Fukushima nuclear disaster | EPFL Graph Search

On 11 March 2011, a nuclear accident occurred at the Fukushima Daiichi Nuclear Power Plant in Ōkuma, Fukushima, Japan. The proximate cause of the disaster was the Tōhoku earthquake and tsunami, which remains the most powerful earthquake ever recorded in Japan. The earthquake triggered a powerful tsunami, with 13- to 14-meter-high waves damaging the nuclear power plant's emergency diesel generators, leading to a loss of electric power. The result was the most severe nuclear accident since the Chernobyl disaster in 1986, classified as level seven on the International Nuclear Event Scale (INES) after initially being classified as level five, and thus joining Chernobyl as the only other accident to receive such classification. While the 1957 explosion at the Mayak facility was the second worst by radioactivity released, the INES ranks incidents by impact on population, so Chernobyl (335,000 people evacuated) and Fukushima (154,000 evacuated) rank higher than the 10,000 evacuated from the

Impact of Insurance on Earthquake Risk Territories in Japan

Rémy Zgraggen

The subject of the present PhD thesis is the qualitative, interdisciplinary analysis of the impact of earthquake insurance on earthquake risk territories in Japan. On the one hand, the Japanese Earthquake Insurance System (JEI) will be analyzed; on the other hand, the liability insurance system for nuclear risks in Japan will be examined. The first research question (R1) focuses on the impact of insurance on earthquake risk territories, whereas the second research question (R2) concerns the adequacy and efficiency of earthquake insurance and the nuclear liability insurance system in Japan. These two research questions will be analyzed in the following two case studies: the Great Hanshin Earthquake in 1995 in the Kobe-Osaka area, and the Tohoku Earthquake, including the following nuclear disaster at Fukushima, in 2011 in the northern part of Japan. In the first case study, the functioning and influence of the JEI will be examined, whereas in the second case study, the liability insurance system for nuclear risks in Japan will be analyzed.

EPFL2015

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

The goal of the present PhD research is to establish a methodology of nuclear data uncertainty quantification (NDUQ) for MCNPX, the continuous-energy Monte-Carlo (M-C) code. The high fidelity (continuous-energy treatment and flexible geometry modelling) of MCNPX makes it the choice of routine criticality safety calculations at PSI/LRS, but also raises challenges for NDUQ by conventional sensitivity/uncertainty (S/U) methods. The methodology developed during this PhD research is fundamentally different from the conventional S/U approach: nuclear data are treated as random variables and sampled in accordance to presumed probability distributions. When sampled nuclear data are used in repeated model calculations, the output variance is attributed to the collective uncertainties of nuclear data. The NUSS (Nuclear data Uncertainty Stochastic Sampling) tool based on this sampling approach, is implemented to work with MCNPX's ACE format of nuclear data, which also gives NUSS compatibility with MCNP and SERPENT M-C codes. In contrast, multigroup uncertainties are used for the sampling of ACE-formatted pointwise-energy nuclear data in a groupwise manner due to the more limited quantity and quality of nuclear data uncertainties. Conveniently, the usage of multigroup nuclear data uncertainties allows consistent comparison between NUSS and other methods (both S/U and sampling-based) that employ the same nuclear data uncertainty format. The first stage of NUSS development focuses on applying simple random sampling algorithm for uncertainty quantification. The effect of combining multigroup and ACE format on the propagated nuclear data uncertainties is assessed. It is found that the number of energy groups has minor impact on the precision of the multiplication factor (k-eff) uncertainty as long as the group structure reflects the neutron flux spectrum. Successful verification of the NUSS tool for propagating nuclear data uncertainties through MCNPX and quantifying MCNPX output parameter uncertainties is obtained. The second stage of NUSS development is motivated by the need for an efficient sensitivity analysis methodology based on global sampling and coupled with MCNPX. For complex systems, the computing time for obtaining a breakdown of the total uncertainty contribution by individual inputs becomes prohibitive when many MCNPX runs are required. The capability of determining simultaneously the total uncertainty and individual nuclear data uncertainty contributions is thus researched and implemented into the NUSS-RF tool. It is based on the Random Balance Design algorithm and is validated by three mathematical test cases for both linear and nonlinear models and correlated inputs. NUSS-RF is then applied to demonstrate the efficient decomposition of total uncertainty by individual nuclear data. However an attempt to decompose total uncertainty into individual contributions using the conventional S/U method shows different decomposition results when the inputs are correlated. The investigation and findings of this PhD work are valuable because of the introduction of global sensitivity analysis into the existing repertoire of nuclear data uncertainty quantification methods. The NUSS tool is expected to be useful for expanding the types of MCNPX-related applications, such as an upgrade to the current PSI criticality safety assessment methodology for Swiss application, for which nuclear data uncertainty contributions can be quantified.

EPFL2015

Providing and Optimizing a Robotic Construction Plan for Rescue Operations

Hadi Ardiny, Francesco Mondada, Stefan John Witwicki

After a terrible disaster such as an earthquake or a nuclear accident, finding victims and isolating them from hazards are usually the first priorities for rescuers. As the security of rescuers and the stabilization of the environment are critical components of the first rescue phase, we assume that robots could be used to secure the environment by performing construction tasks, to stabilize large structures, and/or protect the victims. In this paper we suggest an approach consisting of using mobile robots to construct protective walls on a site affected by a nuclear disaster. Protective walls can help to block radiation from toxic sources and protect both victims and rescuers. On the other hand, the robot’s vulnerability to radiation restricts its freedom of movements into unsafe regions. Therefore, building protective walls needs a plan (construction plan) that involves three competing objectives: victim safety, rescuer safety, and robot safety. Weighting these factors is a societal choice, is not trivial, and impacts the whole system. In this paper, we provide and optimize the construction plan using a genetic algorithm based on three objectives. We analyze the construction plan performance with respect to execution time. We also analyze the trade-offs involved between these competing objectives in different environments with ranging physical complexity (e.g., a number of victims or sources).

2015

Sampling-Based Nuclear Data Uncertainty Quantification for Continuous Energy Monte Carlo Codes

Ting Zhu

EPFL2015