First integrated core-edge fluid simulation of ITER’s Limiter–Divertor transition with SolEdge-HDG
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11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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IDAES-PSE 2.0 Release
The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.0.0 Release Highlights Removal of deprecated features from IDAES v1 Update to Pyomo v6.5 – this required a number of updates to support the new NL solver writer and to address some changes in Pyomo Creation of new testing suite for backward compatibility, model robustness and verification More general implementation of the Helmholtz EoS. This brings some new features like standard property diagrams, choice of mass or mole basis, and new state variable options Standardizing names in Heat Exchanger models (breaking change from v2.0.0a2): Control Volumes named hot_side and cold_side Ports names hot_side_inlet, hot_side_outlet, cold_side_inlet and cold_side_outlet Config Blocks names hot_side_config and cold_side_config Config arguments for user provided names for each side: hot_side_name and cold_side_name. Updating Keras surrogate tool to use v1.1 of OMLT New prototype API for model initialization (idaes.core.initialization) The new API uses "Model Initializer" objects instead of class methods, allowing for the definition of multiple initialization routines for a single model A number of common, model agnostic initialization routines have also been defined, including initialization from data, block-decomposition and a general hierarchical approach equivalent to the existing method for common unit models New metadata for thermophysical properties – valid_range This can be used to record the range of values over which a property value can be trusted, such as the range of experimental data used to regress parameters A number of new utility functions have been added to check for properties with values outside the valid range and to set bounds based on this metadata Updated construction of balance expressions in Control Volumes to remove unneeded terms In the past, unneeded terms were added as a constant 0 term, however they will now be dropped entirely from the expression This was necessary due to more strict unit checking in the new Pyomo solver writer which no longer ignores 0 terms Updates to metadata for thermophysical properties to better define known properties and units of measurement This results in more strict enforcement of standard naming for thermophysical and reaction properties Users can still define custom properties, but these must be done explicitly using the define_custom_properties() method instead of being implicitly created by add_property() Updated convergence tester utility tool to support definition of benchmark files (JSON format) and comparison of performance to benchmarks Set default iteration limit for IPOPT in IDAES config to 200 iterations Update scaling of example models to work with new Pyomo NL solver writer Improve testing of extensions and examples infrastructure to avoid need for downloading files Updated distillation column to centralize common functionality and remove a number of Pyomo warnings
IDAES↗
Toroidal modeling of runaway electron loss due to 3D fields in ITER
Mitigation of runaway electrons (REs) by three-dimensional (3D) magnetic field perturbations is numerically investigated for the ITER 15 MA baseline D–T scenario, utilizing the MARS-F code (Liu et al Phys. Plasmas 7 3681) with a drift orbit test particle tracing module. Considered are two types of 3D fields: the n = 3 (n is the toroidal mode number) resonant magnetic perturbation (RMP) utilized for the purpose of controlling the edge localized modes in ITER, and perturbations generated by the n = 1 magneto-hydrodynamic (MHD) instabilities in a post-disruption plasma. The RMP field, applied to a pre-disruption plasma, is found to be moderately effective in mitigating the RE seeds in ITER when vacuum field model is assumed. Up to ~40% loss fraction is possible at 90 kA-turn coil current. The mitigation efficiency is however substantially reduced, down to less than 5%, when the plasma response is taken into account. This is due to strong screening of the resonant magnetic field components by the plasma response resulting in much less field line stochasticity. On the other hand, the MARS-F modeling, based on the DINA-simulated post-disruption equilibria, shows that the n = 1 resistive kink instabilities develop in these plasmas, as the edge safety factor qa evolves and drops below integer numbers. RE mitigation by these MHD instabilities is sensitive to the eigenmode structure. The best mitigation is achieved as q a drops below 3, when a global kink instability occurs that encompasses both internal and external components. This global instability is found to be capable of mitigating over 80% MeV-level passing RE orbits at a field perturbation |δB|/B 0 that is comparable to that observed in DIII-D experiments, and full mitigation if the perturbation amplitude is doubled. The 'wetted' area on the ITER limiting surface, due to MHD instability induced RE loss, generally increases with the perturbation amplitude (together with increasing loss fraction). At the highest perturbation level assumed in this study, the wetted area reaches ~60% of the total limiting surface area. Lastly, the lost RE orbits mainly strike the outer divertor region of the limiting surface, with some fraction also hitting a wide area along the inboard side of the surface.
