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The JET neutron camera is used to monitor a 2D profile of the neutron emission from the plasma, using 19 sightlines with plastic scintillators suited for measuring neutrons from the D + T → n + ⁴He (DT) reaction. This paper describes an independent, first-principles physics method for estimating the volume integrated DT neutron yield with the neutron camera. This is performed for a selection of shots from the two recent DT campaigns at JET, the DTE2 and DTE3 JET campaigns. It covers the data reduction methods from a light yield calibration of the scintillators to treatment of pile-up, which is prevalent during high yield DT experiments. Several models of the camera geometry are used to account for scattering and transmission effects in the neutron transport. The neutron yield is estimated using models of the neutron emission profile, which are fitted to measurement data. The neutron yield estimates from this method are compared to corresponding estimates from the JET fission chambers. Our estimates with the neutron camera are on average 34% and 41% higher than the fission chamber estimates for DTE2 and DTE3, respectively. The reasons for the discrepancies between the two systems are presently unknown and prompt further investigation. In this paper, we detail the methods used to reach the neutron yield estimate from the neutron camera, along with their strengths, weaknesses, and potential points of failure. This method is an evolution of an earlier work that estimated the deuterium–deuterium neutron yield using similar methods.

Data from the magnetic proton recoil spectrometer (MPRu) high-resolution neutron spectrometer has been used to estimate two fusion plasma quantities, the plasma rotation and the thermonuclear neutron emission. This paper presents a framework for this method and the current results for a selection of plasma discharges from the JET DTE3 campaign. Data collection during DTE3 was preceded by a hardware upgrade in the form of new digitizers, and an update to the data reduction software. The method involves simulations with the TRANSP code, the DRESS code, and a detector response function. The plasma rotation and thermonuclear neutron emission are estimated through a fit of the simulated detector response to the MPRu measurement data. This exploratory analysis studied a selection of DTE3 discharges with high neutron rates. It was found that the rotation was typically in the range 1.5 x 10⁵ to 2.5 x 10⁵ rad s⁻¹ and that neutrons from thermonuclear reactions constitute about 10%–30% of the total neutron emission. For the studied discharges, the neutron emissivity is strongly weighted to the plasma core. Due to the MPRu line of sight passing through the plasma core twice, the plasma rotation and the thermonuclear neutron emission are estimated in the core. This method has the potential to provide complementary data points to other diagnostics.

This data set contains raw timestamped list-mode data obtained using an array of HPGe detectors for the purpose of testing coincidence spectrometry in the context of measurement on air filter samples. Data from one air-sampling station managed by the Swedish Defense Research Agency (FOI) is made available. This air-sampling station is located in Umeå, Sweden (Latitude 63.85°N, Longitude 20.34°E, 46 m above sea level). In addition to the air filter sample, data from a blank filter as well as a filter that was spiked with a known activity of radionuclides is made available in this data set. The detector setup used to collect this data set is made up of five individual HPGe detectors, with one of them surrounded by an active BGO Compton suppression shield. The data set provides a testing ground for investigating the use of multi-fold coincidence spectrometry as a tool to lower the minimum detectable activity of anthropogenic radionuclides in air filter samples. Access to this data set allows researchers to explore and evaluate analysis methodologies for coincidence γ-ray spectrometry on real samples. 

The Nuclear data Evaluation Pipeline of Uppsala University (NEPU) has been developed to perform reproducible data driven nuclear data evaluations. Until now, the pipeline has been used to perform evaluation above the resonance regions for structural materials. Cross-sections for neutron reactions of neutron energies in the range 100 keV to 5-10 MeV in structural materials fluctuate due to resonances in the excited states of the compound system.In this work, we describe the changes we have made to NEPU to allow it also to include energy ranges of cross-sections for neutron energies ranging from 1 to 5 MeV for Fe-56. NEPU uses TALYS. TALYS relies on the assumption that resonance fluctuations are averaged out. This assumption can be described as a model defect in the intermediate incident neutron energy region, where a large number of overlapping resonances causes the cross-section to fluctuate around the energy averaged value. We show how we have used Gaussian process regression to describe the model defects of TALYS to include the cross-section fluctuations in our evaluation pipeline.In addition to co-variance matrixes, NEPU produces random files. Each set of random files incorporate information of for example cross-sections and angular dependencies of the various reaction channels. The distribution of the sets of random files is capable of reflecting more complex covariances than the pure covariance matrixes. In addition, the random files can be used in the Total Monte Carlo technique.We will demonstrate the results of this work by showing how the models agree with nuclear data from the EXFOR database, after the model defect correction has been applied.

