• 천문학회지
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• 제29권spc1호
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• pp.63-64
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• 1996

The nature of distant faint blue field galaxies remains a mystery, despite the fact that much attention has been devoted to this subject in the last decade. Galaxy counts, particularly those in the optical and near ultraviolet bandpasses, have been demonstrated to be well in excess of those expected in the 'no-evolution' scenario. This has usually been taken to imply that galaxies were brighter in the past, presumably due to a higher rate of star formation. More recently, redshift surveys of galaxies as faint as B$\~$24 have shown that the mean redshift of faint blue galaxies is lower than that predicted by standard evolutionary models (de-signed to fit the galaxy counts). The galaxy number count data and redshift data suggest that evolutionary effects are most prominent at the faint end of the galaxy luminosity function. While these data constrain the form of evolution of the overall luminosity function, they do not constrain evolution in individual galaxies. We are carrying out a series of observations as part of a long-term program aimed at a better understanding of the nature and amount of luminosity evolution in individual galaxies. Our study uses the luminosity-linewidth relation (Tully-Fisher relation) for disk galaxies as a tool to study luminosity evolution. Several studies of a related nature are being carried out by other groups. A specific experiment to test a 'no-evolution' hypothesis is presented here. We have used the AUTOFIB multifibre spectro-graph on the 4-metre Anglo-Australian Telescope (AAT) and the Rutgers Fabry-Perot imager on the Cerro Tolalo lnteramerican Observatory (CTIO) 4-metre tele-scope to measure the internal kinematics of a representative sample of faint blue field galaxies in the red-shift range z = 0.15-0.4. The emission line profiles of [OII] and [OIII] in a typical sample galaxy are significantly broader than the instrumental resolution (100-120 km $s^{-l}$), and it is possible to make a reliable de-termination of the linewidth. Detailed and realistic simulations based on the properties of nearby, low-luminosity spirals are used to convert the measured linewidth into an estimate of the characteristic rotation speed, making statistical corrections for the effects of inclination, non-uniform distribution of ionized gas, rotation curve shape, finite fibre aperture, etc.. The (corrected) mean characteristic rotation speed for our distant galaxy sample is compared to the mean rotation speed of local galaxies of comparable blue luminosity and colour. The typical galaxy in our distant sample has a B-band luminosity of about 0.25 L$\ast$ and a colour that corresponds to the Sb-Sd/Im range of Hub-ble types. Details of the AUTOFIB fibre spectroscopic study are described by Rix et al. (1996). Follow-up deep near infrared imaging with the 10-metre Keck tele-scope+ NIRC combination and high angular resolution imaging with the Hubble Space Telescope's WFPC2 are being used to determine the structural and orientation parameters of galaxies on an individual basis. This information is being combined with the spatially resolved CTIO Fabry-Perot data to study the internal kinematics of distant galaxies (Ing et al. 1996). The two main questions addressed by these (preliminary studies) are: 1. Do galaxies of a given luminosity and colour have the same characteristic rotation speed in the distant and local Universe? The distant galaxies in our AUTOFIB sample have a mean characteristic rotation speed of $\~$70 km $s^{-l}$ after correction for measurement bias (Fig. 1); this is inconsistent with the characteristic rotation speed of local galaxies of comparable photometric proper-ties (105 km $s^{-l}$) at the > $99\%$ significance level (Fig. 2). A straightforward explanation for this discrepancy is that faint blue galaxies were about 1-1.5 mag brighter (in the B band) at z $\~$ 0.25 than their present-day counterparts. 2. What is the nature of the internal kinematics of faint field galaxies? The linewidths of these faint galaxies appear to be dominated by the global disk rotation. The larger galaxies in our sample are about 2"-.5" in diameter so one can get direct insight into the nature of their internal velocity field from the $\~$ I" seeing CTIO Fabry-Perot data. A montage of Fabry-Perot data is shown in Fig. 3. The linewidths are too large (by. $5\sigma$) to be caused by turbulence in giant HII regions.