3D fields↗
DESIGN ISSUES AND QUALIFICATION OF HYDROFORMED DOUBLE WALLED EXPANSION JOINTS IN VACUUM SERVICE
The Vacuum Auxiliary System for ITER is devoted to pumping out, venting, and purging the vacuum volumes of the tokamak. Over 5000 clients including the cryostat and vacuum vessel, at 8500 m3 and 1400 m3 respectively, are serviced by approximately 150 pumping stations through 6 km of pipework. The piping and functions provided to the clients include component operation, routing of potentially tritiated gases, and timely leak localization. Expansion joints are used to reduce loadings on pumps and piping stresses but also to qualify in-line components having low allowable design loads. The ITER project utilizes vacuum valves that are not designed to withstand typical piping system loadings and require detailed design and evaluation. Loading scenarios prescribed by governing system specifications must be considered. To maintain allowable design loads for components and to keep piping stresses below code requirements, double walled hydroformed expansion joints have been employed in the design. These are not standard items and require custom fabrication. Installation space constraints and adjacent pipe support attachment location availability at ITER limit standard expansion joint installation guidelines provided by the Expasion Joint Manufacturers Association (EJMA). Careful consideration must be given to the analysis model to ensure proper function and life expectancy of the component. These considerations include accurate accounting of thrust forces, thermal movements, seismic accelerations, equipment and building differential displacements. In addition to displacements, the process internal, external, and interspace pressures affect the qualification and selection of the double walled expansion joints. The calculation results shall confirm that deflections, forces, and moments are reasonable for the size and type required for the system’s demands as evaluated against the manufacturer’s design.
Clark, Forrest [ORNL] (ORCID:0009000678106843)↗
Biosensor-driven strain engineering reveals key cellular processes for maximizing isoprenol production in Pseudomonas putida
Synthetic biology generates vast combinatorial designs, yet high-throughput analytical methods to screen them are poorly matched to interrogate this search space. We address this challenge by developing a biosensor-driven, growth-coupled selection strategy in Pseudomonas putida for isoprenol, a potential aviation fuel precursor. We found and characterized a noncanonical signaling pathway, revealing a functional and physical complex between a hybrid histidine kinase and an alcohol dehydrogenase, whose activity is tuned by heterodimerization. Leveraging this biosensor in a pooled CRISPRi library selection, we identified key host limitations. Iterative combinatorial strain engineering derived from these hits yielded a 36-fold titer increase to ~900 milligrams per liter. Integrated omics analysis revealed that metabolic rewiring toward amino acid catabolism was crucial for this improvement. This observation was found to be beneficial by technoeconomic analysis. Our modular workflow provides a powerful strategy for optimizing complex heterologous pathways and uncovering emergent host biology.
