Evaluation of Corrosivity of Atmosphere for Stainless Steels by Meteorological Data

Shinohara, Tadashi; Motoda, Shin-ichi; Nabeshima, Koji; Suzuki, Yohnosuke; Tsujikawa, Shigeo

doi:10.3323/jcorr1991.48.796

Cited by 11 publications

(3 citation statements)

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“…Finally, sea salt deposition rate can be influenced by the distance that coastal wind travels per day or Run of Wind parameter (ROW) and wind force energy (¡ D,C ). 34) ROW and ¡ D,C at Chon Buri were 34 km/day and 316 km 2 /h/day, respectively. While at Phangnga, ROW and ¡ D,C were 47 km/day and 501 km 2 /h/day.…”

Section: Environmental Factorsmentioning

confidence: 86%

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand

et al. 2020

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Galvanic type atmospheric corrosion monitoring (ACM) sensors can distinguish wet and dry conditions of a surface by reporting spiked charge when there is an electrolyte film on the sensor. In this study, FeAg type ACM sensor was exposed to monitor time of wetness (TOW ACM ) on carbon steel and weathering steel coupons at seven test stations in Thailand. Climate parameters were monitored by weather stations. Chloride and sulfur dioxide deposition rates were obtained by dry gauze and lead dioxide cylinder methods, respectively. The objective was to investigate the effects of TOW ACM , climate, and aerosols on corrosion rate in Thailand tropical climate under high temperature, high relative humidity, and frequent monsoon-induced rainfalls. Multivariable linear regression model applied on the first-year corrosion rate yielded predictive equations for (1) inland areas-as a function of temperature (T) and relative humidity (RH) and (2) coastal areas-as a function of rainfall (RF) with TOW ACM or chloride (Cl ¹ ) deposition rate. The first corrosion map of Thailand was proposed based on the predictive equations for corrosion rates of carbon steel and weathering steel.

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Section: Environmental Factorsmentioning

confidence: 86%

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand

et al. 2020

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“…2 and 3. Shinohara et al 12) reported that temperature and relative humidity were distributed according to the normal probability distribution for two years at a building located in a seashore area in Chiba prefecture, Japan. Because the temperature and humidity showed similar distribution, it was concluded that the normal probability plots was useful to obtain the mean temperature and humidity for given day through the monitoring period.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric Corrosivity Estimation at Electrical Control Unit Room by Multichannel Quartz Crystal Microbalance Corrosion Sensors

et al. 2012

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In order to evaluate the atmospheric corrosivity in an electrical control unit room in a steelmaking plant by using quartz crystal microbalance (QCM) sensors, the temperature, relative humidity and corrosion rates of metals on QCM were monitored for five months. The metals employed for sensing corrosive gases were silver, copper and cobalt. The concentrations of the corrosive gases were estimated from the data of corrosion rates for these metals considering relative humidity. The temperature, relative humidity, and the corrosion rates in a given day were distributed according to the normal probability law. The concentrations of H 2 S, SO 2 and NO 2 were estimated from the mean values of the relative humidity and the corrosion rates of silver, copper and cobalt, which derived from the normal probability plots. The concentrations thus obtained were roughly close to those analyzed by conventional analytical methods. The atmospheric corrosivity will be able to estimate based on the monitoring of the corrosion rate with multichannel QCM sensors.

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“…[4][5][6][7][8][9][10][11][12] The results of those studies clarified some significant corrosion behaviors. For example, corrosion is dependent on the critical pitting potential and the corrosion area is proportional to the pitting index, 10) the pit depth increases in proportion to the logarithm of time, 11) corrosion of stainless steel is inactive during almost all periods and grows during a short period when sea salt is deposited, 12) and so on.…”

Section: Introductionmentioning

confidence: 99%

Pit Growth Behavior of SUS443J1 in Atmospheric Environment

Ishii¹,

Ishii²,

Ota³

et al. 2015

ISIJ Int.

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Evaluation of the corrosion resistance of a new stainless steel, SUS443J1, in atmospheric environments is a significant issue. In this study, the growth behavior of pits on the surface of the ferritic stainless steel, SUS443J1, and an austenitic stainless steel, SUS304, were compared by field exposure tests and electrochemical measurements.It is known that the pit growth rate can be approximated as X = at n , where X is pit depth, t is time, a and n are constants. As a result of field exposure tests, pit growth rate values of approximately n = 0.2 were obtained for both 2B and HL surfaces of SUS443J1 and SUS304. From this, it could be predicted that the pit growth behaviors of SUS443J1 and SUS304 were mutually equivalent.The pitting potential values V' c10 of SUS443J1 and SUS304 were almost equivalent, and the repassivation potential showed the same tendency. The pitting potential decreased with increasing the maximum valley depth R v of the surface. The repassivation potential was affected by the turning current density, where the sweep of potential was reversed. The turning current density represents the degree of pit growth. It was suggested that a deep pit would expand at a larger growth rate than that of a shallow pit because the deep pit grows easily and repassivation is difficult in deep pits.

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Evaluation of Corrosivity of Atmosphere for Stainless Steels by Meteorological Data

Cited by 11 publications

References 0 publications

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand

Atmospheric Corrosion Monitoring Sensor in Corrosion Rate Prediction of Carbon and Weathering Steels in Thailand

Atmospheric Corrosivity Estimation at Electrical Control Unit Room by Multichannel Quartz Crystal Microbalance Corrosion Sensors

Pit Growth Behavior of SUS443J1 in Atmospheric Environment

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