Results for “accelerator modelling and simulations” — Page 36

DOE OSTI2023

Crystal growth and nucleation at metal-organic framework / solution interfaces

Metal-organic frameworks (MOFs) represent an incredibly diverse class of materials with equally diverse applications in catalysis, separations, and gas storage. Despite the prevalence of MOF structures in the literature, in many cases the synthesis of pure, crystalline MOF materials remains a significant challenge, with failed syntheses often yielding mixed crystalline or non-porous amorphous products. Our recent work aims to enable targeted MOF synthesis by developing a comprehensive and predictive model of the fundamental processes occurring at the MOF-solution interface under synthetically-relevant conditions. Our recent accomplishments include: (i) development and implementation of a graphics processor-accelerated approach to simulation the nucleation of weak electrolytes, and the extension of these methods to large/complex solutes such as MOFs; and (ii) studies of the structure of ZIF interfaces under conditions relevant to crystal growth; and (iii) addressing important open questions in the mechanism of MOF solvent-assisted linker exchange, and important synthetic pathway for MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Correction of crosstalk effect in the Low Energy RHIC electron Cooler Booster Cavity

we report that the Low Energy Relativistic Heavy Ion Collider (RHIC) electron Cooler (LEReC) is designed to deliver a 1.6 MeV to 2.6 MeV electron beam, with rms dp/p less than 5e-4. The superconducting radiofrequency (SRF) Booster Cavity is the major accelerating component in LEReC. It is a 0.4 cell cavity operating at 2 K, providing a maximum energy gain of 2.2 MeV. It is modified from an experimental Energy Recovery Linac (ERL) photocathode gun, and thus has fundamental power couplers (FPCs), pickup (PU) couplers (field probes) and HOM coupler close to each other on the same side of the cavity. Direct capacitive coupling between the FPC and PU, called the crosstalk effect, combined with microphonic detuning, can induce closed loop voltage fluctuations that exceed the total energy spread requirement of LEReC. The crosstalk effect in this cavity is modelled, simulated, and measured, and a correction method is proposed and demonstrated to suppress the voltage fluctuation so that energy spread requirement can be met.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

42 ENGINEERING↗

DOE OSTI2021

Development of the IES Plug-and-Play Framework

This report discusses the status of the flexible plug-and-play framework development currently ongoing that aims to integrate Modelica/Dymola with the Risk Analysis and Virtual ENvironment (RAVEN) software in terms of both Functional Mock-Up Interface (FMI)/Functional Mock-Up Unit (FMU) construction and repository structures that aim to ease the sharing and simulation of complex dynamic models. This report aims to provide an overview of all the performed activities resolving around the deployment of methods, software infrastructures, guidelines and workflow for the construction and usage of models, encapsulated using the FMI/FMU protocols and standards. In particular, the report is organized in three main macro-subjects, which are connected to each other: - FMI/FMU adaptors for modelica models - HYBRID repository new structure and open-source deployment - RAVEN FMI/FMU exporting capabilities and Artificial Intelligence (AI)-based analysis acceleration. The first part of the report discusses the FMI/FMU adaptors that have been created within the HYBRID repository to allow users to quickly export models, such as FMUs. Several examples are shown that highlight the step-by-step process of converting an existing Modelica model into an FMU for use within the Dymola platform. Simulation results demonstrate that, while minor differences may occur, the overall control, trends, and solution integrity are maintained between standard Modelica simulation and FMU simulation results. However, it is worth noting that, for small systems, the FMU results have a slower simulation time than the Modelica only simulation. Using this process, a company can provide models that contain proprietary information to entities without disclosing any of the information about the model that could be considered business sensitive. Such an ability would allow institutions to bypass the necessity of “whitewashing” data. In the second part of the report, the new structure of the HYBRID repository is discussed with a major focus on the series of updates that has been completed. These updates include the addition of Modelica system-level regression tests and software quality assurance documentation that ensure that modifications to the Modelica models do not alter system-level model results. The third and final part of the report aims to report the work that has been performed for the deployment of methods and workflows for the construction of RAVEN AI-based models compliant with the FMI/FMU standard. Such development represents the key for the deployment of the concept of “Flexible ecosystem” since it allows for the replacement of high-fidelity modelica models (or any other FMI/FMU compliant model) with RAVEN generated AI surrogate models. Overall, extensive work has been completed on developing FMUs and FMIs from existing models, understanding the requirements and limitations of FMUs, and open-sourcing the HYBRID repository with an integrated regression system.