CRISPRi↗
Towards Realistic and High Fidelity Models for Nuclear Reactor Power Synthesis Simulation with Self-Powered Neutron Detectors
As presented in this report, a weighting function–based inferencing method is being applied to synthesize the power distribution in next-generation and university research reactors based on simulated self power neutron detector (SPND) responses. The overall goal is to assess the impacts of sensor uncertainty and true power distribution perturbations on the error in the synthesized power distribution. Regarding sensor uncertainty, the NuScale Small Modular Reactor (SMR) and the Westinghouse AP1000 serve as testbeds for analyzing the impact of varying the sensor uncertainty, as well as varying the number of sensors per sensor string in the reactor core. The reactor models are informed by Monte Carlo N-Particle (MCNP) neutron flux tallies. For the NuScale SMR and Westinghouse AP1000, the SPND response functions (i.e., the response of the SPNDs to individual segments of fuel) were determined homogeneously. Regarding an analysis of power distribution perturbation detection, the Texas A&M Testing, Research, Isotopes, General Atomics Reactor (TAMU TRIGA) reactor was used as a demonstration case with one particular arrangement of SPNDs; the response functions for this reactor model were determined heterogeneously, making this a uniquely high-fidelity demonstration of perturbation detection. Finally, SPND models generated in the Geometry and Tracking 4 (Geant4) code have been generated and tested for comparison with traditionally implemented analytical SPND models, with the intent for Geant4 integration with the full methodological framework. SPND current outputs as a function of distance from some fuel assembly segment in the NuScale SMR are compared with the analytically determined currents. Results from the sensor uncertainty simulations for the NuScale SMR and AP1000 indicate that the average error in the inferred power distribution on the fuel assembly segment level is reasonably low, being slightly less than the random uncertainty applied to all respective SPNDs in both cores. For example, if all SPNDs in the core have a random uncertainty of 5%, then the corresponding fuel assembly segment level error (i.e. difference between the true and inferred local power) is ~2–3%. However, the maximum error in the inferred power distribution on the fuel assembly segment level can be considerably high (>15%) when SPND random uncertainties start to exceed ~3%. In general, the average and maximum errors in the inferred power distribution were slightly higher in the AP1000 as opposed to the NuScale SMR for the sensor string configurations considered herein. Another result determined from analysis of the sensor uncertainty simulations was that increasing the number of SPNDs per string does not clearly reduce inferred power distribution error and can in fact make the error large in some cases; however, this assessment may skewed due to imposed iteration limits. Results from the perturbation detection demonstration using the high-fidelity TAMU TRIGA model indicate that, given the arrangement of 17 SPND strings and 4 SPNDs per string considered herein, there is a clear, provable ability to infer a localized Gaussian-type peak perturbation in the 3D power distribution. Such a perturbation was detected with an average fuel assembly segment level error of 0.19%, and the general visualization of the detected perturbation clearly indicates that the magnitude and shape were appropriately resolved. Finally, the electrical current output generated by the Geant4 modeled SPND indicates significant magnitude differences than the analytically modeled SPND, demonstrating the need for accurate SPND models which account for finite sensor geometry effects to inform the power synthesis work described herein.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Super Resolving Unrolled Neural Networks for Remote Sensing
In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.
47 OTHER INSTRUMENTATION↗
Electron cyclotron emission detection of neoclassical tearing modes for control for ITER
Successful operation of ITER requires control of magnetic instabilities including neoclassical tearing modes (NTMs) that can degrade confinement and lead to disruption. Low latency detection by electron cyclotron emission (ECE) diagnostics has been demonstrated in a few current experiments. Using a synthetic diagnostic, we demonstrate low latency NTM detection for ITER with plasmas described by ITER IMAS database scenarios and with realistic limitations imposed on the instrumentation by these high temperature scenarios. 2/1 NTMs are detected 430 ms after magnetic island seeding and before island locking. The radiometer configuration was optimized using simulation, and the smallest detectable island size was explored. Island sizes of ∼3 cm are detectable at the 2/1 surface. The simulated signals incorporate recent physics models for island growth and rotation, which show early locking and continued island growth after locking and before disruption. This work determines limits for ITER ECE spatial resolution imposed by relativistic broadening of channels, which informs hardware design. Real-time detection is demonstrated in hardware that is required by ITER, including on an NI PXI-7853R FPGA system. Development of a synthetic diagnostic and details of the hardware will be discussed.
