• 식물조직배양학회지
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• 제25권5호
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• pp.329-333
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• 1998

Escherichia coli의 $\beta$-glucuronidase (GUS) 유전자를 고구마의 배발생세포괴에 particle bombardment로 도입하여 재분화 식물체에 발현시켰다. CaMV35S-GUS 융합유전자와 선발표지로서 neomycin phosphotransferase유전자가 들어있는 binary 운반체 pBI121 DNA를 텅스텐 입자로 코팅하여 정단분열 조직 유래의 배발생 세포괴에 bombarding하였다. Bombarding된 세포괴를 1mg/L 2,4-D와 100mg/L kanamycin이 첨가된 MS 배지로 옮겨 한달 간격으로 6개월동안 계대배양하였다. Kanamycin 저항성 캘러스를 0.03mg/L 2iP, 0.03 mg/L ABA 및 50 mg/L kanamycin이 들어있는 MS 배지로 옮겨 체세포배를 유도하였고, kanamycin이 첨가되지 않은 MS 기본배지에서 식물체로 발달시켰다. 토양에서 생육중인 6개체의 식물을 대상으로 PCR과 northern분석을 수행한 결과 GUS 유전자가 식물체 genome에 안정적으로 도입, 발현되었음이 확인되었다. 조직화학적 분석으로 GUS 유전자가 형질전환 식물체에서 발현됨을 밝혔다.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.