• Preventive Nutrition and Food Science
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• 제15권2호
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• pp.130-136
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• 2010

Norovirus (NV) is becoming a major cause of foodborne illness in many countries. At present, very little is known about the survival of NV in the environment or the disinfection procedures needed to remove NV from contaminated surfaces. Feline calicivirus (FCV, $1{\times}10^{6.75}\;TCID_{50}/mL$) was used as a surrogate model for NV to investigate the effectiveness of sanitizing treatments for the viruses attached to food and food contact surfaces. Ammonium chloride (2%), organic acids (3000 ppm), and ethanol (70%) were most effective, providing $4\;log_{10}$ (99.99%) reductions in FCV titers on food or food contact surfaces. The disinfection efficacies of most agents on ceramic and glass surfaces were greater than stainless steel. The results from this study can be applied in the food industry to reduce NV-associated foodborne illnesses.

• 대한기계학회논문집B
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• 제33권1호
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• pp.46-52
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• 2009

The oxidation of surrogate mixtures for gasoline fuel was studied numerically in perfectly stirred reactor(PSR) to develope the needed detailed reaction mechanism. The reaction mechanism was assembled with the mechanisms for the oxidation of iso-octane or kerosene. It was shown that the reaction model predicted reasonably well the concentration profiles of fuel and major species reported in the literature. As the addition of kerosene into iso-octane as fuel was increased, the concentrations of $C_2H_2$ and benzene became high. Especially benzene known as a carcinogen appeared at a very high concentration in the flue gases.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2001년도 가을 학술발표논문집 Vol.28 No.2 (1)
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• pp.208-210
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• 2001

S-S-M(Server-Surrogate PC-Mobile Client)과 같은 3-계층 구조에서 이동 클라이언트를 이용한 공간 데이터 변경을 위해 대리트랜잭션 모델이 소개되었다. 이 모델에서는 이동 클라이언트의 공간 데이터변경을 위한 트랜잭션간의 동시성 제어를 위하여 전통적인 낙관적 병행 기법인 확인 기법을 확장하였다. 분산 환경에서는 참여자가 완료를 요청하면 조정자는 데이터를 공유하는 모든 참여자에게 완료 또는 취소 여부를 물어 최종 완료를 수행하였으나S-S-M 환경에서는 이동 클라이언트가 서버와의 접속을 해제하고 데이터를 변경한다. 따라서, 본 논문은 이동 클라이언트의 변경 완료 요청을 대리 PC를 통해 서버로 전달하고, 서버는 해당 트랜잭션의 충돌 여부를 검증하여 완료 또는 취소하는 완료 규약을 제시하고 이를 검증하기 위해 설계 및 구현한다.

• Steel and Composite Structures
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• 제44권2호
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• pp.211-224
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• 2022

Multi-objective optimization was conducted to obtain the optimal configuration of a composite sandwich structure with honeycomb-foam hybrid core subjected to underwater shock waves, which can fulfill the demand for light weight and energy efficient design of structures against underwater blast. Effects of structural parameters on the dynamic response of the sandwich structures subjected to underwater shock waves were analyzed numerically, from which the correlations of different parameters to the dynamic response were determined. Multi-objective optimization of the structure subjected to underwater shock waves of which the initial pressure is 30 MPa was conducted based on surrogate modelling method and genetic algorithm. Moreover, optimization results of the sandwich structure subjected to underwater shock waves with different initial pressures were compared. The research can guide the optimal design of composite sandwich structures subjected to underwater shock waves.

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• 항공우주시스템공학회지
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• 제18권2호
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• pp.104-116
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• 2024

인공위성은 발사체 모델과 연성하중해석을 수행하여 설계를 최종 검증하게 된다. 연성하중해석 결과의 정확도를 높이기 위해서는 유한요소모델 정확도가 매우 중요하며, 이를 위해 모델 보정은 필수적이다. 일반적으로 모델 보정은 재료 물성치와 두께 등을 하나씩 바꿔가며 수행하게 되는데, 이는 매우 많은 시간과 비용이 소요된다. 따라서 본 논문에서는 최적화 기법을 이용하여 탑재체 유한요소모델의 보정작업을 보다 효율적으로 수행하였다. 분산분석을 통해 중요 변수를 선정하고, 크리깅 대체 모델을 이용하여 해석과 최적화에 필요한 시간과 비용을 절감하였다. 본 논문에서 제안한 보정 방법은 진동 시험 결과만 있으면 적용할 수 있으며, 수치적인 계산 비용과 소요 시간을 대폭 줄일 수 있다는 점에서 효율성 측면에서 큰 장점이 있다.

