• Nuclear Engineering and Technology
• /
• v.49 no.6
• /
• pp.1219-1225
• /
• 2017

In this work, a scalable algorithm for model calibration in nuclear engineering applications is presented and tested. The algorithm relies on the construction of surrogate models to replace the original model within the region of interest. These surrogate models can be constructed efficiently via reduced order modeling and subspace analysis. Once constructed, these surrogate models can be used to perform computationally expensive mathematical analyses. This work proposes a surrogate based model calibration algorithm. The proposed algorithm is used to calibrate various neutronics and thermal-hydraulics parameters. The virtual environment for reactor applications-core simulator (VERA-CS) is used to simulate a three-dimensional core depletion problem. The proposed algorithm is then used to construct a reduced order model (a surrogate) which is then used in a Bayesian approach to calibrate the neutronics and thermal-hydraulics parameters. The algorithm is tested and the benefits of data assimilation and calibration are highlighted in an uncertainty quantification study and requantification after the calibration process. Results showed that the proposed algorithm could help to reduce the uncertainty in key reactor attributes based on experimental and operational data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.99-99
• /
• 2023

기후변화는 물 관리의 가장 큰 리스크 요인이므로 물 관리 계획을 수립하는 과정에서 기후변화의 영향을 고려하는 것이 필수적이다. 기후변화에 대한 수자원 예측 관련 연구가 이루어지고 있으나, 대부분의 연구에는 수문학적 모델링이나 시뮬레이션이 동반되는데, 이 과정에는 시간과 비용이 많이 들어가며, 지역이나 연구목적에 따른 정밀한 매개변수의 보정은 전문지식이 필요하기 때문에 현업에서 연구결과를 의사결정에 활용하기에는 한계가 있다고 볼 수 있다. 이에 따라 수문학적 모델링의 입력 및 출력 결과를 딥러닝의 학습자료로 하여 수문모델을 사용하지 않아도 효율적으로 결과를 도출할 수 있는 딥러닝 기반 Surrogate 모형에 대한 연구가 이루어지고 있으나 수자원 분야에 접목된 사례는 부재한 실정이다. 따라서 이 연구를 통해 국내 유역을 대상으로 Surrogate 모형을 구축한 뒤, 그 성능을 평가하고자 한다. 이를 위한 Surrogate 모형 구축 과정은 다음과 같다. 충주댐 유역을 대상으로 과거 20년간의 강우 및 기온 자료를 수집한 뒤, 이 자료를 바탕으로 기후변화의 영향을 고려한 3,162개의 시나리오를 생성한다. 그 후 장기유출모형 IHACRES에 생성된 시나리오를 입력자료로 하여 유입량 결과를 도출하고, 이 결과를 Python코드 기반의 딥러닝 학습자료로 하여 최적 예측 결과를 도출해내는 Surrogate 모형을 생성한 뒤 기존 장기유출모형과의 성능을 비교하고자 한다. 이와 같은 Surrogate 모형은 추가적인 데이터와 매개변수의 보정 과정이 없어도 장기유출모형과 같은 결과를 짧은 시간내에 상당히 정확하게 모사할 수 있어 시간과 비용을 줄일 수 있으며, 비전문가도 쉽게 사용할 수 있다는 장점을 가진다.

• Smart Structures and Systems
• /
• v.26 no.2
• /
• pp.175-184
• /
• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Bulletin of the Korean Chemical Society
• /
• v.32 no.2
• /
• pp.716-718
• /
• 2011

• Journal of Korean Society of Water and Wastewater
• /
• v.11 no.1
• /
• pp.21-32
• /
• 1997

Optimum design of water distribution network(WDN) in many times means just reducing redundancy. Given only a few situations are taken into consideration for such design, WDN deprived of inherited redundancy may not work properly in some unconsidered cases. Quantifying redundancy and incorporating it into the optimal design process will be a way of overcoming just reduction of redundancy. Expected shortage is developed as a reliability surrogate in WDN. It is an indicator of the frequency, duration and severity of failure. Using this surrogate, Expected Shortage Optimization Model (ESOM) is developed. ESOM is tested with an example network and results are analyzed and compared with those from other reliability models. The analysis results indicate that expected shortage is a quantitative surrogate measure, especially, good in comparing different designs and obtaining tradeoff between cost and. reliability. In addition, compared other models, ESOM is also proved useful in optimizing WDN with reliability and powerful in controlling reliability directly in the optimization process, even if computational burden is high. Future studies are suggested which focus on how to increase applicability and flexibility of ESOM.

• Journal of Korean Society of Water and Wastewater
• /
• v.28 no.2
• /
• pp.171-179
• /
• 2014

In this study, a novel method was proposed to test the integrity of water treatment system specifically equipped with membrane filtration process. We applied the silica particles coated with a fluorescent agent (rhodamine B isothiocyanate) as a surrogate to detect a membrane process integrity and evaluate the reliability of effluent quality in the system. Additionally, a series of experiments was conducted to evaluate the sensitivity of the method through the laboratory scale experiment. The laboratory scale experiments showed that the feasibility of application of proposed method to detect a breach or damaged part on the membrane surface. However, the sensitivity on predicting the area of a breach was significantly influenced by the testing conditions such as a concentration of surrogate, filtration flux, and detection time. The lowest error of predicting the area of breach was 3.5% at the testing condition of surrogate concentration of 80 mg/L injected with flux of $20L/m^2/hr$ for 10 minutes of detection time for the breach having the actual area of $7.069mm^2$. However, the error of estimation was increased at the small breach with area less than $0.785mm^2$. A future study will be conducted to estimate a damaged area with more accuracy and precision.

• Advances in environmental research
• /
• v.1 no.3
• /
• pp.181-189
• /
• 2012

Trihalomethanes, produced as a result of chlorination of drinking water, are considered a potential health hazard. The trihalomethane formation potential (THMFP) of a raw water source may indicate the maximum trihalomethanes (THMs) that are likely to be produced when chlorine reacts with natural organic matter (NOM) present in the water. A study was conducted to evaluate the THMFP in seven different drinking water sources in the vicinity of Kalpakkam, a rural township, on the east coast of India. Water from seven stations were analysed for THMFP. THMFP was compared with surrogate parameters such as dissolved organic carbon (DOC), ultraviolet absorbance ($UV_{254}$) and bromide. The data showed that THMFP was high in water from open wells as compared to closed bore wells, possibly due to more photosynthetic activity. Proximity to sea, and consequently the levels of bromide, was an important factor that influenced THM formation. THM surrogate parameters showed good correlation with THMFP.

• Journal of Korean Society for Atmospheric Environment
• /
• v.16 no.6
• /
• pp.675-684
• /
• 2000

A novel dry and wet deposition collector, which can overcome the several problems such as water evaporation cartridge cracks and high costs founded in the previous collector systems, has been constructed. ENVI-18 SPE adsorption cartridge has been used to measure atmospheric deposition of polycylic aromatic hydrocarbons (PAHs). A surrogate surface, consisted of water and methanol, was filled in the dry deposition funnel to simulate dry deposition onto water surface. A water supply system in order to compensat evaporation of the surrogate surface was used and it was consisted of a piston pump, a tubing pump, a overflow tube and a chamber system. A novel water vaporizing system to supply water onto the wet SPE cartridge system with a constant flow rate was developed. The novel water vaporizing system, consisted of a vacuum pump, a water supply reserviour and tube and a mini space heater, could prevent the PAHs adsorption cartridge cracks occurred in the previous collector and effectively adsorb PAHs. The novel dry and wet deposition collector showed a good adsorption, desorption, and recovery rates of PAHs. By reducing the number of pumps used and employing polypyopylene (PP) instead of teflon as a material of collection funnel, the total construction costs were much reduced as compared with the previous dry and wet deposition collectors.

• Computers and Concrete
• /
• v.32 no.4
• /
• pp.373-381
• /
• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Food Science of Animal Resources
• /
• v.30 no.6
• /
• pp.946-950
• /
• 2010

This study evaluated the antimicrobial effects of meat processing-related organic acids on Burkholderia thailandensis (Burkholderia pseudomallei surrogate) with different water activities. B. thailandensis KACC12027 (4 log CFU/mL) was inoculated in microwell plates containing tryptic soy broth pH-adjusted to 4, 5, 6, and 7 with ascorbic acid, citric acid, and lactic acid and with water activities adjusted to 0.94, 0.96, 0.98, and 1.0 with NaCl, followed by incubation at $35^{\circ}C$ for 30 h. The optical density (OD) of the samples was measured at 0, 3, 6, 12, 24, and 30 h at 595 nm to estimate the growth of B. thailandensis. Growth of B. thailandensis was observed only at water activity of 1.0. In general, more bacterial growth (p<0.05) was observed at pH 6 than at pH 7, and the antimicrobial effects of the organic acids on B. thailandensis were in the following order: Ascorbic acid > lactic acid > citric acid after incubation at $35^{\circ}C$ for 30 h. These results indicate that organic acids in meat processing-related formulations should be useful in decreasing the risk related to an emerging high risk agent (B. pseudomallei).