• Nuclear Engineering and Technology
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• v.53 no.7
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• pp.2151-2161
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• 2021

Nuclear data (ND) sensitivity and uncertainty (S/U) quantification in shielding applications is performed using deterministic and probabilistic approaches. In this paper the validation of the newly developed deterministic program package ASUSD (ADVANTG + SUSD3D) is presented. ASUSD was developed with the aim of automating the process of ND S/U while retaining the computational efficiency of the deterministic approach to ND S/U analysis. The paper includes a detailed description of each of the programs contained within ASUSD, the computational workflow and validation results. ASUSD was validated on two shielding benchmark experiments from the Shielding Integral Benchmark Archive and Database (SINBAD) - the fission relevant ASPIS Iron 88 experiment and the fusion relevant Frascati Neutron Generator (FNG) Helium Cooled Pebble Bed (HCPB) Test Blanket Module (TBM) mock-up experiment. The validation process was performed in two stages. Firstly, the Denovo discrete ordinates transport solver was validated as a standalone solver. Secondly, the ASUSD program package as a whole was validated as a ND S/U analysis tool. Both stages of the validation process yielded excellent results, with a maximum difference of 17% in final uncertainties due to ND between ASUSD and the stochastic ND S/U approach. Based on these results, ASUSD has proven to be a user friendly and computationally efficient tool for deterministic ND S/U analysis of shielding geometries.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1991.10a
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• pp.57-62
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• 1991

For the purpose of developing the method for efficiently calculating the design sensitivity and the reliability for the complicated structure such as ship structure, the probabilistic finite element method is introduced to formulate the deterministic design sensitivity analysis method and incorporated with the second moment reliability methods such as MVFOSM, AFOSM and SORM. Also, the probabilistic design sensitivity analysis needed in the reliability-based design is performed. The reliability analysis is carried out for the initial yielding failure, in which the derivative derived in the deterministic desin sensitivity is used. The present PFEM-based reliability method shows good agreement with Monte Carlo method in terms with the variance of response and the associated probability of failure even at the first or first few iteration steps. The probabilistic design sensitivity analysis evaluates explicitly the contribution of each random variable to probability of failure. Further, the reliability index variation can be easily predicted by the variation of the mean and the variance of the random variables.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.93-100
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• 2012

In this paper, the deterministic optimal design for the tail plane made of composite materials is conducted under the deterministic loading condition and compared with that of the metallic materials. Next, the reliability analysis with five random variables such as loading and material properties of unidirectional prepreg is conducted to examine the probability of failure for the deterministic optimal design results. The MATLAB programing is used for reliability analysis combined with FEA S/W(COMSOL) for structural analysis. The laminated composite is assumed to the equivalent orthotropic material using classical laminated plate theory. The response surface methodology and importance sampling technique are adopted to reduce computational cost with satisfying the accuracy in reliability analysis. As a result, structural weight of composite materials is lighter than that of metals in deterministic optimal design. However, the probability of failure for the deterministic optimal design of the tail plane structures is too high to be neglected. The sensitivity of each variable is also estimated using probabilistic sensitivity analysis to figure out which variables are sensitive to failure. The computational cost is considerably reduced when response surface methodology and importance sampling technique are used. The study of the computationally inexpensive method for reliability-based design optimization will be necessary in further work.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1234-1242
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• 2003

In this paper we present a simple and efficient robust optimal design formulation for MEMS structures and its application to a resonant-type micro probe. The basic idea is to use the gradient index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-effective and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation. This method, although shown for MEMS structures, may as well be easily applied to conventional mechanical structures where information on uncertainties is lacking but robustness is highly important.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1254-1261
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• 2003

In this paper we present a simple and efficient robust optimal design formulation and its application to a resonant-type micro probe. The basic idea is to use the Gradient Index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-efficient and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation.

• Nuclear Engineering and Technology
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• v.47 no.3
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• pp.306-314
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• 2015

This paper examines the difference in the value of the nuclear fuel cycle cost calculated by the deterministic and probabilistic methods on the basis of an equilibrium model. Calculating using the deterministic method, the direct disposal cost and Pyro-SFR (sodium-cooled fast reactor) nuclear fuel cycle cost, including the reactor cost, were found to be 66.41 mills/kWh and 77.82 mills/kWh, respectively (1 mill = one thousand of a dollar, i.e., $10^{-3}$ $). This is because the cost of SFR is considerably expensive. Calculating again using the probabilistic method, however, the direct disposal cost and Pyro-SFR nuclear fuel cycle cost, excluding the reactor cost, were found be 7.47 mills/kWh and 6.40 mills/kWh, respectively, on the basis of the most likely value. This is because the nuclear fuel cycle cost is significantly affected by the standard deviation and the mean of the unit cost that includes uncertainty. Thus, it is judged that not only the deterministic method, but also the probabilistic method, would also be necessary to evaluate the nuclear fuel cycle cost. By analyzing the sensitivity of the unit cost in each phase of the nuclear fuel cycle, it was found that the uranium unit price is the most influential factor in determining nuclear fuel cycle costs.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.2
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• pp.5-20
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• 1987

This paper addresses sensitivity analysis for a deterministic multi-period production and inventory model. The model assumes a piecewise linear cost structure, but permits backlogging of unsatisfied demand. Our approach to sensitivity analysis here can be divided into two basic steps; (1) to find the optimal production policy through a forward dynamic programming algorithm similar to the backward version of Zangwill [1966] and (2) to apply the penalty network approach by the author [1986] in order to derive sensitivity ranges for various model parameters. Computational aspects are discussed and topics of further research are suggested.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.18-24
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• 1988

We consider sensitivity analysis sequencing problems, in which sequence of a finite set of expansion projects is sought to meet a deterministic demand projection in minimum discounted cost. In particular, by characterizing the underlying network structure, we find analytically the sensitivity range for a project cost such that the optimal sequencing policy remains unchanged for any value in the range. A numerical example is presented.

• Nuclear Engineering and Technology
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• v.31 no.3
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• pp.257-266
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• 1999

A simplified statistical methodology is developed in order to both reduce over-conservatism of deterministic methodologies employed for PWR fuel rod internal pressure (RIP) calculation and simplify the complicated calculation procedure of the widely used statistical methodology which employs the response surface method and Monte Carlo simulation. The simplified statistical methodology employs the system moment method with a deterministic approach in determining the maximum variance of RIP The maximum RIP variance is determined with the square sum of each maximum value of a mean RIP value times a RIP sensitivity factor for all input variables considered. This approach makes this simplified statistical methodology much more efficient in the routine reload core design analysis since it eliminates the numerous calculations required for the power history-dependent RIP variance determination. This simplified statistical methodology is shown to be more conservative in generating RIP distribution than the widely used statistical methodology. Comparison of the significances of each input variable to RIP indicates that fission gas release model is the most significant input variable.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1377-1382
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• 1990

In Multi-Layer Perceptron (MLP) which realizes continuous mappings, the output errors is directly affected by the weight errors which may be caused by the limited precision of digital or analog hardware in implementations. So, it is important to study the sensitivity due to the perturbation of connection weights between neurons. In this paper, we derive a sensitivity function to the statistical weight perturbations in MLP with differentiable activation functions. This sensitivity function can be regarded as an ensemble average of deterministic sensitivity measures due to the perturbations of weights. Hence, this sensitivity function can be used as the criteria for selecting weights with the minimum sensitivity among possible sets of connection weights in MLP. For the verification of the validity of the proposed sensitivity function, computer simulations have been performed and through the simulations we find good agreement between the theoretical and simulation results.