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Träfflista för sökning "WFRF:(Rochman Dimitri) srt2:(2020-2022)"

Sökning: WFRF:(Rochman Dimitri) > (2020-2022)

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1.
  • Alhassan, Erwin, et al. (författare)
  • Iterative Bayesian Monte Carlo for nuclear data evaluation
  • 2022
  • Ingår i: NUCLEAR SCIENCE AND TECHNIQUES. - : Springer Nature. - 1001-8042 .- 2210-3147. ; 33:4
  • Tidskriftsartikel (refereegranskat)abstract
    • In this work, we explore the use of an iterative Bayesian Monte Carlo (iBMC) method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library (TENDL) framework. The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data. The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code. The `best' model set with its parameter set was obtained by comparing model calculations with selected experimental data. Three experimental data types were used: (1) reaction cross sections, (2) residual production cross sections, and (3) the elastic angular distributions. To improve our fit to experimental data, we update our 'best' parameter set-the file that maximizes the likelihood function-in an iterative fashion. Convergence was determined by monitoring the evolution of the maximum likelihood estimate (MLE) values and was considered reached when the relative change in the MLE for the last two iterations was within 5%. Once the final 'best' file is identified, we infer parameter uncertainties and covariance information to this file by varying model parameters around this file. In this way, we ensured that the parameter distributions are centered on our evaluation. The proposed method was applied to the evaluation of p+ Co-59 between 1 and 100 MeV. Finally, the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2019, JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.
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2.
  • Fischer, Ulrich, et al. (författare)
  • Nuclear data activities of the EUROfusion consortium
  • 2020
  • Ingår i: ND 2019. - : EDP Sciences. - 9782759891061
  • Konferensbidrag (refereegranskat)abstract
    • The activities of the EUROfusion consortiums on the development of high quality nuclear data for fusion applications are presented. The activities, implemented in the Power Plant Physics and Technology (PPPT) programme of EUROfusion, include nuclear data evaluations for neutron and deuteron induced reactions and the production of related data libraries which satisfy the needs for nuclear analyses of the DEMO fusion power plant and the IFMIF-DONES neutron source. The activities are closely linked to the JEFF initiative of the NEA Data Bank. The evaluation work is complemented by extensive benchmark, sensitivity and uncertainty analyses to check the performance of the evaluated cross-section data and libraries against integral experiments.
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3.
  • Jansson, Peter, et al. (författare)
  • Blind Benchmark Exercise for Spent Nuclear Fuel Decay Heat
  • 2022
  • Ingår i: Nuclear science and engineering. - : Informa UK Limited. - 0029-5639 .- 1943-748X. ; 196:9, s. 1125-1145
  • Tidskriftsartikel (refereegranskat)abstract
    • The decay heat rate of five spent nuclear fuel assemblies of the pressurized water reactor type were measured by calorimetry at the interim storage for spent nuclear fuel in Sweden. Calculations of the decay heat rate of the five assemblies were performed by 20 organizations using different codes and nuclear data libraries resulting in 31 results for each assembly, spanning most of the current state-of-the-art practice. The calculations were based on a selected subset of information, such as reactor operating history and fuel assembly properties. The relative difference between the measured and average calculated decay heat rate ranged from 0.6% to 3.3% for the five assemblies. The standard deviation of these relative differences ranged from 1.9% to 2.4%.
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4.
  • Siefman, Daniel, et al. (författare)
  • Data assimilation of post-irradiation examination data for fission yields from GEF
  • 2020
  • Ingår i: EPJ NUCLEAR SCIENCES & TECHNOLOGIES. - : EDP SCIENCES S A. - 2491-9292. ; 6
  • Tidskriftsartikel (refereegranskat)abstract
    • Nuclear data, especially fission yields, create uncertainties in the predicted concentrations of fission products in spent fuel which can exceed engineering target accuracies. Herein, we present a new framework that extends data assimilation methods to burnup simulations by using post-irradiation examination experiments. The adjusted fission yields lowered the bias and reduced the uncertainty of the simulations. Our approach adjusts the model parameters of the code GEF. We compare the BFMC and MOCABA approaches to data assimilation, focusing especially on the effects of the non-normality of GEF's fission yields. In the application that we present, the best data assimilation framework decreased the average bias of the simulations from 26% to 14%. The average relative standard deviation decreased from 21% to 14%. The GEF fission yields after data assimilation agreed better with those in JEFF3.3. For Pu-239 thermal fission, the average relative difference from JEFF3.3 was 16% before data assimilation and after it was 12%. For the standard deviations of the fission yields, GEF's were 100% larger than JEFF3.3's before data assimilation and after were only 4% larger. The inconsistency of the integral data had an important effect on MOCABA, as shown with the Marginal Likelihood Optimization method. When the method was not applied, MOCABA's adjusted fission yields worsened the bias of the simulations by 30%. BFMC showed that it inherently accounted for this inconsistency. Applying Marginal Likelihood Optimization with BFMC gave a 2% lower bias compared to not applying it, but the results were more poorly converged.
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5.
  • Solans, Virginie, et al. (författare)
  • Optimisation of used nuclear fuel canister loading using a neural network and genetic algorithm
  • 2021
  • Ingår i: Neural Computing & Applications. - : Springer Nature. - 0941-0643 .- 1433-3058. ; 33:23, s. 16627-16639
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents an approach for the optimisation of geological disposal canister loadings, combining high resolution simulations of used nuclear fuel characteristics with an articial neural network and a genetic algorithm. The used nuclear fuels (produced in an open fuel cycle without reprocessing) considered in this work come from a Swiss Pressurised Water Reactor, taking into account their realistic lifetime in the reactor core and cooling periods, up to their disposal in the final geological repository. The case of 212 representative used nuclear fuel assemblies is analysed, assuming a loading of 4 fuel assemblies per canister, and optimizing two safety parameters: the fuel decay heat (DH) and the canister effective neutron multiplication factor keff. In the present approach, a neural network is trained as a surrogate model to evaluate the keff value to substitute the time-consuming-code Monte Carlo transport & depletion SERPENT for specific canister loading calculations. A genetic algorithm is then developed to optimise simultaneously the canister keff and DH values. The keff computed during the optimisation algorithm is using the previously developed artificial neural network. The optimisation algorithm allows (1) to minimize the number of canisters, given assumed limits for both DH and keff quantities and (2) to minimize DH and keff differences among canisters. This study represents a proof-of-principle of the neural network and genetic algorithm capabilities, and will be applied in the future to a larger number of cases.
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