchia Chang, Nadir DagliAbstract:Very compact GaAs/AlGaAs optical waveguides with propagation loss as low as 0.9 dB/cm at λ=1.55 μm were demonstrated using substrate removal and evaporated Si for index loading. Loss components were identified and minimized through process and waveguide design. Process Induced Roughness contributed significantly to overall propagation loss. Therefore a low damage process in addition to proper waveguide design is needed for loss minimization.
Wenqing Tang - One of the best experts on this subject based on the ideXlab platform.
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rain Induced near surface salinity stratification and rain Roughness correction for aquarius sss retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Wenqing Tang, Simon H. Yueh, Akiko Hayashi, Alexander G. Fore, Linwood W. Jones, Andrea Santosgarcia, Maria Marta JacobAbstract:The effect of rain on surface salinity stratification is analyzed to develop a rain Roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1–2 cm within the surface and the calibration reference SSS from HYCOM ( $\text{SSS}_\text{HYCOM}$ ) a few meters below the surface. We used the rain impact model (RIM) to adjust $\text{SSS}_\text{HYCOM}$ to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements ( $\text{SSS}_\text{RIM}$ ). When calibrated with $\text{SSS}_\text{RIM}$ , the residuals, i.e., the difference between measured and model predicted brightness temperature ${{\text T}_{\text B}}$ , are considered as rain-Induced Roughness. It was found that rain-Induced Roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with ( $\text{SSS}_\text{{CAP}\_{RC}}$ ) or without ( $\text{SSS}_\text{CAP}$ ) rain Roughness correction. We find that the simultaneously retrieved wind speed with rain Roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-Induced Roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of $- {0}.{184}\;{\text{psu}}$ ${\left({{\text{mm}}\;{\text{h}^{ - \mathbf{1}}}} \right)^{ - \mathbf{1}}}$ , representing the salinity stratification between the two depths (1–2 cm versus 45 cm).
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Rain-Induced Near Surface Salinity Stratification and Rain Roughness Correction for Aquarius SSS Retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015Co-Authors: Wenqing Tang, Simon H. Yueh, Akiko Hayashi, Alexander G. Fore, Linwood W. Jones, Andrea Santos-garcia, Maria Marta JacobAbstract:The effect of rain on surface salinity stratification is analyzed to develop a rain Roughness correction scheme to reduce the uncertainty of Aquarius sea surface salinity (SSS) retrieved under rainy conditions. Rain freshwater inputs may cause large discrepancies in salinity measured by Aquarius at 1-2 cm within the surface and the calibration reference SSS from HYCOM (SSSHYCOM) a few meters below the surface. We used the rain impact model (RIM) to adjust SSSHYCOM to reflect near surface salinity stratification caused by freshwater inputs accumulated from rain events that occurred over the past 24 h before Aquarius measurements (SSSRIM). When calibrated with SSSRIM, the residuals, i.e., the difference between measured and model predicted brightness temperature TB, are considered as rain-Induced Roughness. It was found that rain-Induced Roughness is larger at lower wind speeds, and decreases as wind increases. The Combined Active Passive algorithm is used to retrieve SSS with (SSSCAP_RC) or without (SSSCAP) rain Roughness correction. We find that the simultaneously retrieved wind speed with rain Roughness correction has significantly improved agreement with the NCEP wind speed with the rain-dependent bias reduced, self-justifying our rain correction approach. SSS retrieved is validated with salinity measured by drifters at a depth of 45 cm. The difference between satellite retrieved and in situ salinity increases with rain rate. With rain-Induced Roughness accounted for, the difference between satellite retrieval and drifter increases with rain rate with slope of -0.184 psu (mm h-1)-1, representing the salinity stratification between the two depths (1-2 cm versus 45 cm).