Browsing by Subject "CASCADES"

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  • Luchkina, Natalia V.; Huupponen, Johanna; Clarke, Vernon R. J.; Coleman, Sarah K.; Keinanen, Kari; Taira, Tomi; Lauri, Sari E. (2014)
  • Liu, Yi-Nan; Ahlgren, T.; Bukonte, L.; Nordlund, K.; Shu, Xiaolin; Yu, Yi; Li, Xiao-Chun; Lu, Guang-Hong (2013)
  • Henriksson, K. O. E. (2016)
    The number of point defects formed in spherical cementite and Cr23C6 inclusions embedded into ferrite (alpha-iron) has been studied and compared against cascades in pure versions of these materials (only ferrite, Fe3C, or Cr23C6 in a cell). Recoil energies between 100 eV and 3 keV and temperatures between 400 K and 1000 K were used. The overall tendency is that the number of point defects-such as antisites, vacancy and interstitials-increases with recoil energy and temperature. The radial distributions of defects indicate that the interface between inclusions and the host tend to amplify and restrict the defect formation to the inclusions themselves, when compared to cascades in pure ferrite and pure carbide cells. (C) 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (
  • Hamedani, Ali; Byggmästar, Jesper; Djurabekova, Flyura; Alahyarizadeh, G.; Ghaderi, R.; Minuchehr, A.; Nordlund, Kai (2021)
    Characterization of the primary damage is the starting point in describing and predicting the irradiation-induced damage in materials. So far, primary damage has been described by traditional interatomic potentials in molecular dynamics simulations. Here, we employ a Gaussian approximation machine-learning potential (GAP) to study the primary damage in silicon with close to ab initio precision level. We report detailed analysis of cascade simulations derived from our modified Si GAP, which has already shown its reliability for simulating radiation damage in silicon. Major differences in the picture of primary damage predicted by machine-learning potential compared to classical potentials are atomic mixing, defect state at the heat spike phase, defect clustering, and recrystallization rate. Atomic mixing is higher in the GAP description by a factor of two. GAP shows considerably higher number of coordination defects at the heat spike phase and the number of displaced atoms is noticeably greater in GAP. Surviving defects are dominantly isolated defects and small clusters, rather than large clusters, in GAP's prediction. The pattern by which the cascades are evolving is also different in GAP, having more expanded form compared to the locally compact form with classical potentials. Moreover, recovery of the generated defects at the heat spike phase take places with higher efficiency in GAP. We also provide the attributes of the new defect cluster that we had introduced in our previous study. A cluster of four defects, in which a central vacancy is surrounded by three split interstitials, where the surrounding atoms are all 4-folded bonded. The cluster shows higher occurrence in simulations with the GAP potential. The formation energy of the defect is 5.57 eV and it remains stable up to 700 K, at least for 30 ps. The Arrhenius equation predicts the lifetime of the cluster to be 0.0725 mu s at room temperature.
  • Levo, Emil; Granberg, Fredric; Nordlund, Kai; Djurabekova, Flyura (2021)
    Multiprincipally designed concentrated solid solution alloys, such as high entropy alloys (HEA) and equiatomic multi-component alloys (EAMC-alloys) have shown much promise for use as structural components in future nuclear energy production concepts. The irradiation tolerance in these novel alloys has been shown to be superior to that in more conventional metals used in current nuclear reactors. However, studies involving irradiation of HEAs and EAMC-alloys have usually been performed at room temperature. Hence, in this study the irradiation damage is investigated computationally in two different Ni-based EAMC-alloys and pure Ni at four different temperatures, ranging from 138 K to 800 K. The irradiation damage was studied by analyzing point defects, defect cluster sizes and dislocation networks in the materials. Dislocation loop mobility calculations were performed to help understanding the formation of different dislocation networks in the irradiated materials. Utilizing the knowledge of the depth distribution of damage, and using simulations of Rutherford backscattering in channeling conditions (RBS/c), we can relate our results to experimental data. The main findings are that the alloys have superior irradiation tolerance at all temperatures as compared to pure Ni, and that the damage is reduced in all materials with an increase in temperature.
  • Castin, N.; Dubinko, A.; Bonny, G.; Bakaev, A.; Likonen, J.; De Backer, A.; Sand, A.E.; Heinola, K.; Terentyev, D. (2019)
    The microstructure changes taking place in W under irradiation are governed by many factors, amongst which C impurities and their interactions with self-interstitial atoms (SIA). In this work, we specifically study this effect by conducting a dedicated 2-MeV self-ions irradiation experiment, at room temperature. Samples were irradiated up to 0.02, 0.15 and 1.2 dpa. Transmission electron microscopy (TEM) expectedly revealed a large density of SIA loops at all these doses. Surprisingly, however, the loop number density increased in a non-monotonous manner with the received dose. Performing chemical analysis with secondary ion spectroscopy measurements (SIMS), we find that our samples were likely contaminated by C injection during the irradiation. Employing an object kinetic Monte Carlo (OKMC) model for microstructure evolution, we demonstrate that the C injection is the likely factor explaining the evolution of loops number density. Our findings highlight the importance of the well-known issue of C injection during ion irradiation experiments, and demonstrate how OKMC models can help to rationalize this effect.
  • Pal, Ranjan; Psounis, Konstantinos; Crowcroft, Jon; Kelly, Frank; Hui, Pan; Tarkoma, Sasu; Kumar, Abhishek; Kelly, John; Chatterjee, Aritra; Golubchik, Leana; Sastry, Nishanth; Nag, Bodhibrata (2020)
    Service liability interconnections among globally networked IT- and IoT-driven service organizations create potential channels for cascading service disruptions worth billions of dollars, due to modern cyber-crimes such as DDoS, APT, and ransomware attacks. A natural question that arises in this context is: What is the likelihood of a cyber-blackout?, where the latter term is defined as the probability that all (or a major subset of) organizations in a service chain become dysfunctional in a certain manner due to a cyber-attack at some or all points in the chain. The answer to this question has major implications to risk management businesses such as cyber-insurance when it comes to designing policies by risk-averse insurers for providing coverage to clients in the aftermath of such catastrophic network events. In this article, we investigate this question in general as a function of service chain networks and different cyber-loss distribution types. We show somewhat surprisingly (and discuss the potential practical implications) that, following a cyber-attack, the effect of (a) a network interconnection topology and (b) a wide range of loss distributions on the probability of a cyber-blackout and the increase in total service-related monetary losses across all organizations are mostly very small. The primary rationale behind these results are attributed to degrees of heterogeneity in the revenue base among organizations and the Increasing Failure Rate property of popular (i.i.d/non-i.i.d) loss distributions, i.e., log-concave cyber-loss distributions. The result will enable risk-averse cyber-riskmanagers to safely infer the impact of cyber-attacks in a worst-case network and distribution oblivious setting.