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The present article describes the preliminary design studies for PETALE (Programme d’Etude en Transmission de l’Acier Lourd et ses Eléments), an oncoming experimental program in the CROCUS reactor. Within the framework of the Venus-Eole-Proteus collaboration, PETALE continues the nuclear data validation efforts required for modeling GEN-III pressurized water reactors with heavy steel reflectors. The inelastic scattering cross sections at around 1 MeV of iron-56, as well as nickel and chromium isotopes, will be studied separately. The water reflector will be replaced successively by sheets of stainless steel alloy and pure metals—iron, nickel, and chromium. Data will be extracted from two sources: the measured neutron flux attenuation using adequate dosimetry and possibly fission chambers in the metal reflector and from the criticality effects of these reflectors. PETALE will also be used with nuclear data adjustment methods because, as a separated and elemental integral experiment, it allows the limiting of compensation effects in the nuclear data adjustments. A parametric study has been carried out with MCNPX for assessing the optimal configuration and the feasibility of the experiments. This study is the first step toward optimizing the global sensitivity of the experiments to the reactions in the energy range of interest, thus assessing the measurements’ target uncertainties and preparing further use of the program results.

Developments in data assimilation theory allow to adjust integral parameters and cross sections with stochastic sampling. This work investigates how two stochastic methods, MOCABA and BMC, perform relative to a sensitivity-based methodology called GLLS. Stochastic data assimilation can treat integral parameters that behave non-linearly with respect to nuclear data perturbations, which would be an advantage over GLLS. Additionally, BMC is compatible with integral parameters and nuclear data that have non-Gaussian distributions. In this work, MOCABA and BMC are compared to GLLS for a simple test case: JEZEBEL-Pu239 simulated with Serpent2. The three methods show good agreement between the mean values and uncertainties of their posterior calculated values and nuclear data. The observed discrepancies are not statistically significant with a sample size of 10000. BMC posterior calculated values and nuclear data have larger uncertainties than MOCABA's at equivalent sample sizes.

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.

Surfactants offer a tunable approach for modulating the exposed surface area of a nanoparticle. They further present a scalable and cost-effective means for suspending single-walled carbon nanotubes (SWCNTs), which have demonstrated practical use as fluorescence sensors. Though surfactant suspensions show record quantum yields for SWCNTs in aqueous solutions, they lack the selectivity that is vital for optical sensing. We present a new method for controlling the selectivity of optical SWCNT sensors through colloidal templating of the exposed surface area. Colloidal nanotube sensors were obtained using various concentrations of sodium cholate, and their performances were compared to DNA-SWCNT optical sensors. Sensor responses were recorded against a library of bioanalytes, including neurotransmitters, amino acids, and sugars. We report an intensity response towards dopamine and serotonin for all sodium cholate-suspended SWCNT concentrations. We further identify a selective, 14.1 nm and 10.3 nm wavelength red-shifting response to serotonin for SWCNTs suspended in 1.5 and 0.5 mM sodium cholate, respectively. Through controlled, adsorption-based tuning of the nanotube surface, this study demonstrates the applicability of sub-critical colloidal suspensions to achieve selectivities exceed- ing those previously reported for DNA-SWCNT sensors.