![](data:image/jpeg;base64,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)
우리학부 유정열 교수가 지난 6월 21-24일 기간에 대구에서 개최되었던 제14회 국제유동가시화심포지움(The 14th International Symposium on Flow Visualization) 에서 “유동가시화” 분야의 탁월한 업적을 인정 받아 아사누마(Asanuma)상을 수상하였다.
본 상은 일본가시화학회(The Visualization Society of Japan)가 매 2년마다 개최되는 국제유동가시화심포지움에서 헌정하는 상이다. 또한, 국제유동가시화심포지움은 20-30개국에서 유동가시화를 전공하는 동일 분야의 연구가들이 200 - 300명 참가하는 중급 규모의 학술회의로 알려져 있다.