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book/README.md

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| T3 | [Improving Reproducibility Through Better Software Practices](abstracts/david-improving.md) | David Bernholdt | Oak Ridge National Laboratory |
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| T4 | [Fusion Neutronics Workshop](abstracts/j.-fusion.md) | Jonathan Shimwell | Proxima Fusion |
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| T5 | [RadModeling](abstracts/alvaro-radmodeling.md) | Alvaro Cubi | Fusion for Energy |
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| T6 | [Thermavip: an open source framework for multi-sensor data acquisition, processing and visualization](abstracts/victor-thermavip.md) | Victor Moncada | CEA |
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| T7 | [Using WarpX, a general purpose particle-in-cell code ](abstracts/arianna-using.md) | Arianna Formenti | Lawrence Berkeley National Laboratory |
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| T8 | [Accelerating Plasma Physics Simulations with Pyccel: A PyGyro Case Study.](abstracts/jalal-accelerating.md) | Jalal Maaouni | The UM6P Vanguard Center, Morocco |
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| T9 | [OFELIA: Openmc-FEnicsx for muLtiphysics tutorIAl](abstracts/lorenzo-ofelia.md) | Stefano Riva | Politecnico di Milano |
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| T10 | [How Software Quality Assurance Standards Can Drive Industry Adoption of Open-Source Codes in Fusion Energy](abstracts/casey-how.md) | Casey Icenhour | Idaho National Laboratory |
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| T11 | [FAIR data management for physics and engineering simulation with open-source and commercial codes relevant to Fusion](abstracts/mark-fair.md) | Mark Norris | openSPDM Ltd |
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| T6 | [Using WarpX, a general purpose particle-in-cell code ](abstracts/arianna-using.md) | Arianna Formenti | Lawrence Berkeley National Laboratory |
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| T7 | [Accelerating Plasma Physics Simulations with Pyccel: A PyGyro Case Study.](abstracts/jalal-accelerating.md) | Jalal Maaouni | The UM6P Vanguard Center, Morocco |
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| T8 | [How Software Quality Assurance Standards Can Drive Industry Adoption of Open-Source Codes in Fusion Energy](abstracts/casey-how.md) | Casey Icenhour | Idaho National Laboratory |
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| T9 | [FAIR data management for physics and engineering simulation with open-source and commercial codes relevant to Fusion](abstracts/mark-fair.md) | Mark Norris | openSPDM Ltd |
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| T10 | [OFELIA: Openmc-FEnicsx for muLtiphysics tutorIAl](abstracts/lorenzo-ofelia.md) | Stefano Riva | Politecnico di Milano |
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## 🗣️ Panel Session: 10:40 - 11:20 (EDT)

book/abstracts/adam-quantifying.md

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[hpcflow](https://github.com/hpcflow/hpcflow-new) is an open-source, cross-platform Python package for designing, running, and sharing large-scale reproducible computational workflows on HPC systems and locally. [MatFlow](https://github.com/hpcflow/matflow-new) is an extension of hpcflow that focuses on building cohesive materials science workflows, and is particularly useful as a framework to link uncertainty quantification (UQ) algorithms with high-fidelity simulations in fusion materials applications. We discuss some recent work that uses MatFlow to predict rare-event failure probabilities via the subset simulation UQ method https://doi.org/b7z8hq, which conceptually partitions an extremely unlikely failure event into a series of conditional failure events that are more computationally accessible. In particular, we apply the workflow to a materials failure problem using full-field crystal plasticity simulations with DAMASK @10.1016/j.commatsci.2018.04.030, where the input microstructure is uncertain. After running an initial set of simulations, MatFlow then runs sets of Markov chains to iteratively direct the input space towards the failure domain. We discuss the features of MatFlow that enable reusable workflows such as these to be developed, and we further discuss how we have used MatFlow to explore and compare the efficiency of selecting different variations of the Markov Chain Monte Carlo algorithm, such as the multi-level approach, which uses simulations at multiple fidelities to improve efficiency.
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# Repository
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https://github.com/hpcflow
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book/abstracts/aidan-nesst.md

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[Neutron Scattered Spectra Tool (NeSST)](https://github.com/aidancrilly/NeSST) is a python library for producing synthetic neutron spectra relevant to nuclear fusion. The tool is primarily focused on inertial fusion, but also has capabilities which are useful for magnetic fusion. NeSST implements literature models for the primary neutron-producing fusion reactions in DT plasmas (DT, DD and TT). It also uses evaluated nuclear data files to calculate scattered spectra, under the single scatter approximation. This is essential in inertial fusion to model the downscatter of neutrons in the target. Scattering is computed using relativistic corrections and the effects of finite temperature and velocity of the scattering medium. Finally, NeSST includes standard diagnostic models for time-of-flight neutron spectral analysis. The code has been used to model experimental data from the OMEGA facility at the Laboratory for Laser Energetics. NeSST makes use of a few other open-source fusion energy projects: [python-ENDF](https://github.com/paulromano/endf-python) and [pydress](https://github.com/jacob-eri/pydress
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# Repository
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https://github.com/aidancrilly/NeSST
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book/abstracts/alberto-f4enix.md