14 SOLAR ENERGY↗

DOE OSTI2020

Feedbacks Between Surface Deformation and Permafrost Degradation in Ice Wedge Polygons, Arctic Coastal Plain, Alaska

In the past three decades, an abrupt, pan-Arctic acceleration of ice wedge melting has transformed tundra landscapes, spurring the formation of hummock-like features known as high-centered polygons (HCPs). This rapid geomorphic transition profoundly alters regional hydrology and influences surface emissions of CO 2 and CH 4 . In Arctic Alaska, most recent instances of ice wedge degradation have arrested within 15–20 years of inception, stabilizing HCP microtopography. However, feedbacks between ground surface deformation and permafrost stability are incompletely understood, limiting our capacity to predict trajectories of landscape evolution in a still warmer future. In this work, we use field data from a site near Prudhoe Bay, Alaska, to develop a modeling-based framework for assessing the strength of positive (i.e., exacerbating) feedbacks on ice wedge degradation, focusing on the importance of heterogeneity in surface drainage and microtopographic conditions. Our simulations suggest that, when troughs are narrow, positive feedbacks on ice wedge melting (associated with thermokarst pool formation) are relatively weak. Positive feedbacks are markedly stronger beneath wide troughs, such as those that form above older, larger ice wedges. Seasonal thaw abruptly accelerates once a talik begins to form beneath wide and deep thermokarst pools. Once a talik initiates, winter severity and snowpack thickness increase in importance as predictors of thaw intensity in summer. Our results indicate that meter-scale heterogeneity in polygonal microtopography potentially exerts strong, nonlinear controls on thermokarst trajectories. These findings are useful for predicting future thermokarst dynamics and for interpreting the results from coarser-resolution land surface models operating at greater spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

A stress-based fracture model for reacting metal ejecta

The evolution of reacting metal ejecta continues to be a topic of interest at the forefront of metals in reactive and extreme environments. Ejecta are small particles formed when the surface of a metal undergoes Richtmyer–Meshkov instability from a strong shock. Experiments have shown that in the case where ejecta are in ambient conditions that induce a reaction, the ejecta behave irregularly. The ejecta temperature rises and then plateaus, and the acceleration profile shows unexpected jumps. These variations are assumed to be related to the exothermic heat release and particle mass loss caused by the reaction. To explain this phenomenon, efforts to model this in simulations have increased. While current models can capture many of these physical processes, they currently assign a constant reaction shell thickness with little physical reasoning. This work remedies this problem by assigning a dynamic physically informed shell thickness to the reacting particles, using solid analysis. The shell thickness of the particles impacts the rate of change of reacted mass in the system, as well as the rate at which the particles react. The model is based on a simple stress–strain relationship and gives a dynamic assignment for when the reacting particle should begin to fracture. We compare our model to the previous computational and simulation data to analyze the effects of different model parameters.

42 ENGINEERING↗

DOE OSTI2024

Multipoint-BAX: a new approach for efficiently tuning particle accelerator emittance via virtual objectives

Abstract Although beam emittance is critical for the performance of high-brightness accelerators, optimization is often time limited as emittance calculations, commonly done via quadrupole scans, are typically slow. Such calculations are a type of multipoint query , i.e. each query requires multiple secondary measurements. Traditional black-box optimizers such as Bayesian optimization are slow and inefficient when dealing with such objectives as they must acquire the full series of measurements, but return only the emittance, with each query. We propose a new information-theoretic algorithm, Multipoint-BAX , for black-box optimization on multipoint queries, which queries and models individual beam-size measurements using techniques from Bayesian Algorithm Execution (BAX). Our method avoids the slow multipoint query on the accelerator by acquiring points through a virtual objective , i.e. calculating the emittance objective from a fast learned model rather than directly from the accelerator. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II). In simulation, our method is 20× faster and more robust to noise compared to existing methods. In live tests, it matched the hand-tuned emittance at FACET-II and achieved a 24% lower emittance than hand-tuning at LCLS. Our method represents a conceptual shift for optimizing multipoint queries, and we anticipate that it can be readily adapted to similar problems in particle accelerators and other scientific instruments.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Concurrent Relaxation through Accelerated Deep Learning

CRADL captures performance metrics of machine learning algorithms operating on mesh data from multiphysics codes This proxy application is a tool to explore scalability of inference on HPC platforms, and also gather performance metrics for inference on new machine learning specific hardware. CRADL is designed to give users as fine a control as possible over an inference simulation. Users may select the number of cycles, amount of data, and batch size to pass to the accelerator of choice. Additionally the user may select a number of performance optimization libraries and flags. CRADL comes packaged with a repository of anonymized multi-physics simulation data, as well as a pretrained model for inference. The code allows a user to load their own pre-trained model and data if they wish. The code can operate in multiple parallelization schemes, with performance enhancing options such as half-precision libraries, PyTorch benchmarking, and pinned memory with non-blocking data transfers.