• International Journal of Aeronautical and Space Sciences
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• 제17권1호
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• pp.29-36
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• 2016

Tensairity girder is a light weight inflatable fabric structural concept which can be used in road emergency transportation. It uses low pressure air to stabilize compression elements against buckling. With the purpose of obtaining the comprehensive target of minimum deflection and weight under ultimate load, the cross-section and the inner pressure of tensairity girder was optimized in this paper. The Variable Complexity Modeling (VCM) method was used in this paper combining the Kriging approximate method with the Finite Element Analysis (FEA) method, which was implemented by ABAQUS. In the Kriging method, the sample points of the surrogate model were outlined by Design of Experiment (DOE) technique based on Optimal Latin Hypercube. The optimization framework was constructed in iSIGHT with a global optimization method, Multi-Island Genetic Algorithm (MIGA), followed by a local optimization method, Sequential Quadratic Program (SQP). The result of the optimization gives a prominent conceptual design of the tensairity girder, which approves the solution architecture of VCM is feasible and efficient. Furthermore, a useful trend of sensitivity between optimization variables and responses was performed to guide future design. It was proved that the inner pressure is the key parameter to balance the maximum Von Mises stress and deflection on tensairity girder, and the parameters of cross section impact the mass of tensairity girder obviously.

• Nuclear Engineering and Technology
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• 제49권2호
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• pp.434-441
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• 2017

Containment venting is one of several essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to venting operations, from a simulation-based perspective, using an integrated severe accident code, THALES2/KICHE. The effectiveness of the containment-venting strategies needs to be verified via numerical simulations based on various settings of the venting conditions. The number of iterations, however, needs to be controlled to avoid cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using a Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. Compared with the case of pure random searches, the number of code queries is largely reduced for the optimum finding. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.

• Structural Engineering and Mechanics
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• 제50권5호
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• pp.653-664
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• 2014

Wind fragility analysis provides a quantitative instrument for delineating the safety performance of civil structures under hazardous wind loading conditions such as cyclones and tornados. It has attracted and would be expected to continue to attract intensive research spotlight particularly in the nowadays worldwide context of adapting to the changing climate. One of the challenges encumbering efficacious assessment of the safety performance of existing civil structures is the possible incompleteness of the structural appraisal data. Addressing the issue of the data missingness, the study presented in this paper forms a first attempt to investigate the feasibility of using the expectation-maximization (EM) algorithm and Bayesian techniques to predict the wind fragilities of existing civil structures. Numerical examples of typical linear or hysteretic shear frames are introduced with the wind loads derived from a widely used power spectral density function. Specifically, the application of the maximum a posteriori estimates of the distribution parameters for the story stiffness is examined, and a surrogate model is developed and applied to facilitate the nonlinear response computation when studying the fragilities of the hysteretic shear frame involved.

• Wind and Structures
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• 제34권4호
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• pp.321-334
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• 2022

Irregularity in plan shape is very common for any type of building as it enhances better air ventilation for the inhabitants. Systematic opening at the middle of the facades makes the appearance of the building plan as a butterfly one. The primary focus of this study is to forecast the force, moment and torsional coefficient of a butterfly plan shaped tall building. Initially, Computational Fluid Dynamics (CFD) study is done on the building model based on Reynolds averaged Navier Stokes (RANS) k-epsilon turbulence model. Fifty random cases of irregularity and angle of attack (AOA) are selected, and the results from these cases are utilised for developing the surrogate models. Parametric equations are predicted for all these aerodynamic coefficients, and the training of these outcomes are also done for developing Artificial Neural Networks (ANN). After achieving the target acceptance criteria, the observed results are compared with the primary CFD data. Both parametric equations and ANN matched very well with the obtained data. The results are further utilised for discussing the effects of irregularity on the most critical wind condition.