**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Person# Dimitri Rochman

Official source

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related units

Loading

Courses taught by this person

Loading

Related research domains

Loading

Related publications

Loading

People doing similar research

Loading

Courses taught by this person (1)

Related publications (15)

People doing similar research (132)

PHYS-461: Nuclear interaction : from reactors to stars

This course will present an overview of the nuclear interactions for neutrons on nuclei below a few hundreds of MeV. The aspect of so-called "nuclear data" will be presented from the perspective of experiments, compilation, calculation, evaluation, processing and applications.

Loading

Loading

Loading

Related research domains (5)

Nuclear fuel is material used in nuclear power stations to produce heat to power turbines. Heat is created when nuclear fuel undergoes nuclear fission.
Most nuclear fuels contain heavy fissile act

Nuclear data represents measured (or evaluated) probabilities of various physical interactions involving the nuclei of atoms. It is used to understand the nature of such interactions by providing the

Spent nuclear fuel, occasionally called used nuclear fuel, is nuclear fuel that has been irradiated in a nuclear reactor (usually at a nuclear power plant). It is no longer useful in sustaining a nuc

Related units (1)

Stefano Caruso, Andreas Pautz, Dimitri Rochman

Decay heat calculations of spent nuclear fuel (SNF) using Polaris and ORIGEN codes in the SCALE code sys-tem, and CASMO5 code, are validated using measurements from the Clab and GE-Morris facilities. Multiple hypothesis testing, relying on permutations and bootstrapping, is conducted to simultaneously analyze the significance of differences between calculations and measurements - namely the biases. The biases are small for the Clab data, and larger and have more variance for the GE-Morris data. Then, sta-tistical tests are applied separately based on the measurement laboratory and the SNF origin. The calcu-lations on the Clab PWRs show to be systematically different from the measurements. The remaining benchmarks show to be insignificantly different from the measurements. The benchmarks are useful to validate decay heat calculations of PWR and BWR fuel assemblies at various cooling times. Nevertheless, the choice of the data and the codes could affect the significance of the obtained biases. (c) 2021 Published by Elsevier Ltd.

, , ,

, , ,

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 k (eff). In the present approach, a neural network is trained as a surrogate model to evaluate the k eff 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 k (eff) and DH values. The k (eff) 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 k (eff) quantities and (2) to minimize DH and k (eff) 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.