Zieb, KristoferJ.↗

DOE OSTI2025

Virtual Engineering: Python framework for engineering process design

Virtual Engineering (VE) is a Python software framework designed to accelerate the research and development of engineering processes that are fundamentally defined by multiple unit operations executed in series. VE supports a wide variety of different multi-physics models and integrates them to simulate a complete end-to-end process. To automate the execution of this model sequence, VE provides (i) a robust method to communicate between models, (ii) a high-level, user-friendly interface to set model parameters and enable optimization, and (iii) an overall model-agnostic approach that allows new computational units to be swapped in and out of workflows. Although the VE framework was developed to support the biochemical conversion of biomass to fuel, we have designed each component to easily accommodate new domains and unit models.

09 BIOMASS FUELS↗

DOE OSTI2024

Searching for Strongly Coupled Dark Sectors with Unsupervised and Generative Learning

Recipient of the URA Early Career Award for groundbreaking searches for dark matter arising from strongly coupled dark sectors with the CMS detector, pioneering work in ML-based model-independent anomaly detection for collider and astrophysics experiments, and leadership in the development of new AI/ML techniques to improve event reconstruction and detector simulation in particle physics, as well as novel strategies to accelerate AI inference and throughput with heterogeneous computing using coprocessors as a service.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Unraveling fundamental mechanisms of silicon nitride crystallization in microelectronics manufacturing

This research project investigates the fundamental mechanisms of silicon nitride (SiN) crystallization, aiming to enhance the understanding of this critical material in microelectronics manufacturing. Through a collaborative effort between Sandia National Laboratories, the University of Tennessee, and the University of Florida, we developed a comprehensive framework that integrates experimental techniques, atomistic modeling, meso-scale simulations, and an integrated multi-scale model to capture this physical phenomenon on multiple time and length scales . The project developed a new machine learning based atomistic potential and utilized advanced phase field modeling to capture the complexities of polycrystalline growth and the influence of mechanical stresses on crystallization dynamics. By employing a grain tracker algorithm, the meso-scale model effectively identified and tracked individual crystal grains, enabling the simulation of anisotropic growth behaviors reflective of SiN’s physical properties. The integration of atomistic simulations with meso-scale modeling created a powerful multi-scale framework that validated atomistic inputs and enhanced predictive accuracy for crystallization dynamics at larger scales, validated experimentally. This adaptable modeling capability not only accelerates development times by informing manufacturing processes but also serves as a valuable starting point for understanding crystallization physics in similar materials. The insights gained from this research unlock new opportunities for the development of advanced materials tailored for future microelectronics and photonics applications. Overall, this project represents a significant advancement in understanding of fundamental physics of SiN and establishes a foundation for future research in material science, bridging the gap between atomic-level phenomena and macroscopic material behaviors for practical applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Measurement of the Medium Energy NuMI Flux Using the Low-$\nu$ and High-$\nu$ Methods at MINERvA

As we continue to improve our experimental capabilities, the measurements we make of accelerator-based neutrino phenomena are becoming increasingly limited by systematic, rather than statistical uncertainties. Among these are uncertainties corresponding to our understanding of the neutrino flux and to the models that we use to simulate neutrino interactions. These already are significant for currently-running neutrino experiments, and will become more important for next-generation neutrino experiments such as DUNE. Dedicated efforts at experiments such as MINERvA are pursuing improved comprehensive modeling of neutrino cross sections and improving our understanding of how to precisely measure neutrino fluxes. This thesis discusses these themes in the context of MINERvA, a neutrino scattering experiment which was located on-axis in the NuMI beamline at Fermilab. A pair of \textit{in situ} measurements of the flux are presented which utilize Medium Energy $\nu_{\mu}$ data colle cted at MINERvA between 2013 and 2017. The low-$\nu$ technique is used to make a direct measurement of the flux from low-inelasticity events, and that flux is used to measure the total charged-current inclusive cross section. The novel ``high-$\nu$'' technique is used to make a second direct measurement of the flux from a complementary selection of high-inelasticity events, and that flux is used to measure a double-differential cross section of charged-current quasielastic-like events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

