Isotopic prediction calculation methodologies: application to Vandellos-II Reactor cycles 7-11

Determining as accurate as possible spent nuclear fuel isotopic content is gaining importance due to its safety and economic implications. Since nowadays higher burn ups are achievable through increasing initial enrichments, more efficient burn up strategies within the reactor cores and the extension of the irradiation periods, establishing and improving computation methodologies is mandatory in order to carry out reliable criticality and isotopic prediction calculations. Several codes (WIMSD5, SERPENT 1.1.7, SCALE 6.0, MONTEBURNS 2.0 and MCNP-ACAB) and methodologies are tested here and compared to consolidated benchmarks (OECD/NEA pin cell moderated with light water) with the purpose of validating them and reviewing the state of the isotopic prediction capabilities. These preliminary comparisons will suggest what can be generally expected of these codes when applied to real problems. In the present paper, SCALE 6.0 and MONTEBURNS 2.0 are used to model the same reported geometries, material compositions and burn up history of the Spanish Van de llós II reactor cycles 7-11 and to reproduce measured isotopies after irradiation and decay times. We analyze comparisons between measurements and each code results for several grades of geometrical modelization detail, using different libraries and cross-section treatment methodologies. The power and flux normalization method implemented in MONTEBURNS 2.0 is discussed and a new normalization strategy is developed to deal with the selected and similar problems, further options are included to reproduce temperature distributions of the materials within the fuel assemblies and it is introduced a new code to automate series of simulations and manage material information between them. In order to have a realistic confidence level in the prediction of spent fuel isotopic content, we have estimated uncertainties using our MCNP-ACAB system. This depletion code, which combines the neutron transport code MCNP and the inventory code ACAB, propagates the uncertainties in the nuclide inventory assessing the potential impact of uncertainties in the basic nuclear data: cross-section, decay data and fission yields

Extended Triple-Junction Solar Cell 3D Distributed Model: Application to Chromatic Aberration-Related Losses

An extended 3D distributed model based on distributed circuit units for the simulation of triple‐junction solar cells under realistic conditions for the light distribution has been developed. A special emphasis has been put in the capability of the model to accurately account for current mismatch and chromatic aberration effects. This model has been validated, as shown by the good agreement between experimental and simulation results, for different light spot characteristics including spectral mismatch and irradiance non‐uniformities. This model is then used for the prediction of the performance of a triple‐junction solar cell for a light spot corresponding to a real optical architecture in order to illustrate its suitability in assisting concentrator system analysis and design process.

Realistic performance prediction in nanostructured solar cells as a function of nanostructure dimensionality and density

The behavior of quantum dot, quantum wire, and quantum well InAs/GaAs solar cells is studied with a very simplified model based on experimental results in order to assess their performance as a function of the low bandgap material volume fraction fLOW. The efficiency of structured devices is found to exceed the efficiency of a non-structured GaAs cell, in particular under concentration, when fLOW is high; this condition is easier to achieve with quantum wells. If three different quasi Fermi levels appear with quantum dots the efficiency can be much higher.

Building intuition of iron evolution during solar cell processing through analysis of different process models

An important aspect of Process Simulators for photovoltaics is prediction of defect evolution during device fabrication. Over the last twenty years, these tools have accelerated process optimization, and several Process Simulators for iron, a ubiquitous and deleterious impurity in silicon, have been developed. The diversity of these tools can make it difficult to build intuition about the physics governing iron behavior during processing. Thus, in one unified software environment and using self-consistent terminology, we combine and describe three of these Simulators. We vary structural defect distribution and iron precipitation equations to create eight distinct Models, which we then use to simulate different stages of processing. We find that the structural defect distribution influences the final interstitial iron concentration ([Fe-i]) more strongly than the iron precipitation equations. We identify two regimes of iron behavior: (1) diffusivity-limited, in which iron evolution is kinetically limited and bulk [Fe-i] predictions can vary by an order of magnitude or more, and (2) solubility-limited, in which iron evolution is near thermodynamic equilibrium and the Models yield similar results. This rigorous analysis provides new intuition that can inform Process Simulation, material, and process development, and it enables scientists and engineers to choose an appropriate level of Model complexity based on wafer type and quality, processing conditions, and available computation time.