Abstract: The International Fusion Materials Irradiation Facility- DEMO Oriented Neutron Source (IFMIF-DONES) is a scientific infrastructure aimed to provide an intense neutron source for the qualification of materials to be used in future fusion power reactors. Its implementation is critical for the construction of the fusion DEMOnstration Power Plant (DEMO). IFMIF-DONES is a unique facility requiring a broad set of technologies. Although most of the necessary technologies have already been validated, there are still some aspects that introduce risks in the evolution of the project. In order to mitigate these risks, a consortium of companies, with the support of research centres and the funding of the CDTI (Centre for the Development of Industrial Technology and Innovation), has launched the DONES EVO Programme, which comprises six lines of research: center dot Improvement of signal transmission and integrity (planning and integration risks) center dot Optimisation of RF conditioning processes (planning and reliability risks) center dot Development of a reliable beam extraction device (reliability risks) center dot Development of technologies for the production of medical isotopes (reliability risks) center dot Improvement of critical parts of the lithium purification system (safety and reliability risks) center dot Validation of the manufacture of critical components with special materials (reliability risk). DONES EVO will focus on developing the appropriate response to the risks identified in the IFMIFDONES project through research and prototyping around the associated technologies.

Abstract: The production of strip sensors for the ATLAS Inner Tracker (ITk) started in 2021. Since then, a Quality Assurance (QA) program has been carried out continuously, by using specific test structures, in parallel to the Quality Control (QC) inspection of the sensors. The QA program consists of monitoring sensor-specific characteristics and the technological process variability, before and after the irradiation with gammas, neutrons, and protons. After two years, half of the full production volume has been reached and we present an analysis of the parameters measured as part of the QA process. The main devices used for QA purposes are miniature strip sensors, monitor diodes, and the ATLAS test chip, which contains several test structures. Such devices are tested by several sites across the collaboration depending on the type of samples (non-irradiated components or irradiated with protons, neutrons, or gammas). The parameters extracted from the tests are then uploaded to a database and analyzed by Python scripts. These parameters are mainly examined through histograms and timeevolution plots to obtain parameter distributions, production trends, and meaningful parameter-to-parameter correlations. The purpose of this analysis is to identify possible deviations in the fabrication or the sensor quality, changes in the behavior of the test equipment at different test sites, or possible variability in the irradiation processes. The conclusions extracted from the QA program have allowed test optimization, establishment of control limits for the parameters, and a better understanding of device properties and fabrication trends. In addition, any abnormal results prompt immediate feedback to a vendor.

Abstract: Geometry construction is a fundamental aspect of any radiation transport simulation, regardless of the Monte Carlo code being used. Typically, this process is tedious, time-consuming, and error-prone. The conventional approach involves defining geometries using mathematical objects or surfaces. However, this method comes with several limitations, especially when dealing with complex models, particularly those with organic shapes. Furthermore, since each code employs its own format and methodology for defining geometries, sharing and reproducing simulations among researchers becomes a challenging task. Consequently, many codes have implemented support for simulating over geometries constructed via Computer-Aided Design (CAD) tools. Unfortunately, this feature is lacking in penRed and other PENELOPE physics-based codes. Therefore, the objective of this work is to implement such support within the penRed framework. New version program summary Program Title: Parallel Engine for Radiation Energy Deposition (penRed) CPC Library link to program files: https://doi.org/10.17632/rkw6tvtngy.2 Developer's repository link: https://github.com/PenRed/PenRed Code Ocean capsule: https://codeocean.com/capsule/1041417/tree Licensing provisions: GNU Affero General Public License v3 Programming language: C++ standard 2011. Journal reference of previous version: V. Gimenez-Alventosa, V. Gimenez Gomez, S. Oliver, PenRed: An extensible and parallel Monte-Carlo framework for radiation transport based on PENELOPE, Computer Physics Communications 267 (2021) 108065. doi:https://doi.org/10.1016/j.cpc.2021.108065. Does the new version supersede the previous version?: Yes Reasons for the new version: Implements the capability to simulate on CAD constructed geometries, among many other features and fixes. Summary of revisions: All changes applied through the code versions are summarized in the file CHANGELOG.md in the repository package. Nature of problem: While Monte Carlo codes have proven valuable in simulating complex radiation scenarios, they rely heavily on accurate geometrical representations. In the same way as many other Monte Carlo codes, penRed employs simple geometric quadric surfaces like planes, spheres and cylinders to define geometries. However, since these geometric models offer a certain level of flexibility, these representations have limitations when it comes to simulating highly intricate and irregular shapes. Anatomic structures, for example, require detailed representations of organs, tissues and bones, which are difficult to achieve using basic geometric objects. Similarly, complex devices or intricate mechanical systems may have designs that cannot be accurately represented within the constraints of such geometric models. Moreover, when the complexity of the model increases, geometry construction process becomes more difficult, tedious, time-consuming and error-prone [2]. Also, as each Monte Carlo geometry library uses its own format and construction method, reproducing the same geometry among different codes is a challenging task. Solution method: To face the problems stated above, the objective of this work is to implement the capability to simulate using irregular and adaptable meshed geometries in the penRed framework. This kind of meshes can be constructed using Computer-Aided Design (CAD) tools, the use of which is very widespread and streamline the design process. This feature has been implemented in a new geometry module named “MESH_BODY” specific for this kind of geometries. This one is freely available and usable within the official penRed package1. It can be used since penRed version 1.9.3b and above.