The objective of thermonuclear fusion consists of producing electricity from the coalescence of light nuclei in high temperature plasmas. The most promising route to fusion envisages the confinement of such plasmas with magnetic fields, whose most studied configuration is the tokamak. Disruptions are catastrophic collapses affecting all tokamak devices and one of the main potential showstoppers on the route to a commercial reactor. In this work we report how, deploying innovative analysis methods on thousands of JET experiments covering the isotopic compositions from hydrogen to full tritium and including the major D-T campaign, the nature of the various forms of collapse is investigated in all phases of the discharges. An original approach to proximity detection has been developed, which allows determining both the probability of and the time interval remaining before an incoming disruption, with adaptive, from scratch, real time compatible techniques. The results indicate that physics based prediction and control tools can be developed, to deploy realistic strategies of disruption avoidance and prevention, meeting the requirements of the next generation of devices. Confining plasma and managing disruptions in tokamak devices is a challenge. Here the authors demonstrate a method predicting and possibly preventing disruptions and macroscopic instabilities in tokamak plasma using data from JET.

In this paper, we discuss the development of a nuclear data evaluation pipeline, based around the TALYS code system. The pipeline focuses on the evaluation of the fast neutron energy range, above the resolved resonances. A strong focus in development lies on automation and reproducibility, as well as the efficient use of large-scale computational infrastructure, to enable rapid testing of new algorithms and modified assumptions. Several novel concepts for nuclear data evaluation methodology are implemented. A particular problem in evaluating the neutron-induced reaction cross-section using TALYS, relates to the intermediate energy range. While TALYS only predicts the smooth energy-averaged cross-section, experiments reveal unresolved resonance-like structures. In this paper, we explore ways to treat this type of model defect using heteroscedastic Gaussian processes to automatically determine the distribution of experimental data around an energy-averaged cross-section curve.

The Total Monte Carlo (TMC) technique has proven to be a powerful tool to propagate uncertainties in nuclear data to the uncertainty in macroscopic quantities, such as neutron fluxes at detector positions and the criticality of reactor cores. Nuclear data uncertainties can be used to create self-consistent sets of cross-sections. Each set contains files generated by variations of nuclear model parameters to properly fit the model to the nuclear data, accounting for their uncertainty. These files are called random files. The random files reflect the covariances of the nuclear data due to the uncertainties of the nuclear physics model parameters. TMC uses particle transport codes, such as MCNP, to transport particles through arbitrarily complex geometries. Each set of random files is used in a separate transport code run. This allows for the propagation of uncertainties in nuclear data, which otherwise could be hard to account for in the transport codes. However, particle transport techniques are well-known to be computationally expensive. The Half Monte Carlo (HMC) technique uses the random files of the TMC technique but does not rely on transport codes to propagate the uncertainties of nuclear data to the uncertainty of the sought macroscopic quantity. Instead, it uses pre-calculated sensitivity matrices to calculate the difference in a macroscopic quantity, given the difference of the random files relative to the best estimate of the nuclear data evaluation. In this work, we demonstrate how to use the HMC technique to calculate the uncertainty of macroscopic quantities in integral experiments for a set of random files relative to the best nuclear data evaluation. In this paper, we demonstrate how HMC can be used to incorporate integral experiments into an automated nuclear data evaluation. After applying the Bayesian Monte Carlo method in conjunction with the HMC technique and random files of uranium-235 from the TENDL library on the Godiva experiment, we conclude that the HMC technique gives similar results to that of the TMC technique: the mean value and the standard deviation of ∆keff is -6.30 pcm and 1220 pcm, respectively.

Postirradiation examination of nuclear fuel is routinely performed to characterize the important properties of current and future fuel. Gamma emission tomography is a proven noninvasive technique for this purpose. Among various measurement elements of the technique, a gamma-ray detector is an important element whose spectroscopic abilities and detection efficiency affect the overall results. Finding a combination of high detection efficiency and excellent energy resolution in a single detector is often a challenge. We have designed a novel planar segmented high-purity germanium detector that offers simultaneous measurement in six lines of sight with excellent energy resolution. The simultaneous detection ability enables faster data acquisition in a tomographic measurement, which may facilitate achieving higher spatial resolution. In this work, we have demonstrated the first use of the detector by performing a full tomographic measurement of mockup fuel rods. Two methods of detector data analysis were used to make spectra, and the images (tomograms) were reconstructed using the filtered back projection algorithm. The reconstructed images validate the successful use of the detector for tomographic measurement. The use of the detector for real fuel measurement is being planned and will be performed in the near future.