OECD-NEA HTTF Benchmark Progress and Updates

General slides discussing High Temperature Test Facility benchmark progress, Prismatic HTGR deployment, modeling and simulation tools for validating systems, and verification and validation issues. Also discuss collaboration and conclusions of RELAP5-3D validation activities based on HTTF with trends in data and international impact to accelerate deployment of prismatic HTGR microreactors by providing an opportunity for designers to assess their codes against experimental data and solutions from other codes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Improving Projections of Antarctic Ice Sheet Contribution to Sea‐Level Change Through 2300 by Capturing Gravitational, Rotational, and Deformational Effects

Ice-mass change induces regionally varying patterns of sea-level change due to gravitational, rotational, and deformational (GRD) effects, which in turn influence marine-based ice stability in Antarctica. For improved projection of the Antarctic Ice Sheet (AIS), there is a need for including GRD effects in modeling and improving understanding of basin-by-basin sensitivity of ice evolution to GRD effects under a range of climate scenarios. We couple a high-resolution, higher-order ice-sheet model with a 1D global sea-level model that fully captures GRD effects, and simulate ice evolution in Antarctica under the Ice Sheet Model Intercomparison Project for CMIP6 experiments. We perform two sets of coupled simulations incorporating 1D Maxwell solid Earth structure suitable for West and East Antarctica and show that the Amundsen Sea Embayment (ASE) in West Antarctica has the highest sensitivity to GRD effects—in high-emission scenarios, grounding-line retreat accelerates by hundreds of kilometers by 2300 without GRD effects, but GRD effects delay this retreat on a timescale of decades. However, we find that delay times do not show a clear relationship to the strength of climate forcing alone. Furthermore, GRD effects can influence ice-sheet dynamics more than the choice of climate model for a given emissions scenario. In contrast, East Antarctica exhibits minimal sensitivity to GRD effects throughout the study period. These findings underscore the critical role of GRD effects in shaping future West AIS evolution, highlighting the importance of constraining the regional 3D Earth structure and bed topography in West Antarctica, particularly the ASE.

58 GEOSCIENCES↗

DOE OSTI2021

Machine learning dielectric screening for the simulation of excited state properties of molecules and materials

Accurate and efficient calculations of absorption spectra of molecules and materials are essential for the understanding and rational design of broad classes of systems. Solving the Bethe–Salpeter equation (BSE) for electron–hole pairs usually yields accurate predictions of absorption spectra, but it is computationally expensive, especially if thermal averages of spectra computed for multiple configurations are required. We present a method based on machine learning to evaluate a key quantity entering the definition of absorption spectra: the dielectric screening. We show that our approach yields a model for the screening that is transferable between multiple configurations sampled during first principles molecular dynamics simulations; hence it leads to a substantial improvement in the efficiency of calculations of finite temperature spectra. We obtained computational gains of one to two orders of magnitude for systems with 50 to 500 atoms, including liquids, solids, nanostructures, and solid/liquid interfaces. Importantly, the models of dielectric screening derived here may be used not only in the solution of the BSE but also in developing functionals for time-dependent density functional theory (TDDFT) calculations of homogeneous and heterogeneous systems. Overall, our work provides a strategy to combine machine learning with electronic structure calculations to accelerate first principles simulations of excited-state properties.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Impact of breech geometry and propellant flow on the release of large pellets for the ITER disruption mitigation system

Studies have been performed on the release mechanism for large pellets using high pressure gas in a shattered pellet injector. Typically, pellets are dislodged from the cryogenic surface and accelerated down a barrel using high pressure gas delivered by a fast-acting propellant valve. The pellets impact an angled surface which shatters the pellet into many small fragments before entering the plasma. This technique was initially demonstrated on DIII-D (Commaux et al 2016 Nucl. Fusion 56 046007) and is now deployed on JET, KSTAR, ASDEX-Upgrade, and other tokamaks around the world in support of ITER's disruption mitigation system design and physics basis. The large hydrogen, 28.5 mm diameter, 2 length-to-diameter ratio, pellets foreseen for ITER SPI operation have low material strength and low heat of sublimation, which cause the pellets to be fragile and highly reactive to the impact of warm propellant gas. Due to the size of the pellets, significantly more propellant gas is required to dislodge and accelerate them. This creates a potentially significant propellant gas removal issue as 2–6 bar-L of gas is expected to be required for release and speed control. The research presented in this paper is an in-depth exploration of the parameters that are keys to reliable pellet release and speed control. Computational fluid dynamics (CFD) modeling of propellant flows through various breech designs was conducted to determine the force generated on the back surface of a pellet. These simulations assumed the use of the ORNL designed flyer plate valve. CFD modeling combined with experimental measurements provide adequate insight to determine a path to an optimal valve and breech design for ITER SPI pellet release and speed control while minimizing propellant gas usage.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