• 지질공학
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• 제23권3호
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• pp.201-216
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• 2013

지하수면과 불포화대의 수분 포화도가 지하투과레이더(GPR) 신호에 미치는 영향을 연구하기 위하여 실내 토조와 충적층 현장에서 GPR 조사를 수행하였다. 실내의 모래 채움 토조 실험에서, 지하수위를 변화시키기 위해 물을 탱크 바닥에 설치된 밸브를 통해 주입하고 배수시켰다. 지하수위와 수분포화도를 추정하기 위하여 모래 채움 토조에서 GPR 수직반사법(이후, VRP) 자료가 획득되었다. 실내 모래 채움 토조에서 획득된 GPR 신호는, 지하수위는 물론 함수율 변화에도 민감하게 반응함을 보여준다. 불포화대에서 GPR 속도는 함수율 변화에 따라 크게 조절되며, 주시 시간의 증가는 포화도의 증가로 해석된다. 함안군 이룡리 낙동강변 충적층에서 220m에 달하는 VRP 조사가, 지하수위를 추정하기 위하여 수행되었다. 현장 조사 결과, 포화 조건에서 GPR 신호의 첫 번째 반사면은 모관 상승에 의한 경계부를 지시하며, 실제 지하수면과는 차이가 있음을 지시한다. 보다 정확한 지하수위를 추정하기 위하여, Well-3호공 주변에서 중앙공심점(common mid-point, 이후, CMP) 방식 GPR 조사를 수행하였다. 그 결과, 모관 상승 경계부와 지하수면으로부터 반사되는 CMP 자료는 쌍곡선 형태를 보였다. NMO(nomal move-out) 보정을 통해, CMP 조사 자료로부터 GPR 신호의 속도를 구하였고, 이는 보다 상세한 지하수면과 심도별 포화도 정보를 제공하였다. 지하수면과 포화도 정보를 포함하는 GPR 조사결과는 통기대의 현장 수리 지질학적 특성 조사에 유용한 수단이다.

• 한국농공학회지
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• 제19권1호
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• pp.4323-4337
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• 1977

The study was to estimate the effect of the rise of water temperature in the warm water pool and to make contribution to the establishment of reducing to a damage of cool water as well as to the planning for warm water pool. This observation was performed in Wudu warm water pool located at Wudu-Dong of Chuncheon for two years from 1975 to 1976. The results were showed as follows; 1. The daily variation of water temperature was the least for inset (No.1; 0.6$^{\circ}C$) the second for middle overflow (No2: 3$^{\circ}C$, No.3; 2.3$^{\circ}C$) and another for outflet (No.4; 3.6$^{\circ}C$, No.5; 3.8$^{\circ}C$) And the highest reaching time of water temperature in each block was later about 1 hour than the time at which air temperature happend in the daytime. So, the variation of water temperature was sensitive to the variation of air temperature 2. The monthly variation of water temperature at each measuring point was plotted to be increased with increase in air temperature till August (Mean monthly rising degree; No.1; 1.15$^{\circ}C$, No.2; 1.7$^{\circ}C$, No.3; 1.73$^{\circ}C$, No.4; 2.08$^{\circ}C$, No.5; 2.0$^{\circ}C$), and expressed gradually descended influence upon water temperature after August. 3. The mean temperature of inflow folwed in warm Water pool was 7.5∼12.5$^{\circ}C$, and outflow temperature was described as 13.4∼22.5$^{\circ}C$ to be climbed. And So, the rising interval of water temperature was shown as 6.7∼10.4$^{\circ}C$. 4. The correlation between the rising of water temperature and the weather condition was found out highly significant. As the result, their correlation coefficents of water temperature depending on mean air temperature, ground temperature, wind velocity and relative humidity were to be 0.93, 0.90, - 0.83 and 0.71 respectively. But there was no confrimation of the correlation on the clouds, sunlight time, volume of evaporation, and heat capacity of horizontal place. 5. The water temperature of balance during the period of rice growing in Chuncheon district was shown as table 10, and the mean of whole period was calculated as about 23.7$^{\circ}C$. 6. The observed value of the outflow temperature passed through the warm water pool was higher than that of computed, the mean difference between two value was marked as 1.15$^{\circ}C$ for blockl, 1.18$^{\circ}C$ for block2, and 0.47$^{\circ}C$ for block3, respectivly. Therefore, the ratio on the rising degree between the observed and computed were shown as 53%, 44%, and 18%, mean 38% through each block warm water pool (referring item $\circled9$ of table 11,12, and 13). Accordingly, formula (4) in order to fit for each block warm water pool was transfromed as follow; {{{{ { theta }_{w } - { theta }_{ 0} =[1-exp LEFT { { 1-(1+2 varphi )} over {cp } CDOT { A} over { q} RIGHT } ] TIMES ( { theta }_{w } - { theta }_{ 0}) TIMES C }}}} Here, correction coefficinent was computed 1.38, and being substituted 1.38 for C in preceding formula, the expected water temperature will be calculated to be able to irrigate the rice paddy. As the result, we can apply the coefficient in order to plan and to construct a new warm water pool.