Cyclotron radiation↗
ITER cold VDEs in the limit of perfectly conducting walls
Recently, it has been shown that a vertical displacement event (VDE) can occur in ITER even when the walls are perfect conductors, as a consequence of the current quench (CQ) [A. H. Boozer, Phys. Plasmas 26, 114501 (2019)]. We used the extended-MHD code M3D-C1 with an ITER-like equilibrium and induced a CQ to explore cold VDEs in the limit of perfectly conducting walls, using different wall geometries. In the case of a rectangular first wall with the side walls far away from the plasma, we obtained very good agreement with the analytical model developed by Boozer that considers a top/bottom flat-plates wall. We show that the solution in which the plasma remains at the initial equilibrium position is improved when bringing the side walls closer to the plasma. When approximating the ITER first wall as a perfect conductor, the plasma remains stable at the initial equilibrium position far beyond the value predicted by the flat-plates wall limit. When considering an opposite limit in which only the inner shell of the ITER vacuum vessel acts as a perfect conductor, the plasma is displaced during the CQ, but the edge safety factor remains above 2 longer in the current decay compared to the flat-plates wall limit. In all the simulated cases, the VDE is found to be strongly dependent on the plasma current, in agreement with a similar finding in the flat-plates wall limit, showing an important difference with hot VDEs in which the CQ is not a necessary condition.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A Proof of the Asymptotic Variance of Path Length Estimators for Single-Collision Monte Carlo Source Iteration in the Thick Diffusion Limit
Here, we prove a theorem relating the variance of path length estimators for single-collision Monte Carlo source iteration to a parameter that becomes infinitesimally small in an important physical regime arising in radiative transfer. In our usage, “single-collision Monte Carlo source iteration” refers to Monte Carlo Boltzmann transport methods in which each Monte Carlo particle history includes no more than a single collision, and the physics of multiple scattering is modeled by lagging the scattering source term and iterating until this term converges. Our theorem can be used to construct variance reduction techniques which improve the order of the estimator variance. This enables calculations that would otherwise require impractically large sample sizes to achieve practical estimator uncertainties. We believe this is the first postulation of a theorem relating estimator variance to a limiting case parameter for single-collision Monte Carlo source iteration, and the first proof of such a theorem. We illustrate the theorem’s value with an example in which the authors of a transport method used the theorem to design a variance reduction technique that improved the uncertainty of their solution by a factor of about 500 for a proxy problem from radiative transfer that contains both optically-thick and optically-thin material.
Mathematics and Computing↗
Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics
In this work, we evaluate the biomolecular dynamics behaviors when conventionally iterating between all-atom (AA) and coarse-grained (CG) molecular dynamics (MD) simulations over multiple cycles. We implemented the workflow to iterate between AA and CG in OpenMM, namely the iterative multiscale MD (iMMD) simulation workflow. In particular, we aim to identify practical applications for iterating between AA and CG simulations in a conventional manner without any constraints or model modifications. We evaluate the iMMD workflow on four representative systems, spanning folding of two soluble proteins and protein-protein as well as protein-lipid interactions of two membrane proteins. We observe that iteration between AA and CG representations could help the soluble proteins exit undesirable metastable states to fold, resulting from random protein structural distortions due to cycling. Consequently, the most reliable use of iterative AA and CG simulations appears to be to accelerating complex lipid mixing for membrane-bound protein systems rather than sampling protein conformational space. Our work explores the practical usages and limitations for iterative AA and CG simulations using readily available AA and CG force fields. The evaluated iMMD workflow in OpenMM is made available at https://github.com/lanl/iMMD.
59 BASIC BIOLOGICAL SCIENCES↗
Divertor-safe nonlinear burn control based on a SOLPS parameterized core-edge model for ITER
Abstract For ITER operations, the range of desirable burning-plasma regimes with high fusion power output will be restricted by various operational constraints. These constraints include the saturation of ITER’s various heating and fueling actuators such as the neutral beam injectors, the ion and electron cyclotron heating systems, the gas puffing system, and the deuterium–tritium pellet injectors. In addition to these actuator constraints, the H-mode power threshold, divertor detachment, and the heat load on the divertor targets may apply limitations to ITER’s operational space. In this work, Plasma Operation Contour (POPCON) plots that map the aforementioned constraints to the temperature-density space are used to investigate which constraints are most limiting towards accessing regimes with high fusion power output. The presented POPCON plots are based on a control-oriented core-edge model that couples the nonlinear density and energy response models for the core-plasma region with SOLPS4.3 parameterizations for conditions in the edge-plasma regions (scrape-off-layer and divertor). Using this control-oriented core-edge model, a nonlinear burn controller, which aims to regulate the plasma temperature and density in the core-plasma region, is constructed in this work. This controller is augmented with an online optimization scheme that governs the control references such that the plasma can be guided towards regimes with high fusion powers while protecting the divertor targets from dangerously high heat loads. A closed-loop simulation study illustrates the capability of this burn control scheme.