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F4Enix also provides a comprehensive and up-to-date documentation that describes the installation, usage, and API of the package. The documentation is hosted online using Read the Docs, a service that builds and serves the documentation whenever the code is updated. Finally, F4Enix is automatically built and uploaded to the PyPi index every time a new version is tagged, making it available through a simple pip install.
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# Repository
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https://github.com/Fusion4Energy/F4Enix
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book/abstracts/alex-collaborative.md

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Accurate cost estimation for fusion power plants is essential for guiding research, optimizing designs, and advancing commercialization. To address the lack of standardized cost frameworks, Woodruff Scientific LTD developed an open-source Python-based cost estimation tool with support from ARPA-E. The tool is now available in a collaborative Google Colab environment, providing a cloud-based, interactive platform for real-time collaboration, rapid prototyping, and integration into design workflows. This Colab implementation enables users to explore diverse fusion device designs and performance scenarios while leveraging a modular and customizable architecture. The ongoing development of this code is supported by the Clean Air Task Force (CATF) through its International Working Group on Fusion Cost Analysis, led by Woodruff Scientific. Applications of this tool include collaborative design-to-cost exercises, automation of standardized cost documentation, and broader accessibility for the global fusion community. This presentation will demonstrate the Colab platform’s capabilities and showcase how it facilitates collaboration and innovation in fusion cost modeling.
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# Repository
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https://github.com/Woodruff-Scientific-Ltd/PyFECONs-GoogleColab
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book/abstracts/alvaro-radmodeling.md

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- Generation of CAD plots identical to those obtained with the MCNP plotter to have a 1:1 pixel comparison.
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- Automatic conversion of toroidal elbows in pipes to a set of cylinders.
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# Repository
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https://github.com/Fusion4Energy/RadModeling
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book/abstracts/dom-workflow.md

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This work presents a system modelling framework for fusion power plant design and costing, with a focus on the development and implementation of automated workflows in the Galaxy platform. The Galaxy workflows are designed to integrate parametric geometry generation with high-fidelity simulations, ensuring a streamlined, reproducible process for evaluating power plant performance, using the ARPA-E open-source fusion cost model (see Higginbottom and Ward in this meeting). Each workflow is developed to take user-defined inputs, such as design parameters and simulation settings, and execute a series of interconnected tasks, including neutronics, thermal, and electromagnetic analyses, in a fully automated manner. These workflows are built to ensure modularity, allowing engineers to customize outputs while preserving input consistency and tracking provenance. In fusion systems, where every input depends intricately on interconnected outputs, traditional methods often fail to establish consistent links across all subsystems. This framework overcomes such challenges by automating these connections, ensuring seamless interaction between components. This work benefits fusion energy researchers, system designers, and policymakers by providing a flexible, efficient, and user-friendly platform for power plant design. By simplifying the evaluation and iteration process, it accelerates the pathway toward developing cost-effective, high-performance fusion power plants
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book/abstracts/george-atomc.md

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Modern tooling is demanded for predicting the transport and reaction characteristics of atoms and molecules, especially in the context of magnetic confinement fusion. DEGAS2 and EIRENE are the most common and capable tools currently in use, and share many fundamental similarities with the OpenMC framework. OpenMC is an open source Monte Carlo transport solver that was primarily developed for neutron and photon transport. We have demonstrated that OpenMC is suitable for atomic transport calculations and have developed a version with the appropriate reactions https://doi.org/10.48550/arXiv.2411.12937. The relative error between the models is small, and the performance of OpenMC is at least comparable to DEGAS2. This is the case even without taking advantage of heterogeneous computing architectures, which is only one of several remarkable new capabilities that this demonstration heralds.
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# Repository
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github.com/gjwilkie/openmc
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book/abstracts/jake-a.md

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# Repository
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https://github.com/jakemarathon/neutronics_workflow
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book/abstracts/m.-open.md

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affiliations:
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- Research & Technological Innovation Department, Eni S.p.A., 20097 San Donato Milanese (MI), Italy
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- name: R. Zanino
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affiliations: NEMO group, Dipartimento Energia, Politecnico di Torino, 10129 Torino, Italy
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affiliations:
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- NEMO group, Dipartimento Energia, Politecnico di Torino, 10129 Torino, Italy
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license: CC-BY-4.0
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exports:
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- format: pdf

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