Physics↗
Evaluating a Commercial Dynamic Line Rating Software with the National PMU Dataset
To accelerate the development of data-driven applications for power systems, the Department of Energy (DOE) supported the collection and curation of a synchrophasor dataset spanning two years of observations from transmission utilities across the US. This National PMU Dataset (NPDS) was anonymized and distributed to awardees of a DOE research grant under nondisclosure agreements (NDAs) but has also been retained at PNNL to enable further research. Agreements with data contributors prevent the data from being shared outside the organization. However, establishing a blind research validation methodology is envisioned to maximize the value proposition of the NPDS. In this validation strategy, researchers may share algorithms/software (potentially as executables to protect intellectual property) with PNNL, and PNNL will share feedback about the software’s performance on subsets of the NPDS. Such a blind methodology ensures that sensitive information about critical infrastructure remains protected, but the value of the NPDS can be extended to research beyond PNNL. Through iterative feedback, the algorithms may be tweaked to address real-world artifacts. As the NPDS data is temporally and geographically diverse, it may capture features absent in smaller datasets used during the development of the algorithm under test. This report presents lessons learned from applying the blind validation methodology to LineID™, a synchrophasor-based dynamic line rating software developed by Topolonet Corporation. Improvements made to the software through iterative feedback, limitations of the validation methodology, as well as how the limitations of the NPDS affected the evaluation process are discussed. Observations indicate that the proposed validation methodology can be valuable for evaluating other tools in the future.
97 MATHEMATICS AND COMPUTING↗
Enabling the broader adoption of fusion simulation on complex geometry
This project addressed a key barrier to advanced fusion and nuclear simulation: the difficulty of performing high-fidelity Monte Carlo neutronics directly on complex, real-world CAD geometry. Traditional workflows require engineers to rebuild CAD models as simplified constructive solid geometry, a time-consuming and error-prone process that limits design iteration and broader adoption of simulation tools. The goal of this Phase I SBIR was to make CAD-based neutronics practical, accessible, and robust for industrial and research users. During the project, Coreform significantly enhanced the Direct Accelerated Geometry Monte Carlo (DAGMC) workflow and fully integrated it into Coreform Cubit as a first-class capability. Major achievements include optimized material assignment and surface meshing workflows, substantial performance improvements to geometry imprinting and preparation, native export of DAGMC models, and new visualization tools to support OpenMC source definition and lost-particle debugging. Coreform also expanded Cubit’s capabilities as a full OpenMC preprocessor, including the ability to convert OpenMC constructive solid geometry models back into CAD for visualization, multiphysics coupling, and debugging. In collaboration with Argonne National Laboratory, the project delivered comprehensive new DAGMC documentation and training materials, transforming DAGMC from a research-oriented tool into a production-ready workflow. Results were disseminated through tutorials, conference training, and multiple well-attended webinars demonstrating integrated CAD-based neutronics and multiphysics workflows. Overall, this project demonstrated that high-fidelity Monte Carlo simulations can be performed directly on complex CAD geometry, reducing setup time, improving usability, and enabling faster, more informed design decisions for fusion and nuclear energy systems.
42 ENGINEERING↗
A linearity preserving nodal variation limiting algorithm for continuous Galerkin discretization of ideal MHD equations
A stabilized continuous Galerkin (CG) method for magnetohydrodynamics (MHD) is presented herein. Ideal, compressible inviscid MHD equations are discretized in space on unstructured meshes using piecewise linear or bilinear finite element bases to get a semi-discrete scheme. Stabilization is then introduced to the semi-discrete method in a strategy that follows the algebraic flux correction paradigm. This involves adding some artificial diffusion to the high order, semi-discrete method and mass lumping in the time derivative term. The result is a low order method that provides local extremum diminishing properties for hyperbolic systems. The difference between the low order method and the high order method is scaled element-wise using a limiter and added to the low order scheme. The limiter is solution dependent and computed via an iterative linearity preserving nodal variation limiting strategy. The stabilization also involves an optional consistent background high order dissipation that reduces phase errors. The resulting stabilized scheme is a semi-discrete method that can be applied to inviscid shock MHD problems and may be even extended to resistive and viscous MHD problems. To satisfy the divergence free constraint of the MHD equations, we add parabolic divergence cleaning to the system. Various time integration methods can be used to discretize the scheme in time. We demonstrate the robustness of the scheme by solving several shock MHD problems.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Overcoming timestep limitations in boosted-frame particle-in-cell simulations of plasma-based acceleration
Explicit electromagnetic Particle-In-Cell (PIC) codes are typically limited by the Courant- Friedrichs-Lewy (CFL) condition, which implies that the timestep multiplied by the speed of light must be smaller than the smallest cell size. In the case of boosted-frame PIC simulations of plasma-based acceleration, this limitation can be a major hinderance as the cells are often very elongated along the longitudinal direction and the timestep is thus limited by the small, transverse cell size. This entails many small-timestep PIC iterations, and can limit the potential speed-up of the boosted-frame technique. Here, by using a CFL-free analytical spectral solver, and by mitigating additional numerical instabilities that arise at large timestep, we show that it is possible to overcome traditional limitations on the timestep and thereby realize the full potential of the boosted-frame technique over a much wider range of parameters.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Loss of energetic particles due to resistive wall mode instability in ITER
Effects of an unstable n = 1 (n is the toroidal mode number) resistive wall mode (RWM) on the energetic particle (EP) confinement and loss are numerically investigated, for an ITER steady state scenario with 10 MA plasma current and 5.3 T toroidal field. The eigenfunction of the RWM is computed, with the associated three-dimensional magnetic field perturbation superposed with the 2D equilibrium field for tracing the EP drift orbits. Considered are mono-energetic EPs at 0.5 MeV and 1 MeV for deuterium ions, and 3.5 MeV for fusion-born alphas, with a range of distribution in the particle pitch angle. Modeling finds that less than 20% of EPs can be lost to the limiting surface in ITER assuming a source distribution uniform in minor radius, due to an unstable RWM that produces 100 Gauss poloidal field perturbation at the outboard mid-plane just inside the (effective) resistive wall surface. On top of the initial prompt drift orbit loss for counter-current EPs, the RWM induced particle loss occurs on a one second time scale, which is comparable to the RWM growth time in ITER. The 'wetted' area, due to the lost EPs striking the limiting surface, is generally found to be large due to the RWM. This is a favorable prediction for ITER. Here, the loss distribution in the poloidal angle is more uniform for co-current EPs. Counter-current EPs experience outward orbit drift when launched from the low-field side, and tend to more often hit the bottom region of the limiting surface.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
The 1 → 3 massive splitting functions from QCD factorization and SCET
Splitting functions are universal functions describing the collinear dynamics of gauge theories, and as such are crucial ingredients for a wide variety of calculations in perturbative QCD. We present analytic results for the triple collinear splitting functions in QCD with a single massive parton. We derive the splitting functions using two distinct methods; first by expanding the squared matrix elements in the collinear limit, and secondly by using soft-collinear effective theory with massive quarks. We find agreement between these two approaches, providing a strong check of our results. Additionally, we also check all iterated and soft limits of our results, finding agreement with predictions from factorization. Our results provide an important ingredient for higher order perturbative calculations involving massive partons, and for the description of the collinear dynamics of heavy flavor